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

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      Smart Cities

      MDPI AG

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          Abstract

          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.

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          Most cited references 40

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          An introduction to ROC analysis

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            The cell transmission model, part II: Network traffic

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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                Smart Cities
                Smart Cities
                MDPI AG
                2624-6511
                June 2021
                May 24 2021
                : 4
                : 2
                : 864-880
                Article
                10.3390/smartcities4020044
                © 2021
                Product
                Self URI (article page): https://www.mdpi.com/2624-6511/4/2/44

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