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3 DEVELOPMENT AND APPLICATION OF INCIDENT DETECTION TECHNIQUES TO IMPROVE INCIDENT MANAGEMENT IN FREEWAY CORRIDORS FINAL REPORT Principal Investigator: YORGOS J. STEPHANEDES, P.E. PROPERTY OF MN/DOT LIBRR' Minnesota Depart, of Transportio Graduate Student: ATHANASIOS P. CHASSIAKOS Department of Civil and Mineral Engineering University of Minnesota January 1993

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5 ii TABLE OF CONTENTS TABLE OF CONTENTS... ii LIST OF FIGURES... iv LIST OF TABLES... vi I. INTRODUCTION I.1. PROBLEM STATEMENT RESEARCH OBJECTIVES REPORT ORGANIZATION... 3 II. INCIDENT DETECTION BACKGROUND II.1. INCIDENTS AND IMPACT ON TRAFFIC OPERATIONS AUTOMATIC INCIDENT DETECTION SYSTEMS Introduction Traffic Detection System Computer Algorithms INCIDENTS AND OTHER TRAFFIC PHENOMENA LITERATURE ON EXISTING INCIDENT DETECTION ALGORITHMS Pattern Recognition (Comparative) algorithms Time Series algorithms Probabilistic approaches Clustering techniques II.4.5 The HIOCC algorithm II.4.6 Methods employing traffic flow modeling OPERATIONAL INCIDENT DETECTION SYSTEMS California Connecticut Florida Illinois M innesota Texas V irginia Washington State O ntario III. DATA DESCRIPTION III.1. INTRODUCTION PRESENCE DETECTORS... 29

6 THE TWIN CITIES FREEWAY TRAFFIC SURVEILLANCE AND CONTROL SYSTEM TEST SITE AND DATA DESCRIPTION IV. EVALUATION OF MAJOR EXISTING ALGORITHMS IV.1. INTRODUCTION AND PROBLEM STATEMENT IV.2. EXISTING ALGORITHM DESCRIPTION IV.2.1 California algorithm IV.2.2 A lgorithm # IV.2.3 Standard Normal Deviation algorithm IV.2.4 Double Exponential algorithm IV.2.5 ARIMA algorithm IV.3. EVALUATION METHODOLOGY...41 IV.4. ALGORITHM EVALUATION IV.4.1 Term ination Test IV.4.2 Pattern Recognition algorithms IV.4.3 Time Series algorithms IV.4.4 A ll algorithm s IV.4.5 Performance comparison with previous studies V. NEW ALGORITHM DEVELOPMENT V.1. INTRODUCTION AND PROBLEM STATEMENT V.2. DETECTION ISSUES TO BE ADDRESSED V.3. PROPOSED ALGORITHM DESCRIPTION VI. NEW ALGORITHM TESTING AND EVALUATION VI.1. INTRODUCTION - METHODOLOGY VI.2. ALGORITHM DETECTION PERFORMANCE VI.2.1 Linear sm oothing VI.2.2 M edian sm oothing VI.2.3 Exponential smoothing VI.2.4 Combination of Exponential and Linear smoothing VI.3. PERFORMANCE COMPARISON WITH EXISTING ALGORITHMS VII. CONCLUSION AND FUTURE RESEARCH NEEDS ACKNOWLEDGEMENTS R E FER EN C E S APPENDIX: DATA DESCRIPTION iii

7 iv LIST OF FIGURES 2.1. Traffic flow and delay during incidents Effect of a severe accident on detector occupancies Incident affecting long road segment Effect of a non-severe incident on detector occupancies Bottleneck effect on detector occupancies Compression wave effect on detector occupancies Presence detector signal associated with a single vehicle passage Study site on 1-35W in Minneapolis Receiver operating characteristic curve Termination test effect on California algorithm performance Operating characteristics for Pattern Recognition algorithms Mean time-to-detect: Pattern Recognition algorithms Performance comparison between two variables: Double Exponential algorithm Operating characteristic curves: Time Series algorithms Mean time-to-detect: Time Series algorithms Performance comparison among existing algorithms Comparison with results from literature: California algorithm Comparison with results from literature: Algorithm # U nusual incident pattern Incident with no complete traffic pattern Noisy detector occupancy data Application of the new algorithm... 75

8 V 6.1. Operating characteristic curves: Average smoothing Operating characteristic curves: Median smoothing Operating characteristic curves: Exponential smoothing Operating characteristic curves: Exponential - Average smoothing Performance comparison: DELOS vs. Standard Deviation Performance comparison: DELOS vs. Double Exponential Performance comparison: DELOS and California algorithms

9 vi LIST OF TABLES 2.1. Summary of Automatic Incident Detection algorithms Description of incident characteristics Detection performance of the California algorithm without and w ith a term ination test Detection performance of the California algorithm with a persistence test Detection performance of the Algorithm #7 with a persistence test Mean time-to-detect for Pattern Recognition algorithms Detection performance of the Standard Deviation algorithm Detection performance of the Double Exponential algorithm Detection performance of the ARIMA algorithm with station occupancy as state variable Average mean time-to-detect for Time Series algorithms Thresholds and performance results: DELOS 1.1 (10, 6) Thresholds and performance results: DELOS 1.1 (10, 8) Thresholds and performance results: DELOS 1.1 (20, 6) Thresholds and performance results: DELOS 1.1 (15, 10) Thresholds and performance results: DELOS 2.2 (9, 5) Thresholds and performance results: DELOS 2.2 (9, 7) Thresholds and performance results: DELOS 2.2 (9, 9) Thresholds and performance results: DELOS 3.3 (0.03, 6) Thresholds and performance results: DELOS 3.3 (0.05, 6) Thresholds and performance results: DELOS 3.3 (0.10, 6)

10 vii Thresholds and performance results: DELOS 3.1 (0.03, 6) Thresholds and performance results: DELOS 3.1 (0.05, 6) Thresholds and performance results: DELOS 3.1 (0.10, 6)... 95

11 INTRODUCTION

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13 I. INTRODUCTION 1.1 PROBLEM STATEMENT Traffic congestion is a rapidly deteriorating problem in urban areas in the U.S. and abroad. Congestion causes over 1.2 billion vehicle-hours of delay, over 1.3 billion gallons of wasted fuel, and over $9 billion annually in user costs. These figures are predicted to increase to about 6.9 billion vehicle-hours of delay, 7.3 billion gallons of wasted fuel, and over $50 billion in user cost by the year Traffic incidents result in one of the greatest losses of efficiency experienced on urban freeways. The magnitude of their impact on traffic operations is such that they must be taken into account by surveillance and control systems. According to Federal Highway Administration estimates, incidents account for 60% of the vehicle-hours lost to freeway congestion (Grenzeback and Woodle, 1992). Fast and reliable freeway incident detection is instrumental in reducing traffic delay, and increasing safety. In particular, with the information from incident detection, optimal control strategies guide the traffic flow towards smooth operation by preventing additional vehicles from entering the freeway upstream of the incident and by communicating relevant information to travelers. In addition, incident detection constitutes the cornerstone for prompt incident management (emergency vehicles can be dispatched to clear the incident), thus, improving safety near the incident location. Existing techniques for the detection of freeway incidents do not provide the necessary reliability for freeway operations. Conventional automated techniques, based on computerized algorithms, are less effective than is desirable for operational use because they generate a high level of false alarms. Operator-assisted methods minimize the false alarm risk, but suffer from missed or delayed detections, are labor intensive, and restrict the potential benefits from advanced, integrated traffic management schemes. 1.2 RESEARCH OBJECTIVES Responding to the need for effective and reliable detection of freeway incidents, an essential element

14 3 for improved traffic management and control in freeway corridors (Stephanedes and Chang, 1991), the authors initiated this research to investigate the performance limitations of conventional automatic incident detection systems and define the specifications for a new algorithmic logic that can lead to improved detection performance. The research initially focused on assessing the ultimate detection performance that can be accomplished with existing and new incident detection systems that use traffic data from presence detectors. A new algorithm was developed and tested against the major existing ones with promising results towards the development of a more-sophisticated detection structure. All tests employed a unified system of performance assessment (Stephanedes and Chassiakos, 1991), suitable for direct algorithm evaluation. The major accomplishments of this project are: * Review of current incident detection algorithms. * Testing major existing algorithm in the Twin Cities Freeway system. * Development of data preprocessing techniques to enhance the incident signal. * Development and testing of incident detection algorithms based on the data preprocessing. 1.3 REPORT ORGANIZATION Chapter II provides a fairly detailed description of traffic incidents and their impact on traffic operations as well as a review of previous research on automatic incident detection and currently operational Incident Detection Systems around the nation. Chapter III describes the presence detector system for traffic information extraction and a description of the test site and data used in this study. Chapter IV contains the evaluation results for algorithms reported in the literature. Chapter V is dedicated to the new algorithm development. Its structure and logic are described in detail. In Chapter VI, the new algorithm is tested and evaluated in terms of its features and its performance is compared against the existing algorithm performance. Finally, Chapter VII summarizes the findings of this study and recommendations for future research in this area.

15 INCIDENT DETECTION BACKGROUND

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17 H. INCIDENT DETECTION BACKGROUND H.1 INCIDENTS AND IMPACT ON TRAFFIC OPERATIONS A number of different definitions for freeway incidents exist in the literature, depending on the particular objective of each study. For the purposes of the present study, an incident is defined as any unplanned physical obstruction or event on the freeway which disturbs the flow of vehicles. The observability of the imposed disturbance in traffic flow is necessary condition for the detection of incidents because detection is accomplished through observing specific changes in traffic flow patterns at the measuring points (detector stations). The majority of incidents, especially during heavy traffic conditions, reduce the capacity of the freeway below the level of the approaching traffic volume. As a result, traffic quickly backs up upstream of the incident location while a region of light traffic develops downstream. The congestion upstream of the incident continues to grow until the incident condition disappears or until the oncoming flow decreases to less than the road capacity at the incident site. Incidents result in one of the greatest losses of efficiency experienced on urban freeways. Although they are statistically rare events, occurring once every 20,000 to 30,000 vehicles-miles, the magnitude of their impact on traffic operations is such that they must be taken into account by surveillance and control systems. Numerous estimations have revealed that nonrecurring congestion due to incidents is responsible for more than half of the total motorist delay in the urban area. Immediate and prompt response to freeway incidents ensures the rapid restoration of congested flow to normal flow conditions and the avoidance of high levels of delay. Figure 2.1 illustrates graphically the delay problem during incidents. An incident occurs at time T1 and reduce the capacity below the oncoming traffic flow. An inefficient traffic management scheme removes the incident at time T2'. After this time, the queue starts to dissipate with rate equal to the freeway capacity. The last vehicle in the queue attains normal speed at time T3'. The area inside the big triangle represents the total delay due to the incident. Traffic delay due to incidents can be reduced in two ways. When a capacity-reducing incident

18 w 2.J 0 [ SDE LAY UL LL -J AIE T1 TIME T2 T2' T3 T3' Figure 2.1. Traffic flow and delay during incidents.

19 7 takes place at some point, the optimal control strategy is to immediately restrict or prevent additional vehicles from entering the freeway upstream by means of traffic signals installed at each ramp. Excess demand is stored or preferably diverted to alternate routes. The decrease of the freeway demand upstream, through diversion, allows favorable operating conditions to be restored sooner. In addition, an efficient approach to respond to the incident is needed. Such approach is the Freeway Incident Management system. The functional elements of this system are detection, response, and clearance. The main objective of incident management is the fast removal of the incident from the freeway. The shaded area in Figure 2.1 represents the delay reduction resulting from prompt incident management and demand diversion guidance. To effectively manage an incident situation, the information required by the surveillance and control system includes the prompt detection of the incident in time and space, the magnitude of the capacity reduction, and the extent of congestion generated by the incident. Provided that the above information is collected, entrance ramp metering rates can be adjusted by the central traffic control, estimates can be made of the delay likely to be encountered by freeway traffic, and this information can be directly relayed to drivers. Consequently, the rapid detection of incidents is undoubtedly one of the most powerful techniques for reducing delay and improving the overall freeway operation. Existing techniques for the detection of freeway incidents did not succeed to yield acceptable detection performance. Automated techniques, based on computerized algorithms to translate the traffic data and ascertain the presence of a capacity-reducing incident, have been seriously hindered by excessive, operationally unacceptable false alarm rates. Non-automatic methods, which require operator involvement for detection, identification, and verification, minimize the false alarm risk, but suffer from missed or delayed detections, and also restrict the potential of advanced, integrated traffic management and driver guidance schemes. As a result, incidents still remain a major operational problem for urban freeways highly contributing in traffic delays, fuel emission and pollution increase, and safety problems by secondary accidents.

