Probabilistic Fleet Sizing and Routing Problem to Minimize Mobility Disparities

Abstract

The objective of this paper is to develop an analytical model to analyze the mobility behavior of a target population and to minimize the disparities. The analysis is conducted using household travel survey data collected from 7993 residents of five boroughs of New York City, where citizens with mobility impairments are considered as our target population. The study starts by quantifying the existing gap in the patterns of the mobility-impaired individuals and the base population followed by the generation of new activities in the agenda of the target population using the patterns of their nearest neighbors in the base population. Activities are generated based on copula sampling method. Attributes of activities forming the new agenda of the target population are the inputs that will be used for the design of an optimum ridesharing system. By integrating probabilistic models with the existing routing models, we develop a methodology to identify the optimum fleet size, optimum route, and optimum schedule such that all requests of the physically impaired travelers are fulfilled within a reasonable wait time for ride requesters.