Using Video Analytics to Improve Traffic Intersection Safety and Performance
Abstract
:1. Introduction
- The volume hotspot detection module computes the peaks and troughs in pedestrian and vehicle volume. It is useful to understand where the volume peaks and spikes are and if there are valleys and troughs in the volume. This information helps a traffic engineer implement signal timing changes to address safety issues only during the peak period instead of throughout the day.
- The conflict hotspot detection module computes the temporal hotspots of P2V and V2V conflicts by the conflict types and movements of the involved trajectories. P2V conflicts occur when a pedestrian and a vehicle come dangerously close, and their two trajectories intersect. V2V conflicts are defined as the occurrence of evasive vehicular actions and are recognizable by braking or weaving maneuvers [5]. If a hotspot pattern emerges in the conflict analysis, a traffic engineer could apply a countermeasure to address those conflicts. The evaluation engine uses a novel visualization scheme to simultaneously represent temporal conflict hotspots with the spatial locations within the intersection where the conflicts occur. We use the reciprocal of the number of conflicts as a measure of intersection safety.
- The intersection-service evaluation module does a fine-grained aggregation of vehicle volume at subcycle levels and outputs an intersection service histogram that bins the number of vehicles entering an intersection in 5-s bins from the start of green. Vehicle volume is collected for 60 s. Vehicles from all cycles within the study period are aggregated to arrive at a single histogram. We use intersection-service evaluation as a proxy for a measure of intersection performance [6]. Specifically, we use the entering vehicle counts for all movements between the 5- and 15-s marks to represent the performance of the intersection. The rationale for aggregating the vehicle counts for 10 s, choosing the start and end points as 5-s and 15-s marks, respectively, is that we exclude the startup loss and measure the volume before saturation headway. This fine-grained aggregation of vehicle volume captures the effect of any temporal issues impacting intersection performance that may otherwise be unnoticeable in hourly aggregations. Examples of temporal issues impacting the performance may be the presence of many pedestrians at the intersection or a change in signal phasing configuration. We use the performance measure to compare two signal phasing configurations at the same intersection.
- We have developed a systematic end-to-end software to analyze intersection data to find intersection safety and performance metrics.
- We have formalized the simultaneous treatment of intersection safety and intersection performance producing performance versus safety trade-off charts
- Additionally, our graphical heatmap output is very helpful to figure out not only the temporal hotspots for pedestrian–vehicle and vehicle–vehicle conflicts, but also the spatial locations. This concise representation that simultaneously captures both the temporal and spatial properties of the conflicts is not described in the existing literature.
2. Related Work
2.1. Surrogate Safety Measures
2.2. Intersection Sensors
2.3. Intersection Safety Analysis Using Video Cameras
3. Background
3.1. Video Analysis
3.2. High-Resolution Controller Log Analysis
3.3. Feature Computation
- Standard near-miss attributes: We compute the standard risk assessment metrics for every event, such as TTC and PET.
- Signal phase information: The fused video and signal phasing dataset is used to determine features, such as the current vehicle signal, current pedestrian signal, and if the event occurs during the beginning (first 10% of the cycle), middle, or end (last 10% of the cycle) of the current signaling phase.
- Trajectory features: The trajectory-related features are the trajectory’s movement, associated phases, and lanes or crosswalks.
- Speed features: These include the current speeds and accelerations for vehicle–vehicle interactions.
- Distance: Spatial distance between two users at the time of the conflict.
3.4. Categorization of Severe Events
- Left turn and opposing through (LOT): A left-turning vehicle in a permitted phase conflicts with an opposing through movement (Figure 2a).
- U-turn and opposing through (UOT): A U-turning vehicle in a permitted phase conflicts with an opposing through movement (Figure 2b).
- Merging right and through (RMT): A right-turning vehicle merging on the same lane as a through vehicle (Figure 2c).
- U-turn and a following left-turn (UFL): A leading U-turn with a following left-turning vehicle (Figure 2d).
- Right turn and a following through (RFT): A leading right-turning vehicle with a following through vehicle (Figure 2e).
- Lane change and adjacent through (LCC): A lane-changing vehicle conflicting with adjacent through (Figure 2f).
- Rear-end conflicts (REC): A leading vehicle moves slower than the following vehicle in the same lane.
- A U-turn and an adjacent right turn.
