考虑有限理性的市域与城轨列车跨线开行方案研究OA

In order to address the problems of growing diversified travel demands,uneven transport resource allocation,and inefficient transportation improvement amid the rapid development of metropolitan areas and urban agglomerations,this paper focused on the cross-line operations of suburban railway and urban rail transit.It analyzed passengers'travel choice behavior under cross-line operations,and constructed a passenger flow allocation model integrating cumulative prospect theory and mixed logit model via a dynamic reference point update mechanism.By establishing a train event-activity network,an optimization model for cross-line operations was developed.It aimed to minimize the capacity-volume discrepancy and passengers'generalized travel cost while considering constraints such as line capacity,train tracking intervals,and unbalanced departure intervals.To solve the model,a chaotic particle swarm optimization algorithm under a hierarchical framework was designed,balancing computational efficiency and feasibility through phased solution space screening.A case analysis was conducted to verify the effectiveness of the proposed model and algorithm.The results show that under moderate passenger flow demand,when the operation ratio of in-line trains to cross-line trains is maintained at about 4∶1.The operation of cross-line trains can effectively improve the transportation efficiency and obtain positive benefits.The bounded rationality-based passenger flow allocation model better matches actual travel behavior than the complete rationality one,with its dynamic reference point update mechanism capturing how travel reference points evolve for passengers with different risk preferences.This research can break the limitation of the traditional complete rationality hypothesis.The results can provide theoretical support and a decision-making framework for refined cross-line operation of multi-standard rail transit.