Demand Forecasting of Online Car-Hailing With Stacking Ensemble Learning Approach and Large-Scale Datasets

Abstract

With the rapid development and convenient service of online car-hailing, it has gradually become the preferred choice for people to travel. Accurate forecasting of car-hailing trip demand not only enables the drivers and companies to dispatch the vehicles and increase the mileage utilization, but also reduces the passengers' waiting-time. The rebalance of spatiotemporal demand and supply could mitigate traffic congestion, reduce traffic emission, and guide people's travel patterns. This study aimed to develop a short-term demand forecasting model for car-hailing services using stacking ensemble learning approach. The spatial-temporal characteristics of online car-hailing demand were analyzed and extracted through data analysis. The region-level spatial characteristics, time features, and weather conditions were added into the forecasting model. Then the stacking ensemble learning model was developed to predict the car-hailing demand at region-level for different time intervals, including 10 min, 15 min, and 30 min. The validation results suggested that the proposed stacking ensemble learning model has reasonable good prediction accuracy for different time intervals. The comparison results show that the short-term demand forecasting model based on stacking ensemble learning is better than single LSTM, SVR, lightGBM and Random Forest models. MAE and RMSE increased by 6.0% and 5.2% respectively at 30 min time interval, which further verifies the effectiveness and feasibility of the proposed model. INDEX TERMS stacking ensemble learning approach, online car-hailing, feature selection.

Proposing Machine Learning Models Suitable for Predicting Open Data Utilization

“…Stacking ensembles are constructed by combining various different single models to achieve better performance, enabling the use of the strengths of each algorithm and the compensation for their corresponding weaknesses. That is, it aims to create a better performing model by combining different models [79]. In this study, single models (random forest, XGBoost, LightGBM, and CatBoost) were used to form a stacking ensemble, and GBM was used as the metamodel for the final prediction [67,80,81].…”

Understanding Travel Mode Choice Behavior: Influencing Factors Analysis and Prediction with Machine Learning Method

“…The random forest method is a bagging method that uses both bagging and feature randomness to construct a multitude of decision trees [58]. The CART model is the base learner of the random forest method.…”

Demand Forecasting of Online Car-Hailing with Combining LSTM + Attention Approaches

“…The relevant research emphasized the historical data mining from temporal or spatial features (e.g., [2][3][4]10]). Although a small number of studies combine the spatiotemporal correlation of online car-hailing features into its demand forecasting model, the demand forecasting of online car-hailing with spatiotemporal features is ignored.…”

“…However, the uncertain spatial and temporal distributions of online car-hailing and passengers still cause problems, with taxis being in short supply or at a surplus in different periods and areas. The problems of imbalance increase the driving mileage of empty taxis and passenger waiting time [2]. In order to promote the efficiency of online car-hailing and provide a convenient service for passengers, it is necessary to narrow the gap between supply and demand.…”