Incident Detection by Spatiotemporal Analysis of GPS Data

被引：0
|
作者：
D'Andrea, Eleonora [1 ]
Marcelloni, Francesco [1 ]
机构：
[1] Univ Pisa, Dept Informat Engn, Largo Lucio Lazzarino 1, I-56122 Pisa, Italy
来源：
2016 IEEE INTERNATIONAL CONFERENCE ON SMART COMPUTING (SMARTCOMP) | 2016年
关键词：
expert system; GPS; incident detection; urban mobility;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
We present a system to detect incidents causing traffic congestion in the road network by analyzing real-time GPS data. These data are collected from tracking devices installed in the vehicles or from drivers' smartphones. After positioning the GPS coordinates on the road map, the system assigns a traffic state to each road segment based on the velocities of vehicles, and generates alerts for incident based on a spatiotemporal analysis of these states. The system is validated by using GPS data simulated in typical traffic conditions in the city of Pisa, Italy. The results show an incident detection rate of 91.6% and an average detection time shorter than 7 minutes.
引用
收藏
页码：26 / 30
页数：5
相关论文
共 50 条
  • [21] Incident detection methods using probe vehicles with on-board GPS equipment
    Asakura, Yasuo
    Kusakabe, Takahiko
    Long Xuan Nguyen
    Ushiki, Takamasa
    TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2017, 81 : 330 - 341
  • [22] INCREMENTAL PRINCIPAL COMPONENT ANALYSIS BASED OUTLIER DETECTION METHODS FOR SPATIOTEMPORAL DATA STREAMS
    Bhushan, Alka
    Sharker, Monir H.
    Karimi, Hassan A.
    ISPRS INTERNATIONAL WORKSHOP ON SPATIOTEMPORAL COMPUTING, 2015, : 67 - 71
  • [23] Anomaly detection in spatiotemporal data via regularized non-negative tensor analysis
    Lin, Chaoguang
    Zhu, Qiuhan
    Guo, Shunan
    Jin, Zhuochen
    Lin, Yu-Ru
    Cao, Nan
    DATA MINING AND KNOWLEDGE DISCOVERY, 2018, 32 (04) : 1056 - 1073
  • [24] Anomaly detection in spatiotemporal data via regularized non-negative tensor analysis
    Chaoguang Lin
    Qiuhan Zhu
    Shunan Guo
    Zhuochen Jin
    Yu-Ru Lin
    Nan Cao
    Data Mining and Knowledge Discovery, 2018, 32 : 1056 - 1073
  • [25] Cloud Incident Data: An Empirical Analysis
    Fiondella, Lance
    Gokhale, Swapna S.
    Mendiratta, Veena B.
    PROCEEDINGS OF THE 2013 IEEE INTERNATIONAL CONFERENCE ON CLOUD ENGINEERING (IC2E 2013), 2013, : 241 - 249
  • [26] Spatiotemporal data analysis with chronological networks
    Ferreira, Leonardo N.
    Vega-Oliveros, Didier A.
    Cotacallapa, Moshe
    Cardoso, Manoel F.
    Quiles, Marcos G.
    Zhao, Liang
    Macau, Elbert E. N.
    NATURE COMMUNICATIONS, 2020, 11 (01)
  • [27] Spatiotemporal data analysis with chronological networks
    Leonardo N. Ferreira
    Didier A. Vega-Oliveros
    Moshé Cotacallapa
    Manoel F. Cardoso
    Marcos G. Quiles
    Liang Zhao
    Elbert E. N. Macau
    Nature Communications, 11
  • [28] Immersive Analysis of Spatiotemporal Racing Data
    Weidinger, Nicole
    El Sayed, Neven
    Luzhnica, Granit
    Schreck, Tobias
    Veas, Eduardo
    ACM SYMPOSIUM ON SPATIAL USER INTERACTION, SUI 2023, 2023,
  • [29] Challenges in Data Integration for Spatiotemporal Analysis
    Le, Yanfen
    JOURNAL OF MAP & GEOGRAPHY LIBRARIES, 2012, 8 (01) : 58 - 67
  • [30] Spatiotemporal analysis of optical imaging data
    Sornborger, A
    Sailstad, C
    Kaplan, E
    Sirovich, L
    NEUROIMAGE, 2003, 18 (03) : 610 - 621
12345