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Qian Yao

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Proceedings Article | 14 February 2024 Open Access

Analysis of parking behavior in the context of urban renewal based on multi-day RP and SP data: a case study in Xi'an, China

Qian Yao, Liang Wang, Jiafei Wang, Keting Wang, Zhenxing Yao

Proceedings Volume 13018, 130184H (2024) https://doi.org/10.1117/12.3024007

KEYWORDS: Data modeling, Design, Surface plasmons, Calibration, Analytical research, Statistical modeling, Statistical analysis

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The relationship between intelligent parking management and regional traffic congestion in the context of urban parcel renewal has attracted academic attention. Reasonable parking behavior analysis can effectively contribute to traffic demand management and alleviate the contradiction between parking supply and demand. To this end, this paper constructs a multinomial logit model based on Python that considers multiple factors affecting parking. First, to accommodate the multi-day behavioral variability and unobserved heterogeneity in individual characteristics ignored by traditional parking surveys, multi-day stated-preference(SP) and revealed-preference(RP) data collected in Xi'an were used to analyze people's parking choice behavioral characteristics. The questionnaires were designed by SPSS software with orthogonal design principles. Secondly, based on multi-day survey data, parameter estimation and significant factor evaluation of Multinomial logit(MNL) model are completed by using Python. Finally, the effects of parking rates, invehicle time and out-vehicle time on parking choice behavior are explored, and the parking behavior of passengers in different scenarios is also simulated. This paper conducted an empirical experiment in Xi'an. The research results show that in-car time and parking rate are significant factors affecting passengers' parking behavior, indicating that it is feasible to guide travelers' parking behavior and alleviate regional traffic congestion by changing the above factors. This study can provide reference for the implementation of smart parking demand management in domestic cities.

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