18 Transportation Research Record: Journal of the Transportation Research Board, No. 2280, Transportation Research Board of the National Academies, Washington, D.C., 2012, pp. 18–27. DOI: 10.3141/2280-03 T. Sayed, M. H. Zaki, and J. Autey, Department of Civil Engineering, University of British Columbia, 6250 Applied Science Lane, Vancouver, British Columbia V6T 1Z4, Canada. K. Ismail, Department of Civil and Environmental Engineering, Carleton University, 1125-3432 Colonel By Drive, Ottawa, Ontario K1S 5B6, Canada. Corresponding author: T. Sayed, [email protected]. of traffic conflicts provides a more complete understanding of the mechanism of failure that leads to a vehicle collision. A traffic conflict is defined as “an observable situation in which two or more road users approach each other in space and time to such an extent that there is a risk of collision if their movements remained unchanged” (2, 3). For this concept to be operational, the safety hierarchy is transferred into measurable parameters based on certain assumptions. For each traffic event in the hierarchy, an asso- ciated severity can be estimated, and thus, the severity represents its location in the hierarchy (Figure 1). Conflict indicators are used to map road user positions to severity measures. Many severity indicators have been developed to mea- sure traffic conflicts. One example is the time to collision (TTC). For two road users on a collision course, TTC is defined as the extrapolated time at which the collision will occur (4). Positive empirical evidence that shows the validity of traffic con- flict techniques comes from the study of Sayed and Zein, in which a statistically significant correlation between the frequency of traffic conflicts and collisions at signalized intersections was found (1). The traffic conflict technique involves observation, recording, and evaluation of the frequency and severity of traffic conflicts at a location by a team of trained human observers; but this traditional method of traffic conflict data collection has been challenged on several accounts. For example, inter- and intraobserver variability is a common challenge to the repeatability and consistency of results from traffic conflict surveys (5). Furthermore, field observations are costly to conduct and demand staff training. Moreover, despite decades of conceptual developments, no universal operational definition of a traffic conflict exists; for example, objectively measurable interpreta- tions of the words “approach,” “risk of,” or “unchanged” exist in the previous conceptual definition (6). Finally, the estimation of objective conflict indicators, such as TTC (4), by the use of field observations can be difficult. Automation of the process of traffic conflict analysis can enable the traffic conflict analysis to take place in an accurate, objective, and cost-efficient way. More precise, successful automation of the process of extraction of conflict information from video sensor data can have considerable benefits for traffic safety studies (7–9). Video data are rich in details, recording devices are becoming less expen- sive, and video cameras are often already installed for monitoring purposes. In addition, video data represent a permanent record of the traffic events analyzed and can be reviewed and validated, unlike data from observer-based surveys conducted in the field. A primary focus of road safety analysis that could greatly benefit from vision-based analysis is the before-and-after (BA) evaluation of safety treatments. The purpose of BA studies is to measure the safety benefits (or absence thereof) derived from a safety treatment. Feasibility of Computer Vision-Based Safety Evaluations Case Study of a Signalized Right-Turn Safety Treatment Tarek Sayed, Karim Ismail, Mohamed H. Zaki, and Jarvis Autey Traditional road safety analysis has often been undertaken with histori- cal collision records. However, limitations on the quality and complete- ness of collision data gave rise to surrogate ways of measuring safety, especially the traffic conflict technique. Traditionally, traffic conflict techniques have relied on field observations, which have some reliability and repeatability problems. Therefore, successfully automating conflict detection with data extracted from video sensors could have considerable benefits for traffic safety studies. Before-and-after safety evaluations could greatly benefit from automated analysis of traffic conflicts, and the main objective of this paper is to demonstrate the use of this analysis technique for such evaluations. A right-turn safety improvement was implemented at an intersection in Edmonton, Alberta, Canada, in 2009 to mitigate the high rate of rear-end and merging collisions. The right- turn ramp was closed, and all right-turning vehicles were brought to the right-turn lane at the intersection, where a “No-Right-Turn-on-Red” sign was installed. In this study, video sensors were the primary source of