Abstract
Accurate prediction of bus passenger flow is an important basis for the dynamic scheduling of urban smart transportation system. In order to accurately predict short-term bus passenger flow and help managers achieve efficient dispatching operations and alleviate traffic pressure, a Multi-feature Gradient Boosting Decision Tree (GBDT) model is proposed. Using the flexibility of Gradient Boosting Decision Tree algorithm in complex data processing, a Gradient Boosting Decision Tree basic model is established. Through data analysis and processing, multiple features such as the week, time and environmental factors that related to passenger flow were mined to construct effective feature engineering, which can make the model’s prediction results more accurate. Experiments running on the real data set of Guangzhou show that the multi-feature Gradient Boosting Decision Tree model can predict bus passenger flow accurately and effectively. The MAPE of the model is 1.152%, the RMSE is 4.58, under the same conditions, which are better than the prediction effect of traditionally used models such as the Linear Regression and the BP Neural Network, etc.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Li, X.: Temporal outlier detection in vehicle traffic data. In: Proceedings of the 25th International Conference on Data Engineering. IEEE (2009)
Han, X.: Research on data mining of public transit IC card and application. In: 2010 International Conference on Intelligent Computation Technology and Automation, vol. 2, pp. 1134–1137. IEEE (2010)
Zhao, X.: Spatio-temporal association rules in bus IC card databases. In: 2nd International Conference on Power Electronics and Intelligent Transportation System, vol. 1, pp. 125–128. IEEE (2009)
Qunyong, W.U.: A map reduce-based method for parallel calculation of bus passenger origin and destination from massive transit data. J. Geo-Inf. Sci. 2(5), 99–110 (2018)
Ma, C.: Sensitivity analysis on urban rail transit passenger flow forecast. In: 2011 International Conference on Electric Technology and Civil Engineering, pp. 1537–1541. IEEE (2011)
He, Z.: Station passenger flow forecast for urban rail transit based on station attributes. In: 3rd International Conference on Cloud Computing and Intelligence Systems, pp. 410–414. IEEE (2014)
Bai, Y.: A multi-pattern deep fusion model for short-term bus passenger flow forecasting. Appl. Soft Comput. 58, 669–680 (2017)
Chen, Q.: The use of LS-SVM for short-term passenger flow prediction. Transport 26(1), 5–10 (2011)
Jung, J.: Deep-learning architecture to forecast destinations of bus passengers from entry-only smart-card data. J. Intell. Transp. Syst. 11(6), 334–339 (2017)
Lin, P.-Q.: Short-term traffic flow forecast of toll station based on multi-feature GBDT model. J. Guangxi Univ. (Nat. Sci. Ed.) 43(3) (2018)
Ma, Q., Xu, B., Sun, B., et al.: Terminal access data anomaly detection based on GBDT for power user electric energy data acquisition system. In: Nutzwertanalysen in Marketing und Vertrieb (2019)
Chen, F.: Prediction of luciferase inhibitors by the high-performance MIEC-GBDT approach based on interaction energetic patterns. Phys. Chem. Chem. Phys. (PCCP) 19(15), 10163 (2017)
Ma, X.: Prioritizing influential factors for freeway incident clearance time prediction using the gradient boosting decision trees method. IEEE Trans. Intell. Transp. Syst. 18(9), 2303–2310 (2017)
Zhu, Z.: GBDT based hierarchical model for commodity distribution prediction. J. Beijing Jiaotong Univ. 42(2), 15–19 + 51 (2018)
Weng, X.: GBDT subway IC card commuter crowd identification based on GBDT algorithm. J. Chongqing Jiaotong Univ. (Nat. Sci.) 38(05), 8–12 (2018)
Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 29(5), 1189–1232 (2011)
Schmidt, R.: Leveraging textual information for improving decision-making in the business process lifecycle. In: International Conference on Intelligent Decision Technologies, pp. 563–574. Springe (2017)
Chen, W.: One-hot residue coding for low delay-power product CMOS design. IEEE Trans. Circ. Syst. II Analog Digit. Sig. Process. 45(3), 303–313 (1998)
Benesty, J., Chen, J., Huang, Y.: Pearson Correlation Coefficient//Noise Reduction in Speech Processing. Springer, Berlin (2009)
Acknowledgment
This study is supported by the following projects: The National Natural Science Foundation of China (61662085, 61662065), The Science Research Fund of Yunnan Provincial Department of Education (2017ZZX227), and The Data-Driven Software Engineering Provincial Science and Technology Innovation Team Project of Yunnan University (2017HC012).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Xu, Z., Zhu, R., Yang, Q., Wang, L., Wang, R., Li, T. (2020). Short-Term Bus Passenger Flow Forecast Based on the Multi-feature Gradient Boosting Decision Tree. In: Liu, Y., Wang, L., Zhao, L., Yu, Z. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2019. Advances in Intelligent Systems and Computing, vol 1074. Springer, Cham. https://doi.org/10.1007/978-3-030-32456-8_73
Download citation
DOI: https://doi.org/10.1007/978-3-030-32456-8_73
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32455-1
Online ISBN: 978-3-030-32456-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)