A Short-Term Prediction Model for the Number of Registered Motor Vehicles Using Facebook Prophet Forecasting Approach
Downloads
Accurate prediction of the total annual number of motor vehicles to be registered in a country is very important to the government because it will help Driver and Vehicle Licensing Authority (DVLA) policymakers to put good road safety measures in place to make their work successful. This study assessed four standard empirical time series methods for the prediction of the total number of motor vehicles to be registered. The evaluated methods were Facebook Prophet, grey, SARIMA, and ARIMA. In developing the time series model, 70% of the data set obtained from DVLA of Ghana was used and the remaining 30% was used to validate the model’s forecasting adequacy. The robustness of the Facebook Prophet, grey, SARIMA, and ARIMA was assessed using Normalised Root Mean Square Error (NRMSE), Correlation Coefficient (R), Variance Accounted For (VAF), and Performance Index (PI). The analytical results revealed that the Facebook Prophet time series model had the best results with the lowest NRMSE (0.2176) and highest R (0.8229), VAF (66.6475%), and PI (1.1260) values. The study concluded that the Facebook Prophet forecasting approach could be useful to vehicle licensing authority managers and policymakers to give accurate and timely information on future projections of the number of motor vehicles to be registered.
Aguilera, H., Guardiola-Albert, C., Naranjo-Fernández, N., and Kohfahl, C. (2019). Towards Flexible Groundwater-Level Prediction for Adaptive Water Management: Using Facebook’s Prophet Forecasting Approach. Hydrological Sciences Journal, 64(12), 1504-1518.
Agunbiade, D. A. and Peter, E. N. (2013). Modeling and Forecasting Vehicle Registration System: An ARMA Approach. Society for Mathematical Services and Standards, 2(1), 1-13.
Akoglu, H. (2018). User's Guide to Correlation Coefficients. Turkish Journal of Emergency Medicine. 18, 91-93.
https://doi.org/10.1016/j.tjem.2018.08.001.
Almazrouee, A. I., Almeshal, A. M., Almutairi, A. S., Alenezi, M. R., and Alhajeri, S. N. (2020). Long-Term Forecasting of Electrical Loads in Kuwait Using Prophet and Holt-Winters Models. Applied Sciences, 10(16), 5627.
Balochian, S. and Baloochian, H. (2020). Improving Grey Prediction Model and its Application in Predicting the Number of Users of a Public Road Transportation System. Journal of Intelligent Systems, 30(1), 104-114.
Boah-Mensah, E. (2013). The Number of Cars in Ghana Increased by 23%. Accessed: March 19, 2015, www.vehicle population in Ghana.
Fullerton, T. M., Novela, G., Torres, D., and Walke, A. G. (2015). Metropolitan Econometric Electric Utility Forecast Accuracy. International Journal of Energy Economics and Policy, 5(3), 738-745.
Ghimire, B. N. (2017). Application of ARIMA Model for River Discharges Analysis. Journal of Nepal Physical Society, 4(1), 27-32.
Huang, J., Tang, Y., and Chen, S. (2018). Energy Demand Forecasting: Combining Cointegration Analysis and Artificial Intelligence Algorithm. Mathematical Problems in Engineering, 1-13. https://doi.org/10.1155/2018/5194810.
Hyndman, J. R., and Athanasopoulos, G. (2018). Forecasting: Principles and Practice.
Hyun, J., Lee, S. H., Son, H. M., Park, J. U., and Chung, T. M. (2020). A Synthetic Data Generation Model for Diabetic Foot Treatment. In Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications: 7th International Conference, FDSE 2020, Quy Nhon, Vietnam, Proceedings 7, Springer Singapore, 249-264.
Islam, M. R., Ali, S. M., Fathollahi-Fard, A. M., and Kabir, G. (2021). A Novel Particle Swarm Optimisation-Based Grey Model for the Prediction of Warehouse Performance. Journal of Computational Design and Engineering, 8(2), 705-727.
