A novel dynamically adjusted regressor chain for taxi demand prediction

Taxi is an essential part of urban traffic, accurately predicts the taxi demand, which not only facilitates people's travel but also promotes the further development of the entire smart city. The gap between demand and the actual amount for taxi causes trouble for travelers. Forecasts for taxi demand do not take into account the possible interactions of taxi demand between areas, which can lead to a decrease in the accuracy of the forecast. In further exploiting the interaction of taxi demand in each area, We propose An extended Maximum Correlation Regressor Chain method (MCRC) and a new MCRC-based Dynamically Adjusted Regressor Chain method. MCRC uses the various relationships existing among the targets, which are evaluated using Spearman's rank correlation coefficient, feature importance matrix, and maximal information coefficient, respectively, to form the maximum correlation chain with higher prediction accuracy. Based on MCRC, DARC dynamically adjusts the base-regressor of the regressor chain. A set of predictive approaches are implemented to compare the performances, and the results show that the maximal information coefficient DARC (DARC_MIC) achieves the best accurate rate by 91.80%. DARC_MIC is not only can provide managers a more rational taxi operation approach but also more proper for dealing with multi-target regression problems with Lots of targets. This idea of first measuring the degree of interaction between targets and then combining algorithms to further exploit this degree of interaction between targets can also be attempted to improve many other multi-target regression algorithms.