20 II.2 AUTOMATIC INCIDENT DETECTION SYSTEMS HI.2.1 Introduction Automatic incident detection involves two major elements, a traffic detection system which provides all the necessary traffic information and a computerized incident detection algorithm which automatically translates the traffic data and ascertains the presence of a capacity-reducing incident. Both elements are vital in the incident detection process so that malfunctioning operation of either one result in poor performance of the whole system. It is noteworthy that automatic incident detection has been primarily the focus of research rather than actual application. In practice, incident detection is mostly non-automatic or semi-automatic requiring operator involvement to directly detect incidents (usually through the CCTV coverage of the freeway) or verify the incident signal produced by the computer algorithm before deciding the appropriate response. II.2.2 Traffic Detection System Incident detection has traditionally relied on the use of inductive loop presence detectors imbedded in the freeway pavement to obtain traffic data. Use of such presence detectors has been in existence for more than 25 years. Most major cities have many miles of highway with 6'x 6' loop detectors placed in all lanes every 1/2 mile. These loops are connected with a computer which interrogates each loop every few milliseconds to ascertain the presence or absence of a vehicle. The information is translated to occupancy and flow measurements. Presence detectors are described in details in Chapter III. Research in U.S. and Europe is now directed towards the development of more effective traffic detection systems (i.e., traffic detection through video image processing) to replace the traditional presence detector system. I.2.3 Computer Algorithms An incident detection algorithm is a specific logical and analytical procedure used along with data

21 9 obtained from freeway surveillance traffic detectors to ascertain the presence of a capacity reducing incident. There are three major indicators of an incident detection algorithm performance; detection rate, false alarm rate, and mean time-to-detect. Detection rate is the ratio of incidents detected out of all incidents that occur during a specified time period. Incidents that occur, continue and end without the system detecting them are classified as missed detections. False alarm rate is the ratio of the number of false alarms to the number of decisions (incident or nonincident) made by the system in non-incident conditions. A false alarm occurs when the system signals an incident and there is none. Mean time-to-detect is the average amount of time required by the system to make a detection, given that there is a valid detection. The above measures of effectiveness are related to each other in a kind of trade-off. Normally, higher detection rate coincides with higher false alarm rate. To lower the mean time-to-detect, prompt decision is required and thus, higher false alarm rates should be allowed. There is no specific rule in deciding what is the best combination of detection and false alarm rate specifications to be implemented. This primarily results from the fact that the cost and consequences of a miss are quite different from that of a false alarm and an optimum decision rule has to take into account the relative costs and minimize the average cost. The most effective variables for detection are those that respond sharply and promptly to the passage of the incident-generated shock-wave. On the other hand, counts of these variables must be available through the surveillance system. With these restrictions, occupancy (percent of time that a detector is occupied by passing vehicles) and volume are the most extensively used traffic features in incident detection algorithms. Occupancy is generally more sensitive to incident related traffic changes

22 10 because incidents typically generate high cross-occupancy discrepancies but only a uniform drop of volume at the nearby stations which may be the result of usual congestion. Composite variables of occupancy and volume have also been investigated in the past as control variables for incident detection (i.e. station kinetic energy defined as the ratio of the square volume divided by occupancy) without, however, yielding any improvement INCIDENTS AND OTHER TRAFFIC PHENOMENA As in every detection problem, missed detections and false alarms are major operational problems. Missed detections usually take place when traffic is not too heavy or the incident impact on traffic operations is not severe. False alarms result from various traffic phenomena which construct traffic patterns similar to incident patterns. Both missed detections and false alarms are undesirable and should be ideally eliminated or minimized to tolerable limits by the Incident Detection System. Figure 2.2 presents the occupancy change plot during an actual incident at the immediate upstream and downstream measuring points. The occupancy at the upstream station rises abruptly to extremely high values while the downstream occupancy drops. The extreme values persist shortly and afterwards, as traffic starts to move again, occupancies are progressively restored to their initial values. An incident affecting larger segment of the freeway is illustrated in Figure 2.3. Decreasing station numbers in the north direction of 1-35W correspond to the flow direction. The plot reveals that the incident took place between stations 53N and 50N. It is noteworthy that the incident effect appears with some delay in stations lying farther upstream or downstream from the incident site. The delay corresponds to difference in time required by the incident-generated shock wave to reach different stations. It must be also mentioned that the incident time does not correspond to the actual occurrence time (which is not known) but rather to the time that the Traffic Management Center (from where information has been gathered) detected the incident. This is the reason that the incident appears occurring several minutes after the onset of congestion. Besides severe incidents, there may be incidents not creating considerable flow discontinuity.

23 > 30-- o incident time (30-sec intervals) -- upstream station -+- downstream station Figure 2.2. Effect of a severe accident on detector occupancies.

24 50 40 bu I I I I I I I V I I I I I I I I incident time (30-second intervals) - station 55N - station 53N -e- station 50N --- station 46N Figure 2.3. Incident affecting long road segment.

25 13 Traffic shock waves, in these cases, do not take place or readily propagate on the freeway. Additionally, the various noise sources and approximations in the data corrupt the signal due to the incident. In such cases, detection is hard to achieve and missed detections usually take place. An example of a non-severe incident with limited impact on traffic operations is shown in Figure 2.4. A number of traffic phenomena tend to form patterns similar to incident patterns. Such phenomena should be distinguished from incidents to avoid false alarms. The most frequent one is the recurrent congestion. Theoretically, congestion results from demand increase above the capacity level while incidents are caused by capacity reduction below the demand level. In practice, however, it is usually hard to discriminate between demand increase and capacity reduction especially during peak periods when traffic flow is unstable. Bottlenecks constitute another source for traffic inhomogeneities which are often confused with incidents. They are formed at places on the freeway with abnormal geometrics (lane drop or addition, entrance or exit ramp carrying high volumes, etc.) and result in permanent spatial density or occupancy discrepancies (in contrast to incidents which affect traffic only temporarily). Bottlenecks are likely to lead to either missed detection or false alarm generation. Figure 2.5 indicates the change in occupancy at three adjacent stations located at a freeway segment involving a lane drop between the first two and a lane addition between the second and third one. Under normal conditions, the three stations operate at different average occupancy levels. This is a typical bottleneck situation. Incidently, the particular graph, which represents actual data, exhibits an abrupt, simultaneous, and opposite occupancy change in stations 60S and 61S corresponding to a typical incident pattern. However, no incident was actually reported by the Traffic Management Center. This case may indicate a missed detection. A significant number of false incident alarms are caused by traveling compression waves. Compression waves occur in heavy, congested traffic and are associated with severe slow-down, speedup vehicle speed cycles. They typically are manifested by a sudden large increase occupancy which propagates through the traffic stream in a direction counter to the traffic flow as time progresses. Such a compression wave is shown in Figure 2.6.

26 40 I- if 35- " 30- u o n. i-\ Am BE r B incident A time (30-second intervals) -E- upstream station -+- downstream station Figure 2.4. Effect of a non-severe incident on detector occupancies.

27 o 5U \ i-T time (30-sec intervals) -e- station 60S --- station 61S -- station 62S Figure 2.5. Bottleneck effect on detector occupancies.

28 o 30 o _ S, _ time (30-sec intervals) - station 42S - station 46S -~ station 50S Figure 2.6. Compression wave effect on detector occupancies.

29 II.4 LITERATURE ON EXISTING INCIDENT DETECTION ALGORITHMS 17 Several ways to classify incident detection algorithms have been reported in the literature. This study attempts to classify algorithms with the same general approach and structure into the same group. Additionally, different kind of data requirements result in another classification. Algorithm structure varies from one based on simple traffic variable comparisons against preselected threshold levels to differentiate between incident and incident-free conditions up to advanced algorithms which involve modeling of traffic flow or other techniques such as traffic data filtering and expert systems technology. Algorithms have also different data requirements. Some of them employ observations from a single measuring point, and others from two points surrounding the incident location with data comparison between them. Some algorithms utilize aggregate information, e.g., average flow and occupancy over 20 to 60-second intervals whereas the rest require detailed data for every vehicle (microscopic methods). A short description of each group of algorithms is given next Pattern Recognition (Comparative) Algorithms Intuitively, when a capacity reducing blockage occurs somewhere on the freeway, the density of traffic upstream of the incident location increases while it decreases downstream. Hence, one can consider designing incident detection algorithms that essentially look for low occupancy at the downstream detector and high occupancy at the upstream detector. In fact, this is the basis of a number of algorithms (Tignor, et al., 1977, Payne, et al., 1978), which are based on simple functions of flow and occupancy crossing calibrated thresholds. In general, the algorithms employ occupancy difference tests between adjacent detector stations, a temporal reduction test at the downstream station following an incident (to distinguish from bottlenecks), alarm persistence requirements (to reduce false alarms), and other tests between stations to reduce the number of alarms caused by compression waves. These algorithms vary from simple ones to others involving several states before signalling an alarm. Levin and Krause (1979a, 1979b) developed and evaluated some algorithms of similar structure.

30 18 II.4.2 Time-Series algorithms Algorithms in this group take into account the recent history of the traffic variable to determine the current traffic flow trends. A time-series model is assumed to describe the traffic flow and is employed to provide short-term forecasts of the traffic variable. These forecasts, based on past trends, can predict recurrent congestion which takes some time to build up while nonrecurrent congestion due to incidents is unpredictable. Three such models are the most important. The simplest one, developed by Dudek and Messer (1974), takes into account the mean and standard deviation of the control variable over the most recent few minutes. Cook and Cleveland (1974) proposed a double exponential smoothing model of the control variable to provide the needed forecast. Ahmed and Cook (1982) found that an Autoregressive Integrated Moving Average (ARIMA) model of order (0,1,3) describes well the traffic flow and proposed such a model to detect discontinuities resulting from incidents Probabilistic approaches These approaches aim to develop mathematical expressions for the probability distributions of the control variable or other variable related to incidents by examining historical incident and incident-free data. Sakasita and May (1975) proposed a model which uses the least squares method to estimate the probability function of the travel time between adjacent stations under normal conditions, assuming that this probability function would be significantly disturbed if an incident occurs. The model was evaluated using simulated traffic data. Levin and Krause (1978) considered the probability distribution of the relative spatial occupancy difference between adjacent stations under incident and incident free conditions. They derived the optimal threshold level by using the Bayes' optimal decision rule. Tsai and Case (1979) proposed distinguishing between incident and false alarms on the basis of their different duration characteristics. Considering the true and false alarm duration probability distributions obtained from historical information, they utilized the Bayes' optimal decision rule to

31 19 determine the optimal decision point separating true and false alarms Clustering techniques Techniques in this group attempt to separate the various traffic operations into different states on the basis of the appropriate traffic variables. An example of such technique is the McMaster algorithm (Persaud, et al., 1990) which is based on a two-dimensional analysis of the traffic data. In particular, it proposes separating the flow-occupancy diagram into three areas corresponding to different states of traffic conditions. Incidents are detected after observing specific changes of the traffic state in a short time period. This approach requires calibration of the boundaries separating different traffic conditions -- algorithm thresholds -- individually for each station, as volume-occupancy characteristics vary across stations. II.4.5 The HIOCC algorithm The HIOCC (HIgh OCCupancy) algorithm (Collins, et al and 1983) operates by identifying the presence of stationary or slow moving vehicles over individual detectors, usually inductive loops. This is achieved by looking for several consecutive seconds of high detector occupancy and, when this occurs, an alarm is initiated. An occupancy threshold value of 100 per cent, lasting for two consecutive seconds is often appropriate to signal a stationary or slow moving vehicle. II.4.6 Methods employing traffic flow modeling While the above algorithms employ only detector output to make a decision, other methods take advantage of insights gained from research in traffic flow modeling. Willsky et al. (1980) proposed using macroscopic traffic modeling to describe the evolution of spatial-average traffic variables (velocities, flows, and densities), thus capturing the dynamic aspect of the traffic phenomena to alleviate the false alarm problem. Although scientifically appealing, the two methods resulting from that research did not attract the interest of practitioners. The lack of interest is owed to the complexity of the methods and the strong data requirements, in terms of type of variable (density, space mean

