Hybrid deep learning models for short-term demand forecasting of online car-hailing considering multiple factors

References

• Ahmed, M. S., and A. R. Cook. 1979. “Analysis of Freeway Traffic time-series Data by Using Box-Jenkins Techniques.” Transportation Research Record 773: 1–9.
   Google Scholar
• An, J. Y., L. Fu, M. Hu, W. H. Chen, and A. Novel Fuzzy-Based. 2019. “Convolutional Neural Network Method to Traffic Flow Prediction with Uncertain Traffic Accident Information.” IEEE Access 99: 1.
   Google Scholar
• Cai, L. R., Z. C. Zhang, J. J. Yang, Y. D. Yu, T. Zhou, and J. Qin. 2019. “A noise-immune Kalman Filter for short-term Traffic Flow Forecasting, Physica A.” Statistical Mechanics and Its Applications 536: 122601. doi:10.1016/j.physa.2019.122601.
   Web of Science ®Google Scholar
• Castro-Neto, M., Y. S. Jeong, M. K. Jeong, and L. D. Han. 2009. “Online-SVR for short-term Traffic Flow Prediction under Typical and Atypical Traffic Conditions.” Expert Systems with Applications 36 (3): 6164–6173. doi:10.1016/j.eswa.2008.07.069.
   Web of Science ®Google Scholar
• Chen, X. Q., C. Q. Chen, L. L. Ni, and L. Li. 2018. “Spatial Visitation Prediction of on-demand Ride Services Using the Scaling Law, Physica A.” Statistical Mechanics and Its Applications 508: 84–94. doi:10.1016/j.physa.2018.05.005.
   Web of Science ®Google Scholar
• Cheng, T., J. Haworth, and J. Wang. 2012. “Spatio-temporal Autocorrelation of Road Network Data.” Journal of Geographical Systems 14 (4): 389–413. doi:10.1007/s10109-011-0149-5.
   Web of Science ®Google Scholar
• Chen, H., and S. Grant-Muller. 2001. “Use of Sequential Learning for short-term Traffic Flow Forecasting.” Transportation Research Part C-Emerging Technologies 9 (5): 319–336. doi:10.1016/S0968-090X(00)00039-5.
   Web of Science ®Google Scholar
• Chen, Z., B. Zhao, Y. H. Wang, Z. T. Duan, and X. Zhao. 2020. “Multitask Learning and GCN-Based Taxi Demand Prediction for a Traffic Road Network.” Sensors 20 (13): 3776. doi:10.3390/s20133776.
   PubMed Web of Science ®Google Scholar
• Chien, S. I.-J., and C. M. Kuchipudi. 2003. “Dynamic Travel Time Prediction with real-time and Historic Data.” Journal of Transportation Engineering 129 (6): 608–616. doi:10.1061/(ASCE)0733-947X(2003)129:6(608).
   Web of Science ®Google Scholar
• Dia, H. 2001. “An object-oriented Neural Network Approach to short-term Traffic Forecasting.” European Journal of Operational Research 131 (2): 253–261. doi:10.1016/S0377-2217(00)00125-9.
   Web of Science ®Google Scholar
• Disbro, J. E., and M. Frame. 2016. “Traffic Flow Theory and Chaotic Behavior, Transportation Research Record.” Journal of the Transportation Research Board 1225 (1989): 109–115.
   Google Scholar
• Forbes, G. J., and F. Hall. 1990. “The Applicability of Catastrophe Theory in Modelling Freeway Traffic Operations, Transportation Research Part A.” General 24: 335–344.
   Google Scholar
• Geng, X., Y. G. Li, L. Y. Wang, L. Y. Zhang, and Y. Liu. “Spatiotemporal Multi-Graph Convolution Network for Ride-Hailing Demand Forecasting.” Proceedings of the AAAI Conference on Artificial Intelligence 33 (2019), 3656–3663.
   Google Scholar
• Jiang, X. M., and H. Adeli. 2005. “Dynamic Wavelet Neural Network Model for Traffic Flow Forecasting.” Journal of Transportation Engineering 131 (10): 771–779. doi:10.1061/(ASCE)0733-947X(2005)131:10(771).
   Web of Science ®Google Scholar
• Jiang, S., W. T. Chen, Z. H. Li, and H. Y. Yu. 2019. “Short-Term Demand Prediction Method for Online Car-Hailing Services Based on a Least Squares Support Vector Machine.” IEEE Access 7: 11882–11891. doi:10.1109/ACCESS.2019.2891825.
   Web of Science ®Google Scholar
• Ke, J. T., H. Yang, H. Y. Zheng, X. Q. Chen, Y. T. Jia, P. H. Gong, and J. P. Ye. 2019. “Hexagon-Based Convolutional Neural Network for Supply-Demand Forecasting of Ride-Sourcing Services.” IEEE Transactions on Intelligent Transportation Systems 11 (11): 4160–4173. doi:10.1109/TITS.2018.2882861.
   Google Scholar
• Ke, J. T., H. Y. Zheng, H. Yang, and X. (. Chen. 2017. “Short-term Forecasting of Passenger Demand under on-demand Ride Services: A spatio-temporal Deep Learning Approach, Transportation Research Part C.” Transportation Research Part C: Emerging Technologies 85: 591–608. doi:10.1016/j.trc.2017.10.016.
