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On the Characterization of Vehicular Mobility

Published: 21 November 2017 Publication History
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  • Abstract

    With the increasing use of participatory sensing networks on mobile devices, several applications have emerged that influence the mobility of people in a city. These applications also provide data that can help identify patterns in the movement of individuals and vehicles. Time, as well as other factors, tends to influence which regions of the city will have a greater focus on vehicles throughout the day. The goal of this study is to identify vehicular traffic hotspots during the daytime hours so that the population objectives can be identified during that particular time of day and verify if there is any similarity to the other days of the month. This analysis can provide further insights into how traffic works in a particular region and possibly help in solving a traffic-related problem.

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    Cited By

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    • (2021)A Novel Dynamic Split and Merge Clustering Scheme for SDVNProceedings of the 11th ACM Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications10.1145/3479243.3487307(71-76)Online publication date: 22-Nov-2021
    • (2020)Computation Offloading and Retrieval for Vehicular Edge ComputingACM Computing Surveys10.1145/339206453:4(1-35)Online publication date: 20-Aug-2020
    • (2020)Improving the Vehicular Mobility Analysis Using Time-Varying Graphs2020 16th International Conference on Distributed Computing in Sensor Systems (DCOSS)10.1109/DCOSS49796.2020.00041(197-204)Online publication date: May-2020
    • Show More Cited By

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    Published In

    cover image ACM Conferences
    DIVANet '17: Proceedings of the 6th ACM Symposium on Development and Analysis of Intelligent Vehicular Networks and Applications
    November 2017
    160 pages
    ISBN:9781450351645
    DOI:10.1145/3132340
    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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    New York, NY, United States

    Publication History

    Published: 21 November 2017

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    Author Tags

    1. location based services
    2. participatory sensing networks
    3. vehicular networks

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    View all
    • (2021)A Novel Dynamic Split and Merge Clustering Scheme for SDVNProceedings of the 11th ACM Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications10.1145/3479243.3487307(71-76)Online publication date: 22-Nov-2021
    • (2020)Computation Offloading and Retrieval for Vehicular Edge ComputingACM Computing Surveys10.1145/339206453:4(1-35)Online publication date: 20-Aug-2020
    • (2020)Improving the Vehicular Mobility Analysis Using Time-Varying Graphs2020 16th International Conference on Distributed Computing in Sensor Systems (DCOSS)10.1109/DCOSS49796.2020.00041(197-204)Online publication date: May-2020
    • (2018)Space and Time Matter: An Analysis About Route Selection in Mobility Traces2018 IEEE Symposium on Computers and Communications (ISCC)10.1109/ISCC.2018.8538475(00958-00963)Online publication date: Jun-2018

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