A Dynamic Travel Time Estimation Model Based on Connected Vehicles

被引:5
|
作者
Tian, Daxin [1 ,2 ,3 ]
Yuan, Yong [1 ]
Qi, Honggang [4 ]
Lu, Yingrong [1 ]
Wang, Yunpeng [1 ,2 ,3 ]
Xia, Haiying [5 ]
He, Anping [6 ]
机构
[1] Beihang Univ, Beijing Key Lab Cooperat Vehicle Infrastruct Syst, Sch Transportat Sci & Engn, Beijing 100191, Peoples R China
[2] Jiangsu Prov Collaborat Innovat Ctr Modern Urban, Beijing 100191, Peoples R China
[3] Minist Publ Secur China, Key Lab Urban ITS Technol Optimizat & Integrat, Hefei 230088, Peoples R China
[4] Univ Chinese Acad Sci, Sch Comp & Control Engn, Beijing 100049, Peoples R China
[5] Minist Transport, Res Inst Highway, Beijing 100088, Peoples R China
[6] Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou 730000, Peoples R China
基金
中国国家自然科学基金;
关键词
D O I
10.1155/2015/903962
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
With advances in connected vehicle technology, dynamic vehicle route guidance models gradually become indispensable equipment for drivers. Traditional route guidance models are designed to direct a vehicle along the shortest path from the origin to the destination without considering the dynamic traffic information. In this paper a dynamic travel time estimation model is presented which can collect and distribute traffic data based on the connected vehicles. To estimate the real-time travel time more accurately, a road link dynamic dividing algorithm is proposed. The efficiency of the model is confirmed by simulations, and the experiment results prove the effectiveness of the travel time estimation method.
引用
收藏
页数:11
相关论文
共 50 条
  • [21] A Dynamic Travel Time Model for Spillback
    Soulaymane Kachani
    Georgia Perakis
    [J]. Networks and Spatial Economics, 2009, 9 : 595 - 618
  • [22] Travel time forecasting and dynamic routes design for emergency vehicles
    Musolino, Giuseppe
    Polimeni, Antonio
    Rindone, Corrado
    Vitetta, Antonino
    [J]. SIDT SCIENTIFIC SEMINAR 2012, 2013, 87 : 193 - 202
  • [23] Simulation Analysis on Optimal Merging Control of Connected Vehicles for Minimizing Travel Time
    Mutsumi Tashiro
    Hiroki Motoyama
    Yuki Ichioka
    Tomio Miwa
    Takayuki Morikawa
    [J]. International Journal of Intelligent Transportation Systems Research, 2020, 18 : 65 - 76
  • [24] Simulation Analysis on Optimal Merging Control of Connected Vehicles for Minimizing Travel Time
    Tashiro, Mutsumi
    Motoyama, Hiroki
    Ichioka, Yuki
    Miwa, Tomio
    Morikawa, Takayuki
    [J]. INTERNATIONAL JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS RESEARCH, 2020, 18 (01) : 65 - 76
  • [25] Real-Time Traffic State Estimation With Connected Vehicles
    Khan, Sakib Mahmud
    Dey, Kakan C.
    Chowdhury, Mashrur
    [J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2017, 18 (07) : 1687 - 1699
  • [26] A Methodology to Assess the Quality of Travel Time Estimation and Incident Detection Based on Connected Vehicle Data
    Iqbal, Md Shahadat
    Khazraeian, Samaneh
    Hadi, Mohammed
    [J]. TRANSPORTATION RESEARCH RECORD, 2018, 2672 (42) : 203 - 212
  • [27] Attention-Based Sequence Learning Model for Travel Time Estimation
    Wang, Zhong
    Fu, Hao
    Liu, Guiquan
    Meng, Xianwei
    [J]. IEEE ACCESS, 2020, 8 : 221442 - 221453
  • [28] Fault Detection and Repairing for Intelligent Connected Vehicles Based on Dynamic Bayesian Network Model
    Zhang, Haibin
    Zhang, Qian
    Liu, Jiajia
    Guo, Hongzhi
    [J]. IEEE INTERNET OF THINGS JOURNAL, 2018, 5 (04): : 2431 - 2440
  • [29] Placement of Roadside Equipment in Connected Vehicle Environment for Travel Time Estimation
    Kianfar, Jalil
    Edara, Praveen
    [J]. TRANSPORTATION RESEARCH RECORD, 2013, (2381) : 20 - 27
  • [30] Dynamic Prediction Model of Travel Time Based on Participatory Learning Method
    Zhou, Zhenghua
    Ding, Yixing
    Li, Yongyi
    Wang, Qichao
    Xu, Lingyu
    [J]. Yingyong Jichu yu Gongcheng Kexue Xuebao/Journal of Basic Science and Engineering, 2021, 29 (01): : 239 - 250