The University of Southampton
University of Southampton Institutional Repository

Limits of predictability for large-scale urban vehicular mobility

Limits of predictability for large-scale urban vehicular mobility
Limits of predictability for large-scale urban vehicular mobility
Key challenges in vehicular transportation and communication systems are understanding vehicular mobility and utilizing mobility prediction, which are vital for both solving the congestion problem and helping to build efficient vehicular communication networking. Most of the existing works mainly focus on designing algorithms for mobility prediction and exploring utilization of these algorithms. However, the crucial questions of how much the mobility is predictable and how the mobility predictability can be used to enhance the system performance are still the open and unsolved problems. In this paper, we consider the fundamental problem of the predictability limits of vehicular mobility. By using two large-scale urban city vehicular traces, we propose an intuitive but effective model of areas transition to describe the vehicular mobility among the areas divided by the city intersections. Based on this model, we examine the predictability limits of large-scale urban vehicular networks and obtain the maximal predictability based on the methodology of entropy theory. Our study finds that about 78%–99% of the location and above 70% of the staying time, respectively, are predicable. Our findings thus reveal that there is strong regularity in the daily vehicular mobility, which can be exploited in practical prediction algorithm design.
1524-9050
2671-2682
Li, Yong
0817e950-114f-47f3-aefe-74bf9ec0e2a3
Jin, Depeng
d5ef5d7e-82a7-4950-85cf-800fe7794cc5
Hui, Pan
f89491e3-a0ed-4475-a0ee-a874e3514e98
Wang, Zhaocheng
70339538-3970-4094-bcfc-1b5111dfd8b4
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Li, Yong
0817e950-114f-47f3-aefe-74bf9ec0e2a3
Jin, Depeng
d5ef5d7e-82a7-4950-85cf-800fe7794cc5
Hui, Pan
f89491e3-a0ed-4475-a0ee-a874e3514e98
Wang, Zhaocheng
70339538-3970-4094-bcfc-1b5111dfd8b4
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80

Li, Yong, Jin, Depeng, Hui, Pan, Wang, Zhaocheng and Chen, Sheng (2014) Limits of predictability for large-scale urban vehicular mobility. IEEE Transactions on Intelligent Transportation Systems, 15 (6), 2671-2682. (doi:10.1109/TITS.2014.2325395).

Record type: Article

Abstract

Key challenges in vehicular transportation and communication systems are understanding vehicular mobility and utilizing mobility prediction, which are vital for both solving the congestion problem and helping to build efficient vehicular communication networking. Most of the existing works mainly focus on designing algorithms for mobility prediction and exploring utilization of these algorithms. However, the crucial questions of how much the mobility is predictable and how the mobility predictability can be used to enhance the system performance are still the open and unsolved problems. In this paper, we consider the fundamental problem of the predictability limits of vehicular mobility. By using two large-scale urban city vehicular traces, we propose an intuitive but effective model of areas transition to describe the vehicular mobility among the areas divided by the city intersections. Based on this model, we examine the predictability limits of large-scale urban vehicular networks and obtain the maximal predictability based on the methodology of entropy theory. Our study finds that about 78%–99% of the location and above 70% of the staying time, respectively, are predicable. Our findings thus reveal that there is strong regularity in the daily vehicular mobility, which can be exploited in practical prediction algorithm design.

Text
TITS2014-Dec.pdf - Other
Download (2MB)

More information

Published date: December 2014
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 372199
URI: https://eprints.soton.ac.uk/id/eprint/372199
ISSN: 1524-9050
PURE UUID: 9761051d-2114-4166-ab98-ceff91068b9f

Catalogue record

Date deposited: 03 Dec 2014 11:16
Last modified: 19 Jul 2019 20:57

Export record

Altmetrics

Contributors

Author: Yong Li
Author: Depeng Jin
Author: Pan Hui
Author: Zhaocheng Wang
Author: Sheng Chen

University divisions

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 https://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.

×