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Jul 2, 2019
The Influence of Travel Distance on Mode Share for Regional Trips in China
Authors: Xuedong Hua [email protected], Wenjie Xie [email protected], Wei Wang [email protected], Yun Xiang [email protected], and Weijie Yu [email protected]Author Affiliations
Publication: CICTP 2019
Abstract
This paper aims to investigate the influence of travel distance on regional mode share in China. Three regional modes, i.e., road, railway, and air transportation, are considered. Urban characteristics as well as mode share and travel distance data, covering 299 cities around China are covered. The mode shares under different travel distances and urban characteristics are covered and the influence of distance, city size, and economy on mode share are analyzed. Then, four models including linear regression (LR), polynomial regression (PR), artificial neural network (ANN), and support vector machine (SVM), are adopted and calibrated to predict the mode choice likelihood of regional passenger travel distance. The results reveal that travel distance and the urban characteristics strongly affect the regional mode share for all three transportation modes. Also, the improved SVM model performs better for train. This paper is possible to improve accuracy and cost-effectiveness of the existing mode share models.
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© 2019 American Society of Civil Engineers.
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Published online: Jul 2, 2019
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Jiangsu Key Laboratory of Urban ITS, Southeast Univ., Nanjing 211189, China; Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, School of Transportation, Southeast Univ., Dong Nan Da Xue Rd. #2, Nanjing 211189, China. E-mail: [email protected]
Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast Univ., Dong Nan Da Xue Rd. #2, Nanjing 211189, China. E-mail: [email protected]
Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast Univ., Dong Nan Da Xue Rd. #2, Nanjing 211189, China. E-mail: [email protected]
Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast Univ., Dong Nan Da Xue Rd. #2, Nanjing 211189, China. E-mail: [email protected]
Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast Univ., Dong Nan Da Xue Rd. #2, Nanjing 211189, China. E-mail: [email protected]
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