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Calibration of parameters in microscopic traffic flow simulation models considering micro-meteorological information
Jian Ma1, Yuchen Zhang1, Liyan Zhang1
1School of Civil Engineering, Suzhou University of Science and Technology, Suzhou, China.
Plos One
|July 7, 2025
Summary
This study enhances car following models by incorporating micro-meteorological factors, improving the Intelligent Driver Model (IDM) for better rainy-day traffic management. The improved Intelligent Driver Model (I-IDM) shows superior accuracy in simulating driver behavior compared to the I-Wiedemann99 model.
Area of Science:
- Transportation Engineering
- Traffic Flow Theory
- Intelligent Transportation Systems (ITS)
Background:
- Micro-meteorological conditions significantly influence driver judgment and vehicle following behavior.
- Existing car-following models lack sufficient detail regarding the impact of specific micro-meteorological factors, especially on rainy days.
- Reduced road traction in rain increases hydroplaning risk and traffic accidents, necessitating improved traffic management strategies.
Purpose of the Study:
- To investigate the impact of micro-meteorological conditions on driver behavior and car-following dynamics.
- To enhance existing car-following models, specifically the Intelligent Driver Model (IDM) and Wiedemann99, to account for micro-meteorological influences.
- To develop and validate improved models (I-IDM and I-Wiedemann99) for more precise real-time vehicle state monitoring and intelligent traffic management.
Main Methods:
- Modified the Intelligent Driver Model (IDM) and Wiedemann99 models by introducing a driver's judgment factor (λ).
- Developed new models: Improved Intelligent Driver Model (I-IDM) and Improved Wiedemann99 (I-Wiedemann99).
- Utilized simulation validation with speed and following distance as performance indicators for parameter calibration, employing the sum of Root Mean Square Percentage Error (RMSPE) as the goodness-of-fit function.
Main Results:
- The improved I-IDM model demonstrated smaller average error and standard deviation compared to the I-Wiedemann99 model.
- Parameter calibration for the I-IDM model showed a maximum RMSPE of 0.4568 and a minimum of 0.1324.
- Parameter calibration for the I-Wiedemann99 model resulted in more dispersed results than the I-IDM, with a maximum RMSPE of 0.4613 and a minimum of 0.1376.
Conclusions:
- The I-IDM model provides a more effective simulation of following behavior under micro-meteorological conditions than the I-Wiedemann99 model.
- The enhanced models offer a theoretical basis for improving car-following theories and supporting IoT-based traffic management systems.
- Findings support refined traffic management strategies, particularly for adverse weather conditions like rain.
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