A

A Microscopic On-Ramp Model Based on Macroscopic Network Flows

Applied Sciences, vol. 14, pp. 9111

Abstract

While macroscopic traffic flow models adopt a fluid dynamic description of traffic, microscopic traffic flow models describe the dynamics of individual vehicles. Capturing macroscopic traffic phenomena accurately remains a challenge for microscopic models, especially in complex road sections. Based on a macroscopic network flow model calibrated to real traffic data and new rules for the acceleration and merging behavior on the on-ramp, we propose a microscopic model for on-ramps. To evaluate the performance of the new flow-based model, we conduct traffic simulations assessing speeds, accelerations, lane change positions, and risky behavior. Our results show that, although the proposed model exhibits some limitations, its performance is superior to the Intelligent Driver Model in the evaluated aspects. While the Intelligent Driver Model simulations are almost free of conflicts, the proposed model evokes a realistic amount and severity of conflicts and therefore can be considered for safety analysis.

Authors 5

  1. RWTH Aachen University

    Affiliation as printed

    Institute of Geometry and Practical Mathematics, RWTH Aachen University, 52062 Aachen, Germany

  2. RWTH Aachen University

    Affiliation as printed

    Institute of Highway Engineering, RWTH Aachen University, 52062 Aachen, Germany

  3. RWTH Aachen University

    Affiliation as printed

    Institute of Highway Engineering, RWTH Aachen University, 52062 Aachen, Germany

  4. RWTH Aachen University

    Affiliation as printed

    Institute of Geometry and Practical Mathematics, RWTH Aachen University, 52062 Aachen, Germany

  5. Federal Highway and Transport Research Institute · RWTH Aachen University

    Affiliation as printed

    Federal Highway Research Institute, 51427 Bergisch Gladbach, Germany

    Institute of Highway Engineering, RWTH Aachen University, 52062 Aachen, Germany

Cited by 3 stored of 3

3 results

No patents citing this paper on Lens.org (checked 2026-10-06).

References 51