A COMPARATIVE STUDY OF CATBOOST AND ARTIFICIAL NEURAL NETWORKS IN ENHANCING TRIP GENERATION MODELLING FOR ILORIN CITY
Abstract
Trip generation plays a crucial role in transportation planning, and the choice of an appropriate model is essential for predicting future travel patterns. This study focuses on comparing the suitability and performance of CatBoost and ANN for trip generation (production and attraction) modelling of Ilorin City. By incorporating Ilorin household and trip characteristics, population data, and maps, this study evaluates the performance of the models. The two models demonstrated high accuracy and performance. In terms of trip production, the CatBoost model displayed exceptional accuracy, attaining an R-squared value of 0.99999992016446, accompanied by an impressively low mean squared error (MSE) of 3.93870930136429e-05. In contrast, the neural network exhibited a slightly lower accuracy of 0.999873850524181, with an error value of 0.0581313408911228. Similarly, for trip attraction, the CatBoost model showcased remarkable accuracy and precision, achieving an accuracy score of 0.9999999999999994 and an extremely low error value of 2.26762031965784e-13. The neural network model demonstrated an accuracy of 0.99999999990335 and a negligible error value of 0.000000041994. These findings underscore the strong predictive capabilities of both models for trip production and attraction, with the CatBoost model notably excelling in achieving nearly flawless accuracy and minimal error values across both aspects in Ilorin. Further research can explore the application of other advanced machine-learning techniques and combine their strengths to enhance the accuracy and robustness of trip-generation models.
References
Tillema, F., Van Zuilekom, K. M., & Van Maarseveen, M. F. A. M. (2006). Comparison of neural networks and gravity models in trip distribution. Computer‐Aided Civil and Infrastructure Engineering, 21(2), 104–119.
Heyns, W., & Jaarsveld, S. (2017). Transportation, Land Use and Integration: Perspectives for Developing Countries. South Africa. WIT Press.
Nwafor, E. O., Aderinlewo, O. O., & Atoo, A. A. (2018). Trip Generation Model for Makurdi Metropolis, Benue State. International Journal of Civil Engineering and Construction Science, 5(1), 17–24.
Chakroborty, P., & Das, A. (2017). Principles of transportation engineering. PHI Learning Pvt. Ltd.
Ahmed, B. (2012). The traditional four steps transportation modeling using a simplified transport network: A case study of Dhaka City, Bangladesh. International Journal of Advanced Scientific Engineering and Technological Research, 1(1), 19–40.
Ogunbodede, E. F., & Ale, A. S. (2015). The Regression Model In The Forecast Of Travel Demand In Akure, Nigeria. Analele Universităţii din Oradea, Seria Geografie, 2, 186–194.
Mannering, F. L., & Washburn, S. S. (2020). Principles of highway engineering and traffic analysis. John Wiley & Sons.
Ha-Minh, C., Tang, A. M., Bui, T. Q., Vu, X. H., & Huynh, D. V. K. (2022). CIGOS 2021, Emerging Technologies and Applications for Green Infrastructure. Springer.
Ahmad, T., & Aziz, M. N. (2019). Data preprocessing and feature selection for machine learning intrusion detection systems. ICIC Express Lett, 13(2), 93–101. https://doi.org/10.24507/icicel.13.02.93
Zhang, Z., Li, M., Lin, X., Wang, Y., & He, F. (2019). Multistep speed prediction on traffic networks: A deep learning approach considering spatio-temporal dependencies. Transportation Research Part C: Emerging Technologies, 105, 297–322. https://doi.org/10.1016/j.trc.2019.05.039
Castro-Neto, M., Jeong, Y.-S., Jeong, M.-K., & Han, L. D. (2009). Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions. Expert Systems with Applications, 36(3), 6164–6173. https://doi.org/10.1016/j.eswa.2008.07.069
Fusco, G., Colombaroni, C., & Isaenko, N. (2016). Short-term speed predictions exploiting big data on large urban road networks. Transportation Research Part C: Emerging Technologies, 73, 183–201. https://doi.org/10.1016/j.trc.2016.10.019
