SURTRAC: Scalable Urban Traffic Control

This paper defines and evaluates a pilot implementation of a recently developed approach to realtime, adaptive traffic signal control. The pilot system, which is called SURTRAC (Scalable Urban Traffic Control), integrates concepts from traffic control theory with recent work in the field of multi-agent planning and has several important distinguishing characteristics. First, to promote scalability and reliability, SURTRAC operates in a totally decentralized manner; each intersection independently and asynchronously allocates its green time, based on current incoming vehicle flows. Second, SURTRAC aims at managing urban (grid-like) road networks with multiple (competing) traffic flows; network-level coordination is accomplished by communicating projected outflows to downstream neighbors, which gives these intersections a more informed basis for locally balancing competing inflows while simultaneously promoting establishment of larger "green corridors". Third, SURTRAC truly operates in real-time; each intersection recomputes its allocation plan and re-communicates projected outflows as frequently as once per second in rolling horizon fashion, enabling both effective operation in tightly spaced signal networks and responsiveness to sudden changes in traffic conditions. After describing our basic approach to adaptive traffic signal control and the pilot implementation of SURTRAC, we present the results of a field test conducted on a nine-intersection road network in the East Liberty section of Pittsburgh, Pennsylvania. In this pilot test, SURTRAC is seen to achieve major reductions in travel times and vehicle emissions over pre-existing signal control. TRB 2013 Annual Meeting Paper revised from original submittal. Smith, Barlow, Xie, and Rubinstein 2 INTRODUCTION Traffic congestion in urban road networks is a substantial problem, resulting in significant costs for drivers through wasted time and fuel, detrimental impact to the environment due to increased vehicle emissions, and increased needs for infrastructure upgrades (1). One of the largest recurring sources of traffic congestion are poorly timed traffic signals (2). Even when signals have been recently retimed, the inability to respond to current traffic patterns can cause pockets of congestion that lead to larger traffic jams. Inefficiencies in traffic signal timing stem from poor allocation of green time, inability to respond to real-time conditions, and poor coordination between adjacent intersections. It is generally recognized that traffic signal improvements offer the biggest payoff for reducing congestion and increasing the effective capacity of existing road networks, and that adaptive traffic signal control systems hold the most promise for improvement (3, 4). This paper investigates the potential of a recently developed approach to real-time adaptive traffic signal control (5, 6) in an actual urban traffic control setting. The approach, which is realized in a system called SURTRAC (Scalable Urban Traffic Control), follows the perspective of recent work in the field of multi-agent planning (7, 8) and formulates traffic signal control as a decentralized, schedule-driven process. In brief, each intersection independently computes a schedule for servicing all currently approaching vehicles. This schedule is used locally to determine when to switch green phases and is recomputed in rolling horizon fashion every few seconds. Network level coordination is then achieved through the exchange of schedule information between neighboring intersections. At each decision point, the scheduled output flows from an intersection’s immediate upstream neighbors are combined with directly sensed traffic inflows to provide an expanded lookahead planning horizon. Additional coordination mechanisms are layered over this basic protocol to cope with specific mis-coordinated situations. The SURTRAC system design has three distinguishing characteristics, each of which offers important advantages. First, decision making in SURTRAC proceeds in a totally decentralized manner. Although more centralized approaches to adaptive traffic signal control have been effectively applied in many settings (e.g., SCOOT (9), BALANCE (10), ACS-lite (11), and SCATS (12)), they nonetheless require tradeoffs that can be limiting. Decentralized control of individual intersections enables maximum responsiveness to real-time traffic conditions. It promotes scalability by allowing incremental addition of intersections over time with minimal change to the existing adaptive network. There is also no centralized computational bottleneck and no single point of failure. A second distinguishing characteristic of the SURTRAC design is its emphasis on real-time responsiveness to changing traffic conditions. Many adaptive traffic control systems (e.g., BALANCE, ACS-Lite and ACDSS(13)) are designed to effect changes to traffic signal timings on the order of minutes based on average flow