conflict data. The video data were analyzed and traffic conflicts were measured with an automated traffic safety tool. The distributions of the calculated conflict indicators before and after the treatment showed a considerable reduction in the frequency and severity of traffic conflicts. This result suggests significant positive changes in rear-end, merging, and total conflicts. The results of this study show the potential benefit of adopting automated conflict analysis for before-and-after safety studies. Traditional road safety analysis has often been undertaken with his- torical collision records. However, the use of collision records for safety analysis is a reactive approach because a significant number of collisions must be recorded before action is taken (1). Further- more, problems well recognized to be associated with collision data are data availability and quality. Therefore, the use of surrogate safety measures, such as traffic conflicts, has been advocated as an alternative or complementary approach to analysis of traffic safety because it offers a perspective broader than that obtained by the use of collision statistics alone (1). For example, traffic conflicts occur more frequently than collisions, the desired sample size for analysis can be obtained over much shorter periods of time, and analysis
Transcript
18
Transportation Research Record: Journal of the Transportation Research Board, No. 2280, Transportation Research Board of the National Academies, Washington, D.C., 2012, pp. 18–27.DOI: 10.3141/2280-03
T. Sayed, M. H. Zaki, and J. Autey, Department of Civil Engineering, University of British Columbia, 6250 Applied Science Lane, Vancouver, British Columbia V6T 1Z4, Canada. K. Ismail, Department of Civil and Environmental Engineering, Carleton University, 1125-3432 Colonel By Drive, Ottawa, Ontario K1S 5B6, Canada. Corresponding author: T. Sayed, [email protected].
of traffic conflicts provides a more complete understanding of the mechanism of failure that leads to a vehicle collision.
A traffic conflict is defined as “an observable situation in which two or more road users approach each other in space and time to such an extent that there is a risk of collision if their movements remained unchanged” (2, 3). For this concept to be operational, the safety hierarchy is transferred into measurable parameters based on certain assumptions. For each traffic event in the hierarchy, an asso-ciated severity can be estimated, and thus, the severity represents its location in the hierarchy (Figure 1).
Conflict indicators are used to map road user positions to severity measures. Many severity indicators have been developed to mea-sure traffic conflicts. One example is the time to collision (TTC). For two road users on a collision course, TTC is defined as the extrapolated time at which the collision will occur (4).
Positive empirical evidence that shows the validity of traffic con-flict techniques comes from the study of Sayed and Zein, in which a statistically significant correlation between the frequency of traffic conflicts and collisions at signalized intersections was found (1).
The traffic conflict technique involves observation, recording, and evaluation of the frequency and severity of traffic conflicts at a location by a team of trained human observers; but this traditional method of traffic conflict data collection has been challenged on several accounts. For example, inter- and intraobserver variability is a common challenge to the repeatability and consistency of results from traffic conflict surveys (5). Furthermore, field observations are costly to conduct and demand staff training. Moreover, despite decades of conceptual developments, no universal operational definition of a traffic conflict exists; for example, objectively measurable interpreta-tions of the words “approach,” “risk of,” or “unchanged” exist in the previous conceptual definition (6). Finally, the estimation of objective conflict indicators, such as TTC (4), by the use of field observations can be difficult.
Automation of the process of traffic conflict analysis can enable the traffic conflict analysis to take place in an accurate, objective, and cost-efficient way. More precise, successful automation of the process of extraction of conflict information from video sensor data can have considerable benefits for traffic safety studies (7–9). Video data are rich in details, recording devices are becoming less expen-sive, and video cameras are often already installed for monitoring purposes. In addition, video data represent a permanent record of the traffic events analyzed and can be reviewed and validated, unlike data from observer-based surveys conducted in the field.
A primary focus of road safety analysis that could greatly benefit from vision-based analysis is the before-and-after (BA) evaluation of safety treatments. The purpose of BA studies is to measure the safety benefits (or absence thereof) derived from a safety treatment.
