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A platform for research: civil engineering, architecture and urbanism
Exploring Travel Behavior of Urban Residents Based on Big Survey Data
Understanding travel behavior is conducive to urban planning and traffic management. In a sustainable city, green traffic modes, such as bus, metro, and bike-sharing, are encouraged to reduce greenhouse gas emissions. Recently, metro and bike-sharing have experienced fast development. Urban residents have more travel choices than ever before with the rapid development of urban economies and the acceleration of urbanization. It is imperative to understand residents’ travel behavior. Large-scale travel surveys of residents could provide various information to analyze travel behaviors. In this paper, five common travel modes are selected to analyze the characteristics of residents’ travel choice. An MNL model was constructed based on individual, family, and travel attributes. The prediction accuracy of the model is 71.0%, which indicates that the model is suitable for predicting residents’ travel choice. This study could give help to understand urban residents’ behavior and travel choices, which promotes the development of urban transportation.
Exploring Travel Behavior of Urban Residents Based on Big Survey Data
Understanding travel behavior is conducive to urban planning and traffic management. In a sustainable city, green traffic modes, such as bus, metro, and bike-sharing, are encouraged to reduce greenhouse gas emissions. Recently, metro and bike-sharing have experienced fast development. Urban residents have more travel choices than ever before with the rapid development of urban economies and the acceleration of urbanization. It is imperative to understand residents’ travel behavior. Large-scale travel surveys of residents could provide various information to analyze travel behaviors. In this paper, five common travel modes are selected to analyze the characteristics of residents’ travel choice. An MNL model was constructed based on individual, family, and travel attributes. The prediction accuracy of the model is 71.0%, which indicates that the model is suitable for predicting residents’ travel choice. This study could give help to understand urban residents’ behavior and travel choices, which promotes the development of urban transportation.
Exploring Travel Behavior of Urban Residents Based on Big Survey Data
Zhang, Hui (author) / Li, Xu (author)
22nd COTA International Conference of Transportation Professionals ; 2022 ; Changsha, Hunan Province, China
CICTP 2022 ; 1725-1735
2022-09-08
Conference paper
Electronic Resource
English
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