TuSeRACT: Turn-Sample-Based Real-Time Traffic Signal Control

arXiv.org Artificial Intelligence

Real-time traffic signal control systems can effectively reduce urban traffic congestion but can also become significant contributors to congestion if poorly timed. Real-time traffic signal control is typically challenging owing to constantly changing traffic demand patterns, very limited planning time and various sources of uncertainty in the real world (due to vehicle detection or unobserved vehicle turn movements, for instance). SURTRAC (Scalable URban TRAffic Control) is a recently developed traffic signal control approach which computes delay-minimising and coordinated (across neighbouring traffic lights) schedules of oncoming vehicle clusters in real time. To ensure real-time responsiveness in the presence of turn-induced uncertainty, SURTRAC computes schedules which minimize the delay for the expected turn movements as opposed to minimizing the expected delay under turn-induced uncertainty. Furthermore, expected outgoing traffic clusters are communicated to downstream intersections. These approximations ensure real-time tractability, but degrade solution quality in the presence of turn-induced uncertainty. To address this limitation, we introduce TuSeRACT (Turn-Sample-based Real-time trAffic signal ConTrol), a distributed sample-based scheduling approach to traffic signal control. Unlike SURTRAC, TuSeRACT computes schedules that minimize expected delay over sampled turn movements of observed traffic, and communicates samples of traffic outflows to neighbouring intersections. We formulate this sample-based scheduling problem as a constraint program, and empirically evaluate our approach on synthetic traffic networks. We demonstrate that our approach results in substantially lower average vehicle waiting times as compared to SURTRAC when turn-induced uncertainty is present.


Smart Urban Signal Networks: Initial Application of the SURTRAC Adaptive Traffic Signal Control System

AAAI Conferences

In this paper, we describe a pilot implementation and field test of a recently developed approach to real-time adaptive traffic signal control. The pilot system, called SURTRAC (Scalable Urban Traffic Control), follows the perspective of recent work in multi-agent planning and implements a decentralized, schedule-driven approach to traffic signal control. Under this approach, each intersection independently (and asynchronously) computes a schedule that optimizes the flow of currently approaching traffic through that intersection, and uses this schedule to decide when to switch green phases. The traffic outflows projected by this schedule are then communicated to the intersection's downstream neighbors, to increase visibility of vehicles entering their respective planning horizons. This process is repeated as frequently as once per second in rolling horizon fashion, to provide real-time responsiveness to changing traffic conditions and coordinated signal network behavior. After summarizing this basic approach to adaptive traffic signal control and the domain challenges it is intended to address, we describe the pilot implementation of SURTRAC and its application to a nine-intersection road network in Pittsburgh, Pennsylvania. Both the SURTRAC architecture for interfacing with the detection equipment, hardware controller and communication network at a given intersection and the extensions required to account for unreliable sensor data are discussed. Finally, we present the results of a pilot test of the system, where SURTRAC is seen to achieve major reductions in travel times and vehicle emissions over pre-existing signal timings.


Dubuque Working on Adaptive Traffic Control System

U.S. News

The purpose of the Smart Traffic Routing with Efficient and Effective Traffic Signals is to improve traffic flow in the city when a major corridor, such as Dodge Street, becomes backed up due to high usage or a crash. The proposed system would adjust traffic signals for an alternative route to move that traffic along.


Smith

AAAI Conferences

In this paper, we describe a pilot implementation and field test of a recently developed approach to real-time adaptive traffic signal control. The pilot system, called SURTRAC (Scalable Urban Traffic Control), follows the perspective of recent work in multi-agent planning and implements a decentralized, schedule-driven approach to traffic signal control. Under this approach, each intersection independently (and asynchronously) computes a schedule that optimizes the flow of currently approaching traffic through that intersection, and uses this schedule to decide when to switch green phases. The traffic outflows projected by this schedule are then communicated to the intersection's downstream neighbors, to increase visibility of vehicles entering their respective planning horizons. This process is repeated as frequently as once per second in rolling horizon fashion, to provide real-time responsiveness to changing traffic conditions and coordinated signal network behavior. After summarizing this basic approach to adaptive traffic signal control and the domain challenges it is intended to address, we describe the pilot implementation of SURTRAC and its application to a nine-intersection road network in Pittsburgh, Pennsylvania. Both the SURTRAC architecture for interfacing with the detection equipment, hardware controller and communication network at a given intersection and the extensions required to account for unreliable sensor data are discussed. Finally, we present the results of a pilot test of the system, where SURTRAC is seen to achieve major reductions in travel times and vehicle emissions over pre-existing signal timings.


Real-Time Planning for Traffic Signal Control

AAAI Conferences

Unusual events, such as sporting events, road works, and natural disasters, can overwhelm city infrastructure such as traffic networks, which have limited capacity.  Recent work on automating traffic flow to reduce congestion and its effects, such as pollution, has formulated the problem of responding to sensed congestion using hybrid domain-independent planning.  This approach employs complex domain-independent solvers and requires them to formulate a complete multi-step plan to eliminate the congestion before the first response to the congestion can be initiated.  In this short paper, we present a much simpler, domain-dependent solver that uses a receding-horizon approach based on real-time heuristic search to select appropriate actions incrementally.  Empirical results comparing this approach to previous work are encouraging, demonstrating reasonable behavior and much better scalability.  These results suggest that smart cities can leverage real-time planning to control traffic signals on a city-wide scale.