Feasibility of Computer Vision-Based Safety EvaluationsCase Study of a Signalized Right-Turn Safety Treatment
Tarek Sayed, Karim Ismail, Mohamed H. Zaki, and Jarvis Autey
Traditional road safety analysis has often been undertaken with histori-cal collision records. However, limitations on the quality and complete-ness of collision data gave rise to surrogate ways of measuring safety, especially the traffic conflict technique. Traditionally, traffic conflict techniques have relied on field observations, which have some reliability and repeatability problems. Therefore, successfully automating conflict detection with data extracted from video sensors could have considerable benefits for traffic safety studies. Before-and-after safety evaluations could greatly benefit from automated analysis of traffic conflicts, and the main objective of this paper is to demonstrate the use of this analysis technique for such evaluations. A right-turn safety improvement was implemented at an intersection in Edmonton, Alberta, Canada, in 2009 to mitigate the high rate of rear-end and merging collisions. The right-turn ramp was closed, and all right-turning vehicles were brought to the right-turn lane at the intersection, where a “No-Right-Turn-on-Red” sign was installed. In this study, video sensors were the primary source of conflict data. The video data were analyzed and traffic conflicts were measured with an automated traffic safety tool. The distributions of the calculated conflict indicators before and after the treatment showed a considerable reduction in the frequency and severity of traffic conflicts. This result suggests significant positive changes in rear-end, merging, and total conflicts. The results of this study show the potential benefit of adopting automated conflict analysis for before-and-after safety studies.
Traditional road safety analysis has often been undertaken with his-torical collision records. However, the use of collision records for safety analysis is a reactive approach because a significant number of collisions must be recorded before action is taken (1). Further-more, problems well recognized to be associated with collision data are data availability and quality. Therefore, the use of surrogate safety measures, such as traffic conflicts, has been advocated as an alternative or complementary approach to analysis of traffic safety because it offers a perspective broader than that obtained by the use of collision statistics alone (1). For example, traffic conflicts occur more frequently than collisions, the desired sample size for analysis can be obtained over much shorter periods of time, and analysis
Sayed, Ismail, Zaki, and Autey 19
The classical collision-based approach to BA studies is based on estimation of the reduction in the frequency of collisions and their consequences that can be attributed to the treatment evaluated. To draw statistically stable conclusions, researchers typically observe collisions for prolonged periods (1 to 3 years) before as well as after the introduction of the treatment. BA studies based on traffic conflicts, however, can be conducted over shorter periods.
ObjeCTiveS
The main objective of this paper is to demonstrate the feasibility of automated analysis of traffic conflicts to conduct a time series (BA) evaluation of the safety performance of the intersection of Yellow-head Trail and Victoria Trail in Edmonton, Alberta, Canada. A right-turn safety improvement was implemented at the inter section in 2009 to mitigate the high rate of occurrence of rear-end and merging col-lisions. The right-turn ramp was closed, and all right-turning vehi-cles were brought to the right-turn lane at the inter section, where a “No-Right-Turn-on-Red” sign was installed. Video data collected 1 day before and 1 day after the treatment to measure traffic con-flicts were analyzed by use of a recently developed automated traffic safety tool.
PReviOuS WORk
The benefits of application of automated computer vision to road safety analysis and evaluation have been established in the past. A thorough survey of the literature can be found in previous reports (7–10). Although the difficulties with the reliance on collision data to conduct BA studies are acknowledged in the literature, few studies that conducted BA evaluations by the use of traffic conflicts as surrogate measures have been published.
An early attempt to apply traffic conflicts as an indicator to BA safety evaluations of urban intersections in Vancouver, British Columbia, Canada, was described (11). In that work, TTC with a threshold value of 1.5 s was used as the main safety measure. A BA study in Atlanta, Georgia, evaluated the effects on safety of changes in left-turn signal phasing by the use of traffic conflicts (12). The conflict analysis was performed manually, and it was not clear how the severity of the conflicts was measured. The effect on safety of scramble pedestrian signal phasing was evaluated by use of manu-ally observed traffic conflicts (13). These studies share the main shortcomings of subjectivity and a lack of reliability associated with the use of human observers to measure traffic conflicts.
To overcome these shortcomings, the use of automated conflict analysis with computer vision has recently been advocated. A BA study was carried out on a crossing road in Bélgida, Valencia, Spain, to evaluate safety after the installation of traffic-calming devices (14). The investigators used a video recording and digital image analysis and proposed that a conflict indicator, called the pedestrian risk index, be used for the safety evaluation. The pedestrian risk index is based on both the duration and severity of the conflict between a vehicle and a pedestrian. Traffic-calming improvements were implemented on the main road in different stages. This BA study was developed during four phases of implementation of the intervention.
