Influence of Autonomous Vehicles on Freeway Traffic Performance for Undersaturated Traffic Conditions

ElSahly, Osama; Abdelfatah, Akmal · 2026 · Crossref

DOI: 10.30958/ajte.7-2-3

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Summary

This study evaluates the impact of autonomous vehicles (AVs) on freeway traffic performance under undersaturated conditions, motivated by the anticipated transition to smart driving technologies and specific adoption goals in Dubai. The research addresses how varying market penetration rates of AVs affect traffic metrics when mixed with conventional vehicles (CVs). The authors aim to quantify improvements in average speed, travel time, and delay as AVs replace CVs, focusing on two demand-to-capacity ratios of 0.6 and 0.8 to represent low-congestion scenarios. The methodology employs the microsimulation software VISSIM to model a 10-km stretch of a major five-lane freeway in Dubai. The simulation accounts for six junctions and assumes a lane capacity of 2,000 vehicles per hour. To distinguish AV behavior from CVs, the model adjusts driving parameters to reflect AV characteristics: smaller standstill distances, reduced time headways, strict adherence to desired speeds, and uniform acceleration and deceleration. The study simulates 18 scenarios across ten AV market penetration rates (0% to 100%) and the two specified demand-to-capacity ratios. Each scenario undergoes five simulation runs with different random seeds, and results are derived from the trimmed average of the middle three values. Statistical significance is assessed using dependent and independent t-tests. The results demonstrate that increasing AV market penetration consistently improves traffic performance. Average speed increases ranged from approximately 5% to 15%, while travel time reductions ranged from 1% to 12%. Delay reductions were more pronounced, ranging from 18% to 97%. The maximum improvements in speed and travel time occurred at a 100% AV penetration rate with a 0.8 demand-to-capacity ratio. Conversely, the highest delay reduction (approximately 97%) was observed at a 0.6 demand-to-capacity ratio with 100% AVs. Statistical t-tests confirmed that average speed differences between consecutive penetration scenarios and between AVs and CVs within the same scenario were statistically significant. The authors attribute these gains to AVs maintaining constant speeds and smaller headways, which smooths traffic flow and increases network capacity. The significance of this research lies in its quantification of AV benefits under specific traffic conditions, supporting the argument that AVs enhance efficiency even at low penetration rates. The findings suggest that positive impacts are more pronounced as congestion increases (higher demand-to-capacity ratios), consistent with prior literature. The study concludes that AVs can significantly reduce delay and improve speed by eliminating human error variability in driving behavior. However, the authors note limitations, including the lack of real-world data calibration and the exclusion of oversaturated conditions, safety analyses, and system failure scenarios. Future work is recommended to expand these simulations to peak-hour congestion, arterial networks, and intersection dynamics to provide a comprehensive assessment of AV integration.

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• traffic density