research-article

Efficient distributed computation of human mobility aggregates through user mobility profiles

Authors:

Mirco Nanni,

Roberto Trasarti,

Giulio Rossetti,

Dino PedreschiAuthors Info & Claims

UrbComp '12: Proceedings of the ACM SIGKDD International Workshop on Urban Computing

Pages 87 - 94

https://doi.org/10.1145/2346496.2346511

Published: 12 August 2012 Publication History

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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.

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Nanni MTrasarti RMonreale AGrossi VPedreschi D(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
https://dl.acm.org/doi/10.1145/2912148
Nanni MTrasarti RCintia PFurletti BRenso CGabrielli LRinzivillo SGiannotti F(2014)Mobility ProfilingData Science and Simulation in Transportation Research10.4018/978-1-4666-4920-0.ch001(1-29)Online publication date: 2014
https://doi.org/10.4018/978-1-4666-4920-0.ch001
(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
https://doi.org/10.4018/978-1-4666-4920-0.chcrf

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Published In

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
University of Illinois at Chicago
,
Program Chair:
Yu Zheng
Microsoft Research Asia, China

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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KDD '12: The 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 12, 2012

Beijing, China

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Nanni MTrasarti RMonreale AGrossi VPedreschi D(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
https://dl.acm.org/doi/10.1145/2912148
Nanni MTrasarti RCintia PFurletti BRenso CGabrielli LRinzivillo SGiannotti F(2014)Mobility ProfilingData Science and Simulation in Transportation Research10.4018/978-1-4666-4920-0.ch001(1-29)Online publication date: 2014
https://doi.org/10.4018/978-1-4666-4920-0.ch001
(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
https://doi.org/10.4018/978-1-4666-4920-0.chcrf

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Mobility management with distributed mobility routing functions

Mobility: A Double-Edged Sword for HSPA Networks: A Large-Scale Test on Hong Kong Mobile HSPA Networks

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