In another example, Ismail et al. demonstrated the feasibility of measuring conflicts and conducting BA analysis with video data collected from a commercial-grade camera in Chinatown, Oakland, California (10, 15). Video sequences from 2 h before and 2 h after a pedestrian scramble phase were automatically analyzed. The results of the automated analysis showed a pattern of decline in conflict frequency, a reduction in the spatial density of conflicts, and a shift in the spatial distribution of conflicts farther from crosswalks.
SiTe ChaRaCTeRiSTiCS and TReaTmenT
The intersection used in the present study is part of Beverly Inter-change, which interfaces with traffic from Victoria Trail (north approach), Yellowhead Trail (west and east approaches), and 118 Ave-nue (south approach). From 2005 to 2009, about 70% of all collisions reported at this intersection occurred on the right-turn off-ramp of Yellowhead Trail to Victoria Trail (referred to as the “ramp”). Further-more, from 2005 to 2009, approximately 86% of total collisions were attributed to a driver following too closely. This high rate of occur-rence of collisions on the right-turn ramp was the main motivation behind the treatment evaluated in this study.
The City of Edmonton identified the intersection of Yellow-head Trail and Victoria Trail to be a high-collision location, and the intersection was accordingly prioritized for improvement. The treatment consisted of decommissioning of the right-turn ramp and replacement of the ramp with an additional lane north of the eastern approach. This lane is dedicated for right-turn movement, which is controlled by a signal with no right turn on red. The outline of the intersection before and after treatment is shown in Figure 2, which also provides images captured from monitoring cameras.
Figure 3 shows the annual collision frequency at the inter section. A detailed BA evaluation of the collision records at this site is not within the scope of this study (and is the subject of an ongoing study). However, the trend apparent from the collision records provides evidence that the treatment, which was implemented on Novem-ber 1, 2009, was effective in reducing collisions: the average num-ber of collisions from the historical record (2005 to 2009) at the intersection was reduced by about 75% after the implementation of the treatment. However, this reduction cannot be relied on, as it is a simple comparison of collision frequencies before and after the treatment and simple BA studies are highly unreliable because they do not account for confounding factors such as collision history, maturation, and regression to the mean.
Field SuRvey
Video data were collected at the area of the intersection where westbound traffic enters and merges with northbound traffic from 118 Avenue. As discussed above, the main purpose of the treatment
FIGURE 1 Traffic safety pyramid showing hierarchy of traffic events (F 5 fatal; I 5 injury) (2).
20 Transportation Research Record 2280
Yellowhead Trail
Victoria Trail Victoria Trail
Yellowhead Trail
(a) (b)
(c) (d)
FIGURE 2 Treated intersection (a and c) before treatment with right-turn ramp and (b and d) after treatment with added right-turn lane.
FIGURE 3 Historical collision frequencies on treated intersection (Yellowhead Trail and Victoria Trail).
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Sayed, Ismail, Zaki, and Autey 21
Before
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Study Area
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FIGURE 4 Locations of Cameras 1 and 2 used in video survey for BA periods. [Source: orthographic image obtained from GeoEdmonton (spatial resolution 5 25 cm/pixel).]
was to reduce collisions that occur or that would have occurred within the right-turn off-ramp of Yellowhead Trail to Victoria Trail. Therefore, two main types of conflicts were studied: rear-end and merging conflicts. The purpose of the field survey was to monitor the areas of the intersection where these types of conflicts were observ-able. The City of Edmonton mounted cameras on light poles. The camera locations and lengths of the video sequences are explained in the following sections.
video data Collection before Treatment
During the period before the treatment, two cameras were used in the video survey. They were attached to light poles at the locations shown
in Figure 4. The installation process took place with assistance from City of Edmonton personnel and the city’s equipment. The durations of the video recordings, made on September 9, 2009, were 180 min for Camera 1 and 316 min for Camera 2. The actual durations of analysis, however, were 132 min for Camera 1 (because of a cor-rupted video subsequence) and 222 min for Camera 2 (because of ramp closure to set up the camera). Sample frames and an outline of the analyzed areas within each scene are shown in Figure 5.
video data Collection after Treatment
During the period after the treatment, two cameras were also used in the video survey. The installation process took place in a fashion
(a) (b)
FIGURE 5 Sample frames from recorded video sequences: (a and b) bright regions representing analyzed area within video images.(continued on next page)
22 Transportation Research Record 2280
similar to that before treatment. The durations of video recording (April 29, 2010, about 6 months after the treatment) were 6.44 h for Camera 1 and 6.22 h for Camera 2. Sample frames and an outline of the analyzed areas in each scene are shown in Figure 6.
