A car-following model with behavioural adaptation to road geometry

TL;DR

This paper develops a car-following model that incorporates behavioural adaptation to road curvature, improving simulation accuracy by including spatial anticipation and curvature-dependent desired speeds, validated with real traffic data.

Contribution

It introduces a novel car-following model that accounts for road geometry effects and spatial anticipation, enhancing traffic microsimulation realism.

Findings

Significant improvement in free-flow traffic modeling.

Enhanced accuracy in high-speed car-following scenarios.

Highlighting the need for more extensive trajectory data.

Abstract

Understanding the effect of road geometry on human driving behaviour is essential for both road safety studies and traffic microsimulation. Research on this topic is still limited, mainly focusing on free-flow traffic and not adequately considering the influence of curvature on car-following dynamics. This work attempts to investigate this issue and model the adaptation of car-following behaviour to horizontal curvature. For this purpose, the maximum desired speed - which mainly determines the free-flow dynamics - is expressed as a parsimonious function of the curvature. A spatial anticipation mechanism is also included in order to realistically describe the driving behaviour when approaching or exiting from curves. The accuracy of the augmented model is evaluated using the Modified Intelligent Driver Model (M-IDM) and trajectory data from free-flow and car-following traffic (Naples data and Zen Traffic Data). The results show that a significant improvement is achieved in free-flow dynamics. In car-following situations, improvements are mainly observed at high speed and are dependent on the observed driver. Overall, the analysis highlights the lack of sufficiently spatially extended trajectory data to calibrate and evaluate such driving behaviours.