Smart City Gnosys
Smart city article details
| Title | A Novel Dynamically Adjusted Regressor Chain For Taxi Demand Prediction |
|---|---|
| ID_Doc | 3322 |
| Authors | Wu Z.; Lian G. |
| Year | 2020 |
| Published | Proceedings of the International Joint Conference on Neural Networks |
| DOI | http://dx.doi.org/10.1109/IJCNN48605.2020.9207160 |
| Abstract | 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. © 2020 IEEE. |
| Author Keywords | multi-target regression; regressor chain; taxi demand; Traffic prediction |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
38444  | 0.878 | Wu M.; Zhu C.; Chen L. | Multi-Task Spatial-Temporal Graph Attention Network For Taxi Demand Prediction | ACM International Conference Proceeding Series (2020) |
| 54463 | 0.878 | Askari B.; Le Quy T.; Ntoutsi E. | Taxi Demand Prediction Using An Lstm-Based Deep Sequence Model And Points Of Interest | Proceedings - 2020 IEEE 44th Annual Computers, Software, and Applications Conference, COMPSAC 2020 (2020) |
| 52828 | 0.872 | Bhanu M.; Priya S.; Moreira J.M.; Chandra J. | St-Agp: Spatio-Temporal Aggregator Predictor Model For Multi-Step Taxi-Demand Prediction In Cities | Applied Intelligence, 53, 2 (2023) |
| 36074 | 0.87 | Munawar A.; Piantanakulchai M. | Machine Learning-Driven Passenger Demand Forecasting For Autonomous Taxi Transportation Systems In Smart Cities | Expert Systems, 42, 3 (2025) |
| 5524 | 0.869 | Xue X.; Zhou C.; Zhang X.; Guo J. | A Taxi Demand Prediction Model Based On Spectral Domain Graph Convolution | Proceedings of SPIE - The International Society for Optical Engineering, 12613 (2023) |
| 21159 | 0.868 | Yang T.; Tang X.; Liu R. | Dual Temporal Gated Multi-Graph Convolution Network For Taxi Demand Prediction | Neural Computing and Applications, 35, 18 (2023) |
| 52523 | 0.859 | Shu P.; Sun Y.; Zhao Y.; Xu G. | Spatial-Temporal Taxi Demand Prediction Using Lstm-Cnn | IEEE International Conference on Automation Science and Engineering, 2020-August (2020) |
| 2199 | 0.858 | Liu X.-H.; Guo L.-M.; Yang B.-W. | A Hybrid Network Model Based On The Construction Of Virtual Service Areas For Taxi Demand Prediction | 4th International Conference on Intelligent Robotics and Control Engineering, IRCE 2021 (2021) |
| 2708 | 0.857 | Boumeddane S.; Hamdad L.; El-Feda Bouregag A.A.; Damene M.; Sadeg S. | A Model Stacking Approach For Ride-Hailing Demand Forecasting : A Case Study Of Algiers | 2020 2nd International Workshop on Human-Centric Smart Environments for Health and Well-Being, IHSH 2020 (2021) |
| 711 | 0.853 | Munawar A.; Piantanakulchai M. | A Collaborative Privacy-Preserving Approach For Passenger Demand Forecasting Of Autonomous Taxis Empowered By Federated Learning In Smart Cities | Scientific Reports, 14, 1 (2024) |