Camera 2 was devoted to observation of rear-end conflicts at the eastern intersection approach. Camera 1 was used to record merg-ing and rear-end conflicts in the northbound traffic as it approached the southern part of the intersection. Camera 2 was used to record rear-end conflicts on the two rightmost adjacent lanes (north). The
lane farthest to the north was used exclusively for right-turn move-ments. The adjacent lane to the south was used for right-turn and through movements. Therefore, rear-end conflicts in both lanes were analyzed.
During the analysis, the researchers investigated whether to include in the analysis the rear-end conflicts induced by vehicles making through movements (westbound vehicles entering from the eastern approach) to isolate the impact of the treatment exempli-fied by forcing of northbound vehicles to use the signalized inter-
(a) (b)
(c) (d)
FIGURE 5 (continued) Sample frames from recorded video sequences: (c and d) sample vehicle tracks automatically extracted from video sequences.
(c) (d)
FIGURE 6 Sample frames from recorded video sequences: (a and b) bright regions representing analyzed area within video images and (c and d) sample vehicle tracks automatically extracted from video sequences.
Sayed, Ismail, Zaki, and Autey 23
section. In some events, it was found to be a challenge to explicate the mode of action that led to the conflict and identify the vehicle that triggered the evasive action, for example, when a westbound vehicle was trailing a northbound vehicle. Westbound vehicles were not undertaking this particular movement because of the treatment, as this was precisely their pattern of intersection use before the treatment. To overcome this challenge and maintain methodologi-cal correctness, the researchers analyzed all rear-end conflicts on the two adjacent rightmost lanes; this approach simplified the analy-sis and helped to build a conservative estimate of the safety benefit. A more detailed analysis of the westbound signal approach could potentially be undertaken during the before period to provide a more accurate estimate of the safety benefit, which can be greater than that which was assessed.
videO analySiS aPPROaCh
Overview of automated Road Safety analysis
For road safety applications, the approach adopted in the study described in this paper relies on the development of two databases: a trajectory database, in which the results of the video processing module are stored, and an interaction database, in which all inter-actions between road users within a given distance are considered and for which various indicators, including TTC and other severity indicators, can be automatically computed.
Identification of traffic conflicts and measurement of other traffic parameters are thus done by mining of the data in these databases. The road user detection and tracking module relies on a feature-based tracking method described previously (16). Feature-based tracking is preferred because it can handle partial occlusion. Partial occlusion occurs when an object is occluded by another, causing the object boundary to be broken by the occluding object. This broken boundary can result in a failure of further processing of the object, such as object recognition and classification.
The tracking of features is done through use of the well-known Kanade–Lucas–Tomasi feature tracker. Stationary features and fea-tures with unrealistic motion are filtered out, and new features are generated to track objects entering the field of view. Because a moving object can have multiple features, the next step is to group the features, that is, decide what set of features belongs to the same object, by the use of cues such as spatial proximity and common motion. A graph connecting the features is constructed over time.
A detailed description of the tracking algorithm has been pre-sented by Saunier and Sayed (16). The tracking accuracy for motor vehicles has been measured to be between 84.7% and 94.4% on three different sets of sequences. This means that the system detects most trajectories.
Camera Calibration
The positional analysis of road users requires accurate estimation of the camera parameters. In this study, six extrinsic parameters (which describe the location and orientation of the camera) and two intrinsic parameters (which represent the projection on the image space) are calibrated. The calibration process enables the recovery of real-world coordinates of points in the video sequence that lie on a reference surface with a known model (pavement surface). All camera parameters were inferred from video observations and an orthographic image of the intersection.
