Predicting Smart City Traffic Models using Adaboost and Gradient Boosting Method

Year 2024, Volume: 14 Issue: 1, 17 - 22, 30.06.2024

Özlem Bezek Güre

https://doi.org/10.36222/ejt.1436180

Abstract

In parallel with the population density in cities, noise, traffic congestion, parking problems and environmental pollution also increase. To address these problems, smart transportation and traffic systems have emerged, which benefit from internet technologies to offer solutions that concern nearly everyone. These systems generate a vast amount of data, often analyzed through machine learning methods. This study has utilized the Adaboost Regression method from the ensemble methods family within the machine learning framework to predict a smart city's traffic model. This method is a combination of many weak learners randomly selected from the data set and created by applying machine learning algorithms to form a strong learner. The Adaboost Regression method has been applied on a smart city traffic models data set found in the Kaggle database. This data set consists of a total of 48,120 rows and 4 columns, including variables such as the number of vehicles, number of intersections, date and time, and ID number. New variables have been created from the date and time variable before starting to analyze the data. The analyses performed with the Adaboost Regression method were carried out in Orange, a free Python-based program. Performance indicators such as Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and the coefficient of determination (R2) have been used in the study. A 10-fold cross-validation method was used to ensure the validity of the model and to avoid overfitting. The analysis resulted in an MSE value of 24.19; RMSE value, 4.91; MAE value, 3.00; and R2, 0.94. In conclusion, it has been observed that the AdaBoost Regression method performs successful predictions with low error rates. The Adaboost Regression method, which estimates with minimum error, is also recommended for applications in areas such as smart grid, smart hospital, and smart home, in addition to smart traffic prediction.

Keywords

Smart City, Vehicle Traffic Prediction, Machine Learning, Ensemble Method, Adaboost Regression

