Capacity of Freeway Merge Areas with Different On-Ramp Traffic Flow

Shen, Jinxing; Li, Wenquan; Qiu, Feng; Zheng, Shukang · 2015 · DOAJ

DOI: 10.7307/ptt.v27i3.1566

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Summary

This paper investigates the capacity of freeway merge areas, addressing limitations in conventional gap-acceptance models that often assume homogeneous driver behavior and deterministic vehicle arrivals. The authors argue that existing theories fail to account for the randomness of on-ramp vehicle arrivals and the significant impact of individual driving behaviors—classified as aggressive, average, or conservative—on merging efficiency. To improve prediction accuracy, the study develops two enhanced calculation models based on the classical gap-acceptance framework, incorporating probabilistic distributions for vehicle headways and distinct minimum headway requirements for different driver types. The research employs Monte-Carlo simulations to validate these models against theoretical calculations. The experimental design adheres to constraints from the Highway Capacity Manual (HCM) 2010, setting the maximum total freeway volume at 3,600 pcu/h and the volume for the primary lane at 1,200 pcu/h to maintain a Level of Service no worse than F. Two cases were analyzed: Case A assumes on-ramp vehicles arrive randomly with exponentially distributed headways, while Case B incorporates specific headway thresholds for aggressive, average, and conservative drivers. Statistical validity was confirmed using T-tests, ensuring the simulation data reliably represented the proposed models. The results demonstrate that the proposed models outperform conventional gap-acceptance theory in predicting merge area capacity. In Case A, the analysis identified an optimal ramp demand volume of 148 pcu/h; exceeding this threshold did not increase the actual merging volume but significantly increased vehicle delays, suggesting the need for ramp metering. In Case B, the actual merging volume was found to be highly sensitive to the proportion of driver types. Specifically, the capacity varied significantly based on the mix of aggressive and conservative drivers, with regression models showing strong correlations (R values exceeding 0.5) between driver behavior distributions and merging outcomes. The study confirms that ignoring driver heterogeneity and arrival randomness leads to inaccurate capacity estimates. The significance of this work lies in its contribution to more realistic microscopic traffic flow simulation models. By integrating random arrival patterns and differentiated driver behaviors, the findings provide a more practical basis for designing freeway merge areas and developing operational strategies. The results support the implementation of ramp metering when demand exceeds specific thresholds to mitigate delays and highlight the importance of considering behavioral variability in traffic engineering. This approach offers a refined tool for traffic management, enhancing the ability to predict breakdown events and optimize the service quality of freeway merge areas.

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• gap acceptance