Multiple-Feature-Based Vehicle Supply–Demand Difference Prediction Method for Social Transportation

This paper presents a multi-vehicle driving method based on fuzzy decision. Fuzzy decision-making is used to realize the process of multi vehicle merging to observe how he turns and joins other teams successfully. Establish the multi-vehicle driving fleet model, and initialize its relevant data, mainly including the speed of multi vehicle driving, the distance between vehicles in the multi vehicle driving fleet and the location from the intersection. Establish the model and data of the vehicle to be inserted into the multi vehicle driving team, mainly including its speed, the position of the vehicle to the intersection, and the intersection of the queue jumping is a fixed T-junction, and the whole process of fleet merging is implemented The method proposed in the paper can be applied in practice.

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Tabular Learning-Based Traffic Event Prediction for Intelligent Social Transportation System

Sun

Cao

et al. 2023

IEEE Trans. Comput. Soc. Syst.

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Multiple-Feature-Based Vehicle Supply–Demand Difference Prediction Method for Social Transportation

Cited by 9 publications

References 25 publications

Gated ensemble of spatio-temporal mixture of experts for multi-task learning in ride-hailing system

Gated ensemble of spatio-temporal mixture of experts for multi-task learning in ride-hailing system

Multi-vehicle mixed formation driving method based on fuzzy decision

Tabular Learning-Based Traffic Event Prediction for Intelligent Social Transportation System

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