Calibrating car‐following parameters for snowy road conditions in the microscopic traffic simulator VISSIM

Asamer, Johannes; van Zuylen, Henk J.; Heilmann, Bernhard · 2013 · OpenAlex-citations

DOI: 10.1049/iet-its.2011.0193

archive: archived pipeline: cataloged verified

Get this paper ↗ (DOI — opens at the source; we link to it, we don't host it)

Summary

This paper addresses the challenge of calibrating microscopic traffic simulation models, specifically VISSIM, to accurately reflect driver behavior under snowy road conditions. Adverse weather alters car-following dynamics, causing drivers to accelerate more slowly, increase following distances, and reduce speeds, which subsequently lowers saturation flow rates at signalized intersections. Accurate simulation of these conditions is critical for optimizing traffic management strategies, such as signal timing and speed adaptation. The study focuses on adapting model parameters to match observed saturation flow rates and start-up delays on through lanes of urban intersections, excluding the influence of snowfall intensity, which previous research indicated had negligible impact on these metrics in low-speed urban environments. The methodology employs a sensitivity analysis followed by a brute-force calibration approach. The researchers used empirical data from three signalized intersections in Vienna, Austria, where saturation flow rates and start-up delays were measured via video recordings during snowy conditions. The target values for calibration were absolute measurements rather than relative reductions from dry conditions. The study utilized the Wiedemann 74 car-following model within VISSIM. A sensitivity analysis, including One-at-a-Time (OAT) variations and the Elementary Effect method, was conducted to identify which parameters significantly influenced the target outputs. Parameters tested included desired speed, desired acceleration, desired deceleration, and minimum following distance components. The results identified three key parameters as sensitive to snowy conditions: desired acceleration, desired speed, and a combined parameter for minimum following distance. Desired deceleration and the range of the desired speed distribution were found to have negligible effects and were excluded from the final calibration. The sensitivity analysis revealed that reducing desired acceleration decreased saturation flow rates and increased start-up delays, while lower desired speeds also reduced saturation flow. By performing a brute-force search across 4,104 parameter combinations, the authors identified a feasible region in the parameter space that reproduced the observed snowy condition metrics (saturation flow rates between 1175–1285 vphgpl and start-up delays between 1.58–2.72 seconds). This feasible region allows for multiple valid parameter settings, providing a constrained subspace for future automated calibration efforts. The significance of this work lies in providing a validated method for adjusting microscopic simulation parameters to winter conditions, thereby improving the reliability of traffic capacity estimates and Intelligent Transportation Systems (ITS) planning. By defining a feasible parameter region, the study enables more accurate evaluation of traffic management strategies under adverse weather. The authors conclude that while the current calibration provides a robust foundation, further investigation into turning lanes and additional data sources, such as GPS tracking, could refine the model further and reduce the ambiguity of parameter selection.

Provenance

The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed.

Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified.

Topics

Ranked by relevance to this paper. Hover a topic for its definition.

• traffic density