Roundabout Capacity Estimation Model Considering Driver Behaviour on the Exiting and Entry Flows

ŽURA, Marijan · 2022 · DOAJ

DOI: 10.7307/ptt.v34i3.3973

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Summary

This study addresses the limitations of existing roundabout capacity estimation models, which often fail to account for the impact of exiting traffic flows and driver behavior on entry lane capacity. Specifically, the research focuses on how drivers exiting a roundabout who do not use right-turn indicators create uncertainty for entering drivers, thereby affecting gap acceptance decisions. The authors aim to develop an analytical model that extends the Brilon-Wu framework by incorporating the share of exiting drivers who disobey traffic rules, thus providing a more accurate estimation of entry capacity and delays. The methodology involves deriving a new analytical capacity equation based on gap acceptance theory and probability distributions. The model assumes that critical gaps for entering drivers follow an Erlang distribution. It distinguishes between two scenarios: drivers with short critical gaps who only consider circulating flow, and those with longer critical gaps who must also assess exiting flow if the distance between the exit and entry points is short. To validate the proposed model, the authors used quasi-observation data generated from microscopic simulations using VISSIM software. The simulations modeled a single-lane roundabout with varying geometric parameters, specifically the distance between the exit and entry points ($l_K$), ranging from 16 to 24 meters. Traffic volumes for entry, circulating, and exiting flows were varied from 0 to 500 PCU/h. The simulation results were compared against established empirical and analytical models, including Bovy’s, Yap’s, and Brilon-Wu’s models. The results demonstrate that the proposed model outperforms existing methods in estimating both capacity and delays. Using the Root Mean Square Error (RMSE) index, the proposed model achieved an RMSE of 0.36, which was lower than the values for Bovy (0.45), Brilon-Wu (0.43), Yap (0.44), and Yap2 (0.42). This represents a reduction in error of 23.4% compared to Bovy’s model and 19.7% compared to Brilon-Wu’s model. Regression analysis further confirmed the model's superior accuracy, showing a higher coefficient of determination ($R^2$) when comparing estimated delays to simulated data. The study found that considering the behavior of exiting drivers, particularly those not signaling, significantly improves the precision of capacity estimates. The significance of this research lies in its contribution to traffic engineering by providing a more robust analytical tool for roundabout design and performance analysis. By accounting for the "pseudo-conflict" caused by exiting vehicles and driver non-compliance with signaling rules, the model offers a more realistic representation of traffic dynamics. This improvement allows for better prediction of entry delays and capacity, which is crucial for optimizing roundabout geometry and managing traffic flow in urban networks. The findings suggest that future capacity estimation models should integrate driver behavior variables to enhance accuracy.

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Topics

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• gap acceptance