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City-Scale Social Event Detection and Evaluation with Taxi Traces

Published: 20 May 2015 Publication History

Abstract

A social event is an occurrence that involves lots of people and is accompanied by an obvious rise in human flow. Analysis of social events has real-world importance because events bring about impacts on many aspects of city life. Traditionally, detection and impact measurement of social events rely on social investigation, which involves considerable human effort. Recently, by analyzing messages in social networks, researchers can also detect and evaluate country-scale events. Nevertheless, the analysis of city-scale events has not been explored. In this article, we use human flow dynamics, which reflect the social activeness of a region, to detect social events and measure their impacts. We first extract human flow dynamics from taxi traces. Second, we propose a method that can not only discover the happening time and venue of events from abnormal social activeness, but also measure the scale of events through changes in such activeness. Third, we extract traffic congestion information from traces and use its change during social events to measure their impact. The results of experiments validate the effectiveness of both the event detection and impact measurement methods.

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    Published In

    cover image ACM Transactions on Intelligent Systems and Technology
    ACM Transactions on Intelligent Systems and Technology  Volume 6, Issue 3
    Survey Paper, Regular Papers and Special Section on Participatory Sensing and Crowd Intelligence
    May 2015
    319 pages
    ISSN:2157-6904
    EISSN:2157-6912
    DOI:10.1145/2764959
    • Editor:
    • Huan Liu
    Issue’s Table of Contents
    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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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 20 May 2015
    Accepted: 01 October 2014
    Revised: 01 September 2014
    Received: 01 January 2014
    Published in TIST Volume 6, Issue 3

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    Author Tags

    1. Social event
    2. social impact
    3. taxi traces
    4. traffic condition

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    Cited By

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    • (2024)An entropy-based measurement for understanding origin-destination trip distributions: a case study of New York City taxisBig Earth Data10.1080/20964471.2024.23635488:4(673-702)Online publication date: 9-Jul-2024
    • (2023)Diagnosing urban traffic anomalies by integrating geographic knowledge and tensor theoryGIScience & Remote Sensing10.1080/15481603.2023.229034761:1Online publication date: 15-Dec-2023
    • (2022)Patterns of Nighttime Crowd Flows in Tourism Cities Based on Taxi Data—Take Haikou Prefecture as an ExampleRemote Sensing10.3390/rs1406141314:6(1413)Online publication date: 15-Mar-2022
    • (2022)Clustering Methods Based on Stay Points and Grid Density for Hotspot DetectionISPRS International Journal of Geo-Information10.3390/ijgi1103019011:3(190)Online publication date: 11-Mar-2022
    • (2022)Graph Convolutional Adversarial Networks for Spatiotemporal Anomaly DetectionIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2021.313617133:6(2416-2428)Online publication date: Jun-2022
    • (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
    • (2021)BusBeat: Early Event Detection with Real-Time Bus GPS TrajectoriesIEEE Transactions on Big Data10.1109/TBDATA.2018.28725327:2(371-382)Online publication date: 1-Jun-2021
    • (2020)Optimizing Taxi Driver Profit Efficiency: A Spatial Network-Based Markov Decision Process ApproachIEEE Transactions on Big Data10.1109/TBDATA.2018.28755246:1(145-158)Online publication date: 1-Mar-2020
    • (2020)Privacy-preserving spatial keyword location-to-trajectory matchingDistributed and Parallel Databases10.1007/s10619-020-07290-2Online publication date: 24-Apr-2020
    • (2019)A Spatiotemporal Constraint Non-Negative Matrix Factorization Model to Discover Intra-Urban Mobility Patterns from Taxi TripsSustainability10.3390/su1115421411:15(4214)Online publication date: 4-Aug-2019
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