Zhang, L., Liu, Q., Yang, W., Wei, N., & Dong, D. (2013). An Improved K-nearest Neighbor Model for Short-term Traffic Flow Prediction. Procedia - Social and Behavioral Sciences, 96, 653–662. https://doi.org/10.1016/j.sbspro.2013.08.076
Habtemichael, F. G., & Cetin, M. (2016). Short-term traffic flow rate forecasting based on identifying similar traffic patterns. Transportation Research Part C: Emerging Technologies, 66, 61–78. https://doi.org/10.1016/j.trc.2015.08.017
Cai, P., Wang, Y., Lu, G., Chen, P., Ding, C., & Sun, J. (2016). A spatiotemporal correlative k-nearest neighbor model for short-term traffic multistep forecasting. Transportation Research Part C: Emerging Technologies, 62, 21–34. https://doi.org/10.1016/j.trc.2015.11.002
Hamner, B. (2010). Predicting Travel Times with Context-Dependent Random Forests by Modeling Local and Aggregate Traffic Flow. In 2010 IEEE International Conference on Data Mining Workshops (pp. 1357–1359). IEEE. https://doi.org/10.1109/ICDMW.2010.128
Jiber, M., Mbarek, A., Yahyaouy, A., Sabri, M. A., & Boumhidi, J. (2020). Road Traffic Prediction Model Using Extreme Learning Machine: The Case Study of Tangier, Morocco. Information, 11(12), 542. https://doi.org/10.3390/info11120542
Ma, X., Tao, Z., Wang, Y., Yu, H., & Wang, Y. (2015). Long short-term memory neural network for traffic speed prediction using remote microwave sensor data. Transportation Research Part C: Emerging Technologies, 54, 187–197. https://doi.org/10.1016/j.trc.2015.03.014
Gu, Y., Lu, W., Xu, X., Qin, L., Shao, Z., & Zhang, H. (2020). An Improved Bayesian Combination Model for Short-Term Traffic Prediction With Deep Learning. IEEE Transactions on Intelligent Transportation Systems, 21(3), 1332–1342. https://doi.org/10.1109/TITS.2019.2939290
Ma, X., Dai, Z., He, Z., Ma, J., Wang, Y., & Wang, Y. (2017). Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction. Sensors, 17(4), 818. https://doi.org/10.3390/s17040818
Wang, J., Gu, Q., Wu, J., Liu, G., & Xiong, Z. (2016). Traffic Speed Prediction and Congestion Source Exploration: A Deep Learning Method. In 2016 IEEE 16th International Conference on Data Mining (ICDM) (pp. 499–508). IEEE. https://doi.org/10.1109/ICDM.2016.0061
Li, Y., Yu, R., Shahabi, C., & Liu, Y. (2017). Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. arXiv preprint arXiv:1707.01926. https://doi.org/10.48550/arXiv.1707.01926
Ke, J., Zheng, H., Yang, H., & Chen, X. (Michael). (2017). Short-term forecasting of passenger demand under on-demand ride services: A spatio-temporal deep learning approach. Transportation Research Part C: Emerging Technologies, 85, 591–608. https://doi.org/10.1016/j.trc.2017.10.016
Polson, N. G., & Sokolov, V. O. (2017). Deep learning for short-term traffic flow prediction. Transportation Research Part C: Emerging Technologies, 79, 1–17. https://doi.org/10.1016/j.trc.2017.02.024
Defferrard, M., Bresson, X., & Vandergheynst, P. (2016). Convolutional neural networks on graphs with fast localized spectral filtering. Advances in neural information processing systems, 29. https://doi.org/10.48550/arXiv.1606.09375
Adeke, P. T., Inalegwu, O. J., & Jirgba, K. (2019). Prediction of Bus Travel Time on Urban Routes Without Designated Bus Stops in Makurdi Town, Benue State, Nigeria. Arid Zone Journal of Engineering, Technology and Environment, 15(2), 406–417.
Eromietse, E. J., & Joseph, O. O. (2019). Comparative Assessment of Radial Basis Function Neural Network and Multiple Linear Regression Application to Trip Generation Modelling in Akure, Nigeria. International Journal for Traffic And Transport Engineering, 9(2), 163–176. https://doi.org/10.7708/ijtte.2019.9(2).03
Stuart, R., & Peter, N. (2016). Artificial intelligence-a modern approach 3rd ed. Berkeley.
Zheng, F. (2011). Modelling urban travel times. PhD Thesis, Faculty of Civil Engineering and Geosciences, Delft University, Netherlands.
