Taxi travel time prediction based on fusion of traffic condition features. (January 2023)

• Record Type: Journal Article
• Authors: Sheng, Zhaoyu; Lv, Zhiqiang; Li, Jianbo; Xu, Zhihao; Sun, Haokai; Liu, Xue; Ye, Rongkun
• Is Part Of: Computers & electrical engineering. Volume 105(2023)
• Publication Date: 2023-01
• Subjects: Travel time prediction -- Traffic condition -- Hadamard product -- Spatial correlation -- Temporal dependence; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2022.108530
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: The research on travel time prediction has shown its importance and research significance in rational planning of travel arrangements and alleviating road congestion. As the level of urbanization increases, the number of cars has increased sharply. Traffic congestion has gradually become a norm in many first-tier and medium-sized cities. Existing models lack effective traffic information, especially traffic condition, to predict taxi travel time. The accuracy of the research model in urban areas is low. This will affect passenger travel arrangements. This paper defines urban congestion and taxi flow to represent traffic condition. A method for capturing traffic condition is proposed, and traffic condition and trajectory data are merged. Secondly, this work integrates two indirect factors of departure time and travel date to improve the accuracy of the model. The experimental results show that our model has some advantages over some classical models in travel time prediction.