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A Passenger Flow Transfer Prediction Model for Collinear Stations Based on Connection Model of New Station

Published: 02 October 2021 Publication History

Abstract

In order to solve the problem that the subway attracts less passenger flow, there are too many overlapping lines between the bus and the subway, resulting in traffic congestion of other conventional bus lines along the subway. For this reason, we take Xiamen as the sample point, and consider the number of subway stations and the length of subway bus configuration. The mathematical model of matching bus line density and traffic line length is referred. Based on the accessibility analysis, a new bus connecting station model with more than five stations on the same line is designed. We propose two models for the alignment adjustment of the number of common stations of subway and public transport. It is shown that the average connecting time from bus to subway is reduced by 15.58 minutes, and the passenger flow attracted by subway is increased by 2.15%-19.99%, compared with the unadjusted road network layout.

References

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G. Chen. Study on Optimization of feeder bus network considering passenger flow balance at rail transit stations [D]. Chang'an University, 2016.
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Q. Tang. Selection of bus stations and optimization of bus network based on rail transit [D]. Southwest Jiaotong University, 2012.
[3]
X. Fang. Research on layout and optimization method of urban rail transit connecting bus lines [D]. Southwest Jiaotong University, 2013.
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W. Zhang, Y. Wang, X. Xie, et.al. Real-time Travel Time Estimation with Sparse Reliable Surveillance Information[C]. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (Ubicomp), 2020: 1-23.
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Z Zhou, Y. Wang, X. Xie, STUaNet: Understanding uncertainty in spatiotemporal collective human mobility[C]. Proceedings of the 30th Web Conference, 2021.
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Z Zhou, Y Wang, X Xie, L Chen, Foresee Urban Sparse Traffic Accidents: A Spatiotemporal Multi-Granularity Perspective[J]. IEEE Transactions on Knowledge and Data Engineering, 2020(99):1-14.
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Z. Zhou, Y. Wang, X. Xie, et.al. RiskOracle: A Minute-level Citywide Traffic Accident Forecasting Framework[C]. Proceedings of the 34th AAAI Conference on Artificial Intelligence, 2020, pp. 1258-1265.

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        cover image ACM Other conferences
        ACM TURC '21: Proceedings of the ACM Turing Award Celebration Conference - China
        July 2021
        284 pages
        ISBN:9781450385671
        DOI:10.1145/3472634
        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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        Association for Computing Machinery

        New York, NY, United States

        Publication History

        Published: 02 October 2021

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