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<p>This study investigates the combined impact of roadside interactions-specifically on-street parking and pedestrian flow-and traffic parameters on vehicle operating speeds along the main highway in Finote Selam Town, Ethiopia. While urban traffic flow is well-researched, the interplay between parking, pedestrians, and vehicles remains poorly understood in low- and middle-income contexts. Using empirical mixed-traffic data, macroscopic modeling, and regression analysis, this research fills that gap. The Greenberg model demonstrated the strongest fit (R<sup>2</sup> = 0.695), estimating a free-flow speed of 66.99&#xa0;km/h and a jam density of 78.24 veh/km. The study found that on-street parking significantly reduces effective capacity: legal parking by 23% and double parking by 27%, culminating in a total 50% loss. Pedestrian flow was the dominant factor reducing speed (0.564&#xa0;km/h per additional pedestrian), followed by parking occupancy and traffic flow. A quadratic model best captured the speed-flow relationship (R<sup>2</sup> = 0.8), revealing a significant concave curve where speed reduction tapers off at high volumes-consistent with platooning and saturation. This nonlinear dynamic reflects congestion behavior and capacity-drop phenomena. Sensitivity analysis confirmed the robustness of this relationship and showed that peak combined conditions of parking, pedestrians, and traffic can reduce speeds by up to 75%, while moderate interactions cause a 34.4% decline, indicating that congestion occurs well before saturation. These findings extend classical speed-flow-density theory by integrating the lateral friction from pedestrians and parked vehicles, underscoring the limitations of traditional models in mixed-traffic environments. Practically, the results support integrated policy strategies, including parking regulation, enhanced pedestrian infrastructure, park-and-ride systems, and adaptive traffic management. The analytical framework offers a transferable approach for similar urban settings across Sub-Saharan Africa and South Asia, aiding evidence-based strategies to improve corridor efficiency, safety, and multimodal mobility.</p>

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Modeling and analyzing of vehicle speed under roadside interactions and traffic stream parameters along the main highway in Finote Selam, Ethiopia

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月10日

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  • Gedefaye Geremew

摘要

This study investigates the combined impact of roadside interactions-specifically on-street parking and pedestrian flow-and traffic parameters on vehicle operating speeds along the main highway in Finote Selam Town, Ethiopia. While urban traffic flow is well-researched, the interplay between parking, pedestrians, and vehicles remains poorly understood in low- and middle-income contexts. Using empirical mixed-traffic data, macroscopic modeling, and regression analysis, this research fills that gap. The Greenberg model demonstrated the strongest fit (R2 = 0.695), estimating a free-flow speed of 66.99 km/h and a jam density of 78.24 veh/km. The study found that on-street parking significantly reduces effective capacity: legal parking by 23% and double parking by 27%, culminating in a total 50% loss. Pedestrian flow was the dominant factor reducing speed (0.564 km/h per additional pedestrian), followed by parking occupancy and traffic flow. A quadratic model best captured the speed-flow relationship (R2 = 0.8), revealing a significant concave curve where speed reduction tapers off at high volumes-consistent with platooning and saturation. This nonlinear dynamic reflects congestion behavior and capacity-drop phenomena. Sensitivity analysis confirmed the robustness of this relationship and showed that peak combined conditions of parking, pedestrians, and traffic can reduce speeds by up to 75%, while moderate interactions cause a 34.4% decline, indicating that congestion occurs well before saturation. These findings extend classical speed-flow-density theory by integrating the lateral friction from pedestrians and parked vehicles, underscoring the limitations of traditional models in mixed-traffic environments. Practically, the results support integrated policy strategies, including parking regulation, enhanced pedestrian infrastructure, park-and-ride systems, and adaptive traffic management. The analytical framework offers a transferable approach for similar urban settings across Sub-Saharan Africa and South Asia, aiding evidence-based strategies to improve corridor efficiency, safety, and multimodal mobility.