Integrated Optimization of Bus Route Adjustment With Passenger Flow Control for Urban Rail Transit

The contradiction between transport capacity and passenger demand in urban rail transit is usually prominent during peak hours in some megacities of China, and some passenger flow control measures have been adopted to alleviate passenger congestion. To better save passengers' travel time when taking passenger flow control measures, this paper proposes an integrated optimization method of bus route adjustment with network-level passenger flow control for urban rail transit, in which the controlled passengers can freely choose to shift to bus or to retain in urban rail transit for pursuing a lower travel cost. With the objectives of minimizing average additional travel time for all affected passengers and maximizing the operating revenue of urban rail transit, an integer non-linear programming model is formulated to determine the inbound passenger volumes and bus adjustment schemes. To solve this proposed model effectively, a multi-objective particle swarm optimization based on dual-population co-evolution is designed. Finally, three sets of numerical experiments, including an integrated optimization experiment and two independent optimization experiments of passenger flow control, are implemented to demonstrate the feasibility and benefits of the proposed method.