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浙江大学学报(工学版)  2020, Vol. 54 Issue (3): 546-556    DOI: 10.3785/j.issn.1008-973X.2020.03.015
计算机技术与图像处理     
智慧公路多源数据下的交通出行演化模型
孙超1,2(),李孟晖3,韩飞4
1. 江苏大学 汽车与交通工程学院,江苏 镇江 212013
2. 南京伯克利交通科技有限公司,江苏 南京 210017
3. 中国港湾工程有限责任公司,北京 100027
4. 长安大学汽车学院,陕西 西安 710064
Traffic evolution model with multi-source data of intelligent highway
Chao SUN1,2(),Meng-hui LI3,Fei HAN4
1. School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China
2. Nanjing Berkeley Traffic Science and Technology Co. Ltd, Nanjing 210017, China
3. China Harbour Engineering Co. Ltd, Beijing 100027, China
4. School of Automobile, Chang’an University, Xi’an 710064, China
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摘要:

为探索智慧公路对出行者路径选择行为的影响,将网络可靠性和经验学习理论引入出行者的路径选择过程. 运用交通流理论统一化由智慧公路采集的多源数据类型,进一步采用最小方差加权平均方法融合多源数据. 智慧公路上的出行者根据融合信息实时决策出行路径;普通公路上的出行者根据前一天的路网交通状况和历史出行经验选择出行路径. 采用不动点理论证明模型解的等价性、存在性和稳定性条件. 算例结果表明:道路流量行为系数和感知时间误差的增大均会导致模型进入不稳定状态;在模型解稳定性方面,具有冒险倾向的出行者显著优于具有风险规避倾向的出行者,且多源融合数据演化的路网交通流量更具鲁棒性.

关键词: 智慧公路出行演化数据融合交通网络不确定性不动点    
Abstract:

The network reliability and experimential learning theory was introduced into travelers’ route choice decision process, to explore the influence of intelligent highway on path choice behavior. The traffic flow theory was used to transform the multi-source data of intelligent highway to a uniform format. Moreover, the method of minimum variance weighted average was proposed to fuse the multi-source data. Based on the fuse information, the travelers on intelligent highway chose their travel paths. While, the travelers on the ordinary road made their travel decision through considering the road network traffic conditions on the last day and the historical travel experiences. The fixed point theory was adopted to prove the equivalency, existence, and stability of the solutions of the built model. The numerical examples demonstrate that both the increase of road traffic flow behavior coefficient and perception travel time error cause the model to enter an unsteady state; in the aspect of stability of model solutions, the risk-prone travelers are significantly better than the risk-averse travelers; the evolved network traffic flows with fused multi-source data have better robustness.

Key words: intelligent highway    travel evolution    data fusion    uncertainty of traffic network    fixed point
收稿日期: 2019-02-22 出版日期: 2020-03-05
CLC:  U 491  
基金资助: 国家自然科学基金资助项目(71801115);江苏省交通运输科技与成果转化资助项目(JSZC-G2018-176);国家重点研发计划重点专项资助(2018YFB1600503);博士后创新人才支持计划资助项目(BX20180268);江苏省自然科学基金资助项目(BK20190845)
作者简介: 孙超(1990—),男,讲师,博士,从事交通规划与交通网络建模研究. orcid.org/0000-0002-1543-5790. E-mail: chaosun@ujs.edu.cn
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引用本文:

孙超,李孟晖,韩飞. 智慧公路多源数据下的交通出行演化模型[J]. 浙江大学学报(工学版), 2020, 54(3): 546-556.

Chao SUN,Meng-hui LI,Fei HAN. Traffic evolution model with multi-source data of intelligent highway. Journal of ZheJiang University (Engineering Science), 2020, 54(3): 546-556.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2020.03.015        http://www.zjujournals.com/eng/CN/Y2020/V54/I3/546

图 1  随机网络下出行时间概率曲线
图 2  随机网络下智慧公路交通出行演化模型框架
图 3  测试网络拓扑结构和路段属性
图 4  不同行为参数下的交通量逐日演化过程
图 5  不同参数下的均衡解逐日演化过程
图 6  多源和单源观测数据下智慧公路流量演化过程
图 7  Nguyen-Dupuis网络拓扑结构、路段属性和交通需求量
图 8  不同行为参数下评价指标演化过程
图 9  不同风险态度下模型稳定性比较
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