Optimizing Centralized Dispatching of Flexible Feeder Transit considering Transfer Coordination with Regular Public Transit

Optimizing centralized dispatching of flexible feeder transit to provide transport and transfer services is important and theoretically challenging for real-world applications. Considering transfer coordination with regular public transit, a multiobjective optimization model that can output an operation plan containing vehicle routes and a timetable for a bus fleet is proposed. By establishing constraints for parameters such as maximum acceptable advance or delay time of transfer, rated passenger capacity, and maximum travel time of a single trip, the proposed model attempts to maximize the successful response ratio, minimize the passengers’ average time costs, and minimize the operating costs of a single passenger. A genetic algorithm was designed to solve the optimal solution, and computational experiments were conducted in a residential area in Beijing. Results reveal that the proposed model and algorithm can be applied in the operation of flexible feeder transit. Moreover, compared with the distributed dispatching method, the value of the optimal objective function in the proposed model was improved by 26%. Although the successful response ratio showed a 29.3% increase and the average passenger time cost showed a small drop, the operating costs per passenger were reduced by 30.7%. The different weight coefficients of the subobjective function and maximum acceptable advance or delay time of transfer could result in different optimal operation plans. Essentially, the optimization procedures for the successful response ratio and the operating costs are in the same direction, whereas the one for the passenger’ cost is in the opposite direction. However, operators should select appropriate values to optimize operation plans.

Impact of Automated Vehicles on Optimal Demand-Responsive Feeder Transit Network Design

This article aims to examine the economic benefits of automating flexible demand responsive feeder transit systems using a developed feeder bus routing optimization algorithm. The objective function of the algorithm is to minimize total passengers' and operating costs of the system. The results showed that when unit operating costs decline, total operating costs, and total costs obviously decline. Furthermore, when unit operating costs decline, the average passenger travel distance and total passenger travel costs decline while the ratio of total operating costs per unit operating costs increases. That means if unit operating costs decrease, the portion of passenger travel costs in the total costs increases, and the optimization process tends to reduce passenger costs more while reducing total costs. Assuming that automation of the vehicles reduces the operating costs, it will reduce not only total operating costs and total costs, but also total passenger travel costs.

Designing High-Freedom Responsive Feeder Transit System with Multitype Vehicles

The last mile travelling problem is the most challenging part when using public transit. This study designs a high-freedom responsive feeder transit (HFRFT) system to serve at the transfer station, given vehicle routes, departure time, and service area based on demand. The proposed feeder transit system employs a travelling mode with multitype vehicles. In order to improve the operation of the HFRFT system, the optimization design methods are suggested for vehicle routes, scheduling, and service area. A mixed integer programming model and its hybrid of a metaheuristic algorithm are proposed to efficiently and integrally solve the vehicle routes and scheduling parameters according to the reservation requirements. A heuristic method is proposed to optimize the service area based on the equilibrium of system supply and demand. Case studies show that the mixed running mode of multiple models can significantly improve the seat utilization, which can also significantly reduce the number of departures and the average travel distance per passenger. The proposed service area optimization method is proved to be feasible to improve the last mile travel.

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

This study proposed a mathematical model for designing a feeder transit service for improving the service quality and accessibility of transportation hubs (such as airport and rail station). The proposed model featured an integrated framework, which simultaneously guided passengers to reach their nearest stops to get on and off the bus, designed routes to transport passengers from these selected pick-up stops to the transportation hubs, and calculated their departure frequencies. In particular, the maximum walking distance, the upper and lower limits of route frequencies, and the load factor rate of each route were fully accounted for in this study. The main objective of the proposed model was to simultaneously minimize the total walking, riding time, and waiting time of all passengers. As this study explored an NP-hard problem, a two-stage genetic algorithm combining the Dijkstra search method was further developed to yield metaoptimal solutions to the model within an acceptable time. Finally, a test instance in Chongqing City, China, demonstrated that the proposed model was an effective tool to generate a pedestrian, route, and operation plan; it reduced the total travel time, compared with the traditional model.