Improving Coverage Rate for Urban Link Travel Time Prediction Using Probe Data in the Low Penetration Rate Environment

被引:0
作者
Tang, Ruotian [1 ]
Kanamori, Ryo [2 ]
Yamamoto, Toshiyuki [3 ]
机构
[1] Nagoya Univ, Dept Civil Engn, Nagoya, Aichi 4648603, Japan
[2] Nagoya Univ, Inst Innovat Future Soc, Nagoya, Aichi 4648603, Japan
[3] Nagoya Univ, Inst Mat & Syst Sustainabil, Nagoya, Aichi 4648603, Japan
基金
日本学术振兴会; 日本科学技术振兴机构;
关键词
urban travel time prediction; low penetration rate; coverage rate; vehicles in the crossing direction; short-term; probe vehicle data; TIMING ESTIMATION; FREQUENCY; RELIABILITY; MODEL;
D O I
10.3390/s20010265
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Short-term travel time prediction is an important consideration in modern traffic control and management systems. As probe data technology has developed, research interest has moved from highways to urban roads. Most research has only focused on improving the prediction accuracy on urban roads because it is the key index of evaluating a model. However, the low penetration rate of probe vehicles at urban networks may result in the low coverage rate which restricts prediction models from practical applications. This research proposed a non-parametric model based on Bayes' theorem and a resampling process to predict short-term urban link travel time, which can enhance the coverage rate while maintaining the prediction accuracy. The proposed model used data from vehicles in both the target link and its crossing direction, so its coverage rate can be expanded, especially when the data penetration rate is low. In addition, the utilization of relationships between vehicles in both directions can reflect the influence of signal timing. The proposed model was evaluated in a computer simulation to test its robustness and reliability under different data penetration rates. The results implied that the proposed model has a high coverage rate, demonstrating stable and acceptable performance at different penetration rates.
引用
收藏
页数:20
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共 39 条
  • [1] Alrukaibi F., 2018, ADV TRANSP STUD, V44, P79
  • [2] [Anonymous], 1996, TRANSPORTATION RES R, DOI DOI 10.3141/1537-03
  • [3] Connected vehicle penetration rate for estimation of arterial measures of effectiveness
    Argote-Cabanero, Juan
    Christofa, Eleni
    Skabardonis, Alexander
    [J]. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2015, 60 : 298 - 312
  • [4] Signal timing estimation based on low frequency floating car data
    Axer, Steffen
    Friedrich, Bernhard
    [J]. WORLD CONFERENCE ON TRANSPORT RESEARCH - WCTR 2016, 2017, 25 : 1648 - 1664
  • [5] Modelling travel time uncertainty in urban networks based on floating taxi data
    Bauer, Dietmar
    Tulic, Mirsad
    Scherrer, Wolfgang
    [J]. EUROPEAN TRANSPORT RESEARCH REVIEW, 2019, 11 (01)
  • [6] Cooperative Vehicular Traffic Monitoring in Realistic Low Penetration Scenarios: The COLOMBO Experience
    Bellavista, Paolo
    Caselli, Federico
    Corradi, Antonio
    Foschini, Luca
    [J]. SENSORS, 2018, 18 (03):
  • [7] Bloomberg L., 2000, P I TRANSP ENG ANN M
  • [8] A trade-off analysis between penetration rate and sampling frequency of mobile sensors in traffic state estimation
    Bucknell, Christopher
    Herrera, Juan C.
    [J]. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2014, 46 : 132 - 150
  • [9] A spatiotemporal correlative k-nearest neighbor model for short-term traffic multistep forecasting
    Cai, Pinlong
    Wang, Yunpeng
    Lu, Guangquan
    Chen, Peng
    Ding, Chuan
    Sun, Jianping
    [J]. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2016, 62 : 21 - 34
  • [10] Value of travel time reliability: A review of current evidence
    Carrion, Carlos
    Levinson, David
    [J]. TRANSPORTATION RESEARCH PART A-POLICY AND PRACTICE, 2012, 46 (04) : 720 - 741