A Traffic Flow Simulation Framework for Learning Driver Heterogeneity from Naturalistic Driving Data using Autoencoders

A Traffic Flow Simulation Framework for Learning Driver Heterogeneity from Naturalistic Driving Data using Autoencoders

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This paper proposes a novel data-centric framework for microscopic traffic flow simulation with intra and inter driver heterogeneity.We utilized a naturalistic driving corpus of 46 different drivers to learn and model the behavior divergence of Japanese drivers.First, ego-driver behavior signals are used to extract unique features of each driver with an auto-encoder.Then, using Boyfriend Jean these features, drivers are divided into groups using unsupervised clustering algorithms.For each driver group, a feedforward neural network is trained for predicting the desired speed given the road topology.

The YOUTH WETSUITS trained network is then used in a microscopic traffic flow model for simulations.We used a macroscopic traffic survey conducted in Japan to evaluate the proposed framework.Our findings indicate that the proposed framework can simulate a realistic traffic flow with high driver heterogeneity.