1. ITE Western District 2012 Annual Meeting
Applications of High-Resolution Traffic Event Data: Managing Oversaturated Arterials
Dr. Xinkai Wu, Assistant Professor
Department of Civil Engineering
California State Polytechnic University Pomona
ITE Western District 2012 Annual Meeting

2. High-Resolution Event Data
ITE Western District 2012 Annual Meeting

3. ITE Western District 2012 Annual Meeting
SMART-SIGNAL
Terminal Box
DAC
ITE Western District 2012 Annual Meeting

4. Trunk Highway 55 and Boone Ave (Golden Valley, MN)
ITE Western District 2012 Annual Meeting

5. Oversaturation
Gazis (1963): An oversaturated intersection is defined as one in which the demand exceeds the capacity.
Little research has been conducted on the identification and quantification of oversaturated conditions
Mostly qualitative and incomplete
However, traffic arrivals are usually hard to predict or measure.
Therefore, a network of intersections would become oversaturated when the system is overloaded with heavy demand which exceeds the total capacity of the network

6. Detrimental Effects
Temporally, characterized by a residual queue at the end of cycle.
Residual vehicles cannot be discharged due to insufficient green splits
Creating detrimental effects on the following cycle by occupying a portion of green time.
Spatially, characterized by a spill-over from a downstream intersection.
Vehicles cannot be discharged even in green phase due to spill-over
Creating detrimental effects by reducing useable green time for upstream movements

7. Oversaturation Severity Index (OSI)
OSI: the ratio between unusable green time and total available green time in a cycle.
Further differentiate OSI into T-OSI and S-OSI.
Temporal dimension (T-OSI)
The “unusable” green: because of the residual queue from the last cycle
Spatial dimension (S-OSI)
The “unusable” green: because of the downstream blockage

8. Measure T-OSI & S-OSI T-OSI: S-OSI:
Estimate the length of residual queue at the end of cycle
S-OSI:
Identify spillover
Calculate the reduction of green time of upstream intersections

9. T-OSI & S-OSI Measure Using High-Resolution Traffic Event Data
ITE Western District 2012 Annual Meeting

10. Queue Length Estimation
Instead of traditional input-output approach, we estimate queue length by taking advantage of queue discharge process
Based on LWR shockwave theory

11. Queue Length Estimation
Utilize the data collected by advance detector
Identify Critical Points: A, B, C
Point A: Shockwave v1 arrives to detector, indicating traffic state changes from (qa,ka) to (0,kj)
Point B: Shockwave v2 arrives to detector, indicating traffic state changes from (0,kj) to (qm,km)
Point C: Shockwave v3 arrives to detector, indicating traffic state changes from (qm,km) to (qa,ka)
Identify from high-resolution event-based data
Vehicle Gap
Detector occupied time
Point A: Long occupy time (>3 sec) or occupancy > 1 for 3 sec
Point B: Occupy time changes to normal value (< 1 sec) or occupancy changes to less than 1
Point C: Large gap time (>2.5) or occupancy = 0 for about 3 sec

12. Break Point Identification from High-Resolution Detector Data

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Field Tests
Test Site: TH55 (6 intersections)
Independently evaluated by Alliant Engineering, Inc.
At Rhode Island Ave.
Three morning peaks (7:00am-9:00am)
Jul. 23rd, 2008
Occ. 29th, 2008
Dec. 10th, 2008
ITE Western District 2012 Annual Meeting

14. SOSI: Identify Queue-over-detector (QOD) Caused by Spillover
Due to cyclic signal timing, vehicles slow down and stop for red phase or joining the queue, and then resume travel as the light turns green or queue is clear. Such deceleration-stop-acceleration process rapidly changes the occupancy at some locations. In other words, if a vehicle stays on the detector during the queuing process, the occupancy is significantly changed because of the relatively prolonged detector occupation time. We call this phenomenon as “Queue-Over-Detector” (QOD). This phenomenon is clearly indicated in the high-resolution data by large occupation time (2 sec, for example) or occupancy being 100% for several seconds. Generally, there are two kinds of QOD in signalized arterials: the first is caused by cyclic signal timing, i.e. the red phase; the second is because the queue spills back from downstream intersections, i.e. spillover. If the second QOD has been identified, the oversaturation at an arterial/route can be diagnosed.

15. S-OSI: Identification of Spillover
Identify QOD-II.
High-resolution data.
Rhode Island phase 6
ITE Western District 2012 Annual Meeting

16. Managing Oversaturation: A Simple Forward-Backward Procedure
ITE Western District 2012 Annual Meeting

17. A Simple Forward-Backward Procedure
Based on TOSI and SOSI measurements
Respond and mitigate traffic congestion quickly
Simple and effective
Reactive
ITE Western District 2012 Annual Meeting

18. ITE Western District 2012 Annual Meeting
Problem Setting
N intersections along an oversaturated path
At control period t, decisions are made according to the average TOSI and SOSI values at the control period t-1, i.e.,
ITE Western District 2012 Annual Meeting

19. Basic Mitigation Strategies
The TOSI and SOSI values can help identify the causes of arterial traffic congestion
Positive SOSI indicates the spill-back of downstream queue
Positive TOSI indicates that the available green time is insufficient for queue discharge
Therefore for a single intersection, three basic strategies can be applied.
ITE Western District 2012 Annual Meeting

20. ITE Western District 2012 Annual Meeting
TOSI > 0
Extending green
ITE Western District 2012 Annual Meeting

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SOSI > 0
Reducing red at the downstream intersection
ITE Western District 2012 Annual Meeting

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SOSI > 0
Gating (Reducing traffic arrivals & giving green to other approaches)
ITE Western District 2012 Annual Meeting

23. ITE Western District 2012 Annual Meeting
Handling Spillover
ITE Western District 2012 Annual Meeting

24. Forward-Backward Procedure
Forward Process (Seeking the available green)
Follow the flow direction to eliminate spillovers and residual queues
Boundary condition
ITE Western District 2012 Annual Meeting

25. Forward-Backward Procedure
Backward Process (Gating or metering)
Follow the opposing flow direction to check the arc capacity
Boundary condition
ITE Western District 2012 Annual Meeting

26. Simulation Test
22 intersections, Pasadena, CA
Offline control

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Simulation Test
TOSI/SOSI Changes
Fair Oaks Ave SB
Colorado Blvd. WB
ITE Western District 2012 Annual Meeting

28. ITE Western District 2012 Annual Meeting
Future Work
The Fundamental Diagram: Congestion
Safety
Environment
Control
ITE Western District 2012 Annual Meeting