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Open AccessArticle

Bicycle Speed Modelling Considering Cyclist Characteristics, Vehicle Type and Track Attributes

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College of Automobile and Traffic Engineering, Nanjing Forestry University, Longpan Road 159#, Nanjing 210037, China
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School of Maritime and Transportation, Ningbo University, Fenghua Road 818#, Ningbo 315211, China
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School of Transportation, Southeast University, Dongnandaxue Road 2#, Jiangning Development Zone, Nanjing 211189, China
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National Demonstration Center for Experimental Road and Traffic Engineering Education (Southeast University), Dongnandaxue Road 2#, Jiangning Development Zone, Nanjing 211189, China
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School of Architecture and Transportation, Guilin University of Electronic Technology, Jinji Road 1#, Guilin 541004, China
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China Design Group Co., Ltd., Ziyun Road 9#, Nanjing 210014, China
*
Author to whom correspondence should be addressed.
World Electr. Veh. J. 2021, 12(1), 43; https://doi.org/10.3390/wevj12010043
Received: 6 January 2021 / Revised: 5 March 2021 / Accepted: 8 March 2021 / Published: 12 March 2021
Cycling is an increasingly popular mode of transport as part of the response to air pollution, urban congestion, and public health issues. The emergence of bike sharing programs and electric bicycles have also brought about notable changes in cycling characteristics, especially cycling speed. In order to provide a better basis for bicycle-related traffic simulations and theoretical derivations, the study aimed to seek the best distribution for bicycle riding speed considering cyclist characteristics, vehicle type, and track attributes. K-means clustering was performed on speed subcategories while selecting the optimal number of clustering using L method. Then, 15 common models were fitted to the grouped speed data and Kolmogorov–Smirnov test, Akaike information criterion, and Bayesian information criterion were applied to determine the best-fit distribution. The following results were acquired: (1) bicycle speed sub-clusters generated by the combinations of bicycle type, bicycle lateral position, gender, age, and lane width were grouped into three clusters; (2) Among the common distribution, generalized extreme value, gamma and lognormal were the top three models to fit the three clusters of speed dataset; and (3) integrating stability and overall performance, the generalized extreme value was the best-fit distribution of bicycle speed. View Full-Text
Keywords: bicycling characteristics; speed modelling; K-means clustering; L method; distribution model; model comparison bicycling characteristics; speed modelling; K-means clustering; L method; distribution model; model comparison
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MDPI and ACS Style

Yan, X.; Ye, X.; Chen, J.; Wang, T.; Yang, Z.; Bai, H. Bicycle Speed Modelling Considering Cyclist Characteristics, Vehicle Type and Track Attributes. World Electr. Veh. J. 2021, 12, 43. https://doi.org/10.3390/wevj12010043

AMA Style

Yan X, Ye X, Chen J, Wang T, Yang Z, Bai H. Bicycle Speed Modelling Considering Cyclist Characteristics, Vehicle Type and Track Attributes. World Electric Vehicle Journal. 2021; 12(1):43. https://doi.org/10.3390/wevj12010043

Chicago/Turabian Style

Yan, Xingchen; Ye, Xiaofei; Chen, Jun; Wang, Tao; Yang, Zhen; Bai, Hua. 2021. "Bicycle Speed Modelling Considering Cyclist Characteristics, Vehicle Type and Track Attributes" World Electr. Veh. J. 12, no. 1: 43. https://doi.org/10.3390/wevj12010043

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