Demir, S., & Sahin, E. K. (2023). Predicting occurrence of liquefaction-induced lateral spreading using gradient boosting algorithms integrated with particle swarm optimization: PSO-XGBoost, PSO-LightGBM, and PSO-CatBoost. Acta Geotechnica, 18(6), 3403–3419. https://doi.org/10.1007/s11440-022-01777-1
Wang, Q., Li, Z., Cai, J., Zhang, M., Liu, Z., Xu, Y., & Li, R. (2023). Spatially adaptive machine learning models for predicting water quality in Hong Kong. Journal of Hydrology, 622, 129649. https://doi.org/10.1016/j.jhydrol.2023.129649
Landry, M., Erlinger, T. P., Patschke, D., & Varrichio, C. (2016). Probabilistic gradient boosting machines for GEFCom2014 wind forecasting. International Journal of Forecasting, 32(3), 1061–1066. https://doi.org/10.1016/j.ijforecast.2016.02.002
Gong, M., Bai, Y., Qin, J., Wang, J., Yang, P., & Wang, S. (2020). Gradient boosting machine for predicting return temperature of district heating system: A case study for residential buildings in Tianjin. Journal of Building Engineering, 27, 100950. https://doi.org/10.1016/j. jobe.2019.100950
Dorogush, A. V., Ershov, V., & Gulin, A. (2018). CatBoost: gradient boosting with categorical features support. arXiv preprint arXiv:1810.11363. https://doi.org/10.48550/arXiv.1810.11363
Bakhareva, N., Shukhman, A., Matveev, A., Polezhaev, P., Ushakov, Y., & Legashev, L. (2019). Attack Detection in Enterprise Networks by Machine Learning Methods. In 2019 International Russian Automation Conference (RusAutoCon) (pp. 1–6). IEEE. https://doi.org/10.1109/RUSAUTOCON.2019.8867696
Kataev, G., Varkentin, V., & Nikolskaia, K. (2020). Method to estimate pedestrian traffic using convolutional neural network. Transportation Research Procedia, 50, 234–241. https://doi.org/10.1016/j.trpro.2020.10.029
Zhu, J. Z., Cao, J. X., & Zhu, Y. (2014). Traffic volume forecasting based on radial basis function neural network with the consideration of traffic flows at the adjacent intersections. Transportation Research Part C: Emerging Technologies, 47, 139–154. https://doi.org/10.1016/j.trc.2014.06.011
Fu, R., Zhang, Z., & Li, L. (2016). Using LSTM and GRU neural network methods for traffic flow prediction. In 2016 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC) (pp. 324–328). IEEE. https://doi.org/10.1109/YAC.2016.7804912
Ma, Y., Zhang, Z., & Ihler, A. (2020). Multi-Lane Short-Term Traffic Forecasting With Convolutional LSTM Network. IEEE Access, 8, 34629–34643. https://doi.org/10.1109/ACCESS.2020.2974575
More, R., Mugal, A., Rajgure, S., Adhao, R. B., & Pachghare, V. K. (2016). Road traffic prediction and congestion control using Artificial Neural Networks. In 2016 International Conference on Computing, Analytics and Security Trends (CAST) (pp. 52–57). IEEE. https://doi.org/10.1109/CAST.2016.7914939
Kolidakis, S., Botzoris, G., Profillidis, V., & Lemonakis, P. (2019). Road traffic forecasting — A hybrid approach combining Artificial Neural Network with Singular Spectrum Analysis. Economic Analysis and Policy, 64, 159–171. https://doi.org/10.1016/j.eap.2019.08.002
Loumiotis, I., Demestichas, K., Adamopoulou, E., Kosmides, P., Asthenopoulos, V., & Sykas, E. (2018). Road traffic prediction using artificial neural networks. In 2018 South-Eastern European Design Automation, Computer Engineering, Computer Networks and Society Media Conference (SEEDA_CECNSM) (pp. 1–5). IEEE. https://doi.org/10.23919/seeda- cecnsm.2018.8544943
Sun, S., Wu, H., & Xiang, L. (2020). City-Wide Traffic Flow Forecasting Using a Deep Convolutional Neural Network. Sensors, 20(2), 421. https://doi.org/10.3390/s20020421
Do, L. N. N., Vu, H. L., Vo, B. Q., Liu, Z., & Phung, D. (2019). An effective spatial-temporal attention based neural network for traffic flow prediction. Transportation Research Part C: Emerging Technologies, 108, 12–28. https://doi.org/10.1016/j.trc.2019.09.008
Xia, Y., He, L., Li, Y., Liu, N., & Ding, Y. (2020). Predicting loan default in peer‐to‐peer lending using narrative data. Journal of Forecasting, 39(2), 260–280. https://doi.org/10.1002/for.2625
