Abstract
One important objective of urban traffic signal control is to reduce individual delay and improve safety for travelers in both private car and public bus transit. To achieve signal control optimization from the perspective of all users, this paper proposes a platoon-based adaptive signal control (PASC) strategy to provide multimodal signal control based on the online connected vehicle (CV) information. By introducing unified phase precedence constraints, PASC strategy is not restricted by fixed cycle length and offsets. A mixed-integer linear programming (MILP) model is proposed to optimize signal timings in a real-time manner, with platoon arrival and discharge dynamics at stop line modeled as constraints. Based on the individual passenger occupancy, the objective function aims at minimizing total personal delay for both buses and automobiles. With the communication between signals, PASC achieves to provide implicit coordination for the signalized arterials. Simulation results by VISSIM microsimulation indicate that PASC model successfully reduces around 40% bus passenger delay and 10% automobile delay, respectively, compared with signal timings optimized by SYNCHRO. Results from sensitivity analysis demonstrate that the model performance is not sensitive to the number fluctuation of bus passengers, and the requested CV penetration rate range is around 20% for the implementation.
Highlights
Signal light plays a significant role in urban traffic management and control
Recent studies found that the transit signal priority control strategies (TSP) inevitably interrupt automobile traffic flow and increase the control delay for automobile users [3]
For every 30 seconds, vehicular information was extracted for the optimization, and CPLEX was used to find the optimal solution, so that the generated signal timings can be implemented into the VISSIM simulations [24]
Summary
Signal light plays a significant role in urban traffic management and control. Adaptive signal control, a state-of-the-art type of traffic control, can remarkably improve the mobility around signalized intersections compared with fixed or actuated controls [1, 2]. Optimization-based strategies attempt to minimize total disutility (delay, queue length, and stop numbers) by employing nonlinear [9], mix-integer linear [10], or dynamic programming [11] One advantage of such a control strategy is the nonlimitation on the number of conflicting priorities in the optimization model, compared with rule-based strategies. To address the disadvantages of the conventional strategies in modelling the mixed traffic conditions, recent research efforts have been dedicated to developing new control methods based on the CV data. He et al [13] proposed a novel control model called PAMSCOD, which clusters vehicles into platoons to incorporate the arrival patterns subject to upstream intersections.
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