# A Microscopic On-Ramp Model Based on Macroscopic Network Flows
## 4 Conclusions  
In this paper, we proposed a microscopic car-following model for on-ramps derived from a macroscopic traffic flow model. The macroscopic model was calibrated based on real traffic data collected at a highway junction in Germany. The microscopic model includes a few additional assumptions for the acceleration and synchronization between the on-ramp and the adjacent lane, which are neglected in macroscopic models, in order to ensure realistic merging behavior at on-ramps. To the best of our knowledge, our model is the first approach to include possibly risky merging behavior from traffic data in a microscopic simulator. By evaluating the proposed model in a traffic simulation, we found that the speeds and accelerations were larger than in real traffic, but they were more realistic than in the Intelligent Driver Model. The distribution of the lane change positions was also more realistic than in the IDM. The proposed model is suitable for road safety analysis, as it creates a realistic amount and severity of conflicts. For example, it can be applied to evaluate different designs of on-ramps (regarding lengths, speed limits, ramp-metering strategies, etc.) in traffic simulations by incorporating fitted on-ramp models of comparable on-ramp situations in microscopic simulations. On a larger scale, the composition of various microscopic merging models of this kind into complex motorway junctions might assist in evaluating the throughput for different architectures. Since the calibration was only performed on the macroscopic level, the accelerations in both the IDM and the flow-based model were larger than in the real data. With microscopic calibration, which involves an expensive fitting of the parameters \(\lambda\) and \(\tau\), a better resemblance between the model and the real traffic might be achieved in future work. As it stands, extensive vehicle trajectory data at on-ramps have been scarce to come by, and the recording and image processing of the considered data have been costly (see Section 2.1). Further validation will be necessary in future work. We note, however, that given suitable data, the presented framework is transferable to similar on-ramps and junctions, at which two road segments merge into a third one, as shown in Figure 2. Extended geometries and different types of junctions require model extensions on the macroscopic scale, as well as more data for their calibration. The current study serves as a proof of concept, as only the flow maximization junction model has been considered; further modeling approaches including data driven neural networks could be considered. Combining the advantages of both macroscopic and microscopic models is a promising approach to obtain models that represent both microscopic driver behavior and macroscopic traffic phenomena well. However, even with thorough calibration, on-ramps remain a challenging road section to model. Figure 6: (left) Mean velocity on the main lane in simulations with varied \(\rho_{max}^{2}\) and in the traffic data. (right) Corresponding densities on the main lane. **Author Contributions:** Conceptualization, N.K., M.B. and E.K. ; Formal analysis, N.K., M.B. and E.K. ; Funding acquisition, M.H. and M.O. ; Investigation, N.K., M.B. and E.K. ; Methodology, N.K., M.B. and E.K. ; Supervision, M.H. and M.O. ; Validation, N.K., M.B. and E.K. ; Visualization, N.K., M.B. and E.K. ; Writing--original draft, N.K., M.B. and E.K. ; Writing--review and editing, M.H. and M.O. All authors have read and agreed to the published version of the manuscript. **Funding:** The work presented in this paper is part of the project NeMo (Neue Ansatze der Verkehsmodellierung unter Berucksichtigung komplexer Geometrien und Daten - New traffic models considering complex geometries and data), project number 461365406, funded by the German Research Foundation (DFG).