Abstract
Crowds are an important feature of high-dense Mass Rail Transit (MRT), assessing its crowding status is a critical step in crowd management. In this chapter, a pedestrian crowd classification method based on an improved ant colony clustering algorithm (ACCA) is developed for MRT systems. First, survey data from Automatic Fare Collection (AFC) regarding three statuses (check-in/check-out and sum). Second, the PCI-influenced factors were also considered in the method, which included average daily ridership intensity, the duration of crowd, and the scope of crowd influence. Third, to classify the pedestrian crowd, an improved ant colony clustering model and its solving algorithm were presented. The results show that, for the two types of time scale, the passengers’ time–space characteristics present a clear image of M, the variation trend of morning and evening peak hour is obvious in the MRT.
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References
Johansson A, Batty M, Hayashi K, Al Bar O, Marcozzi D, Memish ZA (2012) Crowd and environmental management during mass gatherings. Lancet Infect Dis 12(2):150–156
Vuchic VR (2007) Urban transit systems and technology. Wiley, New York
Garber N, Hoel L (2014) Traffic and highway engineering. Cengage Learning, Stamford
Cascetta E (2013) Transportation systems engineering: theory and methods. Springer Science & Business Media
Si B, Fu L, Liu J, Shiravi S, Gao Z (2016) A multi-class transit assignment model for estimating transit passenger flows-a case study of Beijing subway network. J Adv Transp 50(1):50–68
Khalid R, Baten M, Nawawi M, Mohd K, Ishak N (2016) Analyzing and optimizing pedestrian flow through a topological network based on M/G/C/C and network flow approaches. J Adv Transp 50(1):96–119
Xu X, Li K, Li X (2016) A multi-objective subway timetable optimization approach with minimum passenger time and energy consumption. J Adv Transp 50(1):69–95
CAM (2014) Annual report of china urban mass transit in 2014. China Association of Metros, Beijing, China
De Palma A, Kilani M, Proost S (2015) Discomfort in mass transit and its implication for scheduling and pricing. Transp Res Part B: Methodol 71(2):1–18
Acknowledgements
The work described in this paper was mainly supported by the Public Technology Application Foundation of Zhejiang Province of China (No. 2016C33256), the open fund for the Key Laboratory for Traffic and Transportation Security of Jiangsu Province (No. TTS2016-04, TTS2017-07), Natural Science Foundation of Zhejiang Province, China (LY17E080013), Philosophy and Social Science Program of Zhejiang Province, China (17NDJC130YB), Natural Science Foundation of Ningbo City, China (No. 2015A610298, 2016A610112). The anonymous reviewers are appreciated for their constructive comments and valuable suggestions to improve the quality of the paper.
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Zhou, Jb., Dong, S., Zhao, Pf., Chen, Yr. (2019). A Pedestrian Crowd Classification Method Based on the AFC Data in the Urban Rail Transit. In: Wang, W., Bengler, K., Jiang, X. (eds) Green Intelligent Transportation Systems. GITSS 2017. Lecture Notes in Electrical Engineering, vol 503. Springer, Singapore. https://doi.org/10.1007/978-981-13-0302-9_11
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DOI: https://doi.org/10.1007/978-981-13-0302-9_11
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