In recent years, machine learning technology, with its powerful data-driven capability, has shown a broad application prospect in intelligent transportation. This paper adopts a systematic literature review approach to comprehensively analyze the application of machine learning in intelligent transportation systems, including such key areas as traffic congestion management, traffic safety enhancement, public transportation resource optimization, and environmental pollution control. It is found that advanced algorithms represented by Neural Network and Graph Neural Network have made breakthroughs in spatio-temporal prediction of traffic flow, accident risk assessment, and public transport scheduling optimization. Machine learning technology promotes the intelligent transportation system to realize the transformation from theoretical innovation to practice, which not only significantly improves the operational efficiency of the existing transportation infrastructure but also provides a new paradigm for solving urban transportation management problems.
Zhang et al. (Mon,) studied this question.
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