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<p>Urban intersections in India often suffer from congestion due to mixed traffic conditions and limited capacity. A key factor in intersection performance is the Saturation Flow Rate (SFR), which represents the maximum number of vehicles that can pass through an intersection during the green phase. Traditional methods use fixed vehicle conversion values (PCUs) and assume uniform vehicle behaviour, which may not reflect the complexity of real traffic conditions. This study introduces a data-driven approach to estimating SFR using dynamic PCU values that account for differences in vehicle types, driver behaviour, and location-specific factors such as nearby business activity. Data were collected from five intersections across three Indian cities using video surveys and manual measurements. A linear regression model was developed based on variables including approach width, city tier, distance from business areas, and driver aggressiveness. The model showed strong predictive performance and was validated against field data. Results revealed that wider approaches and more aggressive driving behaviour increase SFR, while proximity to busy areas can reduce it. This method provides more accurate SFR estimates and can help traffic planners design better intersection strategies for mixed traffic environments.</p>

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Empirical assessment of saturation flow rate with dynamic PCUs at signalized intersections under mixed traffic conditions

  • 期刊论文

  • 发表日期：

    2025年7月28日

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  • Kamalnath Maddu,
  • Pratik Potdar,
  • Momi Deb,
  • Suprava Jena

摘要

Urban intersections in India often suffer from congestion due to mixed traffic conditions and limited capacity. A key factor in intersection performance is the Saturation Flow Rate (SFR), which represents the maximum number of vehicles that can pass through an intersection during the green phase. Traditional methods use fixed vehicle conversion values (PCUs) and assume uniform vehicle behaviour, which may not reflect the complexity of real traffic conditions. This study introduces a data-driven approach to estimating SFR using dynamic PCU values that account for differences in vehicle types, driver behaviour, and location-specific factors such as nearby business activity. Data were collected from five intersections across three Indian cities using video surveys and manual measurements. A linear regression model was developed based on variables including approach width, city tier, distance from business areas, and driver aggressiveness. The model showed strong predictive performance and was validated against field data. Results revealed that wider approaches and more aggressive driving behaviour increase SFR, while proximity to busy areas can reduce it. This method provides more accurate SFR estimates and can help traffic planners design better intersection strategies for mixed traffic environments.