32 20 speed) and short time-space discretization. These restrictions limited testing of the methods to a small number of simulated incident patterns. Cremer (1981) proposed a similar approach applicable to congested cross country freeways in Europe, where detectors are located several kilometers apart. While Willsky models an incident as having a capacity reduction effect, Cremer proposes that modeling the attenuation of the road capacity by an additional (fictitious) volume input at the location of the incident can improve detection. Kiuhne (1989) proposed the use of high-order continuum models to calculate the standard deviation of the speed distribution, and found that the deviation broadens when density approaches the critical value in which stop-start traffic movement is observed. He concludes that detecting such broadening could be the basis for an early warning strategy and incident detection. However, his work has not produced a practical incident detection algorithm. Table 2.1 summarizes the major Automatic Incident Detection algorithms, including the new algorithm that is presented in chapter V, based on their data requirements (Stephanedes, et al., 1992a). The Table indicates, for instance, that the McMaster algorithm employs volume, occupancy, and (optionally) speed data, averaged over 30-second periods from a single or two adjacent stations OPERATIONAL INCIDENT DETECTION SYSTEMS Several DOT's have implemented Incident Detection Systems around the country and in Canada. The characteristics of these systems are presented in this section. The information includes type of traffic measurements, incident detection algorithms, incident verification capabilities, and system performance assessment. Reliability of detector data and rates of detector failures are also presented since the quality of traffic information affects the overall performance of the Incident Detection System. The information was gathered through phone conversation with persons supervising Incident Detection Systems at state DOT's California The Los Angeles Automated Traffic Surveillance and Control (ATSAC) System use loop detector

33 Table 2.1. Summary of Automatic Incident Detection algorithms. Algorithm Traffic Variables Time (sec) Discretization Number of Stations Vol. Occ. Spd Single Adjacent PROPOSED ALG x x x COMPARATIVE x x X TIME SERIES x x x x x McMASTER x x x x x x HIOCC x X ( 2) x x WILLSKY x x x ( 2) X ( 3) CREMER x x ( 2) X (" Optional (2) Not typically obtained from existing loop detector systems (3) Requires closely spaced stations along the freeway

34 22 occupancy data, averaged across all lanes at 30 second intervals. The California Algorithm #5 (California algorithm with persistence test), which works well under shock wave conditions during congestion, and the California Algorithm #3 (California algorithm without DOCCTD check) during light traffic conditions are used to detect incidents. Switching between the two is done automatically. Statistics are not regularly gathered on the performance of the system, but it is perceived to have a very high false alarm rate. The high false alarm rate has limited reliance on system alarms. An incident is typically identified and confirmed by CCTV inspection of a possible incident site, a callbox notification or a report from a roving tow patrol. System performance has steadily improved as problem areas in the system are rooted out and recalibrated. Stations are chosen for recalibration if they signal incidents very frequently. One hundred and fifty detector locations have special thresholds. These thresholds, which are determined by trial and error, are adjusted until a certain location signals incident only occasionally on an offline run over the most recent historical data. The output of the system is in the form of a map board with embedded colored light bulbs. Green, yellow, and flushing red bulbs indicate the degree of severity of the traffic situation at the corresponding point on the freeway. A flushing red indicates an incident. Detector failures are not a major problem because automatic error correction occurs in such situations. Since all of the freeways are at least 3 lanes wide in each direction, if a detector fails in a single lane the average is simply taken over the remaining lanes; thus accurate data are transmitted from almost all stations. Typically, 95 % of all detectors are providing useable data. The rest fails only intermittently. This 5% can come from any detectors in the system -- not the same 5% failing repeatedly Connecticut The Connecticut program is not currently operational. A demonstration project is being prepared that will include overhead mounted radar detectors. These detectors will return speed and volume data to an algorithm that will compare the data directly with thresholds. If the speed data drops below the thresholds for a certain period of time, an incident will be signaled. The signaled incident will most

35 likely be verified with CCTV. Loop detectors, although exist in Connecticut's freeway systems, are not used in conjunction with the incident detection/management program. 23 H.5.3 Florida Florida is just in the beginning stages of development for any kind of freeway data acquisition system. Closed circuit cameras are presently being installed and variable message signs will be operating by the first half of In the latter half of 1993, loop and infrared detectors will be installed. II.5.4 Illinois Over 600 mainline detectors covering 130 miles of freeway in the Chicago Metropolitan area are sampled every 1/60th of a second. From these samples, the one minute occupancy is developed. In terms of incident detection algorithm, the Bayesian approach was used in the past. The method produced good results, but required extensive computer time. Currently, the occupancy difference between one upstream and one downstream detector is used as an incident indicator. If this difference continuously exceeds a threshold for a five minute period an incident is signaled. The persistence test imposes generally long detection and response times. However, this is preferred to the alternative -- responding to false alarms -- because emergency crews have tended to ignore very frequent alarms in the past on the assumption that they were false alarms. The thresholds are set by empirical experimentation on typical, historical data. The McMaster algorithm was examined offline as a possible substitute for the current system. It was found to give a good detection rate after a difficult calibration period, but the corresponding false alarm rate was far worse than with any other algorithm tested. Malfunctioning detectors only accentuated this problem. It was found that the algorithm was totally incompatible with a system that consistently exhibited many detector problems. Incident alarms are produced in the form of a printout on a line printer. The operator on duty must review raw data to determine whether the signal was a false alarm or too minor an incident to respond to. Heavy reliance on operator expertise in verification of incidents reduces the high false

36 24 alarm rate exhibited by the system. II.5.5 Minnesota One minute occupancy data are updated every 30 seconds in the Twin Cities Metro area. These data are used for incident detection through the original California Algorithm. Three thresholds are used; the first defines the transition between low and medium occupancy, the second the transition between medium and high occupancy, and the final the transition between high occupancy and an incident. The output consists of color computer graphic maps where each traffic condition is represented by a specific color on the map. A low occupancy is indicated by a green color, a medium occupancy by a yellow, a high occupancy by a red color, and an incident by flashing red. An incident signal is confirmed by viewing the area in question on closed circuit TV. An operator can then make a determination as to the nature and severity of the incident, and can make an appropriate response. In this way a high false alarm rate is negated. II.5.6 Texas The Texas DOT in Austin is currently in the process of setting up an incident detection/management system. They are at the stage of preparing software, but have not settled on an algorithm. The proposed system has the following characteristics. The signal from a detector is sampled 100 times per second. A local computer unit housed in a cabinet alongside the freeway develops the 20 second average every 20 seconds for 24 trap detectors and 24 non-trap detectors. This local unit is in charge of two ramp meters. The main system can receive from 64 local units and make system-wide decisions from this data. The system will include dynamic signs, lane control signals, and gates to close ramps. In addition, arterials will be monitored with data provided every minute. A third layer to this system will include a non-real time manager which will handle such tasks as reports and plotting. II.5.7 Virginia The traffic data consists from loop detector occupancy measurements collected every quarter second

37 25 from the detectors. From the raw data the one minute average is produced every minute. The California Algorithm is used for incident detection. Thresholds are changed automatically, but guards are placed on the changes i.e., the thresholds can only vary within 10% of historical threshold data. Manual calibration of thresholds is performed annually. The output of a detection alarm consists of an "incident page" of data which includes the time, location, and occupancy level of the incident. Information regarding the positioning of CCTV cameras is provided. All incidents are verified through CCTV inspection of the incident site. Once verified, integrated signals can be activated and other appropriate measures can be initiated. The system performance has been measured at a 10% false alarm rate for a 70% detection rate. Such performance is considered unacceptable; an upgrade to the Autoscope system is underway and should be complete by The system will not replace the current system but will run in conjunction with it. H.5.8 Washington State Traffic data are collected every second in Seattle's freeways. The one minute average of occupancy data is developed every 20 seconds. The modified California Algorithm with a persistence check which requires the thresholds to be broken for two consecutive 20 second periods is used. Thresholds have been set by "trial and error". Output is presented in the form of a date, time and location on a log printer. Performance data have not been gathered on any detection indices. Detector failures are observed, but failure rates are not known. Detector failures are preempted by validity checks. The system can work around isolated loop failures. The tunnel system operates independently of the rest of the freeway (Washington State DOT, 1990). Volume and occupancy data are collected every 20 second and speed data are calculated from these parameters. These data are sent as input every 20 seconds to the main computer. One of five different incident detection algorithms is used based on the time of day. The selection of periods was done by considering patterns of historical data.

38 26 * Algorithm A, a modification of the California Algorithm which operates best under moderate traffic conditions, uses the following tests: #25 OCCDF: the difference in adjacent station occupancies, #26 OCCRDF: the ratio between #25 and single station occupancy upstream of the incident, and #27 DOCCTD: the ratio of the change in downstream occupancy between the previous and current periods. * Algorithm B, which works well under moderate to heavy flows, utilizes a persistence check which requires that conditions for an incident be met for two consecutive periods. The following tests are used: #24 DOCC: downstream occupancy (averaged over all lanes), #25, and #26 as described above. * Algorithm C, is well suited to peak periods. Tests #24, #25, and #26 are used along with #27, DOCCTD: the ratio of the change in downstream occupancy between the previous and current periods. It includes the same persistence check as B and is best used in conjunction with another algorithm. Its stated purpose is to reduce false alarms due to compression waves. * Algorithm D is identical to algorithm C except that no persistence check is performed. It is best suited to moderately heavy traffic. * Algorithm E, operates differently based on flow conditions. During moderate flow, a comparison similar to algorithm B is performed. During light flow the change in upstream speeds between the previous and current periods is used. Tests #24, #25, and #26, which were described previously, are used along with #2 OCC: occupancy at the upstream station (averaged over all lanes), and #13 SPDTF: ratio of change of speed at upstream station.

39 27 Output is provided on interactive screens. Verification of an incident may be done by an operator through a closed circuit television system. Two tunnels are installed with this system. The Mount Baker Ridge tunnel system seems to be operating fairly well, but the Mercer Island system has a high false alarm rate although both tunnels were calibrated after the system had been in operation for 6 months. II.5.9 Ontario, Canada Ontario's Queen Elizabeth Way is the second most heavily travelled highway in North America. Highly variable traffic patterns arise due to many closely spaced interchanges, collector/distributer lanes, and geometry changes present in the system. Detector stations, spaced every 600m provide speed, volume, and occupancy data, averaged over a twenty second period at twenty second intervals. The California Algorithm has been implemented on three separate versions -- one for low occupancy, one for medium occupancy, and one for high occupancy. Switching to the appropriate version is done automatically. Thresholds were chosen after an extensive offline computer analysis in which characteristic data sets were run multiple times with incremental changes in the thresholds. Performance curves could then be examined and desired rates selected. False alarm rates of 10 to 20 per hour per detector station were observed for detection rates between 50% and 75 %. True incidents were identified from operator logs. The high false alarm rates resulted in little response to incidents. Output is presented on multi-color computer graphics maps which allows the operator to interactively select red circles indicating incidents and receive appropriate information on the situation. A trial period running the McMaster Algorithm offline on a 24-hour basis has produced some promising results. For detection rates of 75%, false alarms rates of 1 every 64 hours per station have been achieved.