Fan, J., Wang, X., Zhang, F., Ma, X., & Wu, L. (2020). Predicting daily diffuse horizontal solar radiation in various climatic regions of China using support vector machine and tree-based soft computing models with local and extrinsic climatic data. Journal of Cleaner Production, 248, 119264. https://doi.org/10.1016/j.jclepro.2019.119264
Yang, H., & Bath, P. A. (2020). The Use of Data Mining Methods for the Prediction of Dementia: Evidence From the English Longitudinal Study of Aging. IEEE Journal of Biomedical and Health Informatics, 24(2), 345–353. https://doi.org/10.1109/JBHI.2019.2921418
Coma-Puig, B., & Carmona, J. (2019). Bridging the Gap between Energy Consumption and Distribution through Non-Technical Loss Detection. Energies, 12(9), 1748. https://doi.org/10.3390/en12091748
Punmiya, R., & Choe, S. (2019). Energy Theft Detection Using Gradient Boosting Theft Detector With Feature Engineering-Based Preprocessing. IEEE Transactions on Smart Grid, 10(2), 2326–2329. https://doi.org/10.1109/TSG.2019.2892595
Najm, S. M., Trzepieciński, T., & Kowalik, M. (2023). Modelling and parameter identification of coefficient of friction for deep-drawing quality steel sheets using the CatBoost machine learning algorithm and neural networks. The International Journal of Advanced Manufacturing Technology, 124(7–8), 2229–2259. https://doi.org/10.1007/s00170-022-10544-1
Zhang, Y., Zhao, Z., & Zheng, J. (2020). CatBoost: A new approach for estimating daily reference crop evapotranspiration in arid and semi-arid regions of Northern China. Journal of Hydrology, 588, 125087. https://doi.org/10.1016/j.jhydrol.2020.125087
Jabeur, S. Ben, Gharib, C., Mefteh-Wali, S., & Arfi, W. Ben. (2021). CatBoost model and artificial intelligence techniques for corporate failure prediction. Technological Forecasting and Social Change, 166, 120658. https://doi.org/10.1016/j.techfore.2021.120658
Nguyen, N., Duong, T., Chau, T., Nguyen, V.-H., Trinh, T., Tran, D., & Ho, T. (2022). A Proposed Model for Card Fraud Detection Based on CatBoost and Deep Neural Network. IEEE Access, 10, 96852–96861. https://doi.org/10.1109/ACCESS.2022.3205416
Zhang, F., Fleyeh, H., & Bales, C. (2022). A hybrid model based on bidirectional long short-term memory neural network and Catboost for short-term electricity spot price forecasting. Journal of the Operational Research Society, 73(2), 301–325. https://doi.org/10.1080/01605682.2020.1843976
Zhang, S., Liu, H., Yang, Y., Zhang, S., Zhang, Z., Wang, C., & Wang, M. (2023). Prediction of traffic accident impact range based on CatBoost ensemble algorithm. In A. Palanisamy Muthuramalingam & K. Subramaniam (Eds.), Second International Conference on Algorithms, Microchips, and Network Applications (AMNA 2023) (Vol. 12635, p. 54). SPIE. https://doi.org/10.1117/12.2679147
Sergoyan, H. (2020). Estimating Time of Driver Arrival with Gradient Boosting Algorithms and Deep Neural Networks. Mathematical Problems of Computer Science, 53, 29–38. https://doi.org/10.51408/1963-0050
Reddy, D. K. K., & Behera, H. S. (2022). CatBoosting Approach for Anomaly Detection in IoT-Based Smart Home Environment. In Computational Intelligence in Data Mining: Proceedings of ICCIDM 2021 (pp. 753–764). Springer. https://doi.org/10.1007/978-981-16-9447-9_56
Liu, W., Deng, K., Zhang, X., Cheng, Y., Zheng, Z., Jiang, F., & Peng, J. (2020). A Semi-Supervised Tri-CatBoost Method for Driving Style Recognition. Symmetry, 12(3), 336. https://doi.org/10.3390/sym12030336
National Population Commission (NPC). (2006). National Population Census of Federal Republic of Nigeria Official Gazette (2nd ed., Vol. 96).
National Bureau of Statistics (NBS) Federal Republic of Nigeria (2012). (n.d.). Annual Abstract of Statistics.
Fasakin, J.O., Basorun, J. O., Bello, M. O., Enisan, O. F., Ojo, B., and Popoola, O. O. (2018). Effect of Land Pricing on Residential Density Pattern in Akure, Nigeria. Advances in Social Sciences Research Journal, 5(1). https://doi.org/10.14738/assrj.51.4003
Adam, A. M. (2020). Sample Size Determination in Survey Research. Journal of Scientific Research and Reports, 26, 90–97. https://doi.org/10.9734/jsrr/2020/v26i530263
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