Optimal Design of Demand-Responsive Feeder Transit Services with Passengers’ Multiple Time Windows and Satisfaction

Abstract

This paper presents a mixed-integer linear programming model for demand-responsive feeder transit services to assign vehicles located at different depots to pick up passengers at the demand points and transport them to the rail station. The proposed model features passengers' one or several preferred time windows for boarding vehicles at the demand point and their expected ride time. Moreover, passenger satisfaction that was related only to expected ride time is fully accounted for in the model. The objective is to simultaneously minimize the operation costs of total mileage and maximize passenger satisfaction. As the problem is an extension of the nondeterministic polynomial problem with integration of the vehicle route problem, this study further develops an improved bat algorithm to yield meta-optimal solutions for the model in a reasonable amount of time. When this was applied to a case study in Nanjing City, China, the mileage and satisfaction of the proposed model were reduced by 1.4 km and increased by 7.1%, respectively, compared with the traditional model. Sensitivity analyses were also performed to investigate the impact of the number of designed bus routes and weights of objective functions on the model performance. Finally, a comparison of Cplex, standard bat algorithm, and group search optimizer is analyzed to verify the validity of the proposed algorithm.

Optimal Integrated Model for Feeder Transit Route Design and Frequency-Setting Problem with Stop Selection

“…Complete integrated operations handle decisions of each subproblem and hence are much more complex than the subproblem [31]. Further, some exact and heuristic approaches were proposed to deal with large-scale problems [4,5,[28][29][30][31][32][33].…”

An Optimization Model for Demand-Responsive Feeder Transit Services Based on Ride-Sharing Car

“…In the route-building method, a set of feasible routes is firstly generated and then used to search and fine-tune initial solutions according to the proposed constraints at a reasonable computational cost. In meta-heuristics method, simulated annealing [25][26][27], tabu search [18,23,28], genetic algorithms [29][30][31][32], and colony algorithm approach [33,34] are widely used to contend with DRTs.…”

Special Issue “New Perspectives in Intelligent Transportation Systems and Mobile Communications towards a Smart Cities Context”

“…A programming prototype for demand-responsive feeder transportation services to designate vehicles placed at various stations, aimed at picking up passengers at the demand points and carrying them to the rail station is proposed in [2]. The main aim is to reveal the relationship between the total mileage and passenger satisfaction.…”