Abstract
With continuous development of urban construction, the city’s economic level has grown, and the traffic of residents’ lives has also changed. More and more people have entered the well-off level, and the family-to-vehicle ratio has gradually increased, resulting in an increase in urban traffic vehicles, and the city’s rail transit passenger traffic has also expanded. Solving the problem of traffic passenger traffic has become a top priority. Nowadays, there are few researches on the prediction and solution of traffic passenger flow (PF). The main goal of this paper is to predict the PF of urban traffic and to study the establishment of its model. In this paper, the gray model method and the artificial neural network prediction are used to predict PF. According to the influence of factors of the choice of travel mode, it is concluded that the people with high income in the middle and young ages generally prefer the mode of travel to the car.
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References
Bao, Lei. 2017. Real-time prediction model of urban rail-transit passenger flow. Urban Rail Transit Research 20 (5): 104–106.
Yuan, Jian, Peng Wang, Wei Wang, et al. 2017. Passenger flow forecasting method for urban rail transit based on time and space characteristics. Journal of Beijing Jiaotong University 41 (6): 42–48.
Li, Dewei, Yixing Yan, Xianfeng Zeng, et al. 2017. Short-term combined forecasting model for urban rail transit passenger flow. Metropolitan Rapid Transit (1): 54–58.
Tang, Qiu-sheng, Cheng Peng, and Na Li. 2017. Short-term prediction of urban rail transit passenger flow based on GSO-BP neural network. Journal of Transportation Science and Technology 19 (1): 1–4.
Lu, Zhiyi, Weicong Nie, and Lizhen Chen. 2018. Passenger flow forecast of urban rail transit based on ARMA model. Henan Science 36 (5): 12–17.
Yao, Enjian, Wenhua Zhou, and Yongsheng Zhang. 2018. Forecast of passenger flow in real-time inbound and outbound stations in the initial stage of urban rail transit new station. China Railway Science 39 (2).
Cheng, Yan, Xiafei Ye, Zhi Wang, et al. 2018. Prediction model of starting point matrix between stations in urban rail transit during peak hours. Journal of Tongji University (Natural Science) 46 (3).
Qiu, Jianhua, Kai Shang, and Yawei Zhang. 2019. Prediction of large passenger flow in urban rail transit based on correlation vector machine. Journal of Dalian Jiaotong University 40 (1): 16–20.
Lu, Gongyuan, Wei Ma, Kun Wang, et al. 2017. Integer programming model for passenger flow control of urban rail transit lines. Journal of SouthWest Jiaotong University 52 (2): 319–325.
Li, Jianzhong, and Hai Dong. 2017. Research on traffic flow prediction model based on spark framework and ARIMA-BPNN. Computer Programming Techniques & Maintenance 20: 41–44.
Ma, Wei. 2017. Application of GM(1,1) model in passenger flow forecast of spring transportation railway. Journal of Transportation Science and Technology 19 (6): 9–11.
Gao, Bo. 2017. Research on traffic flow forecasting model based on PLS. Journal of Transportation Science and Engineering 33 (2): 100–104.
Zhang, Haiyan. 2017. Design and implementation of mathematical model for passenger flow scheduling based on multi-ship technology. Ship Science and Technology (6): 160–162+165.
Jiang, Tianhe. 2017. Application of ARIMA model and BP neural network model in passenger traffic forecast of railway spring festival—Taking Guangdong Province as an example. Science & Technology Economic Market 7: 4–6.
Han, Qi, Jiansheng Guo, Taoyong Gu, et al. 2018. Optimization of passenger traffic at airport security checkpoint based on Queuing Theory model. Science and Technology Innovation Review, Vol. 15, No. 439 (7): 88–92.
Zhao, Wei, Jieshuang Dong, and Weiwei Liu. 2018. Prediction model of slow traffic demand based on space syntax. Logistics Science and Technology, Vol. 41, No. 275 (7): 93–96.
Bai, L. 2016. Research on the computer algorithm application in urban rail transit holiday passenger flow prediction[C]. International Conference on Network & Information Systems for Computers.
Zhou, W., B. Han, and C. Feng. 2017. A review of passenger flow assignment model and algorithm for urban rail transit network. Systems Engineering-Theory & Practice 37 (2): 440–451.
Liu, S., E. Yao, C. Xin, et al. 2017. Evaluating the impact of new lines on entrance/exit passenger flow of adjacent existing stations in urban rail transit system. Transportation Research Procedia 25: 2629–2642.
Li, M., Y. Wang, J. Jin, et al. 2017. Network congestion evolution law of urban rail transit based on network passenger flow mode. Journal of Southeast University 47 (2): 404–409.
Yang, L., and Y. Wang. 2017. Urban rail transit platform passenger alighting and boarding movement and experiment research.
Liu, M.Y., and J.W. Wang. 2017. Pricing method of urban rail transit considering the optimization of passenger transport structure. Journal of Transportation Systems Engineering & Information Technology 17 (3): 53–59.
Zheng, Z., Z. Zhang, and Z. Jing. 2017(cii). Characteristics of urban rail transit passenger flow in Chengdu. Urban Transport of China.
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Zhang, D., Liu, Z. (2020). Forecast and Model Establishment of Urban Rail—Transit Passenger Flow. In: Huang, C., Chan, YW., Yen, N. (eds) Data Processing Techniques and Applications for Cyber-Physical Systems (DPTA 2019). Advances in Intelligent Systems and Computing, vol 1088. Springer, Singapore. https://doi.org/10.1007/978-981-15-1468-5_112
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DOI: https://doi.org/10.1007/978-981-15-1468-5_112
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