   Web of Science ®Google Scholar
• Li, X. L., G. Pan, Z. H. Wu, G. D. Qi, S. J. Li, D. Q. Zhang, W. S. Zhang, and Z. H. Wang. 2012. “Prediction of Urban Human Mobility Using large-scale Taxi Traces and Its Applications.” Frontiers of Computer Science 1: 111–121.
   Google Scholar
• Ma, X. L., Z. Dai, Z. B. He, J. H. Ma, Y. Wang, and Y. P. Wang. 2017. “Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction.” Sensors 17 (4): 818. doi:10.3390/s17040818.
   PubMed Web of Science ®Google Scholar
• Moreira-Matias, L., J. Gama, M. Ferreira, J. Mendes-Moreira, and L. Damas. 2013. “Predicting Taxi-Passenger Demand Using Streaming Data.” IEEE Transactions on Intelligent Transportation Systems 3 (3): 1393–1402. doi:10.1109/TITS.2013.2262376.
   Google Scholar
• Nihan, N. L., and K. O. Holmesland. 1980. “Use of the Box and Jenkins Time Series Technique in Traffic Forecasting.” Transportation 9 (2): 125–143. doi:10.1007/BF00167127.
   Web of Science ®Google Scholar
• Okutani, I., and Y. J. Stephanedes. 1984. “Dynamic Prediction of Traffic Volume through Kalman Filtering Theory, Transportation Research Part B.” Methodological 18: 1–11.
   Google Scholar
• Shi, X. J., Z. R. Chen, H. Wang, D. Y. Yeung, W. K. Wong, and W. C. Woo. 2015. “Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting.” NIPS 802–810.
   Google Scholar
• Sitorus, C. M., A. Rizal, and M. Jajuli. 2020. “Prediksi Risiko Perjalanan Transportasi Online Dari Data Telematik Menggunakan Algoritma Support Vector Machine.” JuTISI 6 (2): 254–265. doi:10.28932/jutisi.v6i2.2672.
   Google Scholar
• Smith, B. L., and M. J. Demetsky. 1994. “Short-term Traffic Flow Prediction: Neural Network Approach.” Transportation Research Record 1453: 98–104.
   Google Scholar
• Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin. 2017. “Attention Is All You Need.” NIPS 5998–6008.
   Google Scholar
• Vlahogianni, E. I., M. G. Karlaftis, and J. C. Golias. 2014. “Short-term Traffic Forecasting: Where We are and Where We’re Going, Transportation Research Part C.” Emerging Technologies 43: 3–19. doi:10.1016/j.trc.2014.01.005.
   Web of Science ®Google Scholar
• Wang, N., J. H. Guo, X. Liu, and T. Fang. 2020. “A Service Demand Forecasting Model for one-way Electric car-sharing Systems Combining Long short-term Memory Networks with Granger Causality Test.” Journal of Cleaner Production 244: 118812. doi:10.1016/j.jclepro.2019.118812.
   Web of Science ®Google Scholar
• Xu, J., R. Rahmatizadeh, L. Boloni, and D. Turgut. 2018. “Real-Time Prediction of Taxi Demand Using Recurrent Neural Networks.” IEEE Transactions on Intelligent Transportation Systems 19 (8): 2572–2581. doi:10.1109/TITS.2017.2755684.
   Web of Science ®Google Scholar
• Yu, F., and X. Z. Xu. 2014. “A short-term Load Forecasting Model of Natural Gas Based on Optimized Genetic Algorithm and Improved BP Neural Network.” Applied Energy 134: 102–113. doi:10.1016/j.apenergy.2014.07.104.
   Web of Science ®Google Scholar
• Zhang, K., Z. Y. Feng, S. Z. Chen, K. M. Huang, and G. L. Wang. “A Framework for Passengers Demand Prediction and Recommendation.” 2016 IEEE International Conference on Services Computing (2016), 340–347.
   Google Scholar
• Zhang, H., X. M. Wang, J. Cao, M. N. Tang, and Y. R. Guo. 2018. “A Multivariate short-term Traffic Flow Forecasting Method Based on Wavelet Analysis and Seasonal Time Series.” Applied Intelligence 48 (10): 3827–3838. doi:10.1007/s10489-018-1181-7.
   Web of Science ®Google Scholar
• Zhao, Z., W. H. Chen, X. M. Wu, P. C. Y. Chen, and J. M. Liu. 2017. “LSTM Network: A Deep Learning Approach for short-term Traffic Forecast.” IET Intelligent Transport Systems 11 (2): 68–75. doi:10.1049/iet-its.2016.0208.
   Web of Science ®Google Scholar
• Zhao, J. D., Y. Gao, Y. C. Qu, H. D. Yin, Y. M. Liu, and H. J. Sun. 2018. “Travel Time Prediction: Based on Gated Recurrent Unit Method and Data Fusion.” IEEE Access 6: 70463–70472. doi:10.1109/ACCESS.2018.2878799.
   Web of Science ®Google Scholar
• Zheng, H. F., F. Lin, X. X. Feng, and Y. J. Chen. 2021. “A Hybrid Deep Learning Model with Attention-Based Conv-LSTM Networks for Short-Term Traffic Flow Prediction.” IEEE Transactions on Intelligent Transportation Systems 22 (11): 6910–6920. doi:10.1109/TITS.2020.2997352.
   Web of Science ®Google Scholar