Abstract
Although demand responsive feeder bus operation is possible with human-driven vehicles, it has not been very popular and is mostly available as a special service because of its high operating costs due to intensive labor costs as well as requirement for advanced real-time information technology and complicated operation. However, once automated vehicles become available, small-sized flexible door-to-door feeder bus operation will become more realistic, so preparing for such automated flexible feeder services is necessary to take advantage of the rapid improvement of automated vehicle technology. Therefore, in this research, an algorithm for optimal flexible feeder bus routing, which considers relocation of buses for multiple stations and trains, was developed using a simulated annealing algorithm for future automated vehicle operation. An example was developed and tested to demonstrate the developed algorithm. The algorithm successfully handled relocation of buses when the optimal bus routings were not feasible using the buses available at certain stations. Furthermore, the developed algorithm limited the maximum degree of circuity for each passenger while minimizing the total cost, including total vehicle operating costs and total passenger in-vehicle travel time costs.
Highlights
In the past, automated transit was regarded as personal rapid transit or automated rail transit
The demand responsive feeder bus routing problem stems from the vehicle routing problem with simultaneous pickup and delivery (VRPSPD), which is an extension of the VRP that considers the situation in which feeder buses can alight and board passengers simultaneously
Dik ! 0: Total Dk ! 0: ATi ! 0:WTi ! 0: UCi ! 0: ICi ! 0: where I is the number of passengers, K is the number of available vehicles, dij is the direct distance between passengers i and j, di0 is the direct distance between passenger i and station, CT is the value per passenger hour, CO is the unit operating cost of vehicle per kilometer, Speed is the vehicles’ speed, DOC is the degree of circuity, cycle time is 20 min, C is the vehicles’ capacity, and M is a sufficiently large number for modeling the expression
Summary
In the past, automated transit was regarded as personal rapid transit or automated rail transit. Before procuring and operating automated transit vehicles, it is extremely important to determine what future transit customers will want and expect from them; For example, depending on whether transit customers prefer a traditional type of transit service (e.g., fixed route) with an automated small-sized feeder transit or a more flexible service, similar to that provided by TNCs, transit agencies should prepare their future transit service while considering recent improvements in the availability of multisource and heterogeneous traffic data. These technologies will open new opportunities for TNCs to implement automated feeder transit systems as well [4]. These studies will help predict users’ travel behaviors and modal choices between automated ridesharing/carsharing operation and automated feeder services for mass transit
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