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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
ac705db5-b891-4d14-ac43-a87acd05cdd7
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
ac705db5-b891-4d14-ac43-a87acd05cdd7
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.

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Published date: December 2014
Organisations: Southampton Wireless Group

Identifiers

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

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Date deposited: 03 Dec 2014 11:16
Last modified: 14 Mar 2024 18:33

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Contributors

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

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