@article{ref1,
title="A traffic flow simulation framework for learning driver heterogeneity from naturalistic driving data using autoencoders",
journal="International journal of automotive engineering",
year="2019",
author="Yurtsever, Ekim and Miyajima, Chiyomi and Takeda, Kazuya",
volume="10",
number="1",
pages="86-93",
abstract="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 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 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.<p /> <p>Language: en</p>",
language="en",
issn="2185-0984",
doi="10.20485/jsaeijae.10.1_86",
url="http://dx.doi.org/10.20485/jsaeijae.10.1_86"
}