A Sound-based Machine Learning to Predict Traffic Vehicle Density
DOI:
https://doi.org/10.32871/rmrj2109.01.05Keywords:
vehicle density, sound features, sound intensity, machine learning, traffic prediction, random forest, artificial neural network, support vector machineAbstract
Traffic flow mismanagement is a significant challenge in all countries especially in crowded cities. An alternative solution is to utilize smart technologies to predict traffic flow. In this study, frequency spectrum describing traffic sound characteristics is used as an indicator to predict the next five-minute vehicle density. Sound frequency and vehicle intensity are collected during a thirteen-hour data gathering. The collected sound intensity and frequency are then used to learn three machine-learning models - support vector machine, artificial neural network, and random forest and to predict vehicle intensity. It was found out that the performances of the three models based on root-mean-square-error values are 12.97, 16.01, and 10.67, respectively. These initial and satisfactory results pave a new way to predict traffic flow based on traffic sound characteristics which may serve as a better alternative to conventional features.
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Celikoglu, H.B. (2013). An approach to dynamic classification of traffic flow patterns.
Computer-Aided Civil and Infrastructure Engineering, 28(4), 273-288. https://doi.org/10.1111/j.1467-8667.2012.00792.x
Chang, H. C., Kuo, C. C., Chen, Y. T., Wu, W. B., & Piedad, E. J. (2018, August 19-21). Energy consumption level prediction based on classification approach with machine learning technique [Paper presentation]. 4th World Congress on New Technologies (NewTech’18), Madrid, Spain. https://www.semanticscholar.org/paper/Energy-Consumption-Level-Prediction-Based-on-with-Chang-Kuo/f54dd7d4ac0cd1723f4725ef0326d35d8ddb54ed
Chen, Y. T., Piedad, E., & Kuo, C. C. (2019). Energy consumption load forecasting using a level-based random forest classifier. Symmetry, 11(8), 956. https://doi.org/10.3390/sym11080956
The city of the future – sustainable social, economic and environmental management. (2016, April 4). Planet Energies. https://www.planete-energies.com/en/medias/close/city-future-sustainable-social-economic-and-environmental-Management
Halim, H., & Abdullah, R. (2014). Equivalent noise level response to number of vehicles: A comparison between a high traffic flow and low traffic flow highway in Klang Valley, Malaysia. Frontiers in Environmental Science, 2, 1-4. https://doi.org/10.3389/fenvs.2014.00013
Hegina, A. J. (2015, January 20). PH traffic ranks 9th worst in the world-study. Inquirer.Net. https://globalnation.inquirer.net/117785/ph-traffic-ranks-9th-worst-in-the-world-study
Kumar, K., Parida, M., & Katiyar, V.K. (2015). Short term traffic flow prediction in heterogeneous conditions using artificial neural network. Transport, 30(4), 397-405. https://doi.org/10.3846/16484142.2013.818057
Lisangan, E. A., & Sumarta, S. C. (2017, October 26-27). Route selection based on real time traffic condition using Ant Colony System and Fuzzy Inference System [Conference session]. 2017 3rd International Conference on Science in Information Technology (ICSITech), Bandung, Indonesia. https://doi.org/10.1109/ICSITech.2017.8257087
Litman, T. (2006). Smart transportation investments II reevaluating the role of public transit for improving urban transportation. ETDEWEB. https://www.osti.gov/etdeweb/biblio/20818730
Piedad, E. J., Le, T. T., Aying, K., Pama, F. K., & Tabale, I. (2019, October 17-20). Vehicle count system based on time interval image capture method and deep learning Mask R-CNN [Conference session]. IEEE Region 10 Conference (TENCON) 2019. https://doi.org/10.1109/tencon.2019.8929426
Silvano, A., & Bang, K. L. (2016). Impact of speed limits and road characteristics on free-Flow speed in urban areas. Journal of Transportation Engineering, 142(2). https://doi.org/10.1061/(ASCE)TE.1943-5436.0000800
Tan, L. (2016, September 15). Waze: Cebu is the worst place in the world for drivers. CNN. https://cnnphilippines.com/news/2016/09/15/Cebu-Metro-Manila-Philippines-traffic-Waze.html#:~:text=GPS%2Dbased%20traffic%20app%20Waze,countries%20and%20235%20metro%20areas
Vergel, K. N., Cacho, F. T., & Capiz, C. L. E. (2004). A study on roadside noise generated by tricycles. Philippine Engineering Journal, 25(12), 1–22. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.919.8016&rep=rep1&type=pdf
Vlahogianni, E. I., & Karlaftis, M. G., & Golias, J.C. (2014). Short-term traffic forecasting: Where we are and where we’re going. Transportation Research Part C: Emerging Technology, 43(Part 1), 3-19. https://doi.org/10.1016/j.trc.2014.01.005
Wild, D. (1997). Short-term forecasting based on a transformation and classification of traffic volume time series. International Journal of Forecasting, 13(1), 63–72. https://doi.org/10.1016/S0169-2070(96)00701-7
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Published
2021-05-29
How to Cite
Flores, G., Piedad, E. J., Figueroa, A., Tumamak, R., & Berdon, N. J. M. (2021). A Sound-based Machine Learning to Predict Traffic Vehicle Density. Recoletos Multidisciplinary Research Journal, 9(1), 55–62. https://doi.org/10.32871/rmrj2109.01.05
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