Abstract
As an important public transportation, Taxi is used for passengers every day, which is one of the primary causes for traffic jams. For passengers, knowing the difficulty degree of taking a taxi at a particular time and place can help us plan the journey effectively. Nevertheless, the existing predication models for traffic are not able to express the difficulty degree of choosing a taxi. In order to solve this problem, we can use historical data of taxi status to analysis and predict the possibility of taxi-hailing at a specific time and place. In this paper, we present a classification and predication framework for taxi-hailing. In this framework, firstly we use K-Means clustering algorithm to divide the taxi data into different clusters. Then we use Echarts to extract the features of each cluster in order to show the different difficulty degree. Next we use neural network to generate the predication result using the result of K-Means. On this basis, we propose a method to make the predication of taxi-hailing at a particular time and place, which can calculate the possibility score of taxi-hailing. Finally, we make a prediction using this framework and compare the predication results with the actual travelling data report. The comparison results verify the reliability of this framework.
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References
de Brébisson, A., Simon, É., Auvolat, A., Vincent, P., Bengio, Y.: Artificial neural networks applied to taxi destination prediction. https://arxiv.org/abs/1508.00021
Zhang, Z., Wang, L., Jia, L., Li, F., Zhang, L., Zhao, M.: Projective label propagation by label embedding: a deep label prediction framework for representation and classification. Knowl.-Based Syst. 119, 94–112 (2017)
Shi, A., Weiming, K.: Prediction of urban traffic abnormity based on causal network. In: 2015 Sixth International Conference on Intelligent Systems Design and Engineering Applications (ISDEA), pp. 574–577 (2015)
Münz, G., Li, S., Carle, G.: Traffic anomaly detection using k-means clustering. In: GI/ITG Workshop MMBnet (2007)
Xuewu, Z., Yongjun, L.: The city taxi quantity prediction via GM-BP model. In: 2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER), pp. 1594–1598 (2015)
Manasseh, C., Sengupta, R.: Predicting driver destination using machine learning techniques. In: 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013), pp. 142–147 (2013)
Ahmad, B.I., Murphy, J.K., Godsill, S., Langdon, P.M., Hardy, R.: Intelligent interactive displays in vehicles with intent prediction: a Bayesian framework. IEEE Sig. Process. Mag. 34(2), 82–94 (2017)
Tak, S., Kim, S., Oh, S., Yeo, H.: Development of a data-driven framework for real-time travel time prediction. Comput.-Aided Civil Infrastruct. Eng. 31(10), 777–793 (2016)
Liebig, T., Piatkowski, N., Bockermann, C., Morik, K.: Dynamic route planning with real-time traffic predictions. Inf. Syst. 64, 258–265 (2017)
Hu, W., Yan, L., Wang, H., Du, B., Tao, D.: Real-time traffic jams prediction inspired by Biham, Middleton and Levine (BML) model. Inf. Sci. 381, 209–228 (2017)
Oliveira, T.P., Barbar, J.S., Soares, A.S.: Computer network traffic prediction: a comparison between traditional and deep learning neural networks. IJBDI 3(1), 28–37 (2016)
Li, H.: Research on prediction of traffic flow based on dynamic fuzzy neural networks. Neural Comput. Appl. 27(7), 1969–1980 (2016)
Bezuglov, A., Comert, G.: Short-term freeway traffic parameter prediction: application of grey system theory models. Expert Syst. Appl. 62, 284–292 (2016)
Li, J., Mei, X., Prokhorov, D., Tao, D.: Deep neural network for structural prediction and lane detection in traffic scene. IEEE Trans. Neural Netw. Learn. Syst. 28(3), 690–703 (2017)
Yi, H., Jung, H., Bae, S.: Deep neural networks for traffic flow prediction. In: BigComp, pp. 328–331 (2017)
Ma, X., Dai, Z., He, Z., Ma, J., Wang, Y., Wang, Y.: Learning Traffic as Images: A Deep Convolution Neural Network for Large-scale Transportation Network Speed Prediction. CoRR abs/1701.04245 (2017)
Mitrovic, N., Asif, M.T., Dauwels, J., Jaillet, P.: Low-dimensional models for compressed sensing and prediction of large-scale traffic data. IEEE Trans. Intell. Transp. Syst. 16(5), 2949–2954 (2015)
Park, J., Li, D., Murphey, Y.L.: Real time vehicle speed prediction using a neural network traffic model. In: The 2011 International Joint Conference on Neural Networks (IJCNN), pp. 2991–2996 (2011)
Luo, Q.: On discovering regional taxi service disequilibrium with geographical collaborative filtering. In: 2014 International Conference on Informative and Cybernetics for Computational Social Systems (ICCSS) (2014)
Akhmetov, D.F., Dote, Y., Ovaska, S.J.: Fuzzy neural network with general parameter adaptation for modeling of nonlinear time-series. IEEE Trans. Neural Netw. 12(1), 148–152 (2001)
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Yin, C., Lin, Y., Yang, C. (2017). A Classification and Predication Framework for Taxi-Hailing Based on Big Data. In: Huang, DS., Hussain, A., Han, K., Gromiha, M. (eds) Intelligent Computing Methodologies. ICIC 2017. Lecture Notes in Computer Science(), vol 10363. Springer, Cham. https://doi.org/10.1007/978-3-319-63315-2_65
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DOI: https://doi.org/10.1007/978-3-319-63315-2_65
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