Abstract
Demand and supply are crucial elements of the ride-hailing business. After the evolution of the GPS supported mobile-based ride-hailing systems, hotspots detection in a spatial region is one of the most discussed topics among the urban planners and researchers. Due to the high non-linearity and complexity of traffic flow, traditional methods cannot satisfy the requirements of prediction tasks and often they neglect the dynamic constraint of a spatial region. To address this issue, this research considered the road network graph and transform hotspots detection problem into a node-wise decision-making problem and extracted subgraphs as hotspots. In this paper, the authors propose a graph neural networks enable reinforcement learning agents to learn the dynamic behaviour of a road network graph and use it for a subgraph extraction in a road network graph. The Graph Neural Networks (GNNs) can extract node features like the pickup requests and events in the city and generate the subgraphs by stacking multiple neural network layers. Experiments show that the proposed model effectively captures comprehensive spatio-temporal correlations and outperforms state-of-the-art approaches on real-world taxi datasets.
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
References
United Nations: World urbanization prospects: The 2014 revision. Technical report, United Nations (2014)
Lee, J., Shin, I., Park, G.-L.: Analysis of the passenger pick-up pattern for taxi location recommendation. In: 2008 Fourth International Conference on Networked Computing and Advanced Information Management Analysis, pp. 199–204 (2008)
Chang, H.W., Tai, Y.C., Hsu, J.Y.J.: Context-aware taxi demand hotspots prediction. Int. J. Bus. Intell. Data Mining 5(1), 3–18 (2010)
Verma, N., Baliyan, N.: PAM clustering based taxi hotspot detection for informed driving. In: Proceedings of the 8th International Conference on Computing, Communication and Networking Technologies, pp. 1–7 (2001)
Tang, J., Liu, F., Wang, Y., Wang, H.: Uncovering urban human mobility from large scale taxi GPS data. Phys. A 438, 140–153 (2015)
Ester, M., Kriegel, H.-P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, pp. 226–231 (1996)
Cui, Z., Henrickson, K., Ke, R., Wang, Y.: Traffic graph convolutional recurrent neural network: a deep learning framework for network-scale traffic learning and forecasting, arXiv preprint arXiv:1802.07007 (2018)
Zheng, C., Fan, X., Wang, C., Qi, J., GMAN: a graph multi-attention network for traffic prediction. Proc. AAAI 34, 1234–1241 (2020)
Guo, S., Lin, Y., Feng, N., Song, C., Wan, H.: Attention based spatial-temporal graph convolutional networks for traffic flow forecasting. Proc. AAAI 33, 922–929 (2019)
Yu, H., Li, Z., Zhang, G., Liu, P., Wang, J.: Extracting and predicting taxi hotspots in spatiotemporal dimensions using conditional generative adversarial neural networks. IEEE Trans. Veh. Technol. 69(4), 3680–3692 (2020)
Xia, D., et al.: A parallel grid-search-based SVM optimization algorithm on Spark for passenger hotspot prediction. Multim. Tools Appl. 88, 1–27 (2022)
Liu, Z., Li, J., Wu, K.: Context-aware taxi dispatching at city-scale using deep reinforcement learning. IEEE Trans. Intell. Transp. Syst. 23, 1996–2009 (2020)
Dwork, C., Kumar, R., Naor, M., Sivakumar, D.: Rank aggregation methods for the web. In: Proceedings of the 10th International Conference on World Wide Web, pp. 613–622 (2001)
Bohm, C., Ooi, B.C., Plant, C., Yan, Y.: Efficiently processing continuous k-NN queries on data streams. In: ICDE, pp. 156–165 (2007)
Li, M., He, D., Zhou, X.: Efficient kNN search with occupation in large-scale on-demand ride-hailing. In: Borovica-Gajic, R., Qi, J., Wang, W. (eds.) ADC 2020. LNCS, vol. 12008, pp. 29–41. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-39469-1_3
Shekhar, S., Feiner, S.K., Aref, W.G.: Spatial computing. Comm. ACM 59(1), 72–81 (2016)
Ailon, N., Charikar, M., Newman, A.: Aggregating inconsistent information: ranking and clustering. J. ACM 55(5), 23 (2008)
Korn, F., Muthukrishnan, S., Srivastava, D.: Reverse nearest neighbor aggregates over data streams. In: PVLDB, pp. 814–825 (2002)
Li, C., Gu, Y., Qi, J., Yu, G., Zhang, R., Yi, W.: Processing moving kNN queries using influential neighbor sets. Proc. VLDB Endow. 8(2), 113–124 (2014)
Khetarpaul, S., Gupta, S.K., Malhotra, S., Subramaniam, L.V.: Bus arrival time prediction using a modified amalgamation of fuzzy clustering and neural network on spatio-temporal data. In: Sharaf, M.A., Cheema, M.A., Qi, J. (eds.) ADC 2015. LNCS, vol. 9093, pp. 142–154. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-19548-3_12
Li, M., Bao, Z., Sellis, T., Yan, S.: Visualization-aided exploration of the real estate data. In: Cheema, M.A., Zhang, W., Chang, L. (eds.) ADC 2016. LNCS, vol. 9877, pp. 435–439. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46922-5_34
Cheema, M., Zhang, W., Lin, X., Zhang, Y., Li, X.: Continuous reverse k nearest neighbors queries in Euclidean space and in spatial networks. VLDB J. 21(1), 69–95 (2012)
Yu, B., Yin, H., Zhu, Z:. Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting. In: Proceedings of IJCAI, pp. 3634–3640 (2018)
Zhang, Y., Cheng, T.: Graph deep learning model for network-based predictive hotspot mapping of sparse spatio-temporal events. Comput. Environ. Urban Syst. 79, 101403 (2020)
Yang, T., Tang, X., Liu, R.: Dual temporal gated multi-graph convolution network for taxi demand prediction. Neural Comput. Appl. 1–16 (2021). https://doi.org/10.1007/s00521-021-06092-6
Mishra, S., Khetarpaul, S.: Optimal placement of taxis in a city using dominating set problem. In: Qiao, M., Vossen, G., Wang, S., Li, L. (eds.) ADC 2021. LNCS, vol. 12610, pp. 111–124. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-69377-0_10
Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: PVLDB, pp. 946–957 (2005)
Klicpera, J., Bojchevski, A., Günnemann, S.: Predict then propagate: graph neural networks meet personalized pagerank. arXiv preprint. arXiv:1810.05997 (2018)
Shan, C., Shen, Y., Zhang, Y., Li, X., Li, D.: Reinforcement learning enhanced explainer for graph neural networks. Adv. Neural Inf. Process. Syst. 34 (2021)
https://www1.nyc.gov/site/tlc/about/tlc-trip-record-data.page
https://data.cityofnewyork.us/City-Government/NYC-Permitted-Event-Information-Historical/bkfu-528j
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Mishra, S., Khetarpaul, S. (2022). Predicting Taxi Hotspots in Dynamic Conditions Using Graph Neural Network. In: Hua, W., Wang, H., Li, L. (eds) Databases Theory and Applications. ADC 2022. Lecture Notes in Computer Science, vol 13459. Springer, Cham. https://doi.org/10.1007/978-3-031-15512-3_7
Download citation
DOI: https://doi.org/10.1007/978-3-031-15512-3_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-15511-6
Online ISBN: 978-3-031-15512-3
eBook Packages: Computer ScienceComputer Science (R0)