A mixed-feature camera calibration approach was introduced in previous work (17). Each calibration feature imposes a condition based on its shape, position, and length in both image and world spaces. An additional calibration feature based on the parallelism of a calculated vertical line (depicted in blue in Figure 7) to a manually annotated vertical direction (from light poles) is also necessary to enhance the accuracy of the camera calibration. The accuracy of the estimated parameters was tested with a set of line segments of true length estimated from the orthographic image. This set of observa-tions was not used in the calibration. The calibration error is repre-sented by the discrepancy between calculated and annotated segment lengths normalized by the length of each segment.
The accuracy of the final estimates was very good, and no further error in conflict analysis was attributed to inaccurate estimated cam-era parameters. Details of the camera calibration algorithm can be found elsewhere (17).
analysis Components
After video data were collected, the data were analyzed to extract vehicle tracks. The video analysis procedure included several steps. Video data were first encoded to a predefined format. Feature track-ing, in which important points were tracked on moving objects, was then conducted. The subsequent step was to assign points that move at similar speed and satisfy other motion constraints to the same coherent object. This step is called “feature grouping.” Analysis of the severity of events that involve tracked vehicles was then con-ducted. In the context of this study, severity is the spatiotemporal proximity of interacting vehicles. The extrapolation of road user positions was then conducted by matching of a vehicle trajectory to a set of previously learned motion patterns (referred to in this paper as “prototypes”). This is particularly important for extrap- olation of the movements of turning vehicles (8, 9). A common approach described in the literature is to assume constant veloc-ity. This approach can lead to erroneous or unrealistic extrapola-tion. Furthermore, the probability of each extrapolation hypothesis depends on the relative frequency of its observation. Therefore, a frequency-weighted proximity measure can be calculated. In this study, the measure of proximity was TTC. TTC was calculated for every extrapolation hypothesis and then weighted by the relative frequency of each one.
motion Patterns
A subset of the video data was selected to represent the full data set. A segment of video with moderate to high traffic volumes of adequate length to contain many examples of all common traffic movements is ideal for prototype generation. Feature tracking was carried out on the video segment, and the trajectories of a large number of features, usually several thousand, were recorded. This large set of trajectories was reduced to a set of several hundred prototypes through use of a clustering algorithm. The clustering algorithm selected is the longest common subsequence method (9, 18). Feature tracks from vehicles following similar trajectories (for example, right-hand turns) may begin and end at different exact locations but still describe the same pattern of movement. As the name implies, the longest common subsequence algorithm groups feature tracks that contain matching subsequences of an adequate
24 Transportation Research Record 2280
length. In this manner, the initial set of trajectories is reduced to a concise set of prototypes. Figure 8 shows the patterns of motion of vehicle movements captured from four different cameras.
measurement and validation of Conflict indicator
A critical value of a conflict indicator must be drawn from each interaction. The most severe value is typically used to represent the overall severity of a traffic event. For example, if TTC is used to represent severity, then the minimum TTC for the entire traffic event is used. Because of potential noise in road user tracks, a simple filtering technique was used to account for tracking noise. Further-more, selected events were visually reviewed to identify any track-ing errors. In this project, traffic events with an associated minimum TTC of less than 4 s were considered for the safety evaluation. This value was selected on the basis of the work of Sayed and Zein (1). TTC is the primary traffic conflict indicator in the literature and has been extensively used by researchers to estimate the number and severity conflicts, as described earlier. Validation was performed on a subset of 30 events selected from the interactions database.
The scope of the validation was limited to a comparison of an event’s minimum TTC and a corresponding manually calculated TTC. The results demonstrated the accuracy of the automated TTC index estimation.
SummaRy OF FindingS
Reduction in Conflict Frequency
The average frequencies (number of conflicts per hour) of rear-end and merging conflicts, referred to as the average number of hourly conflicts, that occurred before and after the treatment were com-pared. Figure 9 shows the frequency and cumulative distributions of the average number of hourly conflicts at the treated intersection both before and after the treatment. Figure 9 shows a consider-able reduction in conflict frequency (the average number of hourly conflicts) for both conflict types. When events with TTC values of 4.0 s or less are considered, merging conflicts were reduced by 88%, rear-end conflicts were reduced by 66%, and total conflicts were reduced by 82%.
