The University of Southampton
University of Southampton Institutional Repository

Journey time forecasting for dynamic route guidance systems in incident conditions

Hounsell, Nick B. and Ishtiaq, Saeed (1997) Journey time forecasting for dynamic route guidance systems in incident conditions International Journal of Forecasting, 13, (1), pp. 33-42. (doi:10.1016/S0169-2070(96)00698-X).

Record type: Article

Abstract

New in-vehicle systems for route guidance require optimum routes in a network to be calculated based on current and forecast journey times. Following a brief review of forecasting methods developed for normal traffic conditions, this article describes a new method for the more difficult but particularly important situation of traffic incidents which occur in variety of forms in urban networks, e.g. an accident, a vehicle breakdown, illegal parking/ stopping and so on. In such conditions journey times may be increased not only on the incident link, but also on the links which are the upstream links of the incident location, this could lead to serious congestion, a rise in energy consumption and environmental nuisance.

The prediction of the effects of traffic incidents is therefore an important issue for better efficiency and for on-line dynamic route guidance (DRG) systems and other traffic control systems. In this study an incident data base was compiled, based on modelling of several incident/ network/ traffic scenarios using a simulation tool. Generalised statistical models were then developed for predicting the spread of congestion effects following an incident and the required travel time modifications on the incident link and on affected links. The aim was to provide a reasonably robust process for on-line applications, to improve on current ad-hoc methods. The main application of the developed models is in incident management for dynamic route guidance systems particularly in low penetration level (i.e. where the proportion of guided drivers is relatively lowNew in-vehicle systems for route guidance require optimum routes in a network to be calculated based on current and forecast journey times.

Following a brief review of forecasting methods developed for normal traffic conditions, this article describes a new method for the more difficult but particularly important situation of traffic incidents which occur in variety of forms in urban networks, e.g. an accident, a vehicle breakdown, illegal parking/ stopping and so on. In such conditions journey times may be increased not only on the incident link, but also on the links which are the upstream links of the incident location, this could lead to serious congestion, a rise in energy consumption and environmental nuisance. The prediction of the effects of traffic incidents is therefore an important issue for better efficiency and for on-line dynamic route guidance (DRG) systems and other traffic control systems. In this study an incident data base was compiled, based on modelling of several incident/ network/ traffic scenarios using a simulation tool. Generalised statistical models were then developed for predicting the spread of congestion effects following an incident and the required travel time modifications on the incident link and on affected links.

The aim was to provide a reasonably robust process for on-line applications, to improve on current ad-hoc methods. The main application of the developed models is in incident management for dynamic route guidance systems particularly in low penetration level (i.e. where the proportion of guided drivers is relatively low).

Full text not available from this repository.

More information

Published date: March 1997
Keywords: traffic incidents, journey time, dynamic route guidance, network modelling, urban traffic control

Identifiers

Local EPrints ID: 74663
URI: http://eprints.soton.ac.uk/id/eprint/74663
ISSN: 0169-2070
PURE UUID: f458ea36-d387-41a1-945c-6c537e8ad444

Catalogue record

Date deposited: 11 Mar 2010
Last modified: 18 Jul 2017 23:45

Export record

Altmetrics

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×