1. Managing Oversaturated Arterials: from Measurements to Control
Dr. Henry Liu
Professor, Dept. of Civil and Environmental Engr.
Research Professor, Transportation Research Institute
University of Michigan, Ann Arbor
Seminar at University at Buffalo
February 6, 2015
Around 1920, William Potts a Detroit policeman, invented (unpatented) several automatic electric traffic light systems including an overhanging four-way, red, green, and yellow light system. The first to use a yellow light.

2. National Traffic Signal Report Cards
Around 1920, William Potts a Detroit policeman, invented (unpatented) several automatic electric traffic light systems including an overhanging four-way, red, green, and yellow light system. The first to use a yellow light.
Produced by  the National Transportation Operations Coalition (NTOC)
The majority of transportation agencies DO NOT monitor or archive traffic signal data.
What even bothers me further is that all these grades are given in a subjective manner. Usually through a survey of traffic engineer. NO OBJECIVE MEASUREMENTS here. 2

3. Presentation Outline Performance Measurement
Quantifying Oversaturation
Oversaturation Severity Index (OSI)
Managing Oversaturation
A Maximum Flow Based Approach
Simulation Results 3

4. Performance Measurement Using the High-resolution Data
SMART-Signal: Systematic Monitoring
of Arterial Road Traffic Signals

5. 1st Generation Data Collection
Terminal Box
1st Generation Data Collection
DAC

6. 2nd Generation Data Collection
TS-1 type cabinets
TS-2 type cabinets

7. Installation at Pasadena, CA (02/04/15)
170 type cabinets

8. Event-Based Data 8

9. 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

10. 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

11. Break Point Identification from High-Resolution Detector Data

12. Field Tests on TH55 in Minneapolis

13. Independent Evaluation of Performance Measures on TH55
By Alliant Engr. Inc
Queue length
Manually count the vehicles (Two persons per approach)
Four peak hours (July 22nd and 23rd, 2009)

14. Results – Maximum Queue Length14

15. MnDOT Implementation
The system has been installed on more than 80 intersections in Minnesota.
MnDOT Website:

16. Quantifying Oversaturation Using the High-resolution Data

17. What is 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

18. Definition of oversaturation
Gazis (1964)
“a stopped queue cannot be completely dissipated during a green cycle”
Abu-Lebdeh & Benekohal (2003)
“traffic queues persist from cycle to cycle either due to insufficient green splits or because of blockage”
Roess et al. (2004) in Traffic Engineering
“the oversaturated environment is characterized by unstable queues that tend to expand over time with potential of physically blocking intersections (spillback)”
The problem is that queue length is difficult to measure with the existing approach.

19. 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

20. 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

21. 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

22. 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

23. 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.

24. Queue Estimation with Spillover

25. Managing Oversaturation: A Maximum Flow Based Approach

26. 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.,

27. Decision Variables Red time changes Green time changes
Different control variables are designed for different tasks
is responsible for spillover elimination
is responsible for residual queue elimination
Positive SOSI indicates the spill-back of downstream queue
Positive TOSI indicates that the available green time is insufficient for queue discharge
The values of the two control variables can be directly transformed into the values of offset and green duration
Cycle length is unchanged

28. TOSI > 0, SOSI = 0
Extending green

29. SOSI > 0
Reducing red at the downstream intersection

30. SOSI > 0
Gating (Reducing traffic arrivals & giving green to other approaches)

31. Handling Spillover

32. Handling Residual Queue
The total increase of discharging capacity (i.e., increase of effective green time) at intersection n
To eliminate residual queue at control period t

33. Available Green Constraints
The green time increase is constrained by the available green
can be defined in many ways, e.g.,

34. Model formulation Control Objective
Eliminate spillovers and residual queues
Moving the vehicles out of the congested area as soon as possible, i.e., maximizing the discharging capacities

35. Graphical Illustration
A Multi-commodity Maximum Flow Problem

36. Simulation Test
5 intersections, Pasadena, CA

37. Traffic Flow Conditions
Simulation Settings
Traffic Flow Conditions
Control Strategies
Simulation time (Sec)
Traffic Flow Conditions
0~1800
Normal flow condition (a)
1800~5400
Increased flow condition (b)
5400~7200
Control Strategy
Description
Cycle Length 1
Actuated-coordinated 80 2
FBP

38. Network Performance Comparison
Strategy #1
Strategy #2
Value
(%)
Average Delay
(Seconds/per veh.)
81.37
64.28
-21.00
Average # of stops
(per veh.)
2.05
1.60
-21.96
Average Speed
10.95
12.92
+17.96

39. Network Throughput Comparison
Strategy #1
Strategy #3
Value
(%)
Southbound
3021
3762
+24.52
Northbound
1248
1242
- 0.50
Int. 1 minor
1490
1539
+3.27
Int. 2 minor
647
772
+19.28
Int. 3 minor
1120
1180
+5.32
Int. 4 minor
1795
1815
+1.11
Int. 5 minor
1555
1613
+3.73
TOTAL
10880
11925
+9.6

40. Simulation Results Side Street Queue Length
Side street queue decreases due to green time increase on side streets

41. Summary
A quantifiable measure of oversaturation (TOSI and SOSI) is developed.
We can measure TOSI and SOSI using high-resolution signal data.
A simple and effective control strategy is developed to manage oversaturation.

42. Concluding Remarks
Although traffic is traditionally modeled as “continuous flow”, traffic, after all, is discrete.
Measuring traffic flow parameters using the data collected at the individual vehicle level
Embrace the era of “BIG DATA”, but DATA is not everything.
Measurements and Models, you cannot have one without the other.
Technological advances support such data collection at affordable prices, so there is no technical barriers.
The individual vehicle level means that we measure each vehicle’s response to a detector. For example, for a loop detector, we want the information related to when a vehicle touches the detector and when the vehicle leaves the detector. Here I don’t mean vehicle signature, where the detection resolution is even higher.

43. Acknowledgements

44. THANK YOU! Prof. Henry Liu 1-734-764-4354 henryliu@umich.edu