Jameel, A. K., and Evdorides, H. (2019). Review of the Road Crash Data Availability in Iraq. International Journal of Transport and Vehicle Engineering, 13(9), 521-526.
Kordani, A. A., Rooyintan, M., and Boroomandrad, S. M. (2019). Economical and Technical Analysis of Urban Transit System Selection Using TOPSIS Method According to Constructional and Operational Aspects. International Journal of Transport and Vehicle Engineering, 13(7), 398-402.
Ma, D., and Li, J. (2016). Two Novel Grey System Models and their Applications on Landslide Forecasting. Journal of Control Sciences and Engineering, 2016, 1-6.
Papastefanopoulos, V., Linardatos, P., and Kotsiantis, S. (2020). COVID-19: A Comparison of Time Series Methods to Forecast the Percentage of Active Cases Per Population. Applied Science, 10, 1-15. https://doi.org/10.3390/app10113880.
Qasim, T., Alqawasmi, M., Hawash, M., Betar, M., and Qasim, W. (2019). Modeling Jordan University of Science and Technology Parking Using Arena Program. International Journal of Transport and Vehicle Engineering, 13(6), 324-328.
Saini, L. M. (2008). Peak Load Forecasting Using Bayesian Regularization, Resilient and Adaptive Backpropagation Learning-Based Artificial Neural Networks. Electric Power Systems Research, 78(7), 1302–1310. https://doi.org/10.1016/j.epsr.2007.11.003.
Shen, J., Valagolam, D., and McCalla, S. (2020). Prophet Forecasting Model: A Machine Learning Approach to Predict the Concentration of Air Pollutants (PM2.5, PM10, O3, NO2, SO2, CO) in Seoul, South Korea. Peer Journal, 8, 1-18. https://doi.org/10.7717/peerj.9961.
Shomar, S. S. H. (2019). Investigating the Effective Parameters in Determining the Type of Traffic Congestion Pricing Schemes in Urban Streets. International Journal of Transport and Vehicle Engineering, 13(7), 379-385.
Stefenon, S. F., Seman, L. O., Mariani, V. C., and Coelho, L. D. S. (2023). Aggregating Prophet and Seasonal Trend Decomposition for Time Series Forecasting of Italian Electricity Spot Prices. Energies, 16(3), 1371.
Tabachnick, B. G., and Fidell, L. S. (2007). Using Multivariate Statistics, Fifth Edition, Pearson Education, Incorporated.
Taylor, S. J., and Letham, B. (2018). Forecasting at Scale. The American Statistician, 72(1), 37-45. https://doi.org/10.1080/00031305.2017.1380080.
Wei, N., Peng, C., Li, X., Zeng, F., and Lu, X. (2019). Conventional Models and Artificial Intelligence-Based Models for Energy Consumption Forecasting: A Review. Journal of Petroleum Science and Engineering, 181, 1-22. https://doi.org/10.1016/j.petrol.2019.106187.
Weytjens, H., Lohmann, E., and Kleinsteuber, M. (2019). Cash Flow Prediction: MLP and LSTM Compared to ARIMA and Prophet. Electronic Commerce Research, 1-21. https://doi.org/10.1007/s10660-019-09362-7.
Yenidoğan, I., Ҫayır, A., Kozan, O., Dağ, T., and Arslan, ҫ. (2019). Bitcoin Forecasting Using ARIMA and Prophet. Conference Paper, 1-6. https://doi.org/ 10.1109/UBMK.2018.8566476.
Yuan, C., and Chen, D. (2016). Effectiveness of GM (1, 1) Model on Linear Growth Sequence and its Application in Global Primary Energy Consumption Prediction. Emerald, 45(9), 1472-14.
Zhang, X., Zhang, T., Young, A. A., and Li, X. (2014). Applications and Comparisons of Four-Time Series Models in Epidemiological Surveillance Data. Plos One, 9(2), e88075.
