Taxi Demand and Fare Prediction with Hybrid Models: Enhancing Efficiency and User Experience in City Transportation

Taxi Demand and Fare Prediction with Hybrid Models: Enhancing Efficiency and User Experience in City Transportation

An essential part of a city’s transportation infrastructure, taxis allow for regular encounters between drivers and customers. Nevertheless, there are issues with efficiency since there is an imbalance in the supply and demand for taxis. This study describes the creation of a platform that serves bo...

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Bibliographic Details
Main Authors: Ka Seng Chou, Kei Long Wong, Boliang Zhang, Davide Aguiari, Sio Kei Im, Chan Tong Lam, Rita Tse, Su-Kit Tang, Giovanni Pau
Format: Article
Language:English
Published: MDPI AG 2023-09-01
Series:Applied Sciences
Subjects:
taxi demand
taxi fare
LSTM-MDN
ensemble learning
intelligent transportation
Online Access:https://www.mdpi.com/2076-3417/13/18/10192

Internet

https://www.mdpi.com/2076-3417/13/18/10192

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