Access Restriction

Author Necula, E.
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2014
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Special computer methods
Subject Keyword Roads ♦ Vehicles ♦ Global Positioning System ♦ Predictive models ♦ Training ♦ Data models ♦ Hidden Markov models ♦ traffic flow ♦ traffic prediction ♦ VMM ♦ data mining ♦ GPS data ♦ ITS
Abstract This paper presents a solution for traffic flow prediction in a city area. GPS devices offer new opportunities for short-term traffic prediction, especially in arterial road networks where traditional fixed-location sensors are sparse or expensive to install. However, GPS data is often sparse both temporally and spatially. On its own, it is often insufficient for real-time traffic prediction. We consider the fusion of two types of data for the purpose of dynamic traffic prediction: GPS data that is provided as point speeds, rather than trajectories, as well as traffic data that is available from previous tracking. Inspired by the observation that a driver often has its own route selection behavior, we define a mobility pattern as a consecutive series of road segment/link selections that exhibit frequent appearance along all the itineraries of the vehicle. We predict the traffic flow using a hybrid method based on Variable-order Markov Model and adding on top of it the average speed of all the vehicles passing through each road segment. Our solution comes with a highly scalable traffic simulator application that can be used to predict, manage and optimize car traffic in cities. The prediction accuracy is estimated according to various criteria.
Description Author affiliation: Fac. of Comput. Sci., Univ. of Alexandru Ioan Cuza, Iasi, Romania (Necula, E.)
ISBN 9781479965724
ISSN 10823409
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2014-11-10
Publisher Place Cyprus
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Size (in Bytes) 1.10 MB
Page Count 8
Starting Page 922
Ending Page 929

Source: IEEE Xplore Digital Library