Human Car-Following Behavior: Parametric, Machine-Learning, and Deep-Learning Perspectives

Vasebi, Saeed; Hayeri, Yeganeh M.; Jin, Jing

doi:10.1007/978-3-030-50943-9_6

Cited by 2 publications

(6 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In recent years, deep-learning models have shown high performance in regenerating human CF behavior (19)(20)(21)(22)(23)(24)(25)(26)(27)(28). Wang et al proposed a deep-learning based CF model, using a neural network with gated recurrent unit (GRU) cells (23).…”

Section: Ten Trajectory Data From Ngsim Datamentioning

confidence: 99%

“…The model could regenerate the asymmetric characteristics of CF behavior (e.g., hysteresis and intensity difference), and it had a higher accuracy than FVD and RNN models (24). Vasebi et al compared a parametric model (i.e., IDM), two machine learning models (i.e., FNN and RNN), and a deep-learning-with-LSTM model, using the NGSIM dataset (22). The result demonstrated that the deep-learning model has a significantly lower percentage headway error (i.e., 3.2 3 10 27 ) than IDM, FNN, and RNN with 5.8 3 10 22 , 8.9 3 10 23 , and 5.0 3 10 23 , respectively (22).…”

Section: Ten Trajectory Data From Ngsim Datamentioning

confidence: 99%

“…Vasebi et al compared a parametric model (i.e., IDM), two machine learning models (i.e., FNN and RNN), and a deep-learning-with-LSTM model, using the NGSIM dataset (22). The result demonstrated that the deep-learning model has a significantly lower percentage headway error (i.e., 3.2 3 10 27 ) than IDM, FNN, and RNN with 5.8 3 10 22 , 8.9 3 10 23 , and 5.0 3 10 23 , respectively (22). Zhang et al integrated a CF model with a lane-changing model in a deep-learning model with LSTM (28).…”

Section: Ten Trajectory Data From Ngsim Datamentioning

confidence: 99%

“…Conventional data-driven models are not able to learn long-term patterns (e.g., driving style and memory). This unique advantage aids the deep-learning models to achieve a higher accuracy level than traditional CF models (22,25,28).…”

Section: ) Drivers Have Long-term Memory: Deep-learningmentioning

confidence: 99%

“…The deep-learning method is used for this study because multiple studies have shown its outstanding performance in predicting human CF behavior (19)(20)(21)(22)(23)(24)(25)(26)(27)(28). Deep-learning models with LSTM cells have three advantages which make them suitable for CF models: 1) Drivers do not make decisions in a moment:…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Surrounding Vehicles’ Contribution to Car-Following Models: Deep-Learning-Based Analysis

Vasebi

Hayeri

Jin

2021

Transportation Research Record: Journal of the Transportation R

Self Cite

View full text Add to dashboard Cite

Relatively recent increased computational power and extensive traffic data availability have provided a unique opportunity to re-investigate drivers’ car-following (CF) behavior. Classic CF models assume drivers’ behavior is only influenced by their preceding vehicle. Recent studies have indicated that considering surrounding vehicles’ information (e.g., multiple preceding vehicles) could affect CF models’ performance. An in-depth investigation of surrounding vehicles’ contribution to CF modeling performance has not been reported in the literature. This study uses a deep-learning model with long short-term memory (LSTM) to investigate to what extent considering surrounding vehicles could improve CF models’ performance. This investigation helps to select the right inputs for traffic flow modeling. Five CF models are compared in this study (i.e., classic, multi-anticipative, adjacent-lanes, following-vehicle, and all-surrounding-vehicles CF models). Performance of the CF models is compared in relation to accuracy, stability, and smoothness of traffic flow. The CF models are trained, validated, and tested by a large publicly available dataset. The average mean square errors (MSEs) for the classic, multi-anticipative, adjacent-lanes, following-vehicle, and all-surrounding-vehicles CF models are 1.58 × 10−3, 1.54 × 10−3, 1.56 × 10−3, 1.61 × 10−3, and 1.73 × 10−3, respectively. However, the results show insignificant performance differences between the classic CF model and multi-anticipative model or adjacent-lanes model in relation to accuracy, stability, or smoothness. The following-vehicle CF model shows similar performance to the multi-anticipative model. The all-surrounding-vehicles CF model has underperformed all the other models.

show abstract