Estimating Travel Time of a Road Bottleneck Using Bus Probe Data: Toyota City, Japan

被引:0
|
作者
Yang, Jia [1 ]
Cao, Peng [2 ]
Ando, Ryosuke [1 ]
机构
[1] Toyota Transportat Res Inst TTRI, Res Dept, 3-17 Motoshiro Cho, Toyota, Aichi 4710024, Japan
[2] Southwest Jiaotong Univ, Sch Transportat & Logist, 111 Erhuanlu Beiyiduan, Chengdu 610031, Sichuan, Peoples R China
关键词
travel time; road bottleneck; bus location system; Gaussian mixture distribution;
D O I
暂无
中图分类号
U [交通运输];
学科分类号
08 ; 0823 ;
摘要
This paper utilizes low-frequency bus probe data to estimate the travel time of a road bottleneck. The probe data collected from one bus route in six months in Toyota City, Japan, are used for empirical analysis. To investigate the impact of commuting behavior of Toyota Motor Corporation (TMC) which has more than 25,800 workers in Toyota, the Gaussian mixture distribution is applied to fit four groups of bus travel time referring to the combination of working, non-working days of TMC and peak, off-peak periods on weekdays. The major findings indicate that: 1) Gaussian mixture distributions applied for peak and off-peak periods in non-working days of TMC have a bimodal feature; 2) the Gaussian mixture distribution outperforms the Gaussian distribution for the 4 categorized groups, which is indicated by a higher value of the decimal logarithm of likelihood with respect to sample data.
引用
收藏
页码:217 / 226
页数:10
相关论文
共 50 条
  • [31] Measuring Truck Travel Time Reliability Using Truck Probe GPS Data
    Wang, Zun
    Goodchild, Anne
    McCormack, Edward
    JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS, 2016, 20 (02) : 103 - 112
  • [32] Reliable corridor level travel time estimation using probe vehicle data
    Sakhare, Rahul
    Vanajakshi, Lelitha
    TRANSPORTATION LETTERS-THE INTERNATIONAL JOURNAL OF TRANSPORTATION RESEARCH, 2020, 12 (08): : 570 - 579
  • [33] Bus Travel Time Predictions Using Additive Models
    Kormaksson, Matthias
    Barbosa, Luciano
    Vieira, Marcos R.
    Zadrozny, Bianca
    2014 IEEE INTERNATIONAL CONFERENCE ON DATA MINING (ICDM), 2014, : 875 - 880
  • [34] Bus travel time reliability incorporating stop waiting time and in-vehicle travel time with AVL data
    Zhuang, Zixu
    Cheng, Zhanhong
    Yao, Jia
    Wang, Jian
    An, Shi
    INTERNATIONAL JOURNAL OF COAL SCIENCE & TECHNOLOGY, 2022, 9 (01)
  • [35] Bus travel time reliability incorporating stop waiting time and in-vehicle travel time with AVL data
    Zixu Zhuang
    Zhanhong Cheng
    Jia Yao
    Jian Wang
    Shi An
    International Journal of Coal Science & Technology, 2022, 9
  • [36] Road Road Travel Time Prediction using Vehicular Network
    Lim, Sejoon
    INTERNETWORKING INDONESIA, 2016, 8 (01): : 5 - 9
  • [37] Estimating Travel Speed of a Road Section Through Sparse Crowdsensing Data
    Wang, Cheng
    Xie, Zhiyang
    Shao, Lu
    Zhang, Zhenzhen
    Zhou, MengChu
    IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2019, 20 (09) : 3486 - 3495
  • [38] A data fusion algorithm for estimating link travel time
    Choi, K
    Chung, YS
    ITS JOURNAL, 2002, 7 (3-4): : 235 - 260
  • [39] The impact of mandating a driving lesson for elderly drivers in Japan using count data models: Case study of Toyota City
    Yang, Jia
    Yamamoto, Toshiyuki
    Ando, Ryosuke
    ACCIDENT ANALYSIS AND PREVENTION, 2021, 153
  • [40] Estimating the impact of new rail service on travel behaviour of current bus passengers using smart card data
    Kesmez, Firat Enver
    Uz, Volkan Emre
    TRANSPORTATION PLANNING AND TECHNOLOGY, 2025,