Characterization of arterial traffic congestion through analysis of operational parameters (gap acceptance and lane changing).

Gurupackiam, Saravanan; Jones, Steven; Turner, Dan · 2010 · ROSA P / University Transportation Center for Alabama

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Summary

This study investigates the characterization of recurring traffic congestion on signalized urban arterials by analyzing two key operational parameters: gap acceptance and lane changing. The research was motivated by the need to understand how driver behavior changes as traffic transitions from undersaturated to saturated conditions, a factor that significantly impacts the accuracy of microscopic traffic simulation models. Existing models often simplify these parameters, leading to potential inaccuracies when simulating saturated traffic. The project aimed to provide empirical data to enhance these models, specifically focusing on McFarland Boulevard in Tuscaloosa, Alabama, a six-lane corridor experiencing daily recurring congestion. The methodology involved two primary initiatives. First, field data was collected using closed-circuit television cameras and video analysis software to record gap acceptance sizes and lane change durations during AM and PM peak hours and midday periods. Data was categorized into four flow rate bins (10–30, 30–50, 50–70, and 70–90 vehicles per minute of green time). Statistical analysis, including hypothesis testing and distribution fitting, was performed to identify variations in driver behavior across these flow rates. Second, a sensitivity analysis compared observed lane change parameters against embedded parameters in microscopic simulation models (CORSIM, SimTraffic, and AIMSUN) under both free-flow and saturated conditions to assess model accuracy. The findings revealed that driver behavior shifts significantly as traffic flow increases. Statistical analysis confirmed that drivers accept smaller gaps when traffic flow is heavy; the mean accepted gap decreased from 4.33 seconds in the lowest flow bin to 4.04 seconds in the highest (70–90 veh/min). Hypothesis tests indicated that the mean gap at the highest flow rate was significantly smaller than at lower rates. Furthermore, the probability distributions showed that drivers take higher risks in congested conditions, with a higher likelihood of accepting smaller gaps and changing lanes more rapidly. The sensitivity analysis highlighted that existing simulation tools simplify traffic parameters, which may require recalibration to accurately reflect the complex interactions observed in saturated arterial traffic. The significance of this research lies in its contribution to improving the accuracy of microscopic traffic simulation models. By demonstrating that driver behavior is not static but varies with congestion levels, the study provides empirical evidence for recalibrating simulation parameters. This enhances the ability of transportation engineers to model and manage recurring congestion on urban arterials. The findings suggest that current models may underestimate the complexity of saturated traffic flows, and incorporating observed variations in gap acceptance and lane changing can lead to more reliable traffic management strategies and infrastructure planning.

Key finding

Drivers accept smaller gaps and change lanes more rapidly when traffic flow on a signalized arterial approaches saturation compared to moderate flow conditions.

Methodology

naturalistic

Provenance

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Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

Topics

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• lane changing
• traffic density
• gap acceptance
• work zones
• following distance

Information type

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• Empirical Findings: behavioral performance data