Optimal Signal Timing Design for Urban Street Networks Under User Equilibrium Based Traffic Conditions

In the ever-growing travel demand, traffic congestion on freeways and expressways recurs more frequently at a higher number of locations and for longer durations with added severity. This becomes especially true in large metropolitan areas. Particular to the urban areas, excessive crowdedness caused by inefficient traffic control also results in urban street networks operating in near or over-saturated conditions, leading to unpleasant travel experience due to long delays at intersections. As a consequence, the recurrent traffic congestion on roadway segments and vehicle delays at intersections inevitably compromise energy efficiency, traffic mobility improvement, safety enhancement, and environmental impacts mitigation. All too often, neither restraining travel demand nor expanding system capacity is desirable and practical. Conversely, effectively utilizing the capacity of the existing transportation system has been increasingly thought of as the solution to congestion relief. With respect to the urban street networks, developing effective means for urban intersection signal optimization becomes essential to reduce intersection delays. Conventional signal timing optimization methods use historical traffic data and assume that traffic flows will remains unchanged after the implementation of new signal timing plans. Traffic flows are assumed to be constant, but in fact, when signal timing plans change, travel times for some travel routes will alter, which requires drivers in the network to adjust their choice of travel routes to arrive at the destinations, and result in redistribution of traffic in the network. Therefore, the effects of interactions between signal timing plans and traffic flows need to be explicitly taken into consideration. This study introduces a new methodology that jointly considers signal timing optimization and traffic assignment in an overall analytical framework that contains model formulations under assumptions consistent with real world situations. Such a framework is well suited for applications in real world cases. Specifically, the overall optimization framework is formulated as a bi-level optimization problem. In the proposed basic model, at the upper level, a traffic signal timing optimization problem for urban network is introduced to minimize system total travel time by optimizing signal green splits. At the lower level, a static user equilibrium problem is formulated for networkwide traffic assignment. In the vehicle delay estimation, the time-dependent stochastic delay model in the 2010 Highway Capacity Manual (HCM 2010) is employed and formulated as Variational Inequality constraints, what allow the state-of-the-art MPEC solver, GAMS/NLPEC, to solve the problem for a local optimal effectively and efficiently. The bi-level optimization model is first tested using a small network (the test network) and a computational experiment using a subarea network in the Chicago central district is conducted to assess the practicality of the model formulation in real world applications. In order to import more reality to the basic model and also consider the potential system benefit that comes from different signal phasing design, an enhanced model is developed based on the basic model by employing integer and binary variables. Formulating the problem with binary variables allows for the selection of proper phasing design. Heuristic solution methods are proposed and tested using the test network.


  • English

Media Info

  • Media Type: Digital/other
  • Edition: Final Report
  • Features: Bibliography; Figures; Tables;
  • Pagination: 102p

Subject/Index Terms

Filing Info

  • Accession Number: 01616165
  • Record Type: Publication
  • Report/Paper Numbers: NEXTRANS Project No. 019FY02
  • Contract Numbers: DTRT12-G-UTC05
  • Created Date: Oct 31 2016 2:36PM