4. Methodology
4.1. Evaluation Engine Modules
4.1.1. Volume Hotspot Detection Module
4.1.2. Conflict Hotspot Detection Module
4.1.3. Intersection-Service Evaluation Module
4.1.4. Scenario Comparison Module
- Change in signal phasing or sequencing pattern [51].
- Implementation of leading pedestrian interval (LPI) which typically gives pedestrians a 3–7 s head start when entering an intersection with a corresponding green signal in the same direction of travel [52].
- Implementation of exclusive pedestrian phasing (EPP), which stops all vehicular movement and allows pedestrians access to cross in any direction at the intersection [52].
5. Experiments
For weekdays: | |
AM Peak | 07:00–09:30 |
Off-Peak | 09:30–11:00 |
Midday Peak | 11:00–14:30 |
PM Peak | 14:30–18:00 |
5.1. Intersection 1
5.1.1. Pedestrian Volume
5.1.2. P2V Conflicts and Suggested Countermeasures
5.1.3. V2V Conflicts
5.1.4. Countermeasure Evaluation for Performance Metric
5.1.5. Countermeasure Evaluation for Safety Metric
5.1.6. Performance–Safety Trade-Off
5.2. Intersection 2
5.2.1. Pedestrian Volume
5.2.2. P2V Conflicts
5.2.3. V2V Conflicts
5.2.4. Suggested Countermeasures
5.2.5. Countermeasure Evaluation: EPP
6. Discussion
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
UF | University of Florida |
EBT, WBT, SBT, NBT | East/West/South/North bound through |
EBL, WBL, NBL, SBL | East/West/North/South bound left |
EBR, WBR, NBR, SBR | East/West/North/South bound right |
NE, NW, SE, SW | Northeast, Northwest, Southeast, Southwest |
P2V, V2V | Pedestrian–vehicle, Vehicle–vehicle |
LOT | Left opposing through |
RMT | Right merging through |
LPI | Leading Phase Interval |
EPP | Exclusive pedestrian phasing |
RFT | Right following through |
REC | Rear end conflict |
FDOT | Florida Department of Transportation |
USDOT | U.S. Department of Transportation |
SOP | Signal Operating Procedure |
TPS | Thin-plate spline |
DB | Database |
TTC | Time-to-collision |
PET | Post-encroachment time |
ATC | Advanced Traffic Controller |
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ID | Intersection | Speed Limit (mph) | Pedestrian Presence (%) | Left Turn Type | Flashing Yellow Arrow | Right Turn on Red |
---|---|---|---|---|---|---|
1 | NW 23rd Ave. and NW 55th St. | 45/30 | 1.6 | Protected/ Permissive | No | Yes |
2 | University Ave. and 17th St. | 25/25 | 41.8 | Protected/ Permissive | Yes | Yes |
Traffic Involved | Signal Plan 1 | Signal Plan 2 |
---|---|---|
Vehicles going into school (morning) | 5 | 4 |
Remaining intersection vehicles (morning) | 1 | 6 |
Vehicles out of school (afternoon) | 1 | 2 |
Remaining vehicles (afternoon) | 3 | 5 |
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Mishra, A.; Chen, K.; Poddar, S.; Posadas, E.; Rangarajan, A.; Ranka, S. Using Video Analytics to Improve Traffic Intersection Safety and Performance. Vehicles 2022, 4, 1288-1313. https://doi.org/10.3390/vehicles4040068
Mishra A, Chen K, Poddar S, Posadas E, Rangarajan A, Ranka S. Using Video Analytics to Improve Traffic Intersection Safety and Performance. Vehicles. 2022; 4(4):1288-1313. https://doi.org/10.3390/vehicles4040068
Chicago/Turabian StyleMishra, Ahan, Ke Chen, Subhadipto Poddar, Emmanuel Posadas, Anand Rangarajan, and Sanjay Ranka. 2022. "Using Video Analytics to Improve Traffic Intersection Safety and Performance" Vehicles 4, no. 4: 1288-1313. https://doi.org/10.3390/vehicles4040068
APA StyleMishra, A., Chen, K., Poddar, S., Posadas, E., Rangarajan, A., & Ranka, S. (2022). Using Video Analytics to Improve Traffic Intersection Safety and Performance. Vehicles, 4(4), 1288-1313. https://doi.org/10.3390/vehicles4040068