Investigation and findings on reservation-based intersections and managed lanes Real-Time Signal Control and Traffic Stability Congestion on urban arterials is largely centered around intersection control. Traditional traffic signal schemes are limited in their ability to adapt in real time to traffic conditions or by their ability to coordinate with each other to ensure adequate performance. Specifically, there is a tension between adaptivity (as with actuated signals) and coordination through pre-timed signals (signal progression). We propose to investigate whether routing protocols in telecommunications networks can be applied to resolve these problems. Specifically, the backpressure algorithm of Tassiulas & Emphremides (1992) can ensure system stability through decentralized control under relatively weak regularity conditions. It is as yet unknown whether this algorithm can be adapted to traffic signal systems, and if so, what modifications are needed. Traffic systems differ in several significant ways from telecommunication networks: each intersection approach has relatively few queues (lanes) that must be shared among traffic to various definitions. First-in, first-out constraints lead to head-of-line blocking effects, traffic waves move at a much slower speed than data packets, and traffic queues are tightly limited by physical space (finite buffers). Determining whether (and how) the backpressure concept can be adapted to traffic networks requires significant research, and has the potential to dramatically improve signal performance. Improved Models for Managed Lane Operations Managed lanes (ML) are increasingly being considered as a tool to mitigate congestion on highways with limited areas for capacity expansion. Managed lanes are dynamically priced based on the congestion level, and can be set either with the objective of maximum utilization (e.g., a public operator) or profit maximization (e.g., a private operator). Optimization models for determining these pricing policies make restrictive assumptions about the layout of these corridors (often a single entrance and exit) or knowledge of traveler characteristics on behalf of the modeler (e.g., distribution of willingness to pay). Developing new models to address these issues would allow for better utilization of these facilities.

1. Real-time signal control and traffic stability
Improved models for managed lanes operations
Stephen D. Boyles
Associate Professor
The University of Texas at Austin
April 11, 2018
DSTOP updates Boyles

2. PROJECT 125: REAL-TIME
SIGNAL CONTROL AND
TRAFFIC STABILITY

3. The reservation-based intersection
Connected and automated vehicles provide new opportunities for
improving intersection throughput.
DSTOP updates
Project 125: Real-time signal control and
traffic stability Boyles

4. Earlier DSTOP research showed that improving capacity locally may not
improve throughput globally.
DSTOP updates
Project 125: Real-time signal control and
traffic stability Boyles

5. This project explores better ways to prioritize vehicles at reservation-based
intersections.
Conversations with DSTOP colleagues raised the possibility of
backpressure-based intersection priority.
DSTOP updates
Project 125: Real-time signal control and
traffic stability Boyles

6. How to adapt the backpressure concept for roadway networks?
Physical queues: Roadway links have a finite (and often binding) “buffer”
for storing vehicles, which bounds pressure terms.
DSTOP updates
Project 125: Real-time signal control and
traffic stability Boyles

7. How to adapt the backpressure concept for roadway networks?
Physical queues: Roadway links have a finite (and often binding) “buffer”
for storing vehicles, which bounds pressure terms. Solution:
“Chain” queues that spill back by recursively adding pressure
terms for links upstream.
DSTOP updates
Project 125: Real-time signal control and
traffic stability Boyles

8. How to adapt the backpressure concept for roadway networks?
Physical queues: Roadway links have a finite (and often binding) “buffer”
for storing vehicles, which bounds pressure terms. Solution:
“Chain” queues that spill back by recursively adding pressure
terms for links upstream.
Route choice: Vehicles can choose routes independently and selfishly
DSTOP updates
Project 125: Real-time signal control and
traffic stability Boyles

9. How to adapt the backpressure concept for roadway networks?
Physical queues: Roadway links have a finite (and often binding) “buffer”
for storing vehicles, which bounds pressure terms. Solution:
“Chain” queues that spill back by recursively adding pressure
terms for links upstream.
Route choice: Vehicles can choose routes independently and selfishly
Solution: Add equilibrium principle and iterate with
updated routes.
DSTOP updates
Project 125: Real-time signal control and
traffic stability Boyles

10. Simulation environment
DSTOP updates
Project 125: Real-time signal control and
traffic stability Boyles

11. Granting reservations according to the backpressure principle reduced
delays significantly beyond earlier (FCFS) protocols.
DSTOP updates
Project 125: Real-time signal control and
traffic stability Boyles

12. Future work
Test alternative way to address finite buffer (nonlinear transformation
of pressure term)
DSTOP updates
Project 125: Real-time signal control and
traffic stability Boyles

13. Future work
Test alternative way to address finite buffer (nonlinear transformation
of pressure term)
Compare with P0 policy developed by M. J. Smith
DSTOP updates
Project 125: Real-time signal control and
traffic stability Boyles

14. PROJECT 140: IMPROVED
MODELS FOR MANAGED
LANE OPERATIONS

15. Managed lanes
Dynamic toll lanes present new opportunities and challenges for traffic
management.
DSTOP updates
Project 140: Improved models for managed
lane operations Boyles

16. Earlier research is largely confined to single-entrance, single-exit facilities.
DSTOP updates
Project 140: Improved models for managed
lane operations Boyles

17. In facilities with multiple entrances and exits, the number of paths through
the corridor grows exponentially with the number of access points.
DSTOP updates
Project 140: Improved models for managed
lane operations Boyles

18. DSTOP updates
Project 140: Improved models for managed
lane operations Boyles

19. By reformulating the route choice model to operate at diverge node, we
can represent all possible paths with a polynomial set of variables.
DSTOP updates
Project 140: Improved models for managed
lane operations Boyles

20. We used approximate dynamic programming to identify toll policies which
maximize throughput (public facility) and maximize revenue (private
facility).
DSTOP updates
Project 140: Improved models for managed
lane operations Boyles

21. DESE network
Policy Revenue ($) TSTT (hrs) Throughput (veh)
Selected policy VFA1 $217.35 39.71 1586
Selected policy VFA2 $253.53 39.50 1596
Selected policy VFA3 $487.92 41.42 1533
Selected policy VFA4 $41.49 33.88 1639
Density based heuristic $27.87 36.81 1561
Ratio based heuristic $106.65 35.5 1586
Myopic revenue policy $31.44 36.11 1576
LBJ network
Policy Revenue ($) TSTT (hrs) Throughput (veh)
Selected policy VFA1 $554.94 45.80 1185
Selected policy VFA2 $593.78 45.61 1191
Selected policy VFA3 $419.85 43.20 1210
Selected policy VFA4 $88.92 40.69 1266
Density based heuristic $79.92 40.79 1266
Ratio based heuristic $115.29 41.36 1240
Myopic revenue policy $328.05 41.27 1234
DSTOP updates
Project 140: Improved models for managed
lane operations Boyles

22. Future work
Use field data to calibrate/validate model.
Explore methods for estimating value-of-time distributions from data.
Further explore “jam-and-harvest” phenomenon
DSTOP updates
Project 140: Improved models for managed
lane operations Boyles