A data-driven microscopic on-ramp model based on macroscopic network flows

TL;DR

This paper introduces a microscopic on-ramp traffic model derived from macroscopic network flow data, improving realism and safety analysis capabilities over existing models like IDM.

Contribution

A novel microscopic on-ramp model based on macroscopic traffic flow data, bridging the gap between macro and micro traffic modeling for complex road sections.

Findings

Outperforms IDM in most traffic simulation metrics

Produces more realistic conflict scenarios for safety analysis

Maintains consistency with macroscopic traffic flow predictions

Abstract

While macroscopic traffic flow models consider traffic as a fluid, microscopic traffic flow models describe the dynamics of individual vehicles. Capturing macroscopic traffic phenomena remains a challenge for microscopic models, especially in complex road sections such as on-ramps. In this paper, we propose a microscopic model for on-ramps derived from a macroscopic network flow model calibrated to real traffic data. The microscopic flow-based model requires additional assumptions regarding the acceleration and the merging behavior on the on-ramp to maintain consistency with the mean speeds, traffic flow and density predicted by the macroscopic model. To evaluate the model's performance, we conduct traffic simulations assessing speeds, accelerations, lane change positions, and risky behavior. Our results show that, although the proposed model may not fully capture all traffic phenomena of on-ramps accurately, it performs better than the Intelligent Driver Model (IDM) in most evaluated aspects. While the IDM is almost completely free of conflicts, the proposed model evokes a realistic amount and severity of conflicts and can therefore be used for safety analysis.