Development and application of traffic accident density estimation models using kernel density estimation

被引:0
|
作者
Seiji Hashimoto [1 ]
Syuji Yoshiki [2 ]
Ryoko Saeki [3 ]
Yasuhiro Mimura [4 ]
Ryosuke Ando [5 ]
Shutaro Nanba [1 ]
机构
[1] Graduate School of Environmental and Life Science,Okayama University
[2] Department of Civil Engineering,Fukuoka University
[3] Fukuyama Consultants Co.Ltd.  4. International Development Consultants Co.Ltd.  5. Toyota Transportation Research Institute
关键词
Traffic safety; Kernel density estimation(KDE); Hotspots; Zone; 30;
D O I
暂无
中图分类号
U491.31 [交通事故处理、分析与统计];
学科分类号
摘要
Traffic accident frequency has been decreasing in Japan in recent years.Nevertheless,many accidents still occur on residential roads.Area-wide traffic calming measures including Zone 30,which discourages traffic by setting a speed limit of 30 km/h in residential areas,have been implemented.However,no objective implementation method has been established.Development of a model for traffic accident density estimation explained by GIS data can enable the determination of dangerous areas objectively and easily,indicating where area-wide traffic calming can be implemented preferentially.This study examined the relations between traffic accidents and city characteristics,such as population,road factors,and spatial factors.A model was developed to estimate traffic accident density.Kernel density estimation(KDE) techniques were used to assess the relations efficiently.Besides,16 models were developed by combining accident locations,accident types,and data types.By using them,the applicability of traffic accident density estimation models was examined.Results obtained using Spearman rank correlation show high coefficients between the predicted number and the actual number.The model can indicate the relative accident risk in cities.Results of this study can be used for objective determination of areas where area-wide traffic calming can be implemented preferentially,even if sufficient traffic accident data are not available.
引用
收藏
页码:262 / 270
页数:9
相关论文
共 50 条
  • [41] Kernel density estimation in mixture models with known mixture proportions
    Liu, Siyun
    Yu, Tao
    STATISTICS IN MEDICINE, 2021, 40 (28) : 6360 - 6372
  • [42] Kernel density estimation for multiplicative distortion measurement regression models
    Zhang, Jun
    Chen, Aixian
    Wei, Zhenghong
    COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, 2023, 52 (05) : 1733 - 1752
  • [43] Conditional kernel density estimation for some incomplete data models
    Yan, Ting
    Qu, Liangqiang
    Li, Zhaohai
    Yuan, Ao
    ELECTRONIC JOURNAL OF STATISTICS, 2018, 12 (01): : 1299 - 1329
  • [44] Kernel density estimation for partial linear multivariate responses models
    Zhang, Jun
    Lin, Bingqing
    Zhou, Yan
    JOURNAL OF MULTIVARIATE ANALYSIS, 2021, 185
  • [45] Modal regression using kernel density estimation: A review
    Chen, Yen-Chi
    WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL STATISTICS, 2018, 10 (04):
  • [46] Kernel Density Estimation Using Graphical Processing Unit
    Sunarko
    Su'ud, Zaki
    5TH INTERNATIONAL CONFERENCE ON MATHEMATICS AND NATURAL SCIENCES (ICMNS 2014), 2015, 1677
  • [47] Online kernel density estimation using fuzzy logic
    Zarch, Majid Ghaniee
    Alipouri, Yousef
    Poshtan, Javad
    IET SIGNAL PROCESSING, 2015, 9 (08) : 579 - 586
  • [48] Predicting crime using Twitter and kernel density estimation
    Gerber, Matthew S.
    DECISION SUPPORT SYSTEMS, 2014, 61 : 115 - 125
  • [49] NONPARAMETRIC SALIENCY DETECTION USING KERNEL DENSITY ESTIMATION
    Liu, Zhi
    Xue, Yinzhu
    Shen, Liquan
    Zhang, Zhaoyang
    2010 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, 2010, : 253 - 256
  • [50] ESTIMATION OF MUTUAL INFORMATION USING KERNEL DENSITY ESTIMATORS
    MOON, YI
    RAJAGOPALAN, B
    LALL, U
    PHYSICAL REVIEW E, 1995, 52 (03): : 2318 - 2321