Evolving Fuzzy Systems in Taxi Demand Forecasting and Classification
Subject Areas : Transactions on Fuzzy Sets and Systems
Luis Linhares
1
,
Alisson Silva
2
1 - Graduate Program in Mathematical and Computational Modeling, Federal Center of Technological Education of Minas Gerais, Belo Horizonte, MG, Brazil.
2 - Graduate Program in Mathematical and Computational Modeling, Federal Center of Technological Education of Minas Gerais, Belo Horizonte, MG, Brazil.
Keywords: Forecast, Classification, Fuzzy Systems, Evolving Systems, Taxi Demand,
Abstract :
This work presents an approach to taxi demand forecasting and classification. The proposed approach uses historical data from taxi rides and meteorological data. The Kruskal-Wallis variable ranking method is used to identify the most relevant variables. The selected variables are used as input to an evolving fuzzy system to perform the prediction. Once the forecast is made, the demand results are classified by value ranges. Those ranges are also identified by colors that compose a heatmap, displayed at each time interval. In this work, to perform the prediction, four evolving systems are evaluated: Autonomous Learning Multi-Model (ALMMo); evolving Multivariable Gaussian Fuzzy Modeling System (eMG); evolving Fuzzy with Multivariable Gaussian Participatory Learning and Recursive Maximum Correntropy eFCE and; evolving Neo-Fuzzy Neuron (eNFN). Computational experiments were carried out to evaluate the evolving systems in predicting Pick-Up and Drop-Off, at intervals of 15 and 30 minutes, for 86 zones in New York, covering the period from 01/01/2018 to 31/ 10/2018. The results obtained by the evolving systems are compared with each other and state of the art. Among the evolving models, ALMMo presented the best results compared to the state of the art and other evolving models. Performance obtained by the evolving models suggests that the proposed approach is promising an alternative to forecasting and classifying passenger demand.
[1] P. Angelov, Evolving Takagi-Sugeno Fuzzy Systems from Streaming Data (eTS+), Evolving Intelligent Systems: Methodology and Applications, (2010), 21-50. doi:10.1002/9780470569962.ch2.
[2] P. P. Angelov and X. Gu, Applications of autonomous learning multi-model systems, in: Empirical Approach to Machine Learning, (2019), 277-293. doi:10.1007/978-3-030-02384-3 12.
[3] P. Angelov and R. Yager, A new type of simplified fuzzy rule-based system, International Journal of General Systems, 41(2) (2012), 163-185. doi:10.1080/03081079.2011.634807.
[4] P. Angelov and X. Zhou, Evolving fuzzy systems from data streams in real-time, Proceedings of the 2006 International Symposium on Evolving Fuzzy Systems, EFS06 (March), (2006), 29-35. doi:10.1109/ISEFS.2006.251157.
[5] P. P. Angelov, X. Gu and J. C. Principe, Autonomous learning multimodel systems from data streams, IEEE Transactions on Fuzzy Systems, 26(4) (2017), 2213-2224. doi:10.1109/TFUZZ.2017.2769039.
[6] P. Angelov, X. Gu, D. Kangin and J. Principe, Empirical data analysis: A new tool for data analytics, in: 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), IEEE, Budapest, Hungary, (2016), 000052-000059. doi:10.1109/SMC.2016.7844219.
[7] S. Babicki, D. Arndt, A. Marcu, Y. Liang, J. R. Grant, A. Maciejewski and D. S. Wishart, Heatmapper: web-enabled heat mapping for all, Nucleic acids research, 44(W1) (2016), W147-W153.
[8] L. Bai, L. Yao, S. S. Kanhere, Z. Yang, J. Chu and X. Wang, Passenger demand forecasting with multitask convolutional recurrent neural networks,in: Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer, (2019), 29-42. doi:10.1007978-3-030-16145-3 3.
[9] A. A. Bojko, Informative or misleading? heatmaps deconstructed, in: J. A. Jacko (Ed.), Human-Computer Interaction. New Trends, Springer Berlin Heidelberg, Berlin, Heidelberg, (2009), 30-39.
