Peak-Hour Vehicle Routing for First-Mile Transportation: Problem Formulation and Algorithms

Abstract

The first-mile transportation provides a transit service using ridesharing based vehicles, e.g. feeder buses, for passengers to travel from their homes, workplaces or public institutions to the nearest public transportation depots (rapidtransit metro or appropriated bus stations) which are located beyond comfortable walking distance. This paper studies the vehicle routing problem (VRP) for the first-mile transportation, which aims at finding the optimal travel routes for a vehicle fleet to deliver passengers from their doorstep to the depots, where the passengers can continue their journeys using fixedroute buses or trains. We focus on the Peak-Hour VRP (PHVRP) for a limited vehicle fleet capacity to serve a large volume of travel requests, with the aim of maximizing the number of served passengers. The PHVRP generalizes the VRP with time window by considering multiple alternative depots for each travel request, such that a request is satisfied if the passenger is taken to one of his/her nearest depots. We formally formulate the PHVRP with constraints on vehicle capacity, pickup time windows, and quality of service regarding riding time, where a novel trip-based constraint model is used. We proposed an ant-colony optimization algorithm for the PHVRP, which is initialized with pheromone information that jointly considers the temporal-spatial distance as well as depot similarity among different travel requests. We introduced a novel scheme (called trip-by-trip scheme) to construct the travel routes by repeatedly forming a single trip for the vehicle with earliest end time until no vehicle can accept any more trips. In constructing a single trip, the algorithm intelligently decides whether or not to end the trip instead of taking more passengers. The effectiveness of the proposed methods is evaluated by comparing with optimal solutions on small size instances and with heuristic solutions on large size instances, using road network in Singapore and synthetic travel requests that are generated based on real bus travel demands.

Optimizing first-mile ridesharing services to intercity transit hubs

“…In the literature on FM ridesharing, most studies (Shen et al, 2018;Bian and Liu, 2019a,b;Chen et al, 2020;Jiang et al, 2020;Ning et al, 2021;Kumar and Khani, 2021;Huang et al, 2022) focus on FM ridesharing to metro/bus transit hubs, while there is comparatively scarce research on the FM ridesharing to intercity transit hubs, such as airports and railway stations. For instance, Bian and Liu (2019a,b) designed a novel mechanism for FM ridesharing services to metro stations and a heuristic algorithm to solve this problem.…”

Hierarchical Vehicle Scheduling Research on Tide Bicycle-Sharing Traffic of Autonomous Transportation Systems

“…Te actual operation costs need to consider both fxed and fexible costs, as summarized in equation ( 2), which is determined jointly by the value of scheduling vehicles [34], the unit transport cost (i.e., vehicle loss: 1 CNY/km, fuel consumption: 1 CNY/km, and labor cost: 100 CNY/person), and the scheduling distance. 4) suggests that each area can only be served once; equation (5) shows that the transport distance must not exceed the maximum scheduling distance; equation (6) points that the number of bikes loaded by the vehicle must not exceed its maximum capacity, namely, 400; equation ( 7) means x t ij as 0-1 variable; equation (8) proves that the number of bikes deployed by the vehicle is a non-negative integer.…”

Passenger-centric vehicle routing for first-mile transportation considering request uncertainty

“…The work in [33] studies a dynamic passenger-centered ridesharing problem and proposes methods to minimize the overall travel distance of the passengers. The work in [16] considers a scenario of FMT where there are insufficient vehicles to serve all the passengers during peak hours. Its objective is to serve as many passengers as possible by encouraging passengers to provide multiple alternative transit stations, which help improve their chances of being picked up.…”

“…The work in [18] proposed to boost ride-sharing opportunities with alternative destinations to reduce the number of circulating vehicles. Another work [16] encourages passengers to provide multiple alternative destinations, which helps improve the chances of being picked up during peak hours when there are insufficient vehicles. Insufficient vehicles also pose a difficulty in serving all passengers within their preferred pickup time, particularly so for passengers whose requested pickup time and locations are markedly different from others.…”