AI on the Road – A Review of Technologies Enhancing Urban Traffic Safety and Efficiency
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Traffic management is becoming increasingly complicated, posing significant challenges to traditional systems. Adapting the intelligent transportation system (ITS) can resolve this issue. The present study identified peer-reviewed literature published between 2014 and 2023 in the most significant libraries, i.e. Google Scholar, Science Direct, ASCE Library, IEEE and others, to address this issue. Following that, 130 primary studies were identified, and the selected literature conducted systematic analysis. Research findings of RQ: 1 revealed that about 30% of total published articles between the year 2014-23 explored both efficiency and safety shows the significance of the study. To achieve the second objective RQ: 2 this study proposes a solution to improve traffic safety and risk management on the city’s key roadways. The study technique involves gathering data from various sources, including traffic cameras, sensors and vehicle tracking devices. Artificial driving (AD) models are capable of accurately predicting traffic patterns. Intelligent network connections facilitate seamless real-time data sharing between vehicles and buildings. This study thoroughly examines safety by analysing collision rates and reaction times and then compares the results to the outcomes when traditional traffic management (TTM) approach were implemented. Furthermore, it deals with exploring adaptive traffic control and predictive analytics as methods to address safety issues proactively. Case studies demonstrate the positive impact of AD and smart network connectivity on enhancing road safety in urban areas. The review concludes with a discussion of issues raised and recommendations for future research to improve safety assessments and risk management in metro regions with shifting arterial traffic flow.
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