Determining the Viability of a Demand-Responsive Transport System under Varying Demand Scenarios

Abstract

Collaborative transportation has been proposed as a potential solution to decrease congestion, reduce environmental effects of transport, and provide transportation options to those with no or restricted travel options. One such system is demand-responsive transport, in which passengers share a vehicle, usually a bus, but can be picked up or dropped off at a passenger-specified location and time. However, as these systems are expensive to implement and require long trials in order to gain traction, effective simulation is required in order to explore their viability before implementation. Although previous work has concentrated on the number of trip requests, the spatial distribution of these requests has not been considered. This paper explores four spatiallyvarying demand patterns -random, a many-to-one scenario, all short distance trips, all long distance trips -using a simulation of an ad-hoc demand-responsive bus system. It is shown that along with the number of trip requests and the requested trip distances, the spatial distribution of passengers does indeed have an effect on the level of service.

The Fourth Wave of Digitalization and Public Transport: Opportunities and Challenges

“…Demand Responsive Transport (DRT). In DRT, passengers share a vehicle, for instance a small bus, which picks up and drops off the passengers at passenger-specified locations and times [13]. Initially, DRT was designed for disabled or elderly people (see [14,15]).…”

Autonomous car and ride sharing: flexible road trains

“…There are many algorithms in the literature to handle ad-hoc requests [18,8,2,21,19], ranging from simple to complex ones. However, simple heuristic methods, such as greedy insertion, are likely to result in suboptimal solutions.…”

Comparing demand responsive and conventional public transport in a low demand context

“…To this end, instead of an analytical model, an agent based traffic simulation, MATSim, is employed that has the capability of modeling each individual's preferences as well as traffic, which leads to a more realistic model than the previous analytical approaches. Moreover, the embedded dynamic routing algorithm [13] models the most desirable form of DRT, ad-hoc services. Therefore, the presented model in this work is able to accurately demonstrate the extent of DRT's impact on passengers' travel time and find the demand switch point.…”

“…It includes a dynamic routing algorithm developed by Ronald et al [13] for comparing different schemes of DRT. However, this module in MATSim has currently the limitation of performing no iteration, i.e., the agents can only run their initial given plan and are not able to improve their plans over several iterations.…”