Optimization Strategy of Railway Transportation in Guangdong, Hong Kong(China), and Macao(China) Greater Bay Area Based on Intelligent Transportation System (ITS)

Authors

  • Huihong Cao Guangzhou Huaxia Vocational College,China
  • Jinxin Chen Guangzhou Huaxia Vocational College,China

DOI:

https://doi.org/10.61360/BoniGHSS232014240811

Keywords:

intelligent transportation system, Guangdong, Hong Kong, and Macao Greater Bay Area, rail transit, optimization strategy

Abstract

With the rapid development of China's economy, the Guangdong-Hong Kong-Macao Greater Bay Area is experiencing rapid economic growth, increasing population, and extremely high regional foot traffic, which brings great challenges to the transportation system. To support the sustainable development of the Guangdong, Hong Kong(China), and Macao(China) Greater Bay Area, optimizing the rail transportation system of the Guangdong, Hong Kong(China), and Macao(China) Greater Bay Area has become an urgent task. At present, the construction of ITS has become an important trend in modern society, and the efficiency and convenience of transportation can be improved through the application of ITS and the implementation of optimization strategies. By optimizing line planning and design, rationalizing train operation schedules, and implementing intelligent scheduling and operation management to reduce congestion and increase operation speed and frequency, the efficiency of the overall rail transit system can be improved to provide more efficient, convenient, and safe travel options for residents, enterprises, and tourists, and to promote the economic development and sustainable urban development of the Greater Bay Area.

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Published

2023-08-29

Issue

Section

Research Article

How to Cite

Optimization Strategy of Railway Transportation in Guangdong, Hong Kong(China), and Macao(China) Greater Bay Area Based on Intelligent Transportation System (ITS). (2023). Journal of Global Humanities and Social Sciences, 4(04), 207-212. https://doi.org/10.61360/BoniGHSS232014240811

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