[10] G. Box and G. Jenkins, Time Series Analysis: Forecasting and Control, Wiley, Hoboken, Nova Jersey, EUA, (1976).
[11] C. Chatfield, Time-series forecasting, 1st Edition, CRC press, CRC, (2000). doi:10.1201/9781420036206.
[12] J. Crampton, Gis and geographic governance: reconstructing the choropleth map, Cartographica: The International Journal for Geographic Information and Geovisualization, 39(1) (2004), 41-53.
[13] F. D. da Cunha, G. Maia, C. Celes, D. Guidoni, F. de Souza, H. Ramos and L. Villas, Sistemas de Transporte Inteligentes: Conceitos, Aplicacoes e Desafios, in: XXXV simposio Brasileiro de Redes de Computadores e Sistemas Distribu´ ıdos (SBRC) - 2017, no. May, (2017), 1-47.
[14] N. Davis, G. Raina and K. Jagannathan, Taxi Demand Forecasting: A HEDGE-Based Tessellation Strategy for Improved Accuracy, IEEE Transactions on Intelligent Transportation Systems, 19(11) (2018), 3686-3697. arXiv:1805.06619.
[15] S. S. Faghih, A. Safikhani, B. Moghimi and C. Kamga, Predicting Short-Term Uber Demand in New York City Using Spatiotemporal Modeling, Journal of Computing in Civil Engineering, (2019).
[16] M. M. Ferdaus, M. Pratama, S. G. Anavatti and M. A. Garratt, Palm: An incremental construction of hyperplanes for data stream regression,IEEE Transactions on Fuzzy Systems, 27(11) (2019), 2115-2129. doi:10.1109/TFUZZ.2019.2893565.
[17] S. George and A. K. Santra, An improved long short-term memory networks with Takagi-Sugeno fuzzy for traffic speed prediction considering abnormal traffic situation, Computational Intelligence (January), (2020), 1-30.
[18] S. Ishiguro, Y. Fukazawa and S. Kawasaki, Taxi demand forecast using real-time population generated from cellular networks, UbiComp/ISWC 2018 - Adjunct Proceedings of the 2018 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2018 ACM International Symposium on Wearable Computers, (2018), 1024-1032.
[19] S. Jiang, W. Chen and Z. Li, H. Yu, Short-term demand prediction method for online car-hailing services based on a least squares support vector machine, IEEE Access, 7 (2019), 11882-11891. doi:10.1109/ACCESS.2019.2891825.
[20] N. Kasabov and D. Filev, Evolving intelligent systems: Methods, learning, applications, in: 2006 International Symposium on Evolving Fuzzy Systems, IEEE, Ambelside, UK, (2006), 8-18. doi:10.1109/ISEFS.2006.251185.
[21] J. Ke, H. Zheng, H. Yang and X. M. Chen, Short-term forecasting of passenger demand under on-demand ride services: A spatio-temporal deep learning approach, Transportation Research Part C: Emerging Technologies, 85 (2017), 591-608. doi:10.1016/j.trc.2017.10.016.
[22] X. Kong, F. Xia, Z. Fu, X. Yan, A. Tolba and Z. Almakhadmeh, TBI2Flow: Travel behavioral inertia based long-term taxi passenger flow prediction, World Wide Web, 23(2) (2020), 1381-1405.
[23] W. H. Kruskal and W. A. Wallis, Use of ranks in one-criterion variance analysis, Journal of the American statistical Association, 47(260) (1952), 583-621.
[24] D. Leite, I. krjanc and F. Gomide, An overview on evolving systems and learning from stream data, Evolving Systems, (2020). doi:10.1007/s12530-020-09334-5.
[25] A. Lemos, W. Caminhas and F. Gomide, Multivariable gaussian evolving fuzzy modeling system, IEEE Transactions on Fuzzy Systems, 19(1) (2010), 91-104. doi:10.1109/TFUZZ.2010.2087381.
