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
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home International Journal of Crowd Science Article
PDF (1.3 MB)
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Technical paper | Open Access

A novel simulation framework for crowd transportations

Zhihui Li( ), Hongbo Sun
School of Computer and Control Engineering, Yantai University, Yantai, China
Show Author Information

Abstract

Purpose

With the development of the modern economy, vehicles are no longer a luxury for people, which greatly facilitate people's daily life, but at the same time bring traffic congestion. How to relieve traffic congestion and improve its capacity is a hot research area. This paper aims to propose a new simulation framework for crowd transportations to ease traffic congestion.

Design/methodology/approach

This paper establishes related simulation models such as vehicles, traffic lights and advisers. Then the paper describes their relationships, gives their interaction mechanism and solidifies the above into a software implementation framework.

Findings

This paper proposes a simulation framework for crowd transportations.

Originality/value

In this framework, traffic lights are used as a control method to control the road network and road conditions are used as an Affecter to influence individual behavior. The vehicle passing rate is defined by the correlation between endowment and the start time of the traffic lights. In this framework, members are related, dynamically adjusted according to road conditions and dynamically optimized member decisions. The optimal path is dynamic and real-time adjustments are made for each step forward. It is different from the traditional optimal path in which there is only one fixed one and it is different from the macroscopic optimal path that does not exist.

Keywords

Crowd networkCrowd transportationModel of member

References

 

Adacher, L. (2012), “A global optimization approach to solve the traffic signal synchronization problem”, Procedia - Social and Behavioral Sciences, Vol. 54, pp. 1270-1277.
Crossref Google Scholar
 

Chen, X., Li, Y. and Shen, Q. (2019), “A collaborative optimization method for urban traffic network based on two-layer complex networks”, Computer Applications, Vol. 9, pp. 1-11.
Google Scholar
 

Chiou, S. (1999), “Optimization of area traffic control for equilibrium network flows”, Transportation Science, Vol. 33 No. 3, pp. 279-289.
Crossref Google Scholar
 

Hu, Y., Wu, Q. and Zhu, D. (2009), “Topological properties and vulnerability analysis of urban road network”, Complex Systems and Complexity Science.
Google Scholar
 

Huang, J., Hong, Z. and Fan, J. (2015), “An optimized urban traffic signal field control algorithm based on pseudo neural network”, Operations Research Transactions, Vol. 19 No. 3, pp. 71-77.
Google Scholar
 

Liao, S. and Wang, Y. (2012), “Traffic signal timing model and genetic algorithm for single-point intersection”, Highway and Motor Transport, Vol. 3, pp. 45-48.
Google Scholar
 

Li, S., He, Y. and Liu, J. (2017), “Congestion control strategy on complex network with privilege traffic”, International Journal of Modern Physics C, Vol. 28 No. 9, doi: 10.1142/S0129183117501170
Crossref Google Scholar
 
Liu, J. (2009), Research on Urban Traffic Signal Control Based on Multi-Agents, Wuhan: Huazhong University of Science and Technology.
 

Liu, Y. and Chang, G. (2011), “An arterial signal optimization model for intersections experiencing queue spillback and lane blockage”, Transportation Research Part C: emerging Technologies, Vol. 19 No. 1, pp. 130-144.
Crossref Google Scholar
 

Li, Z., Wang, P. and Zhang, C. (2014), “Analysis of the construction mode of the overall architecture of domestic smart transportation”, Transportation Energy Conservation and Environmental Protection.
Google Scholar
 

Li, T., Xu, J. and Wang, L. (2012), “Multi-layer boundary active control for supersaturated traffic networks”, Journal of South China University of Technology: Natural Science Edition, Vol. 40 No. 7, pp. 27-32.
Google Scholar
 

Shen, G. and Yang, Y. (2016), “A dynamic signal coordination control method for urban arterial roads and its application”, Frontiers of Information Technology and Electronic Engineering, Vol. 17 No. 9, pp. 907-918.
Crossref Google Scholar
 

Shen, J., Bai, Y. and Shen, Y. (2012), “Timing optimization of arterial traffic signal based on synchro”, Computer Simulation, Vol. 29 No. 10, pp. 318-322.
Google Scholar
 

Sun, L., Huang, Y. and Chen, Y. (2018), “Vulnerability assessment of urban rail transit based on multi-static weighted method in Beijing, China”, Transportation Research Part A, Vol. 108, pp. 12-24.
Crossref Google Scholar
 

Tang, Z. and Wang, W. (2008), “Small area control of urban traffic signal based on MAS”, Industrial Control Computer, Vol. 21 No. 12, pp. 87-89.
Google Scholar
 

Xu, H., Yan, H. and Cai, C. (2011), “Design and realization of multi-stage traffic signal controller of variable phase”, Applied Mechanics and Materials, Vol. 135-136, pp. 135-136.
Crossref Google Scholar
 

Yang, W., Zhang, L. and Zhu, F. (2018), “A review of the application of multi-agent reinforcement learning in urban traffic network signal control methods”, Computer Application Research, Vol. 35 No. 6, pp. 1613-1618.
Google Scholar
 
Zhang, Y., Chen, Y. and Guan, J. (2014), “The definition, connotation and extension of smart transportation”, Proceedings of the 2014 Ninth China Intelligent Transportation Conference.
 

Zhu, M. and Chen, Y. (2013), “Design of intersection traffic signal timing system based on game”, Computer Simulation, Vol. 30 No. 7, pp. 151-155.
Google Scholar
 

Zhuang, H., Zhou, Y. and Cao, X. (2012), “Optimization of regional traffic signal timing based on improved genetic algorithm”, Transportation System Engineering and Information, Vol. 12 No. 4, pp. 57-63.
Google Scholar
International Journal of Crowd Science
Volume 5 Issue 3,
November 2021
Pages 293-310
DOI: 10.1108/IJCS-07-2021-0019
Cite this article:
Li Z, Sun H. A novel simulation framework for crowd transportations. International Journal of Crowd Science, 2021, 5(3): 293-310. https://doi.org/10.1108/IJCS-07-2021-0019

1171

Views

102

Downloads

0

Crossref

0

Scopus

Altmetrics
Received: 28 July 2021
Revised: 17 September 2021
Accepted: 17 September 2021
Published: 07 October 2021
© The author(s)

Zhihui Li and Hongbo Sun. Published in International Journal of Crowd Science. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
Return