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Article

Macroscopic Lane Change Model—A Flexible Event-Tree-Based Approach for the Prediction of Lane Change on Freeway Traffic

1
School of Engineering, Monash University, Bandar Sunway, Selangor 47500, Malaysia
2
Graduate School of Science and Technology, Gunma University, Kiryu 376-8515, Japan
*
Author to whom correspondence should be addressed.
Academic Editor: Frank Witlox
Received: 18 April 2021 / Revised: 20 May 2021 / Accepted: 20 May 2021 / Published: 24 May 2021
Binary logistic regression has been used to estimate the probability of lane change (LC) in the Cell Transmission Model (CTM). These models remain rigid, as the flexibility to predict LC for different cell size configurations has not been accounted for. This paper introduces a relaxation method to refine the conventional binary logistic LC model using an event-tree approach. The LC probability for increasing cell size and cell length was estimated by expanding the LC probability of a pre-defined model generated from different configurations of speed and density differences. The reliability of the proposed models has been validated with NGSIM trajectory data. The results showed that the models could accurately estimate the probability of LC with a slight difference between the actual LC and predicted LC (95% Confidence Interval). Furthermore, a comparison of prediction performance between the proposed model and the actual observations has verified the model’s prediction ability with an accuracy of 0.69 and Area Under Curve (AUC) value above 0.6. The proposed method was able to accommodate the presence of multiple LCs when cell size changes. This is worthwhile to explore the importance of such consequences in affecting the performance of LC prediction in the CTM model. View Full-Text
Keywords: logistic regression; cell size; multiple lane changes; cell transmission model logistic regression; cell size; multiple lane changes; cell transmission model
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MDPI and ACS Style

Ng, C.; Susilawati, S.; Kamal, M.A.S.; Leng, I.C.M. Macroscopic Lane Change Model—A Flexible Event-Tree-Based Approach for the Prediction of Lane Change on Freeway Traffic. Smart Cities 2021, 4, 864-880. https://0-doi-org.brum.beds.ac.uk/10.3390/smartcities4020044

AMA Style

Ng C, Susilawati S, Kamal MAS, Leng ICM. Macroscopic Lane Change Model—A Flexible Event-Tree-Based Approach for the Prediction of Lane Change on Freeway Traffic. Smart Cities. 2021; 4(2):864-880. https://0-doi-org.brum.beds.ac.uk/10.3390/smartcities4020044

Chicago/Turabian Style

Ng, Christina, Susilawati Susilawati, Md A.S. Kamal, and Irene C.M. Leng. 2021. "Macroscopic Lane Change Model—A Flexible Event-Tree-Based Approach for the Prediction of Lane Change on Freeway Traffic" Smart Cities 4, no. 2: 864-880. https://0-doi-org.brum.beds.ac.uk/10.3390/smartcities4020044

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