Trunk highway passenger flow forecasting method based on comprehensive transportation network

PEI Yu-long; YUWEN Chong; CHANG Zheng; GAO Zhi-xiang; LIU Tao

doi:10.19818/j.cnki.1671-1637.2022.04.020

Volume 22 Issue 4

Aug. 2022

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Article Navigation > Journal of Traffic and Transportation Engineering > 2022 > 22(4): 259-272

PEI Yu-long, YUWEN Chong, CHANG Zheng, GAO Zhi-xiang, LIU Tao. Trunk highway passenger flow forecasting method based on comprehensive transportation network[J]. Journal of Traffic and Transportation Engineering, 2022, 22(4): 259-272. doi: 10.19818/j.cnki.1671-1637.2022.04.020

Citation:

PEI Yu-long, YUWEN Chong, CHANG Zheng, GAO Zhi-xiang, LIU Tao. Trunk highway passenger flow forecasting method based on comprehensive transportation network[J]. Journal of Traffic and Transportation Engineering, 2022, 22(4): 259-272. doi: 10.19818/j.cnki.1671-1637.2022.04.020

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Trunk highway passenger flow forecasting method based on comprehensive transportation network

doi: 10.19818/j.cnki.1671-1637.2022.04.020

School of Traffic and Transportation, Northeast Forestry University, Harbin 150040, Heilongjiang, China

Funds:

National Natural Science Foundation of China 71771047

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Author Bio:
PEI Yu-long(1961-), male, professor, PhD, peiyulong@nefu.edu.cn
Received Date: 2022-03-26
Available Online: 2022-10-08
Publish Date: 2022-08-25

Abstract

Abstract

A trunk highway passenger flow forecasting method integrating multiple transport modes was proposed. By introducing the standard passenger transport unit based on the man-time and the hub nodes conversion method from point to line, the sub-networks of different transport modes, such as highways, railways, airlines and waterways, were integrated, and the comprehensive transportation network model which can reflect the transfer relationship among different transport modes was built. By considering the travel economic cost, travel time, maximum travel recovery time, comfort level and other factors, the impedance functions of different transport modes in the comprehensive transportation network were constructed. The maximum capacities of different transport modes in the comprehensive transportation network were calibrated by using the rated passenger number and the number of departures per unit time. Based on the standard passenger transport unit and comprehensive transportation network model, the passenger flow distribution forecasting model considering the impedance of comprehensive transportation was proposed. On this basis, the passenger flow forecasting model considering the influence of different transport modes was realized. Taking Harbin, Daqing, Suihua and Qiqihar area in Heilongjiang Province as an example, the method was verified. Analysis results show that compared with the actual observation value in 2019, the average error of forecasting results of the passenger flow forecasting method based on the comprehensive transportation network is 5.47%, slightly lower than the 6.14% of the traditional four-stage method when there are no accompanying lines around the characteristic roads. However, the average error of forecasting results of the proposed method is 4.58% when the accompanying lines are around the characteristic roads, far less than the error value 11.89% of the traditional four-stage method. Compared with the traditional four-stage method, the proposed method can better reflect the influence of the transfer passenger on the traffic volume of the trunk highway after the structural change of comprehensive transportation network. Compared with adding waterways, adding high-speed or conventional railways accompanying lines have more obvious impact on the passenger flow of trunk highways, and can promote the transfer of passenger flow from highways to railways. 4 tabs, 12 figs, 32 refs.

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References

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Trunk highway passenger flow forecasting method based on comprehensive transportation network

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