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Title	Analyzing The Temporal And Spatial Characteristics Of Public Transit Passengers' Travel Behavior Using Multiple Logit Models
ID_Doc	9557
Authors	Zhang L.; Lu Y.; Ma Z.; Wei Y.
Year	2023
Published	ACM International Conference Proceeding Series
DOI	http://dx.doi.org/10.1145/3638985.3639022
Abstract	The accelerating process of urbanization has deepened the contradiction between urban traffic supply and residents' travel demand, posing significant challenges to public transportation within cities. Currently, smart city development is being promoted nationwide, and smart mobility plays a pivotal role as an integral component of smart cities. Utilizing big data, the study of passenger travel behavior has been widely applied. This study integrates multiple Logit models and big data visualization methods. Utilizing card-swiping data from May 2020, recorded every day from 4:00 to 23:00, the research analyzes the travel time, frequency, influencing factors, and spatial distribution of residents in the West Coast New Area. It delves deeply into the travel behavior of public transit passengers. The findings revealed that during weekdays, passengers traveling during the morning and evening peaks are 1.33 times more than those during weekends. Age, peak travel times, whether passengers benefit from discounts, and travel distance significantly impact travel frequency. Furthermore, on weekends, there is a 21% increase in the number of public transit passengers in the eastern coastal regions compared to weekdays. © 2023 ACM.
Author Keywords	Data visualization; Intelligent Transportation; Multiple Logit

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