Abstract
Ride-hailing services are growing rapidly and becoming one of the most disruptive technologies in the transportation realm. Accurate prediction of ride-hailing trip demand not only enables cities to better understand people’s activity patterns, but also helps ride-hailing companies and drivers make informed decisions to reduce deadheading vehicle miles traveled, traffic congestion, and energy consumption. In this study, a convolutional neural network (CNN)-based deep learning model is proposed for multi-step ride-hailing demand prediction using the trip request data in Chengdu, China, offered by DiDi Chuxing. The CNN model is capable of accurately predicting the ride-hailing pick-up demand at each 1-km by 1-km zone in the city of Chengdu for every 10 min. Compared with another deep learning model based on long short-term memory, the CNN model is 30% faster for the training and predicting process. The proposed model can also be easily extended to make multi-step predictions, which would benefit the on-demand shared autonomous vehicles applications and fleet operators in terms of supply-demand rebalancing. The prediction error attenuation analysis shows that the accuracy stays acceptable as the model predicts more steps.
Keywords
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
Clewlow, R.R., Mishra, G.S.: The adoption, utilization, and impacts of ride-hailing in the United States. University of California, Davis, Institute of Transportation Studies, Davis, Research Report UCD-ITS-RR-17-07 (2017)
Chang, H.W., Tai, Y.C., Hsu, J.Y.J.: Context-aware taxi demand hotspots prediction. Int. J. Bus. Intell. Data Min. 5(1), 3 (2010)
Moreira-Matias, L., Gama, J., Ferreira, M., Damas, L.: A predictive model for the passenger demand on a taxi network. In: 15th International IEEE Conference on Intelligent Transportation Systems (ITSC), pp. 1014–1019. IEEE (2012)
Moreira-Matias, L., Gama, J., Ferreira, M., Mendes-Moreira, J., Damas, L.: Predicting taxi–passenger demand using streaming data. IEEE Trans. Intell. Transp. Syst. 14(3), 1393–1402 (2013)
Predict New York city taxi demand | NYC Data Science Academy Blog, https://nycdatascience.com/blog/student-works/predict-new-york-city-taxi-demand/. Accessed 21 July 2018
Ke, J., Zheng, H., Yang, H., Chen, X.M.: Short-term forecasting of passenger demand under on-demand ride services: a spatio-temporal deep learning approach. Transp. Res. Part C Emerg. Technol. 85, 591–608 (2017)
Wang, C., Hao, P., Wu, G., Qi, X., Barth, M.: Predicting the Number of Uber Pickups by Deep Learning (No. 18–06738) (2018)
Xu, J., Rahmatizadeh, R., Bölöni, L., Turgut, D.: Real-time prediction of taxi demand using recurrent neural networks. IEEE Trans. Intell. Transp. Syst. (2017)
Liao, S., Zhou, L., Di, X., Yuan, B., Xiong, J.: Large-scale short-term urban taxi demand forecasting using deep learning. In: Proceedings of the 23rd Asia and South Pacific Design Automation Conference, pp. 428–433. IEEE Press (2018)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Sun, S., Zhang, C., Yu, G.: A Bayesian network approach to traffic flow forecasting. IEEE Trans. Intell. Transp. Syst. 7(1), 124–132 (2006)
Matsugu, M., Mori, K., Mitari, Y., Kaneda, Y.: Subject independent facial expression recognition with robust face detection using a convolutional neural network. Neural Netw. 16(5–6), 555–559 (2003)
LeCun, Y.: LeNet-5, convolutional neural networks (2015). http://yann.lecun.com/exdb/lenet/. Accessed 1 June 2016
Ma, X., Dai, Z., He, Z., Ma, J., Wang, Y., Wang, Y.: Learning traffic as images: a deep convolutional neural network for large-scale transportation network speed prediction. Sensors 17(4), 818 (2017)
Ma, X., Tao, Z., Wang, Y., Yu, H., Wang, Y.: Long short-term memory neural network for traffic speed prediction using remote microwave sensor data. Transp. Res. Part C Emerg. Technol. 54, 187–197 (2015)
Duan, Y., Lv, Y., Kang, W., Zhao, Y.: A deep learning based approach for traffic data imputation. In: IEEE 17th International Conference on Intelligent Transportation Systems (ITSC), pp. 912–917. IEEE (2014)
GAIA Open Dataset. https://outreach.didichuxing.com/research/opendata/en/. Accessed 21 July 2018
DiDi – Wikipedia. https://en.wikipedia.org/wiki/DiDi. Accessed 01 Nov 2018
World Weather Online. https://www.worldweatheronline.com/lang/en-us/. Accessed 21 July 2018
Hou, Y., Garikapati, V., Sperling, J., Henao, A., Young, S.: A deep learning approach for TNC trip demand prediction considering spatial-temporal features. In: 98th Annual Meeting of Transportation Research Board (2019)
Performing Convolution Operations. https://developer.apple.com/library/archive/documentation/Performance/Conceptual/vImage/ConvolutionOperations/ConvolutionOperations.html. Accessed 01 Aug 2018
Pooling Layer - Artificial Intelligence. https://leonardoaraujosantos.gitbooks.io/artificial-inteligence/content/pooling_layer.html. Accessed 01 Aug 2018
Acknowledgment
The authors want to thank DiDi Chuxing for providing the data for this study.
This work was authored in part by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding provided by U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Vehicle Technologies Office. The views expressed in the article do not necessarily represent the views of the DOE or the U.S. Government. The U.S. Government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for U.S. Government purposes.
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
Wang, C., Hou, Y., Barth, M. (2020). Data-Driven Multi-step Demand Prediction for Ride-Hailing Services Using Convolutional Neural Network. In: Arai, K., Kapoor, S. (eds) Advances in Computer Vision. CVC 2019. Advances in Intelligent Systems and Computing, vol 944. Springer, Cham. https://doi.org/10.1007/978-3-030-17798-0_2
Download citation
DOI: https://doi.org/10.1007/978-3-030-17798-0_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-17797-3
Online ISBN: 978-3-030-17798-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)