40 DATA DESCRIPTION

41 III. DATA DESCRIPTION INTRODUCTION The detection and identification of incidents on freeways require some type of sensing device along the road so that traffic conditions can be monitored continuously. Electronic presence detectors, having shown advantages over other types in the past, have been installed on hundreds of miles of freeway in the United States providing observations of the traffic conditions. Besides traffic data, information about actual incidents is required for the development and off-line testing of incident detection algorithms. Time and location of incident occurrence are the most important pieces. In addition, incident type, duration, and severity information as well as traffic condition information is helpful. 11I.2 PRESENCE DETECTORS The most widely used type of presence detector is the inductive loop detector. A typical realization is a wire loop set in a square,.6'x 6', centered in the lane and buried in the pavement. These sensors are typically found at one-half mile intervals along the road and normally each lane has a detector. All the detectors across the lanes at one spot of the freeway form a detector station. A computer interrogates the loop every few milliseconds and provides a binary signal indicating the presence or absence of a vehicle in a well defined area of the road around the loop. Figure 3.1 illustrates the detector signal created by a single vehicle passage (Kurkjan, et al., 1977). The analog signal is put through a threshold device to yield a binary signal in time. This signal is sampled times a second. Presence detectors can count the number of vehicles which cross them in some time interval and consequently measure the volume or flow rate quite accurately. In addition, occupancy is an easily obtainable measurement from a presence detector signal. The occupancy of a particular time interval at a given detector is the percent of time interval which the detector signalled vehicles were present. Occupancy quantitatively behaves like density in the detector vicinity. Other measurements, such as speeds etc., can be derived from presence detector signals but with increased effort, special loop configuration (speed trap), and less accuracy.

42 An analog signal resulting from the passage of a single vehicle time presence presence pulse * time I * The analog signal is put through a threshold device to yield a binary signal in time time HIGH LOW * I I time Signal is sampled (15-60 time sec.) 1: "vehicle present" bit 0: "vehicle absent" bit Figure 3.1. Presence detector signal associated with a single vehicle passage.

43 31 III.3 THE TWIN CITIES FREEWAY TRAFFIC SURVEILLANCE AND CONTROL SYSTEM Traffic surveillance and control of the Twin Cities freeway system is done by the Traffic Management Center (TMC). Among all used means for traffic monitoring, loop detectors and closed television (CCTV) are the most important. Detectors are installed in all parts of the freeways. CCTV monitoring, originally covering only a part of 1-35W, is currently being significantly expanded. Loop detectors provide all the quantitative traffic information used to determine the traffic condition along the freeway. The computer connected with them translates the binary signal to volume and occupancy counts corresponding to 1-minute intervals, updated every 30 seconds and averaged over all lanes of the freeway. Alternatively, 5-minute counts separately for each lane are available. For the detection of incidents, which require monitoring of abrupt traffic change, the first type of data is used. Speed counts are not available. The detector system alone, however, is a "blind" system in the sense that it cannot determine the nature of any abnormality on the freeway. Consequently, TV surveillance provides an additional tool for observation of traffic movement leading to identification or verification of possible problems on the road. Through direct observation, the operator can detect the type of the problem and promptly decide the appropriate response. III.4 TEST SITE AND DATA DESCRIPTION The proposed algorithm as well as several algorithms from the literature were tested with a set of actual data. In particular, 140 hours of afternoon peak period (4:00-6:00 pm.) traffic data from a 5.5- mile long segment of 1-35W in Minneapolis (Figure 3.2) were collected through the MnDOT's Traffic Management Center. The freeway segment has three lanes along most of its length. It includes two major bottlenecks, one freeway-to-freeway interchange, and one grade followed by shoulder elimination (Minnehaha Creek). In particular, I-35W has three lanes at station 60, and drops one that enters the HWY 62 east; the two remaining lanes at station 61 increase back to three before station 62, following a lane merging from HWY 62 west. The typical occupancy pattern in these three stations has been presented in Figure 2.5; from the Figure, station 61 appears to operate at occupancies higher

44 Figure 3.2. Study site on I-35W in Minneapolis.

45 33 than those of its neighboring stations during the greatest portion of the test period. This bottleneck spot often creates congestion that spreads to a long freeway segment upstream. The second bottleneck location, often experiencing recurrent congestion, is at the Minnehaha Creek bridge, where a freeway segment with uphill grade is followed by shoulder eliminations at the bridge. A third location that occasionally creates congestion phenomena is at the 46th St., where an exclusive exit lane upstream reduces the number of mainline lanes from four to three followed by an entrance ramp. During the testing period all ramps were metered and no significant bottlenecks were observed at entrance-ramp locations. The test segment includes four entrance and five exit ramps. The traffic data consist of 30-second volume and occupancy measurements from loop detectors, forming 14 detector stations imbedded along the road, miles apart. The 30-second data are averaged across lanes so that individual lane information is not available. The above traffic data is a typical set collected routinely in most U.S. cities. Certain cities may obtain additional information, e.g., measurements for each lane, speed, and shorter-time measurements. Although this is certainly an advantage in terms of potential incident detection performance, algorithms that depend on such features cannot be implemented across all systems. During the testing period, 27 incidents were reported by the traffic operator on duty. The detection of the incidents was accomplished mostly through CCTV cameras installed along the freeway segment. Of all incidents, 15 were accidents; three accidents occurred in the moving lanes and the rest in the shoulder. According to the operator log information, six accidents had severe impact on traffic operations, four happened in an already congested region, three had limited congestion impact on traffic, and the rest were not classified. Besides the incidents, 12 vehicle stalls were observed. All occurred on the shoulder, one had a severe impact on traffic, one occurred in an already congested region, seven produced limited congestion, and three were not classified. Table 3.1 describes the incident set.

46 Table 3.1. Description of incident characteristics. Accidents Stalls Incidents Impact In lanes In shoulder In lanes In shoulder In lanes In shoulder Severe In congestion* Limited Not reported * Incidents occurring at an already congested area

47 EVALUATION OF MAJOR EXISTING ALGORITHMS

48

49 IV. EVALUATION OF MAJOR EXISTING ALGORITHMS IV.1 INTRODUCTION AND PROBLEM STATEMENT Several approaches and algorithms have been developed over the past two decades for the detection of incidents on freeways. Algorithm have been evaluated but such evaluations have been typically limited to a few pairs of detection vs. false alarm rates which are inadequate to provide a complete performance picture. Further, comparative evaluation of several algorithms has not been performed in a single study. On the other hand, algorithm comparison from results produced by different studies cannot be performed since evaluation specifications vary across studies. In this project, we have tested all major algorithms on a common data set from the Twin Cities freeway system. Unlike previous works, this study establishes a unified system for robust evaluation of incident detection algorithms (Chassiakos, 1990). The unified system is based on operating characteristic curves. In signal detection theory, the receiver operating characteristic curve (ROC) is a plot of detection probability (or rate) versus false alarm probability (or rate) which a receiver can achieve. Such a plot is shown in Figure 4.1 (Van Trees, 1968, p. 43). Each curve corresponds to a different "receiver" (in our case algorithm). The coordinates of each point on a curve represent detection and false alarm probabilities corresponding to a specific threshold value. As the threshold decreases, more detections are achieved but at the expense of more false alarms. By plotting the ROC curves of different receivers, their detection performances are directly compared. The receiver with its ROC curve closest to zero false alarm and 100% detection rate axes is the best performing one. The main advantage of the ROC technique to compare detection algorithms lies on the fact that it does not depend on the structure of the particular algorithm. Consequently, algorithms can be compared to each other without regard to the number of tests, traffic variable involved, or type of algorithm. Additionally, the whole range of detection rate (from 0 to 100%) is covered and corresponding pairs of PD and PF are obtained allowing the algorithm user to establish the thresholds which best meet the requirements for the surveillance system. Besides the establishment of a unified system for evaluating incident detection algorithms, the

50 tpd Figure 4.1. Receiver operating characteristic curve. Figure 4.1. Receiver operating characteristic curve.

51 38 evaluation of existing algorithms serves the following purposes. Unlike past studies, that typically compare the performance of any developed algorithm against the most known existing one, the present evaluation includes almost all algorithms which can be evaluated with the available traffic data. Further, the transferability of evaluation results across studies is questionable. Test sites may have different traffic, demand and supply, weather, and driver characteristics. In addition, incident characteristics across data sets may vary significantly. Reasonable comparison, thus, of existing or new algorithms can essentially be done only on a common data basis. The present analysis indicated (as discussed in a later section) that the algorithm performance depends on the traffic and incident data used. IV.2 EXISTING ALGORITHM DESCRIPTION In this section, we describe the major existing algorithms that were evaluated in this study. A major concern in algorithm selection was the availability of the traffic data required by the algorithm. For instance, the HIOCC algorithm requires 1-second data that are not available. The following algorithms were included in the evaluation. From the Pattern Recognition group of algorithms: 1. California algorithm with a termination test 2. California algorithm with a persistence test 3. Algorithm #7 From the Time-Series group: 1. Standard Deviation algorithm 2. Double Exponential algorithm 3. ARIMA algorithm A short description of each of these algorithms follows. IV.2.1 California algorithm The California model consists of three simple comparisons to preset thresholds values that must all be exceeded before an incident is signalled. Where T,, T 2, T 3 are predefined, station-specific thresholds,

52 the three comparisons are: 39 OCCDF - OCC ( i, t) - OCC ( i +1,t) T (4.1) OCCRDF - OCC(i,t) - OCC(i+1,t) T 2 (4.2) OCC (i, t) DOCCTD - OCC(il+1,t-2) - OCC(i+1,t) T3(43) OCC(i+1, t-2) where i indicates the station number increased from upstream to downstream and t indicates the present time (minutes). An incident is detected where upstream occupancy becomes significantly greater than downstream occupancy not only in absolute (test 1) but also in relative values (test 2) and at the same time downstream occupancy has significantly decreased during the past two minutes. Test 3 distinguishes an incident from a bottleneck situation by indicating the reduction in flow past the incident over a short period of time. Besides the original model, a modified version of the California algorithm which involves an incident termination test is reported in the literature. After an incident has been signalled, the relative spatial occupancy (OCCRDF) test is employed to reveal whether the incident continues to affect the traffic during the consecutive time periods. Further, the persistence test requires that two consecutive values of OCCRDF must exceed the threshold before initiating an incident alarm. IV.2.2 Algorithm #7 Algorithm #7 is similar to the California algorithm except it involves a persistence test and replaces the temporal occupancy differences by downstream occupancy (DOCC) in the third test in the California model. The logic behind the persistence test is that random fluctuations of the traffic variable do not last long, but incidents generally result in discontinuities that persist for a period of time. The replacement in the third test was considered to reduce high number of false alarms produced by compression waves, a usual traffic phenomenon in the Los Angeles freeway traffic.

53 40 IV.2.3 Standard Normal Deviation algorithm This model calculates the mean and standard deviation of the control variable (energy or lane occupancy) for the last three to five minutes and detects an incident when the present value differs substantially from the mean in units of standard deviation. In particular, the variable, SND - x, (4.4) s where x is the value detection variable at time t, m and s are the mean and standard deviation of the detection variable over previous n sampling periods respectively, is used. An incident is signaled where SND exceeds a predefined threshold. Two strategies have been proposed. Strategy A requires one SND to be critical. Strategy B requires two successive SND values to be critical. IV.2.4 Double Exponential algorithm In this method, the traffic parameter occupancy is smoothed according to the formulas, and S( t) a OCC(t) + (1-a) S( t-1), (4.5) S 2 (t) - a S{ ( t) + (1-a) S 2 (t-1) (4.6) where OCC(t) is the occupancy for time interval t, and a the smoothing constant. Incident detection is accomplished through the use of a tracking signal, which is the algebraic sum to the present minute of all the previous estimate errors divided by the current estimate of the mean absolute deviation. The tracking signal should dwell around zero because the predictions either match the data or compensate for errors in succeeding time periods. A detection is indicated by a significant deviation of the signal from zero. IV.2.5 ARIMA algorithm The algorithm is based on an Autoregressive Integrated Moving Average, ARIMA (0,1,3), model to

54 41 provide short-term forecasts of the state variable (traffic occupancy) and the associated 95 % confidence limits constructed two standard deviations away from the corresponding point forecasts. These confidence limits are given by X - X, (1) - 2 (4 Xt+ - X,(1) + 2ar. with X,(1) - Xt - z e 1 i(1) - e2 e 2 (1) - 3 e- 3 (1) (4.8) where xt = the traffic occupancy observed at time t, Xt+1 = approximate 95 percent confidence limits for x,+,, Xt(l) = point forecast made at time t, et_(1) = forecast error made at time (t-1), 01,02,03 = parameters of the moving average operator of order 3, and qa, = the estimate of the standard error of the white noise variables. An incident is detected when the observed occupancy value lies outside the confidence limits. IV.3 EVALUATION METHODOLOGY The hugh amount of traffic data (about 240,000 data points) imposed development of software for every one of the above algorithms. Computer programs were written in Microsoft Quick C, v.5.1. They include an input module to read the traffic data as transmitted by the Traffic Management Center, the main module where calculations of needed variables are sequentially done, and the output module which provides detailed information for every alarm produced by the algorithms. Because some minor modifications of the original models were needed to be explored, the software structure has been designed in such a way that any proposed modification can be easily incorporated. It is also important that the procedure to obtain the operating characteristic curves appearing in the following sections be brought out. Every single point of these curves requires design of an

