| Civil Engineering |
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| Speed distribution model for heterogeneous bicycle traffic flow |
| XU Cheng1,2, QU Zhao-wei3, WANG Dian-hai1, JIN Sheng1 |
1. College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China;
2. Department of Traffic Management Engineering, Zhejiang Police College, Hangzhou 310053, China;
3. College of Transportation, Jilin University, Changchun 130022, China |
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Abstract The basic statistical properties of speeds for heterogeneous bicycle traffic flow were analyzed based on the field survey data considering the situation that electric bicycles and regular bicycles ride on the bicycle lane together. A Gaussian mixture model (GMM) for bicycle speed distribution was constructed, and the expectation maximization (EM) algorithm was used for the maximum likelihood estimation of model's parameters through the analysis of various impact factors. The optimal number of components for GMM was determined by using Kolmogorov-Smirnov (K-S) goodness of fit test. Then the effect of different speed limits on bicycles' over-speed percentages was analyzed. Results show that the GMM can fit the field heterogeneous bicycle speed samples well. Three-component model can be used for fitting speed samples under free flow conditions, but five- or six-component model (GMM) should be used under both congested and uncongested conditions.
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Received: 05 January 2017
Published: 08 July 2017
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混合自行车交通流速度分布模型
针对电动自行车和普通自行车在非机动车道上混合运行的问题，基于实测数据分析混合自行车交通流速度的基本统计特性.通过对多种影响因素的分析，构建基于高斯混合模型（GMM）的速度分布函数，采用期望最大化（EM）算法对模型参数进行最大似然估计.通过Kolmogorov-Smirnov（K-S）拟合优度检验优化，得到高斯混合模型的最佳组成数.分析不同限速阈值对自行车超速特性的影响.结果表明，利用高斯混合模型能够有效地拟合混合自行车速度.利用三元高斯混合模型能够拟合自由流状态下的速度数据；针对多种交通状态下的数据，须采用五元或六元高斯混合模型进行拟合.
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| [1] 新华网.电动自行车数量破2亿,"新国标"将出台[EB/OL].2013-10-20. http://news.xinhuanet.com/fortune/2013-10/20/c_125566241.htm.
[2] JIN S, QU X, ZHOU D, et al. Estimating cycleway capacity and bicycle equivalent unit for electric bicycles [J]. Transportation Research Part A, 2015, 77: 22-248.
[3] QU X, YANG Y, LIU Z, et al. Potential crash risks of expressway on-ramps and off-ramps: a case study in Beijing, China [J]. Safety Science, 2014, 70: 58-62.
[4] XU C, YANG Y, JIN S, et al. Potential risk and its influencing factors for separated bicycle paths [J]. Accident Analysis and Prevention, 2016, 87: 59-67.
[5] 周旦,马晓龙,金盛,等.混合非机动车交通流超车次率影响因素模型[J].浙江大学学报：工学版,2015,49(9): 1672-1678. ZHOU Dan, MA Xiao-long, JIN Sheng, et al. Modeling influencing factors of vehicle passing rate in mixed bicycle traffic flow [J]. Journal of Zhejiang University: Engineering Science, 2015, 49(9): 1672-1678.
[6] ALLEN D P, ROUPHALL N, HUMMER J E, et al. Operational analysis of uninterrupted bicycle facilities [J]. Transportation Research Record, 1998, 1636: 29-36.
[7] DEY P P, CHANDRA S, GANGOPADHAYA S. Speed distribution curves under mixed traffic conditions [J]. Journal of Transportation Engineering, 2006, 132(6): 475-481.
[8] CHERRY C R. Electric two-wheelers in China: analysis of environmental, safety, and mobility impacts [R]. Berkeley: University of California, 2007: 101.
[9] LIN S, HE M, TAN Y, et al. Comparison study on operating speeds of electric bicycles and bicycles: experience from field investigation in Kunming, China [J]. Transportation Research Record, 2008, 2048: 52-59.
[10] LI Z, WANG W, LIU P, et al. Modeling bicycle passing maneuvers on multilane separated bicycle paths [J]. Journal of Transportation Engineering, 2013,139(1): 57-64.
[11] DU W, YANG J, POWIS B, et al. Understanding on-road practices of electric bike riders: an observational study in a developed city of China [J]. Accident Analysis and Prevention, 2013, 59: 319-326.
[12] DOZZA M, WERNEKE J. Introducing naturalistic cycling data: what factors influence bicyclists' safety in the real world? [J]. Transportation Research Part F, 2014, 24: 83-91.
[13] SCHEPERS J P, FISHMAN E, DEN HERTOG, et al. The safety of electrically assisted bicycles compared to classic bicycles [J]. Accident Analysis and Prevention, 2014, 73: 174-180.
[14] JIN S, QU X, WANG D. Assessment of expressway traffic safety using Gaussian mixture model based on time to collision [J]. International Journal of Computational Intelligence Systems, 2011, 4(6): 1122-1130.
[15] JIN S, WANG D, XU C, et al. Short-term traffic safety forecasting using Gaussian mixture model and Kalman filter [J]. Journal of Zhejiang University: Science A, 2013, 14(4): 231-243.
[16] PARK B J, ZHANG Y, LORD D. Bayesian mixture modeling approach to account for heterogeneityin speed data [J]. Transportation Research Part B, 2010,44(5): 662-673. |
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