A systematic framework for urban smart transportation towards traffic management and parking

Kai Huang; Chang Jiang; Pei Li; Ali Shan; Jian Wan; Wenhu Qin; Kai Huang; Chang Jiang; Pei Li; Ali Shan; Jian Wan; Wenhu Qin

doi:10.3934/era.2022212

Electronic Research Archive

2022, Volume 30, Issue 11: 4191-4208. doi: 10.3934/era.2022212

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A systematic framework for urban smart transportation towards traffic management and parking

1.
School of Instrument Science and Engineering, Southeast University, China
2.
Wuxi Campus, Southeast University, China
3.
School of Cyber Science and Engineering, Southeast University, China
4.
School of Transportation, Southeast University, China
5.
Institute of Transport Studies, Monash University, Melbourne, Australia
6.
Research and Development Center on ITS Technology and Equipment, China Design Group, China

Received: 09 August 2022 Revised: 03 September 2022 Accepted: 07 September 2022 Published: 21 September 2022

Considering the wide applications of big data in transportation, machine learning and mobile internet technology, artificial intelligence (AI) has largely empowered transportation systems. Many traditional transportation planning and management methods have been improved or replaced with smart transportation systems. Hence, considering the challenges posed by the rising demand for parking spaces, traffic flow and real-time operational management in urban areas, adopting artificial intelligence technologies is crucial. This study aimed to establish a systematic framework for representative transportation scenarios and design practical application schemes. This study begins by reviewing the development history of smart parking systems, roads and transportation management systems. Then, examples of their typical application scenarios are presented. Second, we identified several traffic problems and proposed solutions in terms of a single parking station, routes and traffic networks for an entire area based on a case study of a smart transportation systematic framework in the Xizhang District of Wuxi City. Then, we proposed a smart transportation system based on smart parking, roads and transportation management in urban areas. Finally, by analyzing these application scenarios, we analyzed and predicted the development directions of smart transportation in the fields of smart parking, roads and transportation management systems.
- smart transportation,
- artificial intelligence,
- parking,
- road,
- transportation management
Citation: Kai Huang, Chang Jiang, Pei Li, Ali Shan, Jian Wan, Wenhu Qin. A systematic framework for urban smart transportation towards traffic management and parking[J]. Electronic Research Archive, 2022, 30(11): 4191-4208. doi: 10.3934/era.2022212

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Abstract

Considering the wide applications of big data in transportation, machine learning and mobile internet technology, artificial intelligence (AI) has largely empowered transportation systems. Many traditional transportation planning and management methods have been improved or replaced with smart transportation systems. Hence, considering the challenges posed by the rising demand for parking spaces, traffic flow and real-time operational management in urban areas, adopting artificial intelligence technologies is crucial. This study aimed to establish a systematic framework for representative transportation scenarios and design practical application schemes. This study begins by reviewing the development history of smart parking systems, roads and transportation management systems. Then, examples of their typical application scenarios are presented. Second, we identified several traffic problems and proposed solutions in terms of a single parking station, routes and traffic networks for an entire area based on a case study of a smart transportation systematic framework in the Xizhang District of Wuxi City. Then, we proposed a smart transportation system based on smart parking, roads and transportation management in urban areas. Finally, by analyzing these application scenarios, we analyzed and predicted the development directions of smart transportation in the fields of smart parking, roads and transportation management systems.

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