Research on Urban Traffic Congestion Using Dual-Modal Model of Space Syntax Based on Big Data

Le résumé fourni par la source

With the acceleration of urbanization, urban traffic congestion is becoming increasingly serious, and the uneven accessibility of urban transportation networks is evident. There are obvious shortcomings in using traditional methods to analyze the accessibility of urban transportation networks in terms of efficiency, scalability, and consistency. Therefore, based on the traditional analysis of urban road network accessibility, this paper proposes a Dual Model of Space syntax. Firstly, it introduces the important significance of optimizing the accessibility of urban road networks for the economic development and competitiveness of cities. Secondly, the experience of using spatial syntax to improve urban transportation networks was introduced. Next, by comparing traditional spatial syntactic analysis methods, the research framework of the dual-mode model is introduced. Through Baidu's positioning service, massive map data, subway data, and rapid transportation data can be automatically collected, which can improve the efficiency and accuracy of data. Then, this process involves a comparative analysis of street networks, dual-mode models, and dual-mode model weighted networks, ultimately forming a research method based on spatial syntactic dual-mode models. Finally, selecting the surrounding area of Chengdu as a case study, a comprehensive analysis of the transportation network is conducted to systematically extract and identify urban traffic congestion and bottlenecks.

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