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.
|