Optimal Routing and Charging of Demand-Responsive Feeder Transit Services Considering Departure Time Selection
Abstract
Large suburban communities are rapidly expanding, generating significant subway travel demand and necessitating efficient feeder connections between these communities and subway stations. Against this backdrop, demand-responsive community electric buses, known for their flexibility and eco-friendliness, play a unique microcirculation role in the public transportation system. This study proposes an integrated routing and departure time selection model with charging management. The model simultaneously determines the pick-up and drop-off sequence, the endogenous depot departure time, and the charging schedule, with the objective of minimizing the total system cost, including electric bus travel cost, passenger fare discount cost, and charging cost. The model is solved using a Variable Neighborhood Search-based Simulated Annealing (VNS-SA) algorithm. A small-instance Gurobi benchmark is provided for the routing subproblem with fixed vehicle-request assignment. A fixed-departure-time comparison shows that optimizing the depot departure time endogenously reduces the total system cost in the tested instances. To evaluate the performance of the proposed method, a numerical experiment is conducted based on the real-world road network topology of the Huilongguan community in Beijing. The VNS-SA parameters are calibrated by the Taguchi method, and ten independent runs with fixed random seeds are conducted for reproducibility. Sensitivity analysis reveals that as the fleet size increases, the average route length decreases, and both the average charging cost and charging frequency per route are reduced.