Taxi Demand Prediction Based on a Combination Forecasting Model in Hotspots
Joint Authors
Liu, Zhizhen
Chen, Hong
Zhang, Qi
Li, Yan
Source
Journal of Advanced Transportation
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-07-21
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
Accurate taxi demand prediction can solve the congestion problem caused by the supply-demand imbalance.
However, most taxi demand studies are based on historical taxi trajectory data.
In this study, we detected hotspots and proposed three methods to predict the taxi demand in hotspots.
Next, we compared the predictive effect of the random forest model (RFM), ridge regression model (RRM), and combination forecasting model (CFM).
Thereafter, we considered environmental and meteorological factors to predict the taxi demand in hotspots.
Finally, the importance of indicators was analyzed, and the essential elements were the time, temperature, and weather factors.
The results indicate that the prediction effect of CFM is better than those of RFM and RRM.
The experiment obtains the relationship between taxi demand and environment and is helpful for taxi dispatching by considering additional factors, such as temperature and weather.
American Psychological Association (APA)
Liu, Zhizhen& Chen, Hong& Li, Yan& Zhang, Qi. 2020. Taxi Demand Prediction Based on a Combination Forecasting Model in Hotspots. Journal of Advanced Transportation،Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1175313
Modern Language Association (MLA)
Liu, Zhizhen…[et al.]. Taxi Demand Prediction Based on a Combination Forecasting Model in Hotspots. Journal of Advanced Transportation No. 2020 (2020), pp.1-13.
https://search.emarefa.net/detail/BIM-1175313
American Medical Association (AMA)
Liu, Zhizhen& Chen, Hong& Li, Yan& Zhang, Qi. Taxi Demand Prediction Based on a Combination Forecasting Model in Hotspots. Journal of Advanced Transportation. 2020. Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1175313
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1175313