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This analysis reveals traffic patterns in Vellore, emphasizing predictive models to enhance urban mobility and management.
Urban mobility is a significantly increasing challenge in fast-growing cities, where efficient traffic management is crucial to ensure smooth movement and enhance the overall quality of life. The research consists of an in-depth analysis of urban mobility for the city under study using a data-driven approach to address day-to-day traffic challenges. It includes a fusion of traffic flow analysis and vehicle count data with weekend and weekday indicators to develop predictive models. The study evaluates the city’s traffic data by examining peak and off-peak periods. We focus on simple contextual variables – specifically, temporal indicators – our models provide an efficient framework for traffic forecasting in complex environments. The findings underscore that meaningful traffic forecasts can be used to provide practical and scalable solutions for urban planners and administrators to optimize traffic management in rapidly expanding cities.
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Parkavi et al. (2025) studied this question.
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