(a) (b)
(c) (d)
FIGURE 7 Calibration of video camera: (a and b) before projection and (c and d) after projection of reference grid from world space (a and c) to image space (b and d). (Source: World images taken from Google Maps.)
Sayed, Ismail, Zaki, and Autey 25
(a) (b)
(c) (d)
FIGURE 8 Motion patterns of prevalent road user movement: (a) Camera 1 in before period (used for interaction analysis), (b) Camera 2 in before period, (c) Camera 1 in after period, and (d) Camera 2 in after period.
Reductions in Conflict Severity
The minimum TTC of each event can be mapped to a severity index (Figure 10) with the following transform (8, 9, 19):
SITTC
PRT= −
exp ( )2
221
where SI is the severity index (which is a unitless measure of sever-ity that ranges from 0 to 1, with 0 being uninterrupted passages) and PRT is the perception and braking reaction time, which is assumed to be 2.5 s. Figure 10 depicts this severity mapping.
Aggregation of the severity of all events was conducted. Normal-ization was required to account for differences in observation period and exposure from the before and the after periods. The exposure measure used is the maximum theoretical number of events (expo-
sure), which is the product of the hourly volumes for conflicting traffic streams:
SI rateSI
maximum theoretical exposure million=
ss( ) ( )2
Severity distributions
Figure 11 shows the distributions of the severity indexes of conflicts at the treated intersection both before and after the treatment. Data for rear-end and merging conflicts are displayed separately, and the frequency of conflicts normalized to exposure is plotted over a range of severity values.
The results of a comparison of the safety indexes before and after the study also show considerable safety improvement. Table 1 is a summary of the severity indexes for BA conditions.
(a) (b)
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FIGURE 9 Frequency and cumulative distributions of conflicts at treated intersection before and after treatment (min 5 minimum).
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FIGURE 10 Mapping from TTC to severity index.
FIGURE 11 Distribution of severity indexes for BA conditions according to frequency of severity measurements normalized by maximum theoretical exposure (Emax): (a) merging and (b) rear-end conflicts.
Sayed, Ismail, Zaki, and Autey 27
COnCluSiOnS
The case study presented in this paper has demonstrated the useful-ness of analysis of traffic conflicts in BA safety evaluations. Traf-fic conflicts occur more frequently than collisions, resulting in a sample size that is desirable for analysis and that can be obtained in a much shorter period of time. It was also demonstrated that new developments in the use of computer vision techniques to automate the extraction of traffic conflicts from video data can overcome the shortcomings of the traditional method of manual observation of conflicts.
The conflict analysis undertaken in this study has some limitations, however. These include short observation periods and a lack of con-trol (comparison) sites. Although several proper BA studies are being undertaken by the use of traffic conflict data, the linkage between traffic conflicts and safety (collisions) needs to be clearly established before wider application of the traffic conflicts technique. Therefore, future work should focus on testing and validation of the relationship between traffic conflicts and collisions. This should lead to a wider application of the traffic conflicts technique and better understand-ing of the link between road safety, driver behavior, and dynamic traffic interactions. A comparison of the results of this study and a BA evaluation with historical collision records should further strengthen the validity of the use of the traffic conflict technique.
aCknOWledgmenTS
Funding for this study was provided by the City of Edmonton Office of Traffic Safety. Considerable support and assistance were provided by Gerry Shimko, Stevanus Tjandra, and Asif Iqbal.
ReFeRenCeS
1. Sayed, T., and S. Zein. Traffic Conflict Standards for Intersections. Transportation Planning and Technology, Vol. 22, No. 4, 1999, pp. 309–323.
2. Svensson, Å., and C. Hydén. Estimating the Severity of Safety Related Behaviour. Accident Analysis and Prevention, Vol. 38, No. 2, 2006, pp. 379–385.
3. Amundsen, F., and C. Hydén. Proc., First Workshop on Traffic Conflicts, Lund Institute of Technology, Lund, Sweden, 1977.
4. Hayward, J. C. Near-Miss Determination Through Use of a Scale of Danger. In Highway Research Record 384, HRB, National Research Council, Washington, D.C., 1972, pp. 24–34.
