基于GPS轨迹数据的不同交通状态下交通方式识别流程优化方法

交通运输系统工程与信息 ›› 2020, Vol. 20 ›› Issue (4): 83-89.

• 智能交通系统与信息技术 • 上一篇下一篇

基于GPS轨迹数据的不同交通状态下交通方式识别流程优化方法

杨飞¹ ，姜海航¹，刘好德² ，姚振兴³，霍娅敏^*1 ，周子一⁴

1.西南交通大学交通运输与物流学院，成都 611756；2.交通运输部科学研究院城市交通与轨道交通研究中心，北京 100029；3.长安大学公路学院, 西安 710054；4.东南大学交通学院, 南京 211189

收稿日期:2020-02-26 修回日期:2020-04-16 出版日期:2020-08-25 发布日期:2020-08-25
作者简介:杨飞(1980-)，男，重庆人，教授，博士.
基金资助:
国家重点研发计划/National Key Research and Development Program of China(2018YFB1601300)；中央高校基本科研业务费专项资金 / Fundamental Research Funds for the Central Universities of Ministry of Education of China (300102219301)；国家自然科学基金/National Natural Science Foundation of China(51678505).

Procedure Optimization Method Based on GPS Trajectory Data for Transportation Mode Recognition under Different Traffic Conditions

YANG Fei¹ , JIANG Hai-hang¹ , LIU Hao-de² , YAO Zhen-xing³ , HUO Ya-min¹ , ZHOU Zi-yi⁴

1. School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 611756, China; 2. Urban Transportation Center, China Academy of Transportation Science, Beijing 100029, China; 3. School of Highway, Chang'an University, Xi'an 710054, China; 4. School of Transportation, Southeast University, Nanjing 211189, China

Received:2020-02-26 Revised:2020-04-16 Online:2020-08-25 Published:2020-08-25

摘要/Abstract

摘要：

基于GPS轨迹数据的交通调查技术能够有效弥补传统居民出行调查方式的不足，该技术在高峰拥堵时段的交通方式识别效果有待进一步研究.针对公交车和小汽车识别精度较低的问题，本文提出基于支持向量机(SVM)的流程优化方法，加入基于短时傅里叶变换 (STFT)的频域属性，利用遗传算法(GA)对SVM的惩罚系数和核参数进行联合优化，评估不同交通状态下交通方式和方式转换点的识别效果.结果表明：频域属性的加入能够有效提升交通方式识别精度，在道路畅通状态和一般拥堵状态下，交通方式和方式转换点的识别效果均较为理想；在严重拥堵状态下，机动化方式易与非机动化方式相混淆，方式转换点最大识别误差在13 min以内，相比于基于主观回忆的人工问卷调查方式仍具有参考性.

关键词: 智能交通, 交通方式识别, 支持向量机, GPS轨迹数据, 遗传算法, 频域属性

Abstract:

This study focuses on the transportation mode recognition for the Global Positioning System (GPS)- based travel survey technology. The study proposed a procedure optimization method that is based on the Support Vector Machine (SVM) to improve the recognition accuracy of buses and cars. The proposed model included the new frequency domain features generated from Short- time Fourier Transform (STFT). The Genetic Algorithm (GA) was used to optimize the penalty parameter and the nuclear parameter of SVM. The recognition results of the transportation modes and mode transfer time under different traffic conditions were evaluated, and the result showed the newly added frequency domain features effectively improved the recognition accuracy of the transportation modes. In the free- flow and slightly congested traffic conditions, the transportation mode recognition and mode transfer time both obtained satisfied results. In severe congestions, the motorized modes arerelatively easy to be mixed with the non-motorized modes. The maximum error of mode transfer time is within 13 minutes, which might still be informative compared with traditional manual questionnaire surveys.

Key words: intelligent transportation, transportation mode recognition, support vector machine, GPS trajectory data, genetic algorithm, frequency domain feature

中图分类号:

U491

杨飞，姜海航，刘好德，姚振兴，霍娅敏，周子一. 基于GPS轨迹数据的不同交通状态下交通方式识别流程优化方法[J]. 交通运输系统工程与信息, 2020, 20(4): 83-89.

YANG Fei,JIANG Hai-hang,LIU Hao-de,YAO Zhen-xing,HUO Ya-min,ZHOU Zi-yi. Procedure Optimization Method Based on GPS Trajectory Data for Transportation Mode Recognition under Different Traffic Conditions[J]. Journal of Transportation Systems Engineering and Information Technology, 2020, 20(4): 83-89.

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链接本文: http://www.tseit.org.cn/CN/abstract/abstract20084.shtml

http://www.tseit.org.cn/CN/Y2020/V20/I4/83

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基于GPS轨迹数据的不同交通状态下交通方式识别流程优化方法

Procedure Optimization Method Based on GPS Trajectory Data for Transportation Mode Recognition under Different Traffic Conditions

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