55 42 experiment. First a threshold value or threshold set is assumed. The algorithm is run with the incident data to determine the number of incidents which can be detected with such a threshold. Then, for the same threshold, the whole incident-free data are scanned by the program to identify the number of the algorithm generated false alarms. IV.4 ALGORITHM EVALUATION IV.4.1 Termination Test An incident termination test allows an algorithm to signal the end of the incident as well as to group all successive alarms from a particular station that are produced by a single incident. Counting the number of groups of successive alarms, instead of individual ones, results in more meaningful values of false alarm rates. Figure 4.2 illustrates the false alarm reduction after employing a termination test to the California algorithm. The corresponding detection and false alarm rate values are tabulated in Table 4.1. Termination test involvement is not explicitly reported in the literature for all algorithms. However, we consider termination tests for all algorithms throughout the study. IV.4.2 Pattern Recognition Algorithms Two modified versions of the original California algorithm are evaluated. The first is the one reported in the literature with a termination test. The second involves additionally an alarm persistence test. The persistence test requires that OCCRDF must exceed the corresponding threshold for two consecutive periods before an alarm is signalled. Algorithm #7 is also evaluated. Several pairs of detection and false alarm rate values corresponding to different threshold sets are presented in Tables 4.1, 4.2, and 4.3 for the three algorithms respectively. The operating characteristic curves for the above algorithms are shown in Figure 4.3. The plots consist of scattered points rather than well defined curves as a result of their multi-variable structure which allows various combinations of thresholds to produce the same detection rates but drastically different false alarm rates. The dispersion of points can be considered as a drawback, because the best threshold combinations, leading to the highest detection points, are not obvious or easy to establish. Figure 4.3

56 Table 4.1. Performance of California algorithm without and with a termination test. THRESHOLD VALUES #1 #2 #3 DETECTION RATE (%) FALSE ALARM RATE (%) SIMPLE ALGORITHM TERMINATION TEST

57 100 Detection rate (%) False alarm rate (%) No termination test Termination test Figure 4.2. Termination test effect on California algorithm performance.

58 Table 4.2. Detection performance of California algorithm with a persistence test. THRESHOLD VALUES #1 #2 #3 DETECTION RATE (%) FALSE ALARM RATE (%)

59 Table 4.3. Detection performance of Algorithm #7 with a persistence test. THRESHOLD VALUES #1 #2 # DETECTION RATE (%) 11.i FALSE ALARM RATE (%)

60 100 Detection rate (%) False alarm rate (%) Figure 4.3. Operating characteristics of Pattern Recognition algorithms.

61 Table 4.4. Mean time-to-detect for Pattern Recognition algorithms. AVERAGE MEAN TIME TO DETECT (min) DETECTION RATE (%) CALIFORNIA ALGORITHM TERMINATION PERSISTENCE ALGORITHM #

62 Mean time-to-detect (min) -1.5 r Detection rate (%) Calif.-termination Calif.-persistence i lalgorithm #7 Figure 4.4. Mean time-to-detect: Pattern Recognition algorithms.

63 50 indicates that, for the detection rates of interest (higher than 50%), Algorithm #7 can perform slightly better than both versions of the California algorithm. The finding is in agreement with the original study be Payne, et al., (1978). However, improper choice of threshold values can lead Algorithm #7 to poor performance, significantly lower than the other two. The algorithm sensitivity to threshold selection reduces its attractiveness. Table 4.4 and Figure 4.4 present the mean time-to-detect performance of Pattern Recognition algorithms. Algorithm #7 and the California algorithm with persistence test exhibit a half-minute average delay in response time comparing to the California algorithm with a termination test as a result of their requirement for an additional alarm to indicate an incident. IV.4.3 Time Series Algorithms Two traffic variables (station occupancy and spatial occupancy difference between adjacent stations) are examined for each of the Standard Normal Deviation and Double Exponential algorithms. Only strategy B of the Standard Deviation algorithm, performing better than strategy A (as the original study concluded) is considered. Strategy B utilizes 5-minute average values and persistence of alarm for two consecutive time periods. The performance of both traffic variables is summarized in Table 4.5. The smoothing constant of the Double Exponential algorithm was valued as recommended by the original study. Table 4.6 presents several detection and false alarm pairs for the station occupancy and spatial occupancy difference traffic variables. The ARIMA algorithm requires parameter estimation in each location that the algorithm is applied. Because of strong data requirements, this estimation was not performed in this study, but parameter values reported in the literature were used instead. Although those values were originally derived with data from a detector station in I-35W in Minneapolis, they do not necessarily describe the current situation appropriately. The results of the evaluation are tabulated in Table 4.7. The spatial occupancy difference variable was not included in the evaluation, since no parameters were obtained for this variable in the original study. Figure 4.5 indicates the higher effectiveness of the spatial occupancy difference variable in detecting incidents as compared to the station occupancy for the Double Exponential algorithm. This

64 51 behavior, indicative also for the Standard Deviation algorithm, is quantified to 30-40% false alarm reduction if spatial occupancy difference instead of station occupancy is used. The spatial occupancy difference is the key detection variable in Pattern Recognition algorithms also. Comparison of all three algorithms with station occupancy as state variable and the first two with spatial occupancy difference is performed in Figure 4.6. The Double Exponential algorithm exhibits generally the best performance when station occupancy is employed as detection variable. However, the algorithm fails to detect the least severe incidents. Those incidents require a very low threshold value to detect them. The structure of the algorithm is such that for low thresholds the alarm starts long before and without any connection to the incident, survives because of fluctuations in traffic, continues through the incident duration and ends independently of the incident termination. Such cases practically cannot be considered as detections and are classified as missed detections. With the spatial occupancy difference as detection variable, the Double Exponential algorithm performs still better than the Standard Deviation algorithm for almost all detection rates. Both algorithms perform clearly better with the spatial occupancy difference variable instead of the single station occupancy. The ARIMA algorithm performs poorly comparing to the others. The poor performance may result from improper choice of the model parameters. Regarding the mean time-to-detect, the Standard Deviation and ARIMA algorithms perform much better than the Double Exponential as indicated in Table 4.8 and Figure 4.7. The latter algorithm responds very slowly to traffic changes causing delay in signalling incident alarms. Mean time-to-detect for this algorithm varies from 1 minute in high detection rate levels up to 6 minutes in low levels. In low detection levels, traffic variable values must exceed high threshold values to produce an alarm. The negative values in Table 4.7 result from the fact that the available information on incident occurrence time corresponds to the detection time by the traffic operator on duty rather than to the occurrence time. Negative mean time-to-detect implies that the algorithm detects incidents earlier than the operator in the average.

65 Table 4.5. Detection performance of the Standard Deviation algorithm. STATION OCCUPANCY VARIABLE SPATIAL OCCUPANCY DIFFERENCE VARIABLE THRESH. VALUE DETECT. RATE (%) FALSE AL. RATE (%) THRESH. VALUE DETECT. FALSE AL. RATE (%) RATE (%)

66 Table 4.6. Detection performance of the Double Exponential algorithm. STATION OCCUPANCY VARIABLE SPATIAL OCCUPANCY DIFFERENCE VARIABLE THRESH. DETECT. VALUE RATE (%) FALSE AL. RATE (%) THRESH. DETECT. FALSE AL. VALUE RATE (%) RATE (%) '

67 Table 4.7. Detection performance of the ARIMA algorithm with station occupancy as state variable. THRESH. DETECT. FALSE AL. VALUE RATE (%) RATE (%) THRESH. DETECT. FALSE AL. VALUE RATE (%) RATE (%)

68 Detection rate (%) I False alarm rate (%) Station occupancy E3 Spatial occ. diff. Figure 4.5. Performance comparison between two detection variables: Double Exponential algorithm.

69 100 Detection rate (%) False alarm rate (%) - Q - Standard Deviation*-- Double Exponential-- ARIMA (0,1,3)* --- Standard Deviation*- Double Exponential** State variables: * station occupancy ** spatial occ. difference Figure 4.6. Operating characteristic curves: Time Series algorithms.

70 Table 4.8. Average mean time-to-detect for Time Series algorithms. AVERAGE MEAN TIME TO DETECT (min) DETECTION RATE (%) STANDARD DEVIATION Occup. Occ. Dif. DOUBLE EXPONENTIAL Occup. Occ. Dif. ARIMA Occup

71 h Mean time-to-detect (min) Detection rate (%) E Standard Deviation* Standard Deviation*C Double Exponential* Double Exponential*L ARIMA (0,1,3)* * State variable: station occupancy **State variable: spatial occ. differenc Figure 4.7. Mean time-to-detect: Time Series algorithms.

72 59 IV.4.4 All algorithms The evaluation reveals the superiority in performance of Pattern Recognition algorithms in comparison with Time Series ones. The operating curves of two Pattern Recognition (California algorithm with persistence test and Algorithm #7) and two Time Series algorithms (Standard Deviation and Double Exponential) are compared in Figure 4.8. The Standard Deviation and Double Exponential utilize spatial occupancy difference as the detection variable which was found to best describe the traffic changes. The false alarm rates of Pattern Recognition algorithms range from 0.40 to 0.70 of those which Time Series algorithms produce. It is noteworthy that the Pattern Recognition algorithms have been taken with their upper limit of performance assuming that acquisition of such performance is always feasible. Existing techniques for the automatic detection of freeway incidents are not reliable as they are seriously handicapped by excessive, operationally unacceptable false alarm rates. The poor performance becomes more visible if false alarm rates are translated to actual number of false alarms. The evaluation indicates that for every single detected incident, the California algorithm with persistence test at 37% detection level produces about 22 false alarms, Algorithm #7 at 70% detection rate almost 34 false alarms, and the Double Exponential algorithm at 60% detection rate 77 false alarms. These pairs are indicative of the performance of all existing algorithms. The mean time-to-detect does not vary much among the algorithms (with the exception of the Double Exponential algorithm). The Standard Deviation algorithm presents the lowest response time signalling incident alarms in general 1 to 1.5 minutes earlier than Pattern Recognition algorithms. IV.4.5 Performance comparison with previous studies An interesting inquiry is the comparison of evaluation results produced by different studies for the same algorithm. Two algorithms, the original California algorithm and the Algorithm #7, are used to address the issue in Figures 4.9 and 4.10 respectively. The operating characteristic curves of the California algorithm produced by the original (development and evaluation of a number of algorithms by Payne, et al., 1978, for the FHWA) and present study almost coincide. However, the performance

73 100 Detection rate (%) False alarm rate (%) --9 Calif.-Persistence Algorithm #7 0 Standard Deviation* -0- Double Exponential* *State variable: spatial occ. difference Figure 4.8. Performance comparison among existing algorithms.

74 61 of Algorithm #7 seems to vary in different environments. As indicated in Figure 4.10, the algorithm performance in the original study is quite better than in the present study. A possible reason for such inconsistence is that Algorithm #7 has specifically been designed to eliminate false alarms resulting from compression waves moving the emphasis from the detection of incidents to the prevention of false alarm generated by compression waves. The algorithm, therefore, is expected to perform well in freeway traffic which suffers from numerous compression waves (as for instance the Los Angeles freeway system from where data were used in Payne's study) and not equally well in another freeway system with fewer and less severe compression waves (traffic data from Minneapolis fall in this category).

75 4,~ ~ IUU Detection rate (%) C) False alarm rate (%) ~ original study 9 this study Figure 4.9. Comparison with results from literature: California algorithm.

76 4 IUU Detection rate (%) n False alarm rate (%) - original study this study Figure Comparison with results from literature: Algorithm #7.