[26] B. Li, D. Zhang, L. Sun, C. Chen, S. Li, G. Qi and Q. Yang, Hunting or waiting? discovering passen-gerfinding strategies from a large-scale real-world taxi dataset, in: 2011 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops), IEEE, Seattle, WA, USA, (2011), 63-68. doi:10.1109/PERCOMW.2011.5766967.
[27] S. Liao, L. Zhou, X. Di, B. Yuan and J. Xiong, Large-scale short-term urban taxi demand forecasting using deep learning, Proceedings of the Asia and South Pacific Design Automation Conference, ASP-DAC 2018-Janua, (2018), 428-433. doi:10.1109/ASPDAC.2018.8297361.
[28] C.-Y. Lin, S.-L. Tung, P.-W. Lu and T.-C. Liu, Predictions of taxi demand based on neural network algorithms, International Journal of Intelligent Transportation Systems Research, 19(3) (2021), 477-495. doi:10.1007/s13177-020-00248-9.
[29] Z. Liu, H. Chen, Y. Li and Q. Zhang, Taxi demand prediction based on a combination forecasting model in hotspots, Journal of Advanced Transportation 2020, (2020). doi:10.1155/2020/1302586.
[30] T. Liu, W. Wu, Y. Zhu and W. Tong, Predicting taxi demands via an attention-based convolutional recurrent neural network, Knowledge-Based Systems 206, (2020), 106-294. doi:10.1016/j.knosys.2020.106294.
[31] L. Liu, Z. Qiu, G. Li, Q. Wang, W. Ouyang and L. Lin, Contextualized spatialtemporal network for taxi origin-destination demand prediction, IEEE Transactions on Intelligent Transportation Systems, 20(10) (2019), 3875-3887. doi:10.1109/TITS.2019.2915525.
[32] E. Lughofer, Evolving fuzzy systems: Fundamentals, reliability, interpretability, useability and applications, in: 2015 7th International Joint Conference on Computational Intelligence (IJCCI), 1 (2015), 11-11.
[33] E. Lughofer and P. Angelov, Handling drifts and shifts in on-line data streams with evolving fuzzy systems, Applied Soft Computing Journal, 11(2) (2011), 20572068. doi:10.1016/j.asoc.2010.07.003.
[34] H. Luo, J. Cai, K. Zhang, R. Xie and L. Zheng, A multi-task deep learning model for short-term taxi demand forecasting considering spatiotemporal dependences, Journal of Traffic and Transportation Engineering (English Edition), 8(1) (2021), 83-94. doi:10.1016/j.jtte.2019.07.002.
[35] I. Markou, F. Rodrigues and F. C. Pereira, Real-Time Taxi Demand Prediction using data from the web, in: IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, (2018).
[36] L. Moreira-Matias, J. Gama, M. Ferreira and L. Damas, A predictive model for the passenger demand on a taxi network, in: 2012 15th International IEEE Conference on Intelligent Transportation Systems, IEEE, (2012), 1014-1019. doi:10.1109/ITSC.2012.6338680.
[37] L. Moreira-Matias, J. Gama, M. Ferreira, J. Mendes-Moreira and L. Damas, Predicting taxi-passenger demand using streaming data, IEEE Transactions on Intelligent Transportation Systems, 14(3) (2013), 1393-1402. doi:10.1109/TITS.2013.2262376.
[38] L. Moreira-Matias, J. Gama, M. Ferreira, J. Mendes-Moreira and L. Damas, Time-evolving O-D matrix estimation using high-speed GPS data streams, Expert Systems with Applications, 44 (2016), 275-288. doi:doi.org/10.1016/j.eswa.2015.08.048.
[39] J. D. Novakovic, A. Veljovic, S. S. Ilic, Z. Papic and T. Milica, Evaluation of classification models in machine learning, Theory and Applications of Mathematics & Computer Science, 7(1) (2017), 39-46.
[40] F. Rodrigues, I. Markou and F. C. Pereira, Combining time-series and textual data for taxi demand prediction in event areas: A deep learning approach, Information Fusion, 49 (2019), 120-129. arXiv:1808.05535, doi:10.1016/j.inffus.2018.07.007.
