ABSTRACT
Chauffeured car service based on mobile applications like Uber or Didi suffers from supply-demand disequilibrium, which can be alleviated by proper prediction on the distribution of passenger demand. In this paper, we propose a Zero-Grid Ensemble Spatio Temporal model (ZEST) to predict passenger demand with four predictors: a temporal predictor and a spatial predictor to model the influences of local and spatial factors separately, an ensemble predictor to combine the results of former two predictors comprehensively and a Zero-Grid predictor to predict zero demand areas specifically since any cruising within these areas costs extra waste on energy and time of driver. We demonstrate the performance of ZEST on actual operational data from ride-hailing applications with more than 6 million order records and 500 million GPS points. Experimental results indicate our model outperforms 5 other baseline models by over 10% both in MAE and sMAPE on the three-month datasets.
- T. Cheng, J. Haworth, B. Anbaroglu, G. Tanaksaranond, and J. Wang. Spatiotemporal data mining. In Handbook of Regional Science, pages 1173--1193. Springer, 2014.Google ScholarCross Ref
- A. Ihler, J. Hutchins, and P. Smyth. Adaptive event detection with time-varying poisson processes. In Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 207--216. ACM, 2006. Google ScholarDigital Library
- W. Jiang, T. Wo, M. Zhang, R. Yang, and J. Xu. A Method for Private Car Transportation Dispatching Based on a Passenger Demand Model. Springer International Publishing, 2015.Google ScholarDigital Library
- X. Li, G. Pan, Z. Wu, G. Qi, S. Li, D. Zhang, W. Zhang, and Z. Wang. Prediction of urban human mobility using large-scale taxi traces and its applications. Frontiers of Computer Science, 6(1):111--121, 2012. Google ScholarDigital Library
- L. Matias, J. Gama, J. Mendes-Moreira, and J. Freire, de Sousa. Validation of both number and coverage of bus schedules using avl data. In International IEEE Conference on Intelligent Transportation Systems, pages 131--136, 2010.Google ScholarCross Ref
- L. Moreira-Matias, J. Gama, M. Ferreira, J. Mendes-Moreira, and L. Damas. Predicting taxi--passenger demand using streaming data. Intelligent Transportation Systems, IEEE Transactions on, 14(3):1393--1402, 2013. Google ScholarDigital Library
- D. Zhang, T. He, S. Lin, S. Munir, J. Stankovic, et al. Dmodel: Online taxicab demand model from big sensor data in a roving sensor network. In Big Data (BigData Congress), 2014 IEEE International Congress on, pages 152--159. IEEE, 2014. Google ScholarDigital Library
- Y. Zheng, F. Liu, and H. P. Hsieh. U-air: when urban air quality inference meets big data. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 1436--1444, 2013. Google ScholarDigital Library
- Y. Zheng, X. Yi, M. Li, R. Li, Z. Shan, E. Chang, and T. Li. Forecasting fine-grained air quality based on big data. In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 2267--2276, 2015. Google ScholarDigital Library
Index Terms
- ZEST: A Hybrid Model on Predicting Passenger Demand for Chauffeured Car Service
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