Abstract
This paper investigates the optimal routing design problem of a community shuttle system feeding to metro stations based on demand-responsive service. The solution aims to jointly optimize a set of customized routes and the departure time of each route to provide a flexible shuttle service. Considering a set of on-demand trip requests between bus stops and metro stations, a mixed-integer optimization model is formulated to minimize the total system cost, including the operation cost and passenger’s in-vehicle cost, subject to the constraints on the route length, time window, detours, and vehicle capacity. To solve the problem, two metaheuristic algorithms, i.e. a tabu search (TS) and a variable neighborhood search (VNS), with different internal operators are specifically designed. A case study based on a realistic network is conducted to test the model and the solution, and comparisons of the performance of different algorithms are investigated.
Highlights
Community shuttle services provide flexible mobility to public transit passengers dispersing into a large community to access nearby metro stations
This paper investigates the optimal routing design problem of a community shuttle system feeding to metro stations based on demand-responsive service
Similar to the impact on CkP,1r2, the impact on CrP3 is determined by a critical departure time Di0m,s, which represents the speculated departure time at the depot using the lower limit of the time window of the last pickup service node on r
Summary
Community shuttle services provide flexible mobility to public transit passengers dispersing into a large community (especially in suburbs) to access nearby metro stations. This study focuses on a static routing design of community shuttles for given on-demand trip requests that include their pickup and delivery points, the number of passengers, and some limitations on the service time. It used a tabu list to keep track of recent moves or visited solutions so that they can be forbidden for a number of iterations to avoid cycling Another type of metaheuristics that was quite effectively used in solving the problems is the class of neighborhood-based search algorithms, mainly including the adaptive large neighborhood search (ALNS, e.g. References [10,15]) and variable neighborhood search (VNS, e.g. References [19,23]).
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