[41] F. P. S. Rodrigues, A. M. Silva and A. P. Lemos, Evolving fuzzy system with multivariable gaussian participatory learning and recursive maximum correntropy - efce, in: 2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), (2021), 1-6. doi:10.1109/FUZZ45933.2021.9494451.
[42] F. P. S. Rodrigues, A. M. Silva and A. P. Lemos, An evolving fuzzy predictor with multivariable gaussian participatory learning and multi-innovations recursive weighted least squares - efmi, Evolving Systems, (2022). doi:10.1007/s12530-022-09421-9.
[43] A. Rossi, G. Barlacchi, M. Bianchini and B. Lepri, Modelling Taxi Drivers Behaviour for the Next Destination Prediction, IEEE Transactions on Intelligent Transportation Systems PP, (2019), 1-10. arXiv:1807.08173.
[44] R. Salman and V. Kecman, Regression as classification, in: 2012 Proceedings of IEEE Southeastcon, Institute of Electrical and Electronics Engineers (IEEE), Orlando, Florida, USA, (2012). doi:10.1109/SECon. 2012.6196887.
[45] M. Sayed-Mouchawe and E. Lughofer, Learning in Non-Stationary Environments, 1st Edition, Springer-Verlag New York, (2012). doi:10.1007/978-1-4419-8020-5.
[46] A. M. Silva, W. Caminhas, A. Lemos and F. Gomide, A fast learning algorithm for evolving neo-fuzzy neuron, Applied Soft Computing, 14 (2014), 194-209. doi:10.1016/j.asoc.2013.03.022.
[47] I. Krjanc, Cluster-volume based merging concept for incrementally evolving fuzzy Gaussian clustering - eGAUSS+, IEEE Transactions on Fuzzy Systems, 28(9) (2020), 2222-2231. doi:10.1109/TFUZZ.2019. 2931874.
[48] I. Krjanc, J. Iglesias, A. Sanchis, D. Leite, E. Lughofer and F. Gomide, Evolving fuzzy and neuro-fuzzy approaches in clustering, regression, identification, and classification: A survey, Information Sciences, 490(2019), 344-368. doi:10.1016/j.ins.2019.03.060.
[49] Y. Tong, Y. Chen, Z. Zhou, L. Chen, J. Wang, Q. Yang, J. Ye and W. Lv, The simpler the better: a unified approach to predicting original taxi demands based on large-scale online platforms, in: Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, (2017), 1653-1662. doi:10.1145/3097983.3098018.
[50] U. Vanichrujee, T. Horanont, W. Pattara-atikom, T. Theeramunkong and T. Shinozaki, Taxi demand prediction using ensemble model based on rnns and xgboost, in: 2018 International Conference on Embedded Systems and Intelligent Technology International Conference on Information and Communication Technology for Embedded Systems (ICESIT-ICICTES), (2018). doi:10.1109/ICESIT-ICICTES.2018. 8442063.
[51] Y. Wang, T. Wo, H. Yin, J. Xu, H. Chen and K. Zheng, Origin-destination matrix prediction via graph convolution: A new perspective of passenger demand modeling, in: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, (2019), 1227-1235. doi:10.1145/3292500.3330877.
[52] H. Wei, Y. Wang, T. Wo, Y. Liu and J. Xu, Zest, A hybrid model on predicting passenger demand for chauffeured car service, in: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, (2016), 2203-2208. doi:10.1145/2983323.2983667.
[53] P. Xie, T. Li, J. Liu, S. Du, X. Yang and J. Zhang, Urban flow prediction from spatiotemporal data using machine learning: A survey, Information Fusion, 59 (2020), 1-12. arXiv:1908.10218.
[54] J. Xu, R. Rahmatizadeh, L. Bölöni and D. Turgut, A sequence learning model with recurrent neural networks for taxi demand prediction,in: 2017 IEEE 42nd Conference on Local Computer Networks (LCN), IEEE, (2017), 261-268. doi:10.1109/LCN.2017.31.