5. Glauz, W. D., K. M. Bauer, and D. J. Migletz. Expected Traffic Con-flict Rates and Their Use in Predicting Accidents. In Transportation Research Record 1026, TRB, National Research Council, Washington, D.C., 1985, pp. 1–12.
6. Chin, H. C., and S. T. Quek. Measurement of Traffic Conflicts. Safety Science, Vol. 26, No. 3, 1997, pp. 169–185.
7. Saunier, N., and T. A. Sayed. Automated Road Safety Analysis with Video Data. In Transportation Research Record: Journal of the Transportation Research Board, No. 2019, Transportation Research Board of the National Academies, Washington, D.C., 2007, pp. 57–64.
8. Saunier, N., and T. A. Sayed. Probabilistic Framework for Automated Analysis of Exposure to Road Collisions. In Transportation Research Record: Journal of the Transportation Research Board, No. 2083, Transportation Research Board of the National Academies, Washington, D.C., 2008, pp. 96–104.
9. Saunier, N., T. Sayed, and K. Ismail. Large-Scale Automated Analy-sis of Vehicle Interactions and Collisions. In Transportation Research Record: Journal of the Transportation Research Board, No. 2147, Transportation Research Board of the National Academies, Washington, D.C., 2010, pp. 42–50.
10. Ismail, K., T. Sayed, and N. Saunier. Automated Analysis of Pedestrian–Vehicle Conflicts: Context for Before-and-After Studies. In Transportation Research Record: Journal of the Transportation Research Board, No. 2198, Transportation Research Board of the National Academies, Washington, D.C., 2010, pp. 52–64.
11. Brown, G. R. Traffic Conflicts for Road User Safety Studies. Canadian Journal of Civil Engineering, Vol. 21, No. 1, 1994, pp. 1–15.
12. Tarrall, M. B., and K. K. Dixon. Conflict Analysis for Double Left-Turn Lanes with Protected-Plus-Permitted Signal Phases. In Transportation Research Record 1635, TRB, National Research Council, Washington, D.C., 1998, pp. 105–112.
13. Garder, P. Traffic Conflict Studies Before and After Introduction of RedLight Running Photo Enforcement in Maine. Technical report. New England University Transportation Center, Massachusetts Institute of Technology, Cambridge, 2006.
14. Cafiso, S., A. García, R. Cavarra, R. Romero, and A. Mario. Pedestrian Crossing Safety Improvements: Before and After Study Using Traffic Conflict Techniques. Proc., 4th International Symposium on Highway Geometric Design, Valencia, Spain, Universidad Polytecnica de Valencia, Spain, 2010.
15. Ismail, K., T. Sayed, N. Saunier, and C. Lim. Automated Analysis of Pedestrian–Vehicle Conflicts Using Video Data. In Transportation Research Record: Journal of the Transportation Research Board, No. 2140, Transportation Research Board of the National Academies, Washington, D.C., 2009, pp. 44–54.
16. Saunier, N., and T. Sayed. Feature-Based Tracking Algorithm for Vehi-cles in Intersections. Proc., 3rd Canadian Conference on Computer and Robot Vision, Quebec City, Quebec, Canada, IEEE, New York, 2006, p. 59.
17. Ismail, K., T. Sayed, and N. Saunier. Camera Calibration for Urban Traffic Scenes: Practical Issues and a Robust Approach. Presented at 89th Annual Meeting of the Transportation Research Board, Washington, D.C., 2010.
18. Vlachos, M., G. Kollios, and D. Gunopulos. Elastic Translation Invariant Matching of Trajectories. Machine Learning, Vol. 58, No. 2–3, 2005, pp. 301–334.
19. Ismail, K., T. Sayed, and N. Saunier. Methodologies for Aggregating Traffic Conflict Indicators. In Transportation Research Record: Journal of the Transportation Research Board, No. 2237, Transportation Research Board of the National Academies, Washington, D.C., 2011, pp. 10–19.
The Safety Data, Analysis, and Evaluation Committee peer-reviewed this paper.
TABLE 1 Summary of Conflict Severity Analysis
Conflict Type Before Treatment After Treatment B/A RatioImplied Reduction in Conflicts (%)
