Speed variation during peak and off-peak hours on urban arterials in Shanghai

被引:34
|
作者
Wang, Xuesong [1 ,2 ,3 ]
Fan, Tianxiang [1 ]
Li, Weinan [1 ]
Yu, Rongjie [1 ]
Bullock, Darcy [4 ]
Wu, Bing [1 ]
Tremont, Paul [1 ]
机构
[1] Tongji Univ, Sch Transportat Engn, Shanghai 201804, Peoples R China
[2] Minist Educ, Key Lab Rd & Traff Engn, Beijing, Peoples R China
[3] Jiangsu Prov Collaborat Innovat Ctr Modern Urban, SiPaiLou 2, Nanjing 210096, Jiangsu, Peoples R China
[4] Purdue Univ, Sch Civil Engn, W Lafayette, IN 47907 USA
基金
美国国家科学基金会;
关键词
Urban arterial; Speed variation; Mean speed; Influencing variable; Floating Car Data; Hierarchical model with random variables;
D O I
10.1016/j.trc.2016.02.005
中图分类号
U [交通运输];
学科分类号
08 ; 0823 ;
摘要
Increased speed variation on urban arterials is associated with reductions in both operational performance and safety. Traffic flow, mean speed, traffic control parameters and geometric design features are known to affect speed variation. An exploratory study of the relationships among these variables could provide a foundation for improving the operational and safety performance of urban arterials, however, such a study has been hampered by problems in measuring speeds. The measurement of speed has traditionally been accomplished using spot speed collection methods such as radar, laser and loop detectors. These methods can cover only limited locations, and consequently are not able to capture speed distributions along an entire network, or even throughout any single road segment. In Shanghai, it is possible to acquire the speed distribution of any roadway segment, over any period of interest, by capturing data from Shanghai's 50,000+ taxis equipped with Global Positional Systems (GPS). These data, hereafter called Floating Car Data, were used to calculate mean speed and speed variation on 234 road segments from eight urban arterials in downtown Shanghai. Hierarchical models with random variables were developed to account for spatial correlations among segments within each arterial and heterogeneities among arterials. Considering that traffic demand changes throughout the day, AM peak, Noon off-peak, and PM peak hours were studied separately. Results showed that increases in number of lanes and number of access points, the presence of bus stops and increases in mean speed were all associated with increased speed variation, and that increases in traffic volume and traffic signal green times were associated with reduced speed variation. These findings can be used by engineers to minimize speed differences during the road network planning stage and continuing through the traffic management phase. (C) 2016 Elsevier Ltd. All rights reserved.
引用
收藏
页码:84 / 94
页数:11
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