[55] J. Xu, R. Rahmatizadeh, L. Boloni and D. Turgut, Real-Time prediction of taxi demand using recurrent neural networks,IEEE Transactions on Intelligent Transportation Systems, 19(8) (2018), 2572-2581. doi:10.1109/TITS.2017.2755684.
[56] R. Yager, A model of participatory learning,IEEE Transactions on Systems, Man, and Cybernetics, 20(5) (1990), 1229-1234. doi:10.1109/21.59986.
[57] C. Yang and E. J. Gonzales, Modeling taxi demand and supply in New York city using large-scale taxi GPS data, Springer Geography, (2017), 405-425.
[58] H. Yao, X. Tang, H. Wei, G. Zheng and Z. Li, Revisiting spatial-temporal similarity: A deep learning framework for traffic prediction, in: Proceedings of the AAAI conference on artificial intelligence, 33 (2019), 5668-5675. doi:10.1609/aaai.v33i01.33015668.
[59] H. Yao, F. Wu, J. Ke, X. Tang, Y. Jia, S. Lu, P. Gong, J. Ye and Z. Li, Deep multi-view spatial-temporal network for taxi demand prediction, in: Proceedings of the AAAI Conference on Artificial Intelligence, 32 (2018).
[60] H. Yu, X. Chen, Z. Li, G. Zhang, P. Liu, J. Yang and Y. Yang, Taxi-Based Mobility Demand Formulation and Prediction Using Conditional Generative Adversarial Network-Driven Learning Approaches, IEEE Transactions on Intelligent Transportation Systems, 20(10) (2019), 3888-3899.
[61] G. Zander, Predicting taxi passenger demand using artificial neural networks, Masters thesis, KTH, School of Computer Science and Communication (CSC), (2017).
[62] J. Zhao, C. Chen, H. Huang and C. Xiang, Unifying uber and taxi data via deep models for taxi passenger demand prediction, Personal and Ubiquitous Computing, (2020). doi:10.1007/s00779-020-01426-y.
[63] Y. Zhou, H. Chen, J. Li, Y. Wu, J. Wu and L. Chen, St-attn: Spatial-temporal attention mechanism for multi-step citywide crowd flow prediction,in: 2019 International Conference on Data Mining Workshops (ICDMW), IEEE, Beijing, China, (2019), 609-614. doi:10.1109/ICDMW.2019.00092.
[64] K. Zhang, Z. Liu and L. Zheng, Short-term prediction of passenger demand in multi-zone level: Temporal convolutional neural network with multi-task learning, IEEE Transactions on Intelligent Transportation Systems, 21(4) (2019), 1480-1490. doi:10.1109/TITS.2019.2909571.
[65] D. Zhang, L. Sun, B. Li, C. Chen, G. Pan, S. Li and Z. Wu, Understanding taxi service strategies from taxi gps traces, IEEE Transactions on Intelligent Transportation Systems, 16(1) (2015), 123-135. doi:10.1109/TITS.2014.2328231.
[66] C. Zhang, F. Zhu, Y. Lv, P. Ye and F.-Y. Wang, Mlrnn: Taxi demand prediction based on multi-level deep learning and regional heterogeneity analysis, IEEE Transactions on Intelligent Transportation Systems, (2021). doi:10.1109/TITS.2021.3080511.
[67] C. Zhang, F. Zhu, X. Wang, L. Sun, H. Tang and Y. Lv, Taxi Demand Prediction Using Parallel Multi-Task Learning Model,IEEE Transactions on Intelligent Transportation Systems (August), (2020), 1-10. doi:10.1109/TITS.2020.3015542.
[68] K. Zhu, S. Zhang, W. Zhang and Z. Zhang, A novel hybrid deep learning model for taxi demand forecasting based on decomposition of time series and fusion of text data, Journal of Intelligent & Fuzzy Systems (Preprint), (2021), 1-17. doi:10.3233/JIFS-210657.
[69] Online Learning versus Offline Learning, Machine Learning, 29(1) (1997), 45-63. doi:10.1023/A:1007465907571.