Latest revision as of 20:32, 22 March 2021

Abstract

Old areas of metropolises play a crucial role in their development. The main factors restricting further progress are primitive road transportation planning, limited space, and dense population, among others. Mass transit systems and public transportation policies are thus being adopted to make an old area livable, achieve sustainable development, and solve transportation problems. Identifying old areas of metropolises as a research object, this paper puts forth an improved ant colony algorithm and combines it with virtual reality. This paper predicts traffic flow in Yangpu area on the basis of data obtained through Python, a programming language. On comparing the simulation outputs with reality, the results show that the improved model has a better simulation effect, and can take advantage of the allocation of traffic resources, enabling the transport system to achieve comprehensive optimization of time, cost, and accident rates. Subsequently, this paper conducted a robustness test, the results of which show that virtual traffic simulation based on the improved ant colony algorithm can effectively simulate real traffic flow, use vehicle road and signal resources, and alleviate overall traffic congestion. This paper offers suggestions to alleviate traffic congestion in old parts of metropolises.

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The different versions of the original document can be found in:

• http://dx.doi.org/10.3390/su11041140 under the license https://creativecommons.org/licenses/by

• http://dx.doi.org/10.3390/su11041140 under the license http://creativecommons.org/licenses/by/3.0/

• https://www.mdpi.com/2071-1050/11/4/1140/pdf under the license https://creativecommons.org/licenses/by/4.0

• http://www.mdpi.com/2071-1050/11/4/1140/pdf,

http://dx.doi.org/10.3390/su11041140 under the license cc-by

• https://www.mdpi.com/2071-1050/11/4/1140,

https://doaj.org/toc/2071-1050 under the license https://creativecommons.org/licenses/by/4.0/

• https://www.mdpi.com/2071-1050/11/4/1140,

https://www.mdpi.com/2071-1050/11/4/1140/pdf,

https://ideas.repec.org/a/gam/jsusta/v11y2019i4p1140-d207969.html,

https://academic.microsoft.com/#/detail/2917861039