Microscopic Vehicular Traffic Simulation: Toward Online Calibration.
Winter Simulation Conference(2023)
摘要
The modern world requires accurate and efficient traffic modeling to facilitate commerce and ensure citizens’ safety. Traffic simulations play an important role in this endeavor by allowing traffic engineers to test traffic systems and policies before implementing them. This requires traffic simulation models that have the ability to accurately represent real-world traffic systems, and which are also capable of re-calibrating model parameters when needed through online calibration. This work presents four contributions toward this endeavor. The data science system SCALATION was extended with agent-based modeling and makes use of virtual threads for each vehicle, which improves the efficiency of simulations. The modeling, simulating, and data loading schema were all optimized to enhance the system performance as well. Additionally, a new arrival model strategy was implemented improving the accuracy of the model calibration phase.
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关键词
Traffic Flow,Traffic Simulation,Simulation Model,Multi-agent,Traffic System,Traffic Model,Statistical Models,Artificial Neural Network,Optimization Algorithm,Evaluation Of Function,Mean Absolute Error,Long Short-term Memory,Autoregressive Model,Optimization Procedure,Stochastic Gradient Descent,Poisson Process,Urban Network,Stochastic Approximation,Lane Change,Digital Twin,Traffic Forecasting,Performance Measurement Systems,Driver Model,Inter-arrival Time,Forecast Values,Calibrated Model Parameters,Examples Of Methods,Online Learning,Car Speed,Forecasting Model
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