Intelligent Transport Systems and their Impact on Urban Mobility

Engineering notes

The use of adaptive algorithms combined with predictive analytics significantly reduced delays and improved trip predictability while decreasing traffic flow randomness.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

Purpose: This research investigated the influence of technology on intelligent transportation systems (ITS). The objectives are to determine the impact of these technologies on urban mobility and to assess the factors influencing traffic congestion and safety. Design/Methodology/Approach: This article is an original study that combines a systematic review of relevant research with an analysis of secondary data. This research uses a combination of system and economic analysis, mathematical modelling, and applied transport analytics. Urban transportation data, driver time-loss profiles, traffic density, and results from adaptive traffic management systems were used to evaluate the efficiency of the ITS systems. An integral of total mobility loss was used to evaluate efficiency, and time-series models and machine learning algorithms were used to make predictions. Results were grouped by the characteristics of the city under study and the type of traffic control method utilised. Research Limitation: The research has limitations because it focuses only on two urban networks and on the traffic data collected during the study. The results may not be generalizable to other cities with different infrastructure or local mobility patterns. Findings: The multi-level ITS showed significant improvements in traffic efficiency compared with single-level ITS systems. The use of adaptive algorithms combined with predictive analytics significantly reduced delays and improved trip predictability while decreasing traffic flow randomness. The system performance was positively correlated to the quality of the technical components of the ITS. The ITS also had a positive impact on traffic safety and environmental outcomes. Practical Implication: The findings of this study will assist urban planners in designing ITS strategies that utilise predictive analytics and adaptive traffic management to maximise urban mobility. Social Implication: Improving ITS can help reduce travel times, decrease travel-related stress, and lower emissions, which will contribute to a higher quality of life for citizens in urban areas. Originality/Value: The original contribution of this work is the evolution of present processes. The article develops an extrapolated strategy for a fully integrated ITS technology combined with a holistic model of urban mobility systems.

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