Assessing the impact of different policy decisions on the resource requirements of a Demand Responsive Transport system for persons with disabilities

Abstract

Demand Responsive Transport (DRT) services are frequently offered in the context of door-to-door transportation of elderly and persons with disabilities (PWD), but are expensive to provide. Therefore, the present study aims to determine the impact of different policy decisions on the resource requirements of a DRT system, in terms of vehicles and drivers required and kilometers traveled. A number of policy scenarios are analyzed in the case study of Flanders for the prediction years 2015, 2020 and 2030: a base scenario assuming a geographically covering subsidized DRT system; a more economical scenario limiting the subsidized DRT services; a more accessible public transport; more flexibility of the users of the DRT system; an increase in the service area of the DRT service providers; and a doubling of the current supply of transport by volunteers.

A microscopic simulation of the demand of elderly and PWD for transportation was performed in order to obtain a detailed overview of all transportation requests that need to be processed. Next, each simulated transportation request was assigned to a specific service provider and a separate vehicle routing plan was created, taking into account heterogeneous users (persons with different severity and type of disabilities), heterogeneous vehicles (regular and wheelchair adapted vehicles) and multiple geographically distributed depots. Resource requirements were calculated for each of the considered scenarios.

The results show that changing the modal split (the mix between the different DRT providers), as well as the realization of adequate public transport and an improved flexibility, seem to be key elements in Flanders to minimize the resource requirements of a DRT system. Our applied methodology can easily be transferred to other regions or areas, as the flexibility of the model allows to easily modify the input criteria based on the specific context. This information can be used by governments for the optimization of their DRT system.