predictions, which limits how quickly and effectively a system can respond to locally changing traffic patterns. SURTRAC alternatively adopts the real-time perspective of prior model-based intersection control methods (e.g., ALLONS-D (14), PRODYN (15), OPAC (16), RHODES (17), CRONOS (18), and others (19, 20)) which attempt to compute intersection control plans that optimize actual traffic inflows. By using a novel reformulation of the optimization problem as a single machine scheduling problem, SURTRAC is able to compute near-optimal intersection control plans over an extended horizon on a second-by-second basis. Finally, SURTRAC is designed to aim generally at managing urban (grid-like) road networks, where there are multiple (typically competing) dominant flows that shift dynamically through the day, and where specific dominant flows cannot be pre-specified (as in arterial or major crossTRB 2013 Annual Meeting Paper revised from original submittal. Smith, Barlow, Xie, and Rubinstein 3 road applications). Urban networks also often have tightly spaced intersections requiring tight coordination. The combination of competing dominant flows and densely spaced intersections presents a challenge for all adaptive systems. SURTRAC determines dominant flows dynamically by continually communicating projected outflows to downstream neighbors (in similar fashion to the earlier PRODYN system (15)). This information gives each intersection a more informed basis for locally balancing competing inflows while simultaneously promoting establishment of larger "green corridors" when traffic flow circumstances warrant. To demonstrate the potential of the SURTRAC approach, a pilot implementation was installed at a nine-intersection road network in the East Liberty neighborhood of Pittsburgh, Pennsylvania, and a performance comparison was carried out with the existing traffic signal control scheme at this pilot site. Before the pilot test, control of these nine intersections was accomplished using coordinated-actuated timing plans that were optimized offline for AM and PM rush periods and simple actuated control (free mode) during non-rush periods. A series of “before” and “after” drive through runs were performed for each of 4 different periods of the day (AM rush, Mid Day, PM rush and Evening) and relevant performance metrics (travel time, speed, number of stops, wait time, fuel consumption and emissions) were computed for each test condition. Across all metrics studied, SURTRAC is seen to produce significant performance improvement, ranging from 20%-40% overall. The remainder of the paper is organized as follows. First, the decentralized, scheduledriven approach to real-time, adaptive signal control that underlies the SURTRAC system is summarized. Then the architecture and configuration of the pilot SURTRAC implementation are described. The pilot study design is presented next, followed by a discussion of the results obtained. Finally, some conclusions are drawn. SCHEDULE-DRIVEN TRAFFIC CONTROL As indicated above, the traffic signal control problem is formulated in SURTRAC as a decentralized, schedule-driven process (5, 6). At the lowest level, each intersection is controlled independently by a local scheduler, which maintains a phase schedule that minimizes the total delay for vehicles traveling through the intersection and continually makes decisions to update the schedule according to a rolling horizon. The intersection scheduler communicates outflow information implied by its current schedule to its immediate neighbors, to extend visibility of incoming traffic and achieve network level coordination. Effective consideration of the significance of short-term (second-by-second) variability of traffic flows at the individual intersection level is made tractable by a novel formulation of online planning as a single machine scheduling problem (5). Key to this formulation is an aggregate representation of traffic flows as sequences of clusters (corresponding specifically to anticipated queues (21) and platoons) in a limited prediction horizon. These cluster sequences preserve the non-uniform nature of real-time flows while providing a more efficient scheduling search space. Interpreting each cluster as an input job, the scheduling problem is to construct an optimal sequence of all jobs that preserves the ordering of jobs along each inflow and treats all jobs as non-preemptable. A given sequence dictates the order in which jobs will pass through the intersection and can be associated with an expected phase schedule that fully clears the ordered jobs in the shortest possible time, subject to basic timing and safety constraints. The optimal sequence (schedule) is the one that incurs minimal delay for all vehicles. A forward recursion, dynamic programming process is used to solve this scheduling probTRB 2013 Annual Meeting Paper revised from original submittal. Smith, Barlow, Xie, and Rubinstein 4 lem. From a constructive view, the state space can be org