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Efficient distributed computation of human mobility aggregates through user mobility profiles

Published: 12 August 2012 Publication History

Abstract

A basic task of urban mobility management is the real-time monitoring of traffic within key areas of the territory, such as main entrances to the city, important attractors and possible bottlenecks. Some of them are well known areas, while while others can appear, disappear or simply change during the year, or even during the week, due for instance to roadworks, accidents and special events (strikes, demonstrations, concerts, new toll road fares). Especially in the latter cases, it would be useful to have a traffic monitoring system able to dynamically adapt to reference areas specified by the user.
In this paper we propose and study a solution exploiting on-board location devices in private cars mobility, that continuously trace the position of the vehicle and periodically communicate it to a central station. Such vehicles provide a statistical sample of the whole population, and therefore can be used to compute a summary of the traffic conditions for the mobility manager. However, the large mass of information to be transmitted and processed to achieve that might be too much for a real-time monitoring system, the main problem being the systematic communication from each vehicle to a unique, centralized station.
In this work we tackle the problem by adopting the general view of distributed systems for the computation of a global function, consisting in minimizing the amount of information communicated through a careful coordination of the single nodes (vehicles) of the system. Our approach involves the use of predictive models that allow the central station to guess (in most cases and within some given error threshold) the location of the monitored vehicles and then to estimate the density of key areas without communications with the nodes.

References

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M. Dilman and D. Raz, "Efficient reactive monitoring," in INFOCOM, 2001, pp. 1012--1019.
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I. Sharfman, A. Schuster, and D. Keren, "A geometric approach to monitoring threshold functions over distributed data streams," ACM Trans. Database Syst., vol. 32, no. 4, Nov. 2007.
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C. Song, Z. Qu, N. Blumm, and A.-L. Barabási, "Limits of predictability in human mobility," Science, vol. 327, no. 5968, pp. 1018--1021, 2010.
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R. Trasarti, F. Pinelli, M. Nanni, and F. Giannotti, "Mining mobility user profiles for car pooling," in Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2011, pp. 1190--1198.
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D. Wang, D. Pedreschi, C. Song, F. Giannotti, and A.-L. Barabási, "Human mobility, social ties, and link prediction," in KDD, 2011, pp. 1100--1108.
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G. Andrienko, N. Andrienko, S. Rinzivillo, M. Nanni, D. Pedreschi, and F. Giannotti, "Interactive visual clustering of large collections of trajectories," in IEEE VAST, 2009, pp. 3--10.
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"Octotelematics s. p.a.: http://www.octotelematics.it/."

Cited By

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  • (2016)Driving Profiles Computation and Monitoring for Car Insurance CRMACM Transactions on Intelligent Systems and Technology10.1145/29121488:1(1-26)Online publication date: 23-Aug-2016
  • (2014)Mobility ProfilingData Science and Simulation in Transportation Research10.4018/978-1-4666-4920-0.ch001(1-29)Online publication date: 2014
  • (2013)Compilation of ReferencesData Science and Simulation in Transportation Research10.4018/978-1-4666-4920-0.chcrf(0-0)Online publication date: 31-Dec-2013

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cover image ACM Conferences
UrbComp '12: Proceedings of the ACM SIGKDD International Workshop on Urban Computing
August 2012
176 pages
ISBN:9781450315425
DOI:10.1145/2346496
  • General Chair:
  • Ouri E. Wolfson,
  • Program Chair:
  • Yu Zheng
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 12 August 2012

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Cited By

View all
  • (2016)Driving Profiles Computation and Monitoring for Car Insurance CRMACM Transactions on Intelligent Systems and Technology10.1145/29121488:1(1-26)Online publication date: 23-Aug-2016
  • (2014)Mobility ProfilingData Science and Simulation in Transportation Research10.4018/978-1-4666-4920-0.ch001(1-29)Online publication date: 2014
  • (2013)Compilation of ReferencesData Science and Simulation in Transportation Research10.4018/978-1-4666-4920-0.chcrf(0-0)Online publication date: 31-Dec-2013

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