A simulation study of an on‐demand transportation system

• Date: 01 July 2018
• Author: Claudia Archetti,M. Grazia Speranza,Dennis Weyland
• Published date: 01 July 2018
• DOI: http://doi.org/10.1111/itor.12476

Intl. Trans. in Op. Res. 25 (2018) 1137–1161

DOI: 10.1111/itor.12476

INTERNATIONAL

TRANSACTIONS

IN OPERATIONAL

RESEARCH

A simulation study of an on-demand transportation system

Claudia Archettia, M. Grazia Speranzaaand Dennis Weylandb

aDepartment of Economics and Management, University of Brescia,Italy

bIstituto dalle Molle di Studi sull’Intelligenza Artificiale, Universit´

a della Svizzera italiana, Lugano, Switzerland

E-mail: claudia.archetti@unibs.it[Archetti]; grazia.speranza@unibs.it [Speranza];dennisweyland@gmail.com [Weyland]

Received 15 February2017; received in revised form 21 September 2017; accepted 22 September 2017

Abstract

In this paper we present the results of a simulation study aimed at assessing an on-demand transportation

system. The on-demand system uses minibuses thathave neither fixed itineraries nor fixed stops.The minibuses

are dynamically routed to accommodate the requests received by the users. To use the on-demand service,

users communicate, close to their desired departure time, the origin and destination of the trip. They accept

the service if the estimated arrival time at destination fulfills their service level threshold. In the simulation

users may decide whether to walk, to use a standard bus, to call the on-demand service, and, if none of these

options is satisfactory, to use a private car. We consider different scenarios to assess the potential benefits of

the introduction of an on-demand service. We also analyze the scalability and responsiveness of the service.

The results suggest that an on-demand system may be able to satisfy a large portion of user transportation

requests and may be put beside standard transportation systems in order to provide a better transportation

service to the users and substantially reduce the use of private cars.

Keywords:simulation; demand-responsive transit systems; on-demand transportation;conventional public transportation

1. Introduction

Traffic congestion is a primary problem everywhere. As stated in Kennedy (2002), the automobile

has had a dramatic impact on the society during the last century. A crucial issue has become the

sustainability of such a growing number of cars on the roads, which has led to a renewed interest in

alternative forms of transportation,especially in urban areas. This is witnessed by the wide diffusion

of bicycle and car sharing systems, as well as car pooling. The success of these systems is partly

because they are able to satisfy transportation requests that cannot be handled by the standard

public (and mass) transportation systems. In fact, mass mobility systems typically work on fixed

schedules, which in many situations cannot satisfy the dynamic demand of people. Recent studies

(Diana et al., 2009; Edwards and Watkins, 2013; Navidi et al., 2016) show that conventional public

transport (CPT) systems are suited for areas characterized by high-density demand, such as in huge

C

2017 The Authors.

International Transactionsin Operational Research C

2017 International Federation of OperationalResearch Societies

Published by John Wiley & Sons Ltd, 9600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main St, Malden, MA02148,

USA.

1138 C. Archetti et al. / Intl. Trans. in Op. Res.25 (2018) 1137–1161

cities, while on-demand and flexible systems aresuperior when this condition is not satisfied as they

can offer a service that is closer to the user needs. In fact, when demand density is low, or at least

below a given threshold, CPT systems offer a service whose frequency may be insufficient to satisfy

the dynamic nature of the demand of a vast portion of the users. In any case, the same studies report

that when users require a high-quality service, then demand-responsive transit (DRT) systems are

superior to CPT systems.

DRT systems (also called dial-a-ride systems) have emerged in the last decades as an attempt to

satisfy the dynamic nature of users’ demands. The users rely on flexible services that provide almost

“door-to-door” transportation in small vehicles,with the possibility of prebooking (see Bellini et al.,

2003; Ferreiraet al., 2007; Logan, 2007). DRT systems are nowadaysmainly implemented as services

for small groups of people (e.g., elderly or physically challenged; see Palmer et al., 2004; Cordeau

and Laporte, 2007; Grootenboers et al., 2010). The literature on DRT is also very limited (for

studies on the topic, see Uchimura et al., 2002; Brake et al., 2004, 2007; Palmer et al., 2004; Mulley

and Nelson, 2009; and for planning multileg journeys with fixed-route and demand-responsive

passenger transportation services, see Horn, 2004).

An excellent review of the literature dealing with DRT systems is presented in a recent paper

(Mart´

ınez et al., 2015b), where a minibus service is studied to serve low- and intermediate-density

areas (see also Eir´

o et al., 2007, 2011). The authors propose a classification of DRT systems

previously introduced in Bellini et al. (2003) as follows:

rwith fixed itineraries and stops, where users must prebook the service;

rwith fixed itineraries and stops with possible detours;

rwith unspecified itineraries and predefined stops;

rwith unspecified itineraries and unspecified stops.

The first category is most closely related to the traditional public transportation service. This

category may also include systems with fixed timetables.The difference is that users have to prebook

the service that is performed on demand. As specified in Mart´

ınez et al. (2015b), the last type of

service, which is the most flexible one, can be considered as the closest to the concept of shared

taxis. The shared-taxi system is studied in Mart´

ınez et al. (2015a) where taxis are used to serve more

than one customer at a time. Customers are assigned to shared taxis on the basis of their origin

and destination requests and their service time, whileguaranteeing a predefined threshold of service

level. The authors simulate, in the city of Lisbon, the behavior of a system combining traditional

taxis, that is, taxis serving one person at a time, with shared taxis. Users can hail for a taxi on the

street, go to a taxi stand, or phone to a dispatching company. Shared taxis can only serve customers

who place their service request to a dispatch company. The simulator assigns users to taxis with

the objective of minimizing waiting time for the users and balancing workload (and thus rewards)

of taxi drivers. The indicators used to measure the performance of the system are waiting time for

users, cost for users, and taxi drivers’ revenues. The results show that passengers may benefit from

an average 9% fare reduction compared with the traditional system.

More recently, DRT systems have been referred to as flexible transportation services (see Mulley

and Nelson, 2009) when used as feeder systems for more traditional public transportation services

such as buses or trains.From this point of view,DRT systems are seen as flexible,demand-responsive

transportation services used to ease the user access to massive public transportation means. In a

C

2017 The Authors.

International Transactionsin Operational Research C

2017 International Federation of OperationalResearch Societies