Analysis of Stochasticity and Heterogeneity of Car-Following Behavior Based on Data-Driven Modeling

Analysis of Stochasticity and Heterogeneity of Car-Following Behavior Based on Data-Driven Modeling

Shiomi, Y. (Ritsumeikan University, Biwako-Kusatsu)
Li, G. (TU Delft Transport and Planning)
Knoop, V.L. (TU Delft Transport and Planning)

2023

Traffic dynamics on freeways are stochastic in nature because of errors in perception and operation of drivers as well as the heterogeneity between and within drivers. This stochasticity is often represented in car-following models by a stochastic term, which is assumed to follow a normal distribution for the convenience of mathematical processing. However, the validity of this assumption has not been studied yet. In this study, we focused on the shape of the distribution of a stochastic term in the car-following model that predicts an acceleration after a time step. Based on vehicle trajectory data on a freeway in Japan, a car-following model is first developed by using data-driven methodology in which long short-term memory (LSTM) network is applied. In this LSTM network, the acceleration value is discretized and the model parameters are trained with the focal loss function. The relationship between the predicted distributions’ modality, standard deviation (SD), and (Formula presented.) with respect to traffic states is then examined. The findings demonstrate that: 1) the developed model can accurately predict the accelerations; 2) a probabilistic distribution tends to have a large SD and multimodality around a merging point and at the beginning of and along stop-and-go waves; and 3) driving behavior can be classed in one of four clusters based on the variation of the percentile value that a driver takes within the probability distribution. The proposed model and the insights are helpful for improving microscopic simulation models when considering new traffic management measures.

car-following
microscopic traffic models
models
operations
simulation
traffic flow

http://resolver.tudelft.nl/uuid:29e7c611-85f1-40c4-bc48-422b146d5df4

https://doi.org/10.1177/03611981231169279

2023-11-22

0361-1981

Transportation Research Record, 2677 (12), 604-619

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

journal article

© 2023 Y. Shiomi, G. Li, V.L. Knoop