Traffic congestion has widespread negative impacts on the environment, urban development, and road safety, leading to increased commute times and heightened incidents of road rage and accidents. Evaluating congestion, particularly in relation to speed humps, becomes crucial due to their complex impact on traffic flow. Although few studies have explored delay estimation and lane-changing behaviour at speed humps, the larger issue of traffic congestion has received less attention. Recognizing and measuring congestion levels at these humps can be pivotal in devising specified strategies to alleviate the challenge. The present investigation focused on adapting travel time reliability metrics, specifically the Planning Time Index (PTI) and Travel Time Index (TTI), to consider the influence of speed humps. These adjusted metrics have been used to assess congestion in two critical zones: the area before the speed humps where vehicles slow down and the sections covering the humps. The study took a comprehensive approach by using video analysis to gather data on various vehicles operating on the road. Subsequently, the PTI and TTI were analyzed for their relationships with different speed percentiles (98th, 85th, and 15th). The findings revealed compelling correlations allying PTI, TTI, the 15th and 85th percentile speeds, surpassing the relation with the 98th percentile speed. This analysis formed the basis for a congestion severity index, outlining distinct congestion levels. The study employed K-means clustering, ensuring a logical and data-driven categorization of congestion severity at speed humps. To sum up, this research not only enhances our understanding of traffic congestion at speed humps but also lays the groundwork for implementing targeted measures to effectively mitigate these issues.
Development of Congestion Severity Index for Speed Humps Utilizing Fundamental Parameters and Clustering Techniques - A Case Study in India
2025
Article (Journal)
Electronic Resource
Unknown
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