77 NEW ALGORITHM DEVELOPMENT

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79 V. ALGORITHM DEVELOPMENT V.1 INTRODUCTION AND PROBLEM STATEMENT A number of studies have focused on the development of computer algorithms for the detection of freeway incidents without, however, yielding the desirable detection performance. As a result, many centers for freeway traffic management around the country use non- or semi-automatic techniques to detect incidents. In particular, operators dealing with the traffic surveillance are in charge of detecting incidents through the various means for surveillance (loop detector outputs, CCTV, map display, scanner, metro traffic, etc.). In a few traffic management centers, computer algorithms are used, but the alarms they produce must be verified by the operator before any further action is taken. On the other hand, the need for improved computer algorithm development becomes more and more imperative following the current trend for advanced, integrated traffic management and driver guidance schemes. Past research and operating experience have demonstrated that the detection of incidents can be automated. In this direction, the development of efficient algorithms improving the current level of performance is required. The use of a sophisticated incident identification and verification system would offset the remaining weaknesses of the detection algorithms. Responding to the need for more efficient algorithms, the present research developed a new algorithm which improves the detection performance in terms of false alarm rates, leading to an ultimate performance of algorithms that utilize aggregate traffic data from presence detectors and consist of simple traffic variable comparisons to detect incidents. V.2 DETECTION ISSUES TO BE ADDRESSED Algorithms involving simple volume and occupancy comparisons along with aggregate (20-60 sec) traffic counts have received the most attention in the literature. These algorithms, being intuitively appealing and computationally simple, are most likely to be implemented in freeway control systems. In addition, the traffic data they utilize are simple, easily obtainable and used also for other purposes (e.g., ramp control) besides incident detection. Presence detector system has been and will be the

80 66 primary source for extracting traffic data in the future (at least until another, more capable traffic detection system has been extensively and successfully tested and installed on the freeways). Any incident detection algorithm, thus, should take into account this reality and adjust appropriately its data requirements. Following these specifications, the new algorithm is of the same nature as most existing algorithms. Its development has been motivated by observations on specific deficiencies of existing algorithms that lead to improper operation. The major weakness of existing algorithms is that they are too deterministically structured to follow the rather stochastic nature of traffic flow. As a result, they often fail to detect incidents even if the introduced disturbance in traffic flow is obvious. Referring primarily to Pattern Recognition algorithms, they assume that increase and reduction in occupancy at the upstream and downstream station, following an incident, take place simultaneously. However, it is known that those changes appear with a time delay which depends on the location of the incident relatively to the detector stations and the propagation speed of the produced shock waves, which also varies depending on the flow level and the incident severity. Figure 5.1 illustrates a 5 minute delay in the development of significant cross-occupancy difference between adjacent stations after the occupancy drop in the downstream station. In addition, existing algorithms assume a very specific incident pattern which can never be found exactly leading to missed detections. For instance, the temporal occupancy reduction at the downstream station following an incident may not take place or may not be observable enough to initiate an alarm. This is the case when the downstream occupancy level before the incident is not very high. An example of an actual incident with no real drop in downstream occupancy is shown in Figure 5.2. Usually, although heavy congestion is built upstream, detection is not accomplished. The persistence requirements, which is employed by existing algorithms to reduce false alarms, are often insufficient. The persistence tests require usually two consecutive alarms to be given before signaling an incident. Two consecutive alarms, in 30-second data, correspond to 1-minute time period which is typically smaller than the duration of a random fluctuation in traffic flow. On the other hand, persistence requirements cannot be extended to longer time periods because, even in case of incidents

81 o incident time (30-second intervals) -s- station 55S --- station 60S -*- station 61S Figure 5.1. Unusual incident pattern.

82 0 a cz 0 incident time (30-second intervals) I 1 I -e- upstream station -*- downstream station I Figure 5.2. Incident with no complete traffic pattern.

83 69 with long duration, the detection variable does not remain above the threshold continuously but rather drops for a moment below the threshold, because of fluctuations, and recovers again. A test utilizing an average of the control variable for a few intervals, rather than individual values, could remove the given weakness. Further, Pattern Recognition algorithms do not take sufficiently into account the recent history of traffic. Incorporation of past information is crucial especially when bottleneck situations are encountered. A bottleneck is characterized by permanent occupancy difference between adjacent stations. For instance, in a lane drop location, upstream occupancy is smaller than the downstream one under normal conditions for a substantial amount of time. If an incident takes place at this spot, even if it initializes observable shock waves, they may not be sufficient to increase upstream occupancies enough so that the spatial occupancy difference exceeds the corresponding threshold. In this case, no detection is achieved. Finally, Pattern Recognition algorithms do not handle efficiently the variability of the traffic data in time. Heavily congested traffic produces occupancy signal with high variability. Many of the alarms produced during such periods result from short-term traffic inhomogeneities and can be eliminated only if the variability of the data is properly taken into account. An example of an incident during a period with highly fluctuating traffic is depicted in Figure 5.3. Time Series algorithms take into account the recent history of traffic, however, the traffic models they assume are either very simple and coarse or require parameter estimation which can limit algorithm transferability. Further, they detect abrupt changes in the traffic stream without attempting to distinguish the source of the change. They do not utilize tests specifically designed for incidents and do not typically involve alarm persistence tests. V.3 PROPOSED ALGORITHM DESCRIPTION Our review of Incident Management Systems around the country indicates that algorithms involving simple volume and occupancy comparisons based on aggregate (20-60 sec) traffic counts, such as the California algorithm, have received the most attention by practitioners. In general, algorithms that are

84 _ , 35-1 B S10 o incident time (30-second intervals) -e- upstream station - - downstream station Figure 5.3. Noisy detector occupancy data.

85 71 intuitively appealing, computationally simple, and based on traffic data that are widely available, are most likely to be implemented in freeway control systems. Within this specification framework, the proposed logic aims to develop simple occupancy tests to distinguish incidents from other traffic disturbances. Two major characteristics can be used for this purpose. In particular, incidents result in rapid temporal changes in the traffic conditions or state. Further, the duration of incidents is longer than that of other disturbances. The first characteristic can be used to distinguish incident congestion from bottleneck (recurrent) congestion that evolves slowly as compared to incident congestion. The duration characteristic can differentiate incidents from shortduration traffic disturbances. DELOS (DEtection LOgic with Smoothing) algorithms involve smoothing the occupancy measurements to distinguish short-duration traffic inhomogeneities from incidents (Chassiakos, 1990, Stephanedes and Chassiakos, 1992). When an inhomogeneity is present, smoothing eliminates or diminishes the impact of such fluctuation; on the other hand, in case of incidents, smoothing does not substantially modify the pattern as long as the duration of the incident is greater than the number of terms in the smoother. Although smoothing may conceal the patterns of some non-severe incidents, the large reduction in false alarms compensates for a few possibly missed incidents. Test results indicate significant false alarm reduction of the new algorithm compared to similar algorithms, e.g., Standard Deviation, Double Exponential, and California algorithm, that attempt raw data manipulations. Further, in a manner similar to, but more effective than, in previous algorithms, the proposed structure attempts to distinguish bottleneck from incident congestion on the basis of slow or fast evolution of the congestion respectively. In particular, the distinguishing logic is based on temporal comparison of the detection variable, i.e., the spatial occupancy difference between adjacent stations. For comparison, the incident test of the California algorithm considers the occupancy reduction at the downstream station. However, such reduction is not always observed during incidents. The structure of the detection scheme considers two smoothed values for the detection variable, one representing current traffic conditions, and one past conditions. Assuming an incident occurs at

86 72 time t, we define OCCi(t+k), smoothed occupancy at station i from k occupancy values after t, and OCCi(t), smoothed occupancy at station i from n occupancy values prior to t, where k and n represent the window size to smooth the data for the current and past period respectively. The incident is likely to create congestion in the upstream detector station i and reduce flow in the downstream station i+1 leading to a high value of the spatial occupancy difference variable AOCC(t+k) - OCC, (t+k) - OCC j (tc+k) (5.1) Further, to distinguish from bottleneck congestion, we compare the value of spatial occupancy difference AOCC(t+k) for the current period to the corresponding value from the past period, AOCC ( t) - OCC(t) - OCCj, ( t) (5.2) Both tests, congestion and incident, are normalized by maxocc( t) - max {OCC (t), OCC i 1 ( t)} (5.3) to reflect changes with respect to existing conditions prior to incident. In summary, the proposed detection logic involves the following test, Congestion test AOCC(t+k) Ž (5.4) maxocc (t) Incident test AOCC( tc+k) - AOCC( t) > T (5.5) maxocc(t)

87 73 After an incident has been signaled, the congestion test is employed at consecutive time intervals to indicate the period that the incident is in effect. The alarm is terminated at the first time interval that the congestion test fails. A number of techniques can be used for smoothing traffic occupancy. Selection criteria include simplicity in design, and implementation and detection effectiveness. The major concerns are related to the effectiveness in eliminating undesirable false alarm sources, the extent to which smoothing distorts the information content of incident patterns, and the detection delay imposed from the need to obtain a number of measurements while an incident is in progress. Moving average, a linear transformation, is a simple but effective smoothing technique. The occupancy measurement at time t and detector station i, o,(t), is smoothed via the formula, -1 ~(5.,6) OCC ( t) - -. o 1 (t-1) L 1-0 Moving averages of different order, L=k and L=n, are employed for the current and past periods respectively. Window sizes k and n are selected to optimize the algorithm performance. An additional length constraint is imposed on k as long smoothing windows (e.g., more than 10 samples) would result in excessive delays in algorithm response. The above linear transformation, although effective in removing traffic fluctuations, distorts information-bearing edges (i.e., step-like changes that are due to incidents), possibly obscuring their information content. An alternative, non-linear, transformation employing the statistical median of the data window, OCC ( t) - median [o ( t), o (t-l)),..., o(t-l)] (5.7) has been considered to address this issue. Exponential smoothing is a third smoothing technique, extensively used in several applications. This type of smoothing is particularly important in smoothing past occupancy values and has also been considered in Cook and Cleveland, The advantage of such smoothing is that it considers a long data window while placing a greater weight on the recent occupancy values that are more important in determining the traffic trends. The general form of the smoother is,

88 74 OCC (t) - xa-o(t) + (1-a) -OCC(t-1) (5.8) where the variables are defined above and a represents the smoothing factor. A number of algorithms have been developed along the three major types of smoothing described above. For easy reference the algorithms are coded as DELOS x.y (z,w), where, x and y represent the type of smoother that is used for the past and current period respectively, with the values of 1 for average, 2 for median, and 3 for exponential smoother. Further, z and w represent the past and current period window sizes (n and k respectively) to smooth the data in the average or median smoother case. In exponential smoothers, z represents the smoothing factor a, and w is the time lag k between the end of the past and the end of the current period. In combinations of exponential smoothing for the past data with other types of smoothing for the current, the window size for the current data period represents the above time lag. For example, DELOS 3.1 (0.05, 6) is assigned in the algorithm which smooths past data exponentially with a=0.05 and current data with an average 6-sample window. Figure 5.4 illustrates the application of the average algorithm DELOS 1.1 (10,6). In particular, we consider the occurrence time t of an actual accident that the traffic operator classified as having limited impact on traffic. As the Figure indicates, fluctuations in traffic occupancy exist under nonincident conditions; however, these fluctuations are usually short in duration, tend to cancel each other, and can thus be removed with data averaging. The 10-interval average indicates that the upstream and downstream stations operated at about the same occupancy prior to the incident (spatial occupancy difference value is -2). The incident, on the other hand, created a high, persistent difference (equal to 10.5) in average occupancy between the two stations. The temporal difference before and after { (-2) = 12.5} is an incident indication.

89 ^ 30 el Occupancy (%) Time (30-second intervals) Figure 5.4. Application of the new algorithm.

