Taxi Demand Prediction Based on a Combination Forecasting Model in Hotspots

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 comp...

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Bibliographic Details
Published in:Journal of advanced transportation 2020, Vol.2020 (2020), p.1-13
Main Authors: Zhang, Qi, Li, Yan, Chen, Hong, Liu, Zhizhen
Format: Article
Language:English
Subjects:
Algorithms
Deep learning
Demand
Economic forecasting
Feature selection
Humidity
Kalman filters
Methods
Neural networks
Outdoor air quality
Precipitation
Predictions
Regression analysis
Regression models
Short term
Studies
System theory
Taxicabs
Time series
Traffic flow
Transportation
Weather
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Summary: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.
ISSN:0197-6729
2042-3195
DOI:10.1155/2020/1302586