AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
PDF (1,006.4 KB)
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Research Article | Open Access

Congestion judgment method at entrances and exits of large-scale parking lots based on average vehicle delay

Qianyi Hu1Jian Chen3Jun Chen1,2Chu Zhang1,2( )
School of Transportation, Southeast University, Nanjing, Jiangsu 211189, China
Longquan Transportation Bureau, Lishui, Zhejiang 323700, China
Jiangsu Key Laboratory of Comprehensive Transportation Planning and Simulation, Southeast University, Nanjing, Jiangsu 211189, China
Show Author Information

Abstract

The significant rise in the number of motor vehicles and the increased utilization of land in China has led to the emergence of numerous large-scale parking facilities, resulting in exacerbated congestion at their entrances and exits. To address this issue, it is essential to first establish criteria for assessing congestion. This study employs an average delay model for various vehicle types and takes into account the distribution ratios of incoming vehicles across different lanes. Through quantitative analysis, the research identifies the causes and severity of congestion for different vehicle categories at parking lot entrances and exits. The findings aim to inform effective management and regulation of traffic flow in these critical areas.

References

[1]
National Bureau of Statistics of the People’s Republic of China. China Statistical Yearbook[M]. Beijing: China Statistics Press, 2020.
[2]
Wang, L.W. Research on traffic congestion identification and prediction based on translational nested grid model[D]. Beijing: Beijing Jiaotong University, 2021.
[3]
Li, X.W., Chen, H., Wang, W.B. Application of vissim simulation to traffic influence analysis to solid parking lot[C].//3rd China Annual Conference on ITS Proceedings. Shanghai: Southeast University Press, 2007: 544–550.
[4]
Lu, B.R. Study on optimization of the traffic organization for off-road public parking lot[D]. Lanzhou: Lanzhou Jiaotong University, 2015.
[5]

Chang, L.J., Zheng, L.L., Yang, F. A method of discrimination for traffic state based on (SAGA-FCM)-PNN[J]. Journal of Transport Information and Safety, 2019, 37(2): 120–127.

[6]

Yan, Y.C., Bai, L., Wu, Q.S., et al. Traffic congestion prediction and assessment based on multi-index fuzzy comprehensive evaluation[J]. Application Research of Computers, 2019, 36(12): 3697–3700,3704.

[7]

Saito, K., Kato, T., Shimane, T. Traffic congestion and accident externality: A Japan-US comparison[J]. The B.E. Journal of Economic Analysis & Policy, 2010, 10(1): 14.

[8]

Taylor, M.A., Woolley, J.E., Zito, R. Integration of the global positioning system and geographical information systems for traffic congestion studies[J]. Transportation Research Part C, 2000, 8(1): 257–285.

[9]

Hou, L.P. Study on Evaluation and judgment standard of urban road trafic congestion[J]. Urban Roads Bridges & Flood Control, 2020(3): 9–11,17.

[10]

Wang, L.Y., Chen, J., Cao, X.F., et al. Vehicle delay model applied to dynamic and static traffic impact analysis of large parking lots[J]. Applied Sciences, 2021, 11(20): 9771.

[11]
Yao R.H., Zhang W.S., Yang L., JIN Y. A short-time traffic flow prediction method based on time division[P]. CN110517485A. 2021.
[12]

Zhang, T.F., Yuan, P.C. Short-term traffic flow forecast based on ARIMA model[J]. Intelligent Computer and Applications, 2020, 10(7): 273–278.

Journal of Highway and Transportation Research and Development (English Edition)
Pages 51-57
Cite this article:
Hu Q, Chen J, Chen J, et al. Congestion judgment method at entrances and exits of large-scale parking lots based on average vehicle delay. Journal of Highway and Transportation Research and Development (English Edition), 2024, 18(4): 51-57. https://doi.org/10.26599/HTRD.2024.9480032

258

Views

20

Downloads

0

Crossref

Altmetrics

Received: 25 October 2023
Revised: 05 May 2024
Accepted: 28 May 2024
Published: 31 December 2024
© The Author(s) 2024.

This is an open access article under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0, http://creativecommons.org/licenses/by/4.0/).

Return