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91 NEW ALGORITHM TESTING AND EVALUATION

92

93 VI. NEW ALGORITHM TESTING AND EVALUATION VI.1 INTRODUCTION - METHODOLOGY Results from tests evaluating the effectiveness of the new algorithm are presented in this section. The evaluation included the main features of the algorithm, namely detection rate (the ratio of incidents detected out of all incidents), false alarm rate (the ratio of false alarms out of all decisions, incident or non-incident, made by the system), and mean time-to-detect (the average time duration needed for detection; detection time is measured from the time that incidents are reported in the operator's log rather than from the actual occurrence time). Algorithm performance is assessed via operating characteristic curves, an evaluation method whose effectiveness lies on its independence from algorithm structure. Operating characteristic curves depict the detection and false alarm rates accomplished by an algorithm across threshold values. To construct these curves, the threshold parameters are allowed to vary over a wide range of values. Every threshold set (in this case, a set is a pair) produces a performance point (PD, PF) on the curve. Three types of smoothing have been considered in this study, linear (average), median and exponential. Past and current occupancy measurements are smoothed according to one of the above types and are included in the corresponding version of the algorithm. For each type of smoothing, we have tested several alternative structures, by varying the data window sizes or the value of the smoothing parameter a. For each smoothing type, we have performed a limited sensitivity analysis for the influence of the smoothing parameters on the detection performance. Further, we have compared the performances among the best algorithms in each class. Finally, we have compared these algorithms with the best performing existing algorithms to assess the performance improvement from using smoothed in place of raw data.

94 78 VI.2 ALGORITHM DETECTION PERFORMANCE VI.2.1 Linear smoothing The degree of smoothing with linear transformation depends on the length of the window sizes and the smoothing coefficients. To simplify the sensitivity analysis, we have considered equi-valued coefficients that sum up to unity, so that the signal magnitude is not affected in any way. The sensitivity analysis was held by modifying the window length for past and current data starting from the values initially found to result in good performance in Chassiakos (1990). These values are k=6 and n= 10. We have considered three additional DELOS versions with window sizes {k= 6, n= 20}, {k= 8, n= 10}, and {k= 10, n= 15}. The threshold and performance measures (detection, false alarm rates, and mean detection time) are tabulated in Tables and graphically illustrated in Figure 6.1. Although quite different window sizes were employed, no substantial difference in detection performance is recorded. This is a positive factor for the algorithm since no exact calibration for the window sizes will be required in a different application. The major difference between the four versions is in the mean detection time. This time depends on the size k of the current data window. Tables indicate that DELOS 1.1 (15,10) exhibits average detection time one to two minutes higher than the other three DELOS 1.1 versions. VI.2.2 Median smoothing Unlike linear smoothing, median transformation are more influenced by the data window size. To illustrate, we fixed the past data window to nine intervals and considered three windows for the current data incrementally changing in length by two time intervals, i.e., one minute. The performance results are presented in Tables and in Figure 6.2. In the case of median filtering, increasing the length of the current window results in observable improvement in detection and false alarm performance. In particular, DELOS 2.2 (9,9) performs better than DELOS 2.2 (9,7) and DELOS 2.2 (9,5) at most detection levels. However, because of longer window in the current data, DELOS 2.2 (9,9) presents the highest average detection times.

95 Table 6.1. Thresholds and performance results: DELOS 1.1 (10, 6). Algorithm DELOS1.1 Tc T Detection Rate (%) 89 False Alarm Rate (%) Hourly Number of False Alarms* 11.8 Average Detection Time (min) 0.1 (10, 6) * test site length is 5.5 miles and includes 14 detector stations

96 Table 6.2. Thresholds and performance results: DELOS 1.1 (10, 8). False Hourly Average Detection Alarm Number Detection Rate Rate of False Time Algorithm Tc TI (%) (%) Alarms' (min) DELOS (10, 8) * test site length is 5.5 miles and includes 14 detector stations

97 Table 6.3. Thresholds and performance results: DELOS 1.1 (20, 6). False Hourly Average Detection Alarm Number Detection Rate Rate of False Time Algorithm Tc T, (%) (%) Alarms* (min) DELOS (20, 6) * test site length is 5.5 miles and includes 14 detector stations

98 Table 6.4. Thresholds and performance results: DELOS 1.1 (15, 10). False Hourly Average Detection Alarm Number Detection Rate Rate of False Time Algorithm Tc TI (%) (%) Alarms' (min) DELOS (15, 10) * test site length is 5.5 miles and includes 14 detector stations

99 .4 f% rk IUU Detection rate (%) I I I DELOS 1.1 (20, 6) - DELOS 1.1 (10, 8) DELOS 1.1 (15, 10) False alarm rate (%) I Figure 6.1. Operating characteristic curves: Average smoothing.

100 Table 6.5. Thresholds and performance results: DELOS 2.2 (9, 5). Algorithm DELOS2.2 Tc 0.30 T, 0.30 Detection Rate (%) 93 False Alarm Rate (%) Hourly Number of False Alarms* 13.8 Average Detection Time -- (min) 0.4 (9, 5) * test site length is 5.5 miles and includes 14 detector stations

101 Table 6.6. Thresholds and performance results: DELOS 2.2 (9, 7). False Hourly Average Detection Alarm Number Detection Rate Rate of False Time Algorithm Tc T, (%) (%) Alarms* (min) DELOS (9, 7) * test site length is 5.5 miles and includes 14 detector stations

102 Table 6.7. Thresholds and performance results: DELOS 2.2 (9, 9). False Hourly Average Detection Alarm Number Detection Rate Rate of False Time Algorithm Tc T, (%) (%) Alarms' (min) DELOS (9, 9) * test site length is 5.5 miles and includes 14 detector stations

103 41d r% r% 1UU Detection rate (%) DELOS 2.2 (9, 7) n - DELOS 2.2 (9, 9) I., False alarm rate (%) Figure 6.2. Operating characteristic curves: Median smoothing.

104 VI.2.3 Exponential smoothing 88 While in the previous smoothing techniques the smoothing parameter is the window length, exponential smoothing is determined by the smoothing factor a. Our investigation indicated that sufficient smoothing is achieved by values of a smaller than Tables and Figure 6.3 present the performance result of three DELOS versions with a's equal to 0.03, 0.05, and 0.10 respectively. In each version, the same a value was used for both current and past data. The time lag between the end of the past and the end of the current period is six time intervals in all cases examined. The detection performance graph in Figure 6.3 shows that lower values of a result is slightly improved performance, but the difference is not substantial, at least for the cases a =0.03 and a =0.05. As for the average detection time, Tables indicate that, in general, detection time decreases as a increases. VI.2.4 Combination of Exponential-Linear smoothing The algorithm that employs exponential smoothing for filtering past data and average smoothing for filtering current data intuitively makes the most appropriate distinction between pre- and post-incident conditions. In particular, the exponential smoother averages over a long past period to better assess traffic conditions prior to the incident, and the average smoother acts within a short window to account only for measurements while the incident is in progress. The performance measures for three DELOS 3.1 versions are presented in Tables and Figure 6.4. The values of the smoothing coefficients for exponentially smoothing the past data are, as previously, a =0.03, 0.05, and The length of the window for linearly smoothing current data is six time intervals for all cases. Figure 6.4 indicates that all versions of the algorithm produce performance curves close to each other. Therefore, the smoothing parameter for past data does not substantially influence the performance of the algorithm. Similarities in performance are also observed in the average detection time, as indicated by the Tables.

105 Table 6.8. Thresholds and performance results: DELOS 3.3 (0.03, 6). False Hourly Average Detection Alarm Number Detection Rate Rate of False Time Algorithm Tc T, (%) (%) Alarms' (min) DELOS (0.03, 6) * test site length is 5.5 miles and includes 14 detector stations

106 Table 6.9. Thresholds and performance results: DELOS 3.3 (0.05, 6). I Algorithm DELOS3.3 Tc 0.07 TI 0.07 Detection Rate (%) 85 False Alarm Rate (%) Hourly Number of False Alarms* 6.4 Average Detection Time (min) 1.0 (0.05, 6) * test site length is 5.5 miles and includes 14 detector stations

107 Table Thresholds and performance results: DELOS 3.3 (0.10, 6). False Hourly Average Detection Alarm Number Detection Rate Rate of False Time Algorithm Tc T, (%) (%) Alarms* (min) DELOS (0.10, 6) * test site length is 5.5 miles and includes 14 detector stations

108 S 3.3 (0.03 6) 100 Detection rate (%) DELOS 3.3 (0.05, 6) DELOS 3.3 (0.10, 6) ai I I False alarm 0.4 rate (%) Figure 6.3. Operating characteristic curves: Exponential smoothing.

109 Table Thresholds and performance results: DELOS 3.1 (0.03, 6). False Hourly Average Detection Alarm Number Detection Rate Rate of False Time Algorithm Tc TI (%) (%) Alarms* (min) DELOS (0.03, 6) * test site length is 5.5 miles and includes 14 detector stations

110 Table Thresholds and performance results: DELOS 3.1 (0.05, 6). Algorithm DELOS3.1 Tc 0.25 T, 0.25 Detection Rate (%) 93 False Alarm Rate (%) Hourly Number of False Alarms* 12.4 Average Detection Time (min) 0.1 (0.05, 6) * test site length is 5.5 miles and includes 14 detector stations

111 Table Thresholds and performance results: DELOS 3.1 (0.10, 6). False Hourly Average Detection Alarm Number Detection Rate Rate of False Time Algorithm Tc TI (%) (%) Alarms* (min) DELOS (0.10, 6) * test site length is 5.5 miles and includes 14 detector stations

112 h h 1UU Detection rate (%) I DELOS 3.1 (0.05, 6) DELOS 3.1 (0.10, 6) 0 o False alarm rate (%) Figure 6.4. Operating characteristic curves: Exponential-Average smoothing.

113 97 VI.3 PERFORMANCE COMPARISON WITH EXISTING ALGORITHMS In this section, we present the "best" algorithm from each type of filtering and compare them against existing algorithms. The four smoothing versions are DELOS 1.1 (10,8), DELOS 2.2 (9,9), DELOS 3.3 (0.05,6), and DELOS 3.1 (0.05,6). First, we compare the performance of the DELOS algorithms to that of two older algorithms (Standard Deviation and Double Exponential) that feature a structure similar to DELOS to assess the performance improvement from using smoothed in place of raw data. In particular, the average smoothing algorithm DELOS 1.1 (10,8) is compared to the Standard Deviation, on the basis of detection and false alarm rates, in Figure 6.5. The two exponential algorithms, DELOS 3.3 (0.05,6) and DELOS 3.1 (0.05,6), are compared to the Double Exponential algorithm in Figure 6.6. Both comparisons indicate that smoothing current measurements leads to a substantial reduction in false alarms, thus, accomplishing higher detection performance. The evaluation results for the four alternative smoothing types are shown in Figure 6.7. While the performances of the average and exponential smoothers are comparable, they are all superior to the median algorithm. A possible reason for such behavior is related to the substantial contribution of compression waves to false alarms. More specifically, to avoid excessive detection times, the window size that averages the current data must be kept short. However, compression waves may have duration comparable or longer than this window. In such cases, smoothing fails to attenuate the patterns and a false alarm typically occurs. Median smoothers tend to preserve these compression waves better than linear or exponential smoothing, and thus, produce a higher number of false alarms. To better appreciate the performance of the proposed algorithms, Figure 6.7 includes performance curves from two well known existing algorithms, the modified California and Algorithm 7, both tested in this study. The comparison indicates a high detection improvement of the proposed algorithms, especially at low false alarm rates that are most suitable for operational use. In particular, presenting the false alarm performance in terms of number of alarms per hour indicates the algorithm performance as viewed by the operator; such information could be useful in determining the potential of computerized methods as a primary tool for incident detection. For instance, as the Tables in this chapter indicate, the proposed algorithm, at 60% detection rates, can yield approximately 1 false alarm

114 100 Detection rate (%) False alarm rate (%) Figure 6.5 Performance comparison: DELOS vs. Standard Deviation.

115 ., P% 0 1UU Detection rate (%) False alarm rate (%) Figure 6.6. Performance comparison: DELOS vs. Double Exponential.

116 0%0 Detection rate (%) 1UU ( 20 nr S-+- - DELOS 3.1 (0.05,6) California algorithm +- Algorithm # False alarm rate (%) I Figure 6.7. Performance comparison: DELOS and California algorithms.

