Data-driven Microscopic Traffic Modelling and Simulation using Dynamic LSTM

Abstract

With the increasing popularity of Digital Twin, there is an opportunity to employ deep learning models in symbiotic simulation system. Symbiotic simulation can replicate multiple what-if simulation instances from its real-time reference simulation (base simulation) for short-term forecasting. Hence, it is a useful tool for just-in-time decision making process. Recent trends on symbiotic simulation studies emphasize on its combination with machine learning. Despite its success and usefulness, very few works focus on application of such a hybrid system in microscopic traffic simulation. Existing application of machine (deep) learning models in microscopic traffic simulation is confined to either predictive analysis or offline simulation-based prescriptive analysis. Thus, there is also lack of work on updating parameters of a deep learning model dynamically for real-time traffic simulation. This is necessary if the learning-based model is to be used as part of the base simulation so that "Just-in-time (JIT)" what-if simulation initialized from the model can make better short-term forecasts. This paper proposes a data-driven modelling and simulation framework to dynamically update parameters of Long Short-term Memory (LSTM) for JIT microscopic traffic simulation. Extensive experiments were carried out to demonstrate its effectiveness in terms of more accurate short-term forecasting than other baseline models.