Analysis of Subway Station Setting Based on Passenger Flow Attraction Model

Meng-Nan Li, Xueqing Wang, Ru-Xi Ding () and Jin-Tao Cai
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Meng-Nan Li: Tianjin University
Xueqing Wang: Tianjin University
Ru-Xi Ding: Beijing Institute of Technology
Jin-Tao Cai: Tianjin University

A chapter in Proceedings of the 26th International Symposium on Advancement of Construction Management and Real Estate, 2022, pp 972-984 from Springer

Abstract: Abstract In recent years, many cities in China have vigorously promoted the subway construction, which has eased the city’s traffic pressure and promoted the development of the regional economy. However, after constructing subway in some cities, the intensity of passenger flow has not reached the expected level. The inefficiency of subway operations makes local governments pay more for subway project subsidies. Therefore, on the basis of the distance attenuation theory of passenger flow, this paper used a web crawler to obtain the data of the surrounding houses of the subway line, constructed the subway passenger flow attractiveness (SPFA) model, and reasonably evaluated the passenger flow attractiveness of the subway line. Then, the model was used to compare and evaluate the SPFA of Tianjin, Beijing, and Shanghai and analyze the reasons for the low passenger flow intensity of the Tianjin subway. The results show that the SPFA of Line 1, 2, and 3 in Tianjin is not very different from that of similar subway lines in Beijing and Shanghai, but the daily SPFA of Circle Line 6 is 90% different from that of Beijing and Shanghai; at the same time, the gap of SPFA calculated by the model is also very large, because it avoids dense residential areas in the station setting, and the density of residents close to the station is low. It is recommended that the government should take this issue into account in subsequent subway planning and reduce the phenomenon of setting subway stations in remote areas.

Keywords: Subway passenger flow attractiveness; Subway station position; Web crawler; Segment weight (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/978-981-19-5256-2_76

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