Exploring Spatial and Temporal Patterns of Large-scale Smartphone-based Dangerous Driving Event Data

被引：0
|
作者：
Yang, Di [1 ]
Xie, Kun [2 ]
Ozbay, Kaan [1 ]
Yang, Hong [3 ]
机构：
[1] NYU, Connected Cities Smart Mobil Accessible & Resilie, Dept Civil & Urban Engn, 15 MetroTech Ctr,6th Floor, Brooklyn, NY 11201 USA
[2] ODU, Dept Civil & Environm Engn, 135 Kaufman Hall, Norfolk, VA 23529 USA
[3] ODU, Dept Modeling Simulat & Visualizat Engn, 4700 Elkhorn Ave, Norfolk, VA 23529 USA
来源：
2019 IEEE INTELLIGENT TRANSPORTATION SYSTEMS CONFERENCE (ITSC) | 2019年
关键词：
AGREEMENT; CRASHES;
D O I：
暂无
中图分类号：
U [交通运输];
学科分类号：
08 ; 0823 ;
摘要：
Dangerous driving events data are widely used as surrogates to traffic crashes. Large-scale dangerous driving events data collected from smartphones are explored in this study. Clustering analysis is performed on dangerous driving events counted in spatial cells. Spatial and temporal patterns of the cluster distributions are then explored. Both the existence of spatial autocorrelation and the similarity of cluster distributions for different time periods are uncovered.
引用
收藏
页码：116 / 121
页数：6
相关论文
共 50 条
  • [1] Smartphone-Based Adaptive Driving Maneuver Detection: A Large-Scale Evaluation Study
    Castignani, German
    Derrmann, Thierry
    Frank, Raphael
    Engel, Thomas
    IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2017, 18 (09) : 2330 - 2339
  • [2] Modeling of time-dependent safety performance using anonymized and aggregated smartphone-based dangerous driving event data
    Yang, Di
    Xie, Kun
    Ozbay, Kaan
    Yang, Hong
    Budnick, Noah
    ACCIDENT ANALYSIS AND PREVENTION, 2019, 132
  • [3] Smartphone-Based Identification of Dangerous Driving Situations: Algorithms and Implementation
    Smimov, Alexander
    Kashevnik, Alexey
    Lashkov, Igor
    Baraniuc, Olesya
    Parfenov, Vladimir
    2016 18TH CONFERENCE OF OPEN INNOVATIONS ASSOCIATION AND SEMINAR ON INFORMATION SECURITY AND PROTECTION OF INFORMATION TECHNOLOGY (FRUCT-ISPIT), 2016, : 306 - 313
  • [4] A Data-Driven Method for Trip Ends Identification Using Large-Scale Smartphone-Based GPS Tracking Data
    Zhou, Chaoran
    Jia, Hongfei
    Juan, Zhicai
    Fu, Xuemei
    Xiao, Guangnian
    IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2017, 18 (08) : 2096 - 2110
  • [5] Editorial: Temporal and Large-Scale Spatial Patterns of Plant Diversity and Diversification
    Dimitrov, Dimitar
    Neves, Danilo M.
    Xu, Xiaoting
    FRONTIERS IN ECOLOGY AND EVOLUTION, 2022, 10
  • [6] Conducting a Large-scale Field Test of a Smartphone-based Communication Network for Emergency Response
    Alvarez, Flor
    Almon, Lars
    Lieser, Patrick
    Meuser, Tobias
    Dylla, Yannick
    Richerzhagen, Bjoern
    Hollick, Matthias
    Steinmetz, Ralf
    PROCEEDINGS OF THE 13TH WORKSHOP ON CHALLENGED NETWORKS (CHANTS'18), 2018, : 3 - 10
  • [7] Exploring Microscopic Driving Behaviors Using Large-Scale GPS Trajectory Data
    Yang, Miao
    Zhou, Chengyu
    Li, Ye
    CICTP 2023: INNOVATION-EMPOWERED TECHNOLOGY FOR SUSTAINABLE, INTELLIGENT, DECARBONIZED, AND CONNECTED TRANSPORTATION, 2023, : 1256 - 1266
  • [8] Nudging baseball fans to be active: a large-scale, smartphone-based, quasi-experimental study
    Kamada, Masamitsu
    Hayashi, Hana
    Shiba, Koichiro
    Taguri, Masataka
    Kondo, Naoki
    Lee, I-Min
    Kawachi, Ichiro
    JOURNAL OF PHYSICAL ACTIVITY & HEALTH, 2018, 15 (10): : S78 - S78
  • [9] Spatial and temporal patterns of large-scale droughts in Europe: Model dispersion and performance
    Tallaksen, Lena M.
    Stahl, Kerstin
    GEOPHYSICAL RESEARCH LETTERS, 2014, 41 (02) : 429 - 434
  • [10] Exploring App-Based Taxi Movement Patterns from Large-Scale Geolocation Data
    Zhang, Wenbo
    Xu, Chang
    ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION, 2021, 10 (11)
12345