References

• [1] Bawaneh, M., & Simon, V. (2023). Novel traffic congestion detection algorithms for smart city applications. Concurrency and Computation: Practice and Experience, 35(5), e7563.
• [2] Balasubramanian, S. B., Balaji, P., Munshi, A., Almukadi, W., Prabhu, T. N., Venkatachalam, K., & Abouhawwash, M. (2023). Machine learning based IoT system for secure traffic management and accident detection in smart cities. PeerJ Computer Science, 9, e1259.
• [3] Saleem, M., Abbas, S., Ghazal, T. M., Khan, M. A., Sahawneh, N., & Ahmad, M. (2022). Smart cities: Fusion-based intelligent traffic congestion control system for vehicular networks using machine learning techniques. Egyptian Informatics Journal, 23(3), 417-426.
• [4] Hashem, I. A. T., Chang, V., Anuar, N. B., Adewole, K., Yaqoob, I., Gani, A., ... & Chiroma, H. (2016). The role of big data in smart city. International Journal of information management, 36(5), 748-758.
• [5] Hemanth, D. J. (Ed.). (2022). Machine Learning Techniques for Smart City Applications: Trends and Solutions. Springer Nature.
• [6] Devi, T., Alice, K., & Deepa, N. (2022). Traffic management in smart cities using support vector machine for predicting the accuracy during peak traffic conditions. Materials Today: Proceedings, 62, 4980-4984.
• [7] Djenouri, Y., Michalak, T. P., & Lin, J. C. W. (2023). Federated deep learning for smart city edge-based applications. Future Generation Computer Systems, 147, 350-359.
• [8] Oyewola, D. O., Dada, E. G., & Jibrin, M. B. (2022). Smart City Traffic Patterns Prediction Using Machine Learning. In Machine Learning Techniques for Smart City Applications: Trends and Solutions (pp. 123-133). Cham: Springer International Publishing
• [9] Ismaeel, A. G., Janardhanan, K., Sankar, M., Natarajan, Y., Mahmood, S. N., Alani, S., & Shather, A. H. (2023). Traffic pattern classification in smart cities using deep recurrent neural network. Sustainability, 15(19), 14522.
• [10] Mohammed, O., & Kianfar, J. (2018, September). A machine learning approach to short-term traffic flow prediction: A case study of interstate 64 in Missouri. In 2018 IEEE International Smart Cities Conference (ISC2) (pp. 1-7). IEEE.
• [11] Navarro-Espinoza, A., López-Bonilla, O. R., García-Guerrero, E. E., Tlelo-Cuautle, E., López-Mancilla, D., Hernández-Mejía, C., & Inzunza-González, E. (2022). Traffic flow prediction for smart traffic lights using machine learning algorithms. Technologies, 10(1), 5.
• [12] Ramesh, K. (2021). Smart Traffic Prediction and Congestion Reduction in Smart Cities. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(12), 1027-1033.
• [13] Boukerche, A., & Wang, J. (2020). Machine learning-based traffic prediction models for intelligent transportation systems. Computer Networks, 181, 107530.
• [14] An, C., & Wu, C. (2020). Traffic big data assisted V2X communications toward smart transportation. Wireless Networks, 26, 1601-1610.
• [15] Ibrahim, A., & Hafez, A. (2023). Adaptive IEEE 802.11 ah MAC protocol for Optimization Collision Probability in IoT smart city data traffic Based Machine Learning models.
• [16] Lippmann, R., Fried, D., Piwowarski, K., & Streilein, W. (2003, November). Passive operating system identification from TCP/IP packet headers. In Workshop on Data Mining for Computer Security (Vol. 40).
• [17] Yıldırım, P., Birant, U. K., & Birant, D. (2019). EBOC: Ensemble-based ordinal classification in transportation. Journal of Advanced Transportation, 2019, 1-17.
• [18] Ozbayoglu, M., Kucukayan, G., & Dogdu, E. (2016). A real-time autonomous highway accident detection model based on big data processing and computational intelligence. In 2016 IEEE international conference on big data (Big Data) (pp. 1807–1813).
• [19] Niu, X., Zhu, Y., Cao, Q., Zhang, X., Xie, W., & Zheng, K. (2015). An online-traffic-prediction based route finding mechanism for smart city. International Journal of Distributed Sensor Networks, 11(8), 970256.
• [20] Smart City Traffic Patterns. Available online: https://www.kaggle.com/datasets/utathya/smart-city-traffic-patterns/code (accessed on 15 June 2023).
• [21] Shrestha, D. L., & Solomatine, D. P. (2006). Experiments with AdaBoost. RT, an improved boosting scheme for regression. Neural computation, 18(7), 1678-1710.
• [22] Shanmugasundar, G., Vanitha, M., Čep, R., Kumar, V., Kalita, K., & Ramachandran, M. (2021). A comparative study of linear, random forest and adaboost regressions for modeling non-traditional machining. Processes, 9(11), 2015.
• [23] Gupta, K.K.; Kalita, K.; Ghadai, R.K.; Ramachandran, M.; Gao, X.-Z. Machine Learning-Based Predictive Modelling of Biodiesel Production—A Comparative Perspective. Energies 2021, 14, 1122. [CrossRef]
• [24] Kummer, N., & Najjaran, H. (2014). Adaboost. MRT: Boosting regression for multivariate estimation. Artif. Intell. Res., 3(4), 64-76.
• [25] Dahiya, N., Saini, B., & Chalak, H. D. (2021). Gradient boosting-based regression modelling for estimating the time period of the irregular precast concrete structural system with cross bracing. Journal of King Saud University-Engineering Sciences.
• [26] Gumaei, A., Al-Rakhami, M., Al Rahhal, M. M., Albogamy, F. R., Al Maghayreh, E., & AlSalman, H. (2021). Prediction of COVID-19 confirmed cases using gradient boosting regression method. Comput Mater Continua, 66, 315-329.
• [27] Bentéjac, C., Csörgő, A., & Martínez-Muñoz, G. (2021). A comparative analysis of gradient boosting algorithms. Artificial Intelligence Review, 54, 1937-1967.
• [28] Touzani, S., Granderson, J., & Fernandes, S. (2018). Gradient boosting machine for modeling the energy consumption of commercial buildings. Energy and Buildings, 158, 1533-1543.
• [29] Zhang, Y., & Haghani, A. (2015). A gradient boosting method to improve travel time prediction. Transportation Research Part C: Emerging Technologies, 58, 308-324.
• [30] Yangın, G. (2019). XGboost ve Karar Ağacı tabanlı algoritmaların diyabet veri setleri üzerine uygulaması (Master's thesis, Mimar Sinan Güzel Sanatlar Üniversitesi, Fen Bilimleri Enstitüsü).
• [31] Islam, S., & Amin, S. H. (2020). Prediction of probable backorder scenarios in the supply chain using Distributed Random Forest and Gradient Boosting Machine learning techniques. Journal of Big Data, 7(1), 65.
• [32] Kaur, H., Malhi, A. K. & Pannu, H. S. (2020). Machine learning ensemble for neurological disorders. Neural Computing and Applications, 32, 12697-12714.
• [33] Aksu, G. & Doğan, N. (2018). Learning Methods Used in Data Mining Comparison under Different Conditions. Ankara University Journal of Faculty of Educational Sciences (JFES). 51(3). 71-100.
• [34] Zaman, M., Saha, S., & Abdelwahed, S. (2023, June). Assessing the Suitability of Different Machine Learning Approaches for Smart Traffic Mobility. In 2023 IEEE Transportation Electrification Conference & Expo (ITEC) (pp. 1-6). IEEE.
• [35] Alekseeva, D., Stepanov, N., Veprev, A., Sharapova, A., Lohan, E. S., & Ometov, A. (2021). Comparison of machine learning techniques applied to traffic prediction of real wireless network. IEEE Access, 9, 159495-159514.
• [36] Tiwari, P. (2024). The machine learning framework for traffic management in smart cities. Management of Environmental Quality: An International Journal, 35(2), 445-462.
• [37] Zheng, G., Chai, W. K., & Katos, V. (2019, December). An ensemble model for short-term traffic prediction in smart city transportation system. In 2019 IEEE Global Communications Conference (GLOBECOM) (pp. 1-6). IEEE.
• [38] Savithramma, R. M., Sumathi, R., & Sudhira, H. S. (2022). A comparative analysis of machine learning algorithms in design process of adaptive traffic signal control System. In Journal of Physics: Conference Series (Vol. 2161, No. 1, p. 012054). IOP Publishing.