Abstract
As an important part of the urban public transport system, taxi has been the essential transport option for city residents. The research on the prediction and analysis of taxi demand based on the taxi GPS data is one of the hot topics in transport recently, which is of great importance to increase the incomes of taxi drivers, reduce the time and distances of vacant driving and improve the quality of taxi operation and management. In this paper, we aim to predict the taxi demand based on the ConvLSTM network, which is able to deal with the spatial structural information effectively by the convolutional operation inside the LSTM cell. We also use the LSTM network in our experiment to implement the same prediction task. Then we compare the prediction performances of these two models. The results show that the ConvLSTM network outperforms LSTM network in predicting the taxi demand. Due to the ability of handling spatial information more accurately, the ConvLSTM can be used in many spatio-temporal sequence forecasting problems.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Kong, X., Xu, Z., Shen, G., Wang, J., Yang, Q., Zhang, B.: Urban traffic congestion estimation and prediction based on floating car trajectory data. Future Gener. Comput. Syst. 61, 97–107 (2016)
Zhang, D., et al.: Understanding taxi service strategies from taxi GPS traces. IEEE Trans. Intell. Transp. Syst. 16(1), 123–135 (2015)
Yang, Q., Gao, Z., Kong, X., Rahim, A., Wang, J., Xia, F.: Taxi operation optimization based on big traffic data. In: Proceedings of 12th IEEE International Conference on Ubiquitous Intelligence and Computing, Beijing, China, pp. 127–134 (2015)
Zhao, K., Khryashchev, D., Freire, J., Silva, C., Vo, H.: Predicting taxi demand at high spatial resolution: approaching the limit of predictability. In: Proceedings of IEEE BigData, December 2016, pp. 833–842 (2016)
Li, X., et al.: Prediction of urban human mobility using large-scale taxi traces and its applications. Front. Comput. Sci. 6(1), 111–121 (2012)
Kong, X., Xia, F., Wang, J., Rahim, A., Das, S.: Time-location-relationship combined service recommendation based on taxi trajectory data. IEEE Trans. Ind. Inform. 13(3), 1202–1212 (2017)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Xu, J., Rahmatizadeh, R., Boloni, L.: Real-time prediction of taxi demand using recurrent neural networks. IEEE Trans. Intell. Transp. Syst. 99(1), 1–10 (2017)
Shi, X., Chen, Z., Wang, H., Yeung, D.-Y., Wong, W.-K., Wong, W.-C.: Convolutional LSTM network: a machine learning approach for precipitation nowcasting. In: NIPS (2015)
Graves, A.: Supervised Sequence Labelling with Recurrent Neural Networks, vol. 385. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-24797-2
Kim, S., Hong, S., Joh, M., Song, S.-K.: DeepRain: ConvLSTM network for precipitation prediction using multichannel radar data. In: IWOCI, September 2017
NYC Taxi & Limousine Commission: Taxi and Limousine Commission (TLC) Trip Record Data. http://www.nyc.gov/html/tlc/html/about/trip_record_data.shtml. Accessed Dec 2016
Lv, Y., Duan, Y., Kang, W., Li, Z., Wang, F.-Y.: Traffic flow prediction with big data: a deep learning approach. IEEE Trans. Intell. Transp. Syst. 16(2), 865–873 (2015)
Wikipedia. https://en.wikipedia.org/wiki/Symmetric_mean_absolute_percentage_error. Accessed 20 May 2018
Vanguard Software Homepage. http://www.vanguardsw.com/business-forecasting-101/symmetric-mean-absolute-percent-error-smape/. Accessed 20 May 2018
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Li, P., Sun, M., Pang, M. (2018). Prediction of Taxi Demand Based on ConvLSTM Neural Network. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11305. Springer, Cham. https://doi.org/10.1007/978-3-030-04221-9_2
Download citation
DOI: https://doi.org/10.1007/978-3-030-04221-9_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-04220-2
Online ISBN: 978-3-030-04221-9
eBook Packages: Computer ScienceComputer Science (R0)