117 101 per peak hour in a 5.5-mile long, heavily traveled freeway segment with 14 detector stations. A final comment from the evaluation brings up a positive contribution of computerized algorithms that is usually overlooked, the reduction in missed detection. In particular, algorithms can identify incidents that would not be detected by operators, if no algorithm was used. To investigate this possibility, we isolated the major false alarms produced by the new algorithms and plotted the corresponding data. The experiment revealed that 22 of these alarms exhibit incident-like patterns, including a number of patterns that very strongly resemble incidents. However, since no incident identification can independently verify their occurrence in an off-line evaluation, we have treated such alarms as false.

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119 CONCLUSIONS AND FUTURE RESEARCH NEEDS

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121 VII. CONCLUSIONS AND FUTURE RESEARCH NEEDS The prevention of avoidable accidents and the management of traffic during incidents have been the focus of attention in recent years. The significant contribution of incidents to traffic congestion has necessitated their special treatment by surveillance and control systems. Rapid detection of incidents can substantially reduce traffic delays and is the cornerstone for effective incident management systems. Existing techniques for the automatic detection of freeway incidents are not reliable as they are seriously handicapped by excessive, operationally unacceptable false alarm rates. Consequently, current detection algorithms have found limited acceptance by traffic engineers. The present study focused in assessing the performance limitations of conventional automatic incident detection systems. The research was directed towards two objectives, the performance evaluation of the major existing algorithms and the development of an improved algorithm approaching the ultimate performance of conventional algorithms. All testing and experimentation was accomplished with data from I-35W in Minneapolis. Conventional automatic incident detection algorithms and a new algorithm were evaluated based on their operating characteristic curves. The evaluation revealed that Pattern Recognition algorithms, employing three test variables, can distinguish incidents from other traffic phenomena more effectively than single-variable Time Series algorithms that employ statistical forecasting of traffic. At all detection levels, Pattern Recognition algorithms produce 30-50% fewer false alarms than Time Series algorithms. Comparison of our results with findings from the literature indicates that algorithm performance may exhibit varying degrees of transferability across test locations. While transferability potential increases for algorithms designed with general traffic behavior in mind, it deteriorates when algorithms involve specific tests to account for traffic phenomena (e.g., compression waves) that do not exhibit the same frequency of occurrence and severity across test sites. A detection algorithm has been developed for identifying capacity-reducing incidents in freeway traffic. The algorithm aims to minimize the number of false alarms that previous algorithms generate when temporal random oscillations in the traffic measurements, frequently observed in congested

122 104 flows, occur. The proposed structure involves preprocessing the traffic data with average, median, or exponential smoothers over data windows of approximately five minute length to eliminate or reduce the size of traffic fluctuations. With short-duration fluctuations filtered out, a significant temporal change in the smoothed spatial occupancy difference between two stations indicates a major, in magnitude and duration, traffic change that is most probably due to a capacity-reducing incident. The algorithm was tested with traffic and incident data from 1-35W in Minneapolis and compared with previous algorithms with similar structure but without data preprocessing. The test results suggest that smoothing the data can substantially reduce the false alarm risk. In particular, test results with the new algorithm indicate false alarm reduction up to 80% compared to major existing algorithms. Since the false alarm level is the measure that most probably defines the acceptance for each algorithm, we can restate the above conclusion by noting that the new algorithm detects a higher number of incidents than previous algorithms at all false alarm levels. In addition, the algorithm presents a mean time-to-detect comparable to that of existing algorithms. Although the new algorithm improves on the performance of conventional detection methods, it is still restricted by modeling and hardware limitations. For achieving a lower false alarm rate, appropriate for operational use, further research is ongoing by the authors addressing the need for improved traffic modeling (Chassiakos and Stephanedes, 1992) and more-effective distinction between incident and non-incident alarms. Expert system and neural network technology would further benefit the incident detection process.

123 105 ACKNOWLEDGEMENTS The authors would like to thank Mr. Ron Dahl and Mr. Dave Strege, Traffic Management Center, Minnesota, Department of Transportation, for their cooperation in the data collection. The Center for Transportation Studies, Department of Civil and Mineral Engineering, University of Minnesota is acknowledged for its support.

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125 REFERENCES

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127 REFERENCES 107 Ahmed S.A. and Cook A.R. (1982). Discrete Dynamic Models for Freeway Incident Detection Systems. Transportation Planning and Technology, vol. 7, pp Chassiakos A.P. (1990). An Improved Time-Space Incident Detection Algorithm. M.Sc. Thesis, Dept. of Civil and Mineral Engineering, University of Minnesota. Chassiakos A.P. and Stephanedes Y.J. (1992). Improved Incident Detection for Adaptive Control in IVHS Networks. Proceedings 25th International Symposium on Automotive Technology and Automation, Florence, Italy. Collins J.F., Hopkins C.M., and Martin J.A. (1979). Automatic incident detection - TRRL algorithms HIOCC and PATREG. TRRL Supplementary Report 526. Collins J.F. (1983). Automatic incident detection - experience with TRRL algorithm HIOCC. TRRL Supplementary Report 775. Cook A.R., and Cleveland D.E. (1974). Detection of Freeway Capacity-Reducing Incidents by Traffic-Stream Measurements", Transportation Research Record, no 495, pp Cremer M. (1981). Incident Detection on Freeways by Filtering Techniques. Preprints 8th IFAC Congress, Kyoto, XVII Dudek C.L., and Messer C.J. (1974). Incident Detection on Urban Freeways. Transportation Research Record, no 495, pp Federal Highway Administration. (1982). Incident Detection Algorithms. A Freeway Management Handbook, FHWA Volume 2: Planning and Design, pp Grenzeback L.R., and Woodle C.E. (1992). The True Costs of Highway Congestion. ITE Journal vol. 62, no 3, pp Kiuhne R.D. (1989). Freeway Control and Incident Detection Using a Stochastic Continuum Theory of Traffic Flow. Proceedings of the 1989 ASCE Conference on Applications of Advanced Technologies in Transportation Engineering, San Diego, California. Kurkjan A.L., Gershwin S.B., Houpt P.K., and Willsky A.S. (1977). Dynamic Detection and Identification of Incidents on Freeways, Volume 2: Approaches to Incident Detection using Presence Detectors. M.I.T. Electron. Syst. Lab., Cambridge, MA, Rep. ESL-R-765. Levin M. and Krause G.M. (1978). Incident Detection: A Bayesian Approach. Transportation Research Record, no 682, pp Levin M. and Krause G.M. (1979). Incident Detection Algorithms, Part 1: Off-Line Evaluation. Transportation Research Record, no 722, pp

128 Levin M. and Krause G.M. (1979). Incident Detection Algorithms, Part 2: On-Line Evaluation. Transportation Research Record, no 722, pp Payne H.J. (1971). Models of freeway traffic and control. Math. Models Public Syst., Simulation Council Proc., vol. 28, no 1, pp Payne H.J., Helfenbein E.D., and Knobel H.C. (1976). Development and testing of incident detection algorithms. Fin. Rep., FHWA, FH , vol. 2. Payne H.J. and Tignor S.C. (1978). Freeway Incident Detection Algorithms Based on Decision Trees with States. Transportation Research Record, no 682, pp Persaud B.N., Hall F.L., and Hall L.M. (1990). Congestion Identification Aspects of the McMaster Incident Detection Algorithm. Transportation Research Record, no 1287, pp Sakasita M. and May A.D. (1975). Development and Evaluation of Incident Detection Algorithms for Electronic-Detector Systems on Freeways. Transportation Research Record, no 533, pp Stephanedes Y.J., and Chassiakos A.P. (1991). A Low Pass Filter for Incident Detection." Proceedings of the 1991 ASCE Conf on Applications of Advanced Technologies in Transportation Engineering, Minneapolis, Minnesota, pp Stephanedes Y.J., Chassiakos A.P. and Michalopoulos P.G. (1992a). Comparative Performance Evaluation of Incident Detection Algorithms. Transportation Research Record in press. Stephanedes Y.J. and Chassiakos A.P. (1992). Application of Filtering Techniques for Incident Detection. ASCE Journal of Transportation Engineering in press. Tignor S.C. and Payne H.J. (1977). Improved Freeway Incident Detection Algorithms. Public Roads, vol. 41, no 1, pp Tsai J. and Case E.R. (1979). Development of Freeway Incident Detection Algorithms By Using Pattern-Recognition Techniques. Transportation Research Record, no 722, pp Van Trees, H. (1968). Detection, Estimation, and Modulation Theory Part I. John Wiley & Sons, New York. Washington State DOT. (1990) Tunnel Incident Detection System: A Brief Overview. Willsky A.S., Chow E.Y., Gershwin S.B., Greene C.S., Houpt P.K., and Kurkjan A.L. (1980). Dynamic Model-Based Techniques for the Detection of Incidents on Freeways. IEEE Transactions on Automatic Control, vol. 25, no 3, pp

129 APPENDIX: DATA DESCRIPTION

130

131 TRAFFIC DATA SAMPLE FILE Date: 11/30/1989 Direction: southbound STATION TIME S( l ) 042S S 050S 051S 055S 060S 061S 062S 17:00:30 30(223(326 17:01:00 17:01:30 17:02:00 17:02:30 17:03:00 17:03:30 17:04:00 17:04:30 17:05: TOTALS :05:30 17:06:00 17:06:30 17:07:00 17:07:30 17:08:00 17:08:30 17:09:00 17:09:30 17:10:00 TOTALS :10:30 17:11:00 17:11:30 17:12:00 17:12:30 17:13:00 17:13:30 17:14:00 17:14:30 17:15: (1) Detector station name <2) Station volume (vehicles/minute) (3) Station occupancy (%)

132 INCIDENT OCCURRED ON FREEWAY 1-35W DURING THE TIME PERIOD 4:00-6:00 pm BETWEEN MILEPOINTS AND FROM 6/15/89-1/15/90 South direction mile stations # date time type point affected time clear lanes blocked result 1 6/20 16:55 S S-060S 17:04 2 6/22 16:50 S S-046S 17:00 3 6/28 16:37 A S-060S 17:00 4 7/10 16: S-035S 17:35 5 7/13 17:46 A S-060S -17:57 6 7/19 16:14 A S-062S 16:54 7 7/19 17:11 S S-046S 17:42 8 7/19 17:26? S-061S - 9 7/20 16:48 A S-028S 17: /20 17:49 S S-055S 18: /26 16:48 S S-050S /31 16:39 A S-042S 17: /31 17:36 A S-060S 17: /02 17:01 S S-050S 17: /08 16:05 S S-050S 16: /22 16:44 S S-055S 17: /22 17:17 A S-050S 17: /13 17:43 A S-060S 18: /14 16:22 A S-038S 17: /21 16:21 A S-046S 16: /30 17:15 S S-046S 17: /06 16:28 A S-051S 16: /13 16:11 A S-046S 16: /18 17:38 A S-035S 17: /09 17:06 S S-042S 17: /11 16:26 S S-061S 16: /11 16:33 A S-061S 16:37 LEFT SHOULDER L RIGHT SHOULDER S LANE 2 S RIGHT SHOULDER - RIGHT SHOULDER S LEFT SHOULDER - RIGHT SHOULDER - LANE 2 LANE 1 + RT. SH. S RIGHT SHOULDER T RIGHT SHOULDER RT. & LEFT SHOULDER S RT. SHOULDER & EXIT S LEFT SHOULDER T RIGHT SHOULDER L MEDIAN L MEDIAN A RIGHT SHOULDER L RIGHT SHOULDER A RIGHT SHOULDER S RIGHT SHOULDER A RIGHT SHOULDER A RIGHT SHOULDER RIGHT SHOULDER A T RIGHT SHOULDER T LEFT SHOULDER L RIGHT SHOULDER L

133 SYMBOL EXPLANATION INCIDENT TYPE A - ACCIDENT S - STALL C - LANE CLOSURE T - FLAT TIRE M - MECHANICAL F - VEHICLE FIRE P - PATROL X - ROLLOVER D - SPILLED LOAD L - SPINOUT O - OTHER (EXPLAIN) RESULT N - NO IMPACT ON TRAFFIC T - AFFECTING TRAFFIC - NO CONGESTION L - RESULTED IN LIMITED CONGESTION S - RESULTED IN SEVERE CONGESTION A - ADDED TO EXISTING PROBLEM

134

135 HE 336 E94 $ C.2 Stephanedes, YorgOs 3. Development and application of incident detection