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http://dx.doi.org/10.4218/etrij.2021-0123

Taxi-demand forecasting using dynamic spatiotemporal analysis  

Gangrade, Akshata (Department of Computer Science and Engineering, Maulana Azad National Institute of Technology)
Pratyush, Pawel (Department of Computer Science and Engineering, Maulana Azad National Institute of Technology)
Hajela, Gaurav (Department of Computer Science and Engineering, Maulana Azad National Institute of Technology)
Publication Information
ETRI Journal / v.44, no.4, 2022 , pp. 624-640 More about this Journal
Abstract
Taxi-demand forecasting and hotspot prediction can be critical in reducing response times and designing a cost effective online taxi-booking model. Taxi demand in a region can be predicted by considering the past demand accumulated in that region over a span of time. However, other covariates-like neighborhood influence, sociodemographic parameters, and point-of-interest data-may also influence the spatiotemporal variation of demand. To study the effects of these covariates, in this paper, we propose three models that consider different covariates in order to select a set of independent variables. These models predict taxi demand in spatial units for a given temporal resolution using linear and ensemble regression. We eventually combine the characteristics (covariates) of each of these models to propose a robust forecasting framework which we call the combined covariates model (CCM). Experimental results show that the CCM performs better than the other models proposed in this paper.
Keywords
combined covariates model; ensemble regression models; linear regression; spatiotemporal analysis; taxi demand forecasting;
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1 X. Guo, Prediction of taxi demand based on CNN-BiLSTM-Attention neural network, Neural information processing, Cham, 2020, pp. 331-342.
2 Z. Liu, H. Chen, X. Sun, and H. Chen, Data-driven real-time online taxi-hailing demand forecasting based on machine learning method, Appl. Sci. 10 (2020), no. 19, 6681.   DOI
3 U. Vanichrujee, T. Horanont, W. Pattara-atikom, T. Theeramunkong, and T. Shinozaki, Taxi demand prediction using ensemble model based on RNNs and xgboost, (International Conference on Embedded Systems and Intelligent Technology International Conference on Information and Communication Technology for Embedded Systems, Khon Kaen, Thailand), 2018, pp. 1-6.
4 H. Luo, J. Cai, K. Zhang, R. Xie, and L. Zheng, A multi-task deep learning model for short-term taxi demand forecasting considering spatiotemporal dependences, J. Transp. Eng. (English Edition) 8 (2021), no. 1, 83-94.   DOI
5 C. Antoniades, D. Fadavi, and A. F. Amon, Fare and duration prediction: A study of New York City taxi rides, 2016.
6 A. Safikhani, C. Kamga, S. Mudigonda, S. S. Faghih, and B. Moghimi, Spatio-temporal modeling of yellow taxi demands in New York City using generalized STAR models, Int. J. Forecasting 36 (2020), no. 3, 1138-1148.   DOI
7 P. Shu, Y. Sun, Y. Zhao, and G. Xu, Spatial-temporal taxi demand prediction using LSTM-CNN, (IEEE 16th International Conference on Automation Science and Engineering, Hong Kong, China), Aug. 2020, pp. 1226-1230.
8 T. Kim, S. Sharda, X. Zhou, and R. M. Pendyala, A stepwise interpretable machine learning framework using linear regression (LR) and long short-term memory (LSTM): City-wide demand-side prediction of yellow taxi and for-hire vehicle (FHV) service, Transp. Res. C: Emerg. Technol. 120 (2020), 102786.   DOI
9 Q. Liu, C. Ding, and P. Chen, A panel analysis of the effect of the urban environment on the spatiotemporal pattern of taxi demand, Travel Behav. Soc. 18 (2020), 29-36.   DOI
10 C. Yang and E. J. Gonzales, Modeling taxi demand and supply in New York City using large-scale taxi GPS data, Seeing cities through big data: Research, methods and applications in urban informatics, Springer International Publishing, Cham, 2017, pp. 405-425.
11 Z. Liu, H. Chen, Y. Li, and Q. Zhang, Taxi demand prediction based on a combination forecasting model in hotspots, J. Adv. Transp. 2020 (2020), 13.
12 Y. Zhou, Y. Wu, J. Wu, L. Chen, and J. Li, Refined taxi demand prediction with ST-Vec, (26th International Conference on Geoinformatics, Kunming, China), 2018, pp. 1-6.
13 I. Markou, F. Rodrigues, and F. C. Pereira, Multi-step ahead prediction of taxi demand using time-series and textual data, Transportation Research Procedia 41 (2019), 540-544.   DOI
14 J. Xu, R. Rahmatizadeh, L. Boloni, and D. Turgut, Real-time prediction of taxi demand using recurrent neural networks, IEEE Trans. Intell. Transp. Syst. 19 (2018), no. 8, 2572-2581.   DOI
15 B. Hu, S. Zhang, Y. Ding, M. Zhang, X. Dong, and H. Sun, Research on the coupling degree of regional taxi demand and social development from the perspective of job-housing travels, Phys. A: Stat. Mech. Appl. 564 (2021), 125493.   DOI
16 S. Faghih, A. Shah, Z. Wang, A. Safikhani, and C. Kamga, Taxi and mobility: Modeling taxi demand using ARMA and linear regression, Procedia Comput. Sci. 177 (2020), 186-195.   DOI
17 Z. Chen, B. Zhao, Y. Wang, Z. Duan, and X. Zhao, Multitask learning and GCN-based taxi demand prediction for a traffic road network, Sensors 20 (2020), no. 13, 3776.   DOI
18 P. Rodrigues, A. Martins, S. Kalakou, and F. Moura, Spatiotemporal variation of taxi demand, Transp. Res. Procedia 47 (2020), 664-671.   DOI
19 X. Liu, L. Sun, Q. Sun, and G. Gao, Spatial variation of taxi demand using GPS trajectories and POI data, J. Adv. Transp. 2020 (2020), 7621576.
20 N. Davis, G. Raina, and K. Jagannathan, A multi-level clustering approach for forecasting taxi travel demand, (IEEE 19th International Conference on Intelligent Transportation Systems, Rio de Janeiro, Brazil), 2016, pp. 223-228.
21 T. L. Quy, W. Nejdl, M. Spiliopoulou, and E. Ntoutsi, A neighborhood-augmented LSTM model for taxi-passenger demand prediction, Multiple-aspect analysis of semantic trajectories, Cham, 2020, pp. 100-116.
22 D. Faial, F. Bernardini, E. M. Meza, L. Miranda, and J. Viterbo, A methodology for taxi demand prediction using stream learning, (International Conference on Systems, Signals and Image Processing, Niteroi, Brazil), 2020, pp. 417-422.
23 T. Liu, W. Wu, Y. Zhu, and W. Tong, Predicting taxi demands via an attention-based convolutional recurrent neural network, Knowl. Based Syst. 206 (2020), 106294.   DOI
24 J. Ye, L. Sun, B. Du, Y. Fu, X. Tong, and H. Xiong, Co-prediction of multiple transportation demands based on deep spatio-temporal neural network, (Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Association for Computing Machinery, Anchorage, AK, USA), 2019, pp. 305-313.