How to Promote Urban Intelligent Transportation: A Fuzzy Cognitive Map Study - Zhao, Luwei; Wang, Qing’e; Hwang, Bon-Gang (Frontiers in Neuroscience, 2022)
As an important part of smart city, intelligent transportation is an important breakthrough to solve urban traffic congestion, build an integrated transportation system, realize the intelligence of traffic infrastructure and promote sustainable development of traffic. In order to improve the construction of intelligent transportation in cities, this paper used literature analysis to determine 20 initial variables. A questionnaire was set and distributed to professionals from academia and industry, and 196 valid responses were received. Through exploratory factor analysis, potential grouping was revealed. Finally, using the practical experience and knowledge of domain experts, a causal model containing seven concepts was established. A root cause analysis method based on fuzzy cognitive map (FCM) was proposed to simulate intelligent transportation construction. The results are as follow： (1) The 20 variables were divided into 6 dimensions: policy support (PS), traffic sector control (TSC), technical support (TS), communication foundation (CF), residents' recognition (RR), and talent quality (TQ); and (2) In the FCM model, all six concept nodes have a significant positive correlation with the target concept node TSC. In terms of correlation strength, the ranking of the six dimensions is TS, CF, PS, TSC, RR, and TQ. The findings of this paper can help academics and practitioners better understand the deep-seated determinants of urban in-telligent transportation construction, and provide valuable references for policy makers. And thus, the efficiency of intelligent transportation construction can be improved.