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An efficient technique for event location identification using multiple sources of urban data

Published: 07 November 2017 Publication History

Abstract

The proliferation of smart technologies has produced significant changes in the way people interact in a city. Smart traffic monitoring systems allow citizens and city operators to acquire a real-time view of the city traffic state. Furthermore, alternative means of transport, such as bike sharing systems, have enjoyed tremendous success in many major cities around the world today and provide real-time information regarding the mobility of the users. Such sources of urban data may act as human mobility sensors. Detecting the location and extent of large events in urban environments is a challenging problem. Previous work focuses mainly on identifying traffic flows and extract possible event sources. However, these solutions lack the ability to capture large areas of events, as they rely only on single-source data to identify user mobility or focus on identifying single locations rather than areas. In this paper we model the behavior of two different real-time data sources and we illustrate how they may be combined to acquire the area affected from a social event. We propose "fEEL" (Efficient Event Location identification), a novel algorithm to identify affected areas from social events using multiple heterogeneous sources of urban data. Our experimental evaluations show that fEEL is efficient and practical.

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Cited By

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  • (2023)A holistic approach for modeling and predicting bike demandInformation Systems10.1016/j.is.2022.102129111(102129)Online publication date: Jan-2023
  • (2022)Urban Anomaly Analytics: Description, Detection, and PredictionIEEE Transactions on Big Data10.1109/TBDATA.2020.29910088:3(809-826)Online publication date: 1-Jun-2022
  • (2018)Evaluating the Health State of Urban Areas Using Multi-source Heterogeneous Data2018 IEEE 19th International Symposium on "A World of Wireless, Mobile and Multimedia Networks" (WoWMoM)10.1109/WoWMoM.2018.8449761(14-22)Online publication date: Jun-2018
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    cover image ACM Conferences
    LocalRec'17: Proceedings of the 1st ACM SIGSPATIAL Workshop on Recommendations for Location-based Services and Social Networks
    November 2017
    45 pages
    ISBN:9781450354998
    DOI:10.1145/3148150
    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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    Publication History

    Published: 07 November 2017

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    Author Tags

    1. Smart cities
    2. event location identification
    3. human mobility

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    LocalRec'17 Paper Acceptance Rate 8 of 10 submissions, 80%;
    Overall Acceptance Rate 17 of 26 submissions, 65%

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    Cited By

    View all
    • (2023)A holistic approach for modeling and predicting bike demandInformation Systems10.1016/j.is.2022.102129111(102129)Online publication date: Jan-2023
    • (2022)Urban Anomaly Analytics: Description, Detection, and PredictionIEEE Transactions on Big Data10.1109/TBDATA.2020.29910088:3(809-826)Online publication date: 1-Jun-2022
    • (2018)Evaluating the Health State of Urban Areas Using Multi-source Heterogeneous Data2018 IEEE 19th International Symposium on "A World of Wireless, Mobile and Multimedia Networks" (WoWMoM)10.1109/WoWMoM.2018.8449761(14-22)Online publication date: Jun-2018
    • (2018)Crowdsourcing techniques for smart urban mobility2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)10.1109/PERCOMW.2018.8480244(460-461)Online publication date: Mar-2018

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