Stochastic driver compensation for road gradients across vehicle classes: a machine learning approach

Kai Yuan, Hongye Xing, Weihua Zhang, Rui Jiang, Shixiong Jiang

Abstract Slopes often emerge as traffic bottlenecks, yet not all slopes lead to congestion. The relationship between slope capacity and factors like grade and length is complex and non-linear. Accurately estimating road slope capacity and mitigating traffic congestion remain challenges in traffic management. Drivers instinctively adjust vehicle acceleration or braking to counteract gravity, influencing vehicle speed and road capacity. However, traditional models often overlook these compensatory adjustments, leading to inaccurate predictions. This study introduces a novel approach using vehicle trajectory data and the Expectation-Maximization (EM) algorithm to estimate driver compensations on slopes. The algorithm separates observed acceleration into baseline (flat road) and compensatory components. Data from field experiments with cars, trucks, and buses reveal that compensatory acceleration decreases with speed and remains predictable across different slopes. These findings enhance our understanding of slope impacts on traffic flow and provide valuable insights for traffic management and infrastructure design.