Modeling Framework and Decomposition Scheme for On‐Demand Mobility Services with Ridesharing and Transfer

Abstract

This article presents a modeling framework for the operations of on‐demand mobility services (ODMS) in urban areas. The framework provides the capabilities to analyze ODMS operations while representing emerging services such as ridesharing and transfer. The problem is formulated as a mixed integer program and an efficient decomposition‐based methodology is developed for its solution. The methodology adopts a modified version of the column generation algorithm, which implements iterative decomposition and network augmentation techniques to allow studying networks of moderate size. The results of a set of experiments considering grid and real‐world networks are presented. The results show that increasing the number of passengers willing to rideshare and/or transfer improves the overall performance of ODMS as it increases number of served passengers and associated profit and reduces the number of used vehicles. Although presented as an offline planning tool, the methodology could be adopted for real‐time applications as adequate computational resources become available.

Towards a geographically even level of service in on-demand ridepooling

“…Below we test our techniques modifying the method developed by [2]. Other methods that could make use of our techniques are the ones by [4], [5] (that are based on [2]) and by [13], [14].…”

A survey on demand-responsive public bus systems

“…Real-world data of locations of shared vehicle pickup and drop-off stations (Washington DC (USA)), with high demand levels, is used to test this system. Lotfi et al (2019) study a door-to-door ride-sharing system with transfer services. Passengers need to make a request for transportation between their pickup and drop-off locations, their earliest departure time, and their latest arrival time.…”

“…Rigas et al (2018) compare between an "offline" and an "online" version of their system, and develop different algorithms to solve each system. Lotfi et al (2019) further mention that a dynamic online version would technically be possible if their algorithm was faster but do not make a comparison. They themselves use a matheuristic.…”

Shared Mobility Problems: A Systematic Review on Types, Variants, Characteristics, and Solution Approaches

“…The ride exchange mechanism allowed unmatched riders to purchase an itinerary from a previously matched rider, allowing for a ride exchange accompanied with an exchange of money through the system. There is also work by [28] who introduced a comprehensive modelling framework for the dynamic shared mobility service which is the integration of ridesharing with transfer points. The proposed model assumes heterogeneous customers' in terms of their service preferences.…”

“…A TP allows passengers to be transferred between multiple drivers. The driver drops off passengers at the transfer point and another driver will bring those passengers to their final destination [28]. Transfer services play an important role in mitigating the shortage of vehicle supply.…”