多信号交叉口干线行程时间特性分析

doi:10.16097/j.cnki.1009-6744.2021.04.016

交通运输系统工程与信息 ›› 2021, Vol. 21 ›› Issue (4): 132-139.DOI: 10.16097/j.cnki.1009-6744.2021.04.016

多信号交叉口干线行程时间特性分析

王福建，卢一笑，金盛^*

浙江大学，建筑工程学院，杭州310058

收稿日期:2021-04-27 修回日期:2021-05-10 接受日期:2021-06-08 出版日期:2021-08-25 发布日期:2021-08-23
作者简介:王福建(1969- )，男，安徽阜阳人，副教授。
基金资助:
国家重点研发计划;国家自然科学基金

Travel Time Characteristics Analysis of Arterial Roads with Multiple Signalized Intersections

WANG Fu-jian, LU Yi-xiao, JIN Sheng*

College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China

Received:2021-04-27 Revised:2021-05-10 Accepted:2021-06-08 Online:2021-08-25 Published:2021-08-23
Supported by:
National Key Research and Development Program of China(2018YFB1601000)；National Natural Science Foundation of China(92046011)。

摘要/Abstract

摘要： 城市道路多信号交叉口影响下的行程时间分布及可靠性是交通流理论研究中的重要方向之一。本文基于灰色关联理论建立信号协调控制下的多信号交叉口行程时间影响因素模型。首先，对车牌识别数据进行预处理，得到路段和干线行程时间数据；然后，利用Burr分布和高斯混合模型对数据进行分布拟合，并进行拟合优度检验；最后，利用灰色关联法分析交叉口数量、干线长度和干线流量与行程时间特性之间的关联关系。结果表明，单路段行程时间分布具有明显的双峰现象，高斯混合模型适用于单路段行程时间的拟合；而Burr分布可以较好地描述多信号交叉口干线行程时间分布右偏和高峰值的特征。交叉口数量、干线长度和干线流量与行程时间特性之间有较强的相关性，且干线长度的影响更为显著，随着干线长度的增加，行程时间趋于一个稳定的单峰分布，波动性减小，可靠性增加。

关键词: 交通工程, 行程时间特性, Burr分布, 高斯混合模型, 影响因素

Abstract: The travel time distribution and reliability characteristics of urban arterial roads with multiple signalized intersections are one of the important branches of traffic flow theory. Based on the grey relational analysis, this paper establishes a model of travel time influencing factors of multiple signalized intersections under signal coordination control. Firstly, the automatic number plate recognition (ANPR) data is preprocessed to get travel time data; then, the Burr distribution and the Gaussian mixture model are used to fit the travel time distribution and conduct goodness of fit test; finally, the grey correlation method is used to analyze the relationship between the number of signalized intersections, route length, route traffic flow and the travel time characteristics. The results show that the link travel time distribution is bimodal, and the Gaussian mixture model is suitable for the fitting of link travel time; the Burr distribution can better describe the characteristics of right deviation of the arterial roads with multiple signalized intersections. There is a certain correlation between the number of signalized intersections, route length, route traffic flow and travel time characteristics, and the influence of the route length is more significant. With the increase of route length, the travel time tends to a stable unimodal distribution, and the fluctuation decreases and the reliability increases.

Key words: traffic engineering, travel time characteristics, Burr distribution, Gaussian mixture model, influence factor

中图分类号:

U491

王福建, 卢一笑, 金盛. 多信号交叉口干线行程时间特性分析[J]. 交通运输系统工程与信息, 2021, 21(4): 132-139.

WANG Fu-jian, LU Yi-xiao, JIN Sheng. Travel Time Characteristics Analysis of Arterial Roads with Multiple Signalized Intersections[J]. Journal of Transportation Systems Engineering and Information Technology, 2021, 21(4): 132-139.

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http://www.tseit.org.cn/CN/Y2021/V21/I4/132

参考文献

[1] TAYLOR M A P, SUSILAWATI. Modelling travel time reliability with the Burr distribution[J]. Procedia Social & Behavioral Sciences, 2012, 54(2290): 75-83.

[2] SUSILAWATI S, TAYLOR M, SOMENAHALLI S. Distributions of travel time variability on urban roads[J]. Journal of Advanced Transportation, 2013, 47(8): 720- 736.

[3] 向文凯. 基于轨迹数据网格化方法的快速路旅行时间可靠性研究[D]. 北京: 北京交通大学, 2019. [XIANG W K. Research on expressway travel time reliability based on trajectory data gridding method[D]. Beijing: Beijing Jiaotong University, 2019.]

[4] ZHEN C, WEI D F. Analyzing travel time distribution based on different travel time reliability patterns using probe vehicle data[J]. International Journal of Transportation Science and Technology, 2020, 9(1): 64- 75.

[5] KIM S H, CHANG J H. Exploration on origin-destinationbased travel time variability: Insights from Seoul metropolitan area[J]. Journal of Transport Geography, 2018, 70: 104-113.

[6] 李瑞敏, 钱小东, 武红斌. 城市道路旅行时间高斯混合模型研究[J]. 交通运输系统工程与信息, 2016, 16(4): 178- 184. [LI R M, QIAN X D, WU H B. Gaussian mixture model for urban link travel time analysis[J]. Journal of Transportation Systems Engineering and Information Technology, 2016, 16(4): 178-184.]

[7] PENG C, KAI Y, JIAN S. Application of finite mixture of regression model with varying mixing probabilities to estimation of urban arterial travel times[J]. Transportation Research Record, 2014, 2442(1): 96-105.

[8] SUSILAWATI, TAYLOR M A, SOMENAHALLI S. Travel time reliability and the bimodal travel time distribution for an arterial road[J]. Road & Transport Research, 2010, 19(4): 37-50.

[9] 刘迎, 过秀成, 周润瑄, 等. 基于多源数据融合的干线公交车辆行程时间预测[J]. 交通运输系统工程与信息, 2019, 19(4): 124-129, 148. [LIU Y, GUO X C, ZHOU R X, et al. Travel time prediction of main transit line based on multi-source data fusion[J]. Journal of Transportation Systems Engineering and Information Technology, 2019, 19(4): 124-129, 148.]

[10] 陈明明. 基于统计分布的城市道路行程时间可靠性研究[D]. 西安: 长安大学, 2016. [CHEN M M. Travel time reliability of urban road based on statistical distributions [D]. Xi'an: Chang'an University, 2016.]

[11] 李晓莉, 石建军. 行程时间异常值处理方法研究[J]. 武汉理工大学学报(交通科学与工程版), 2012, 36(1): 116- 119. [LI X L, SHI J J. Research on the filtering method for travel time outliers[J]. Journal of Wuhan University of Technology (Transportation Science & Engineering), 2012, 36(1): 116-119.]

[12] TAYLOR M A P. Fosgerau's travel time reliability ratio and the Burr distribution[J]. Transportation Research Part B: Methodological, 2017, 97: 50-63.

[13] YANG H T, LIANG X Q, ZHANG Z L, et al. Statistical modeling of quartiles, standard deviation, and buffer time index of optimal tour in traveling salesman problem and implications for travel time reliability[J]. Transportation Research Record, 2020, 2674(12): 339-347

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多信号交叉口干线行程时间特性分析

Travel Time Characteristics Analysis of Arterial Roads with Multiple Signalized Intersections

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