Development of Traffic Volume Forecasting Using Multiple Regression Analysis and Artificial Neural Network
The purpose of this study is to develop a model for traffic volume forecasting of the road network in Anamorava Region. The description of the current traffic volumes is enabled using PTV Visum software, which is used as an input data gained through manual and automatic counting of vehicles and interviewing traffic participants. In order to develop the forecasting model, there has been the necessity to establish a data set relying on time series which enables interface between demographic, socio-economic variables and traffic volumes. At the beginning models have been developed by MLR and ANN methods using original data on variables. In order to eliminate high correlation between variables appeared by individual models, PCA method, which transforms variables to principal components (PCs), has been employed. These PCs are used as input in order to develop combined models PCA-MLR and PCA-RBF in which the minimization of errors in traffic volumes forecasting is significantly confirmed. The obtained results are compared to performance indicators such R2, MAE, MSE and MAPE and the outcome of this undertaking is that the model PCA-RBF provides minor errors in forecasting.
Ortuzar, J.D., and Williamson, L.G. “Modelling Transport, Fourth Edition”. United Kingdom: John Wiley and Sons Ltd (2011).
Ministry of Infrastructure-Directorate of Roads. “Data for Traffic Volumes by Automatic Counting for the Period 2004-2016”. Prishtina.
Fu, Miao, J. Andrew Kelly, and J. Peter Clinch. “Estimating Annual Average Daily Traffic and Transport Emissions for a National Road Network: A Bottom-up Methodology for Both Nationally-Aggregated and Spatially-Disaggregated Results.” Journal of Transport Geography 58 (January 2017): 186–195. doi:10.1016/j.jtrangeo.2016.12.002.
Morf, F.T., and Houska, V.F. “Traffic Growth Pattern on Rural Areas”. Highway Research Board Bulletin 194. (1958).
Tennant, B. “Forecasting Rural Road Travel in Developing Countries from Land Use Studies”. Transport Planning in Developing Countries, Proc., Summer Annual Meeting, Planning and Transport Research and Computation Company, Ltd., Univ. of Warwick, Coventry, Warwickshire, England. Accession Number: 00148235, 153-163. (1975).
Neveu, A.J. “Quick Response Procedure to Forecast Rural Traffic”. Transportation Research Record 944, (1982). 47-53.
Fricker, Jon, and Sunil Saha. “Traffic Volume Forecasting Methods for Rural State Highways” (1986). doi:10.5703/1288284314120.
Varagouli, E.G., Simos, T.E., and Xeidakis, G.S. “Fitting a Multiple Regression Line to Travel Demand Forecasting: The Case of Prefecture of Xanthi, Northern Greece”. Mathematical and Computer Modelling, Vol. 42, (2005). 817-836. doi: 10.1016/j.mcm.2005.09.010.
Miksic, Stefica, Maja Miskulin, Brankica Juranic, Zeljko Rakosec, Aleksandar Vcev, Dunja Degmecic, et al. “Depression And Suicidality During Pregnancy.” Psychiatria Danubina 30, no. 1 (March 15, 2018): 85–90. doi:10.24869/psyd.2018.85.
Semeida, A.M. “Derivation of Travel Demand Forecasting Models for Low Population Areas: The Case of Port Said Governorate, North East Egypt”. Journal of Traffic and Transportation Engineering, Vol. 1, No. 3. (2014). 196-208. doi:10.1016/S2095-7564(15)30103-3.
Karlaftis, M.G., and Vlahogianni, E.I. “Statistical Methods versus Neural Networks in Transportation, Research: Differences, Similarities and Some Insights”. Transportation Research an International Journal Part Emerging Technologies. Vol. 19, No. 3 (2011), 387–399. doi:10.1016/j.trc.2010.10.004.
Yun, S.-Y., S. Namkoong, J.-H. Rho, S.-W. Shin, and J.-U. Choi. “A Performance Evaluation of Neural Network Models in Traffic Volume Forecasting.” Mathematical and Computer Modelling 27, no. 9–11 (May 1998): 293–310. doi:10.1016/s0895-7177(98)00065-x.
Adamo, M. “Estimation of Annual Average Daily Traffic Volumes Using Neural Networks”. Faculty of Science, Laurentian University (1994).
Sharma, S.C., Lingras, P., Liu, G.X., and Xu, F. “Estimation of Annual Average Daily Traffic on Low-Volume Roads Factor Approach Versus Neural Networks”. Transportation Research Record, Journal of the Transportation Research Board, Vol. 1719, (2000). 103–111. doi:10.3141/1719-13.
Tang, Y.F., Lam, W.H.K., and Pan, L.P. “Comparison of Four Modelling Techniques for Short-Term AADT Forecasting in Hong Kong”. Journal of Transport Engineering, Vol. 129, No. 3. (2003); 271–277. doi:10.1061/(ASCE)0733-947X (2003)129:3(271).
Duddu, V.R., ASCE, A.M., Pulugurtha, S.S., and ASCE, M. “Principle of Demographic Gravitation to Estimate Annual Average Daily Traffic: Comparisons of Statistical and Neural Network Models”. American Society of Civil Engineers. Vol. 139, No. 6, (2013). 585-595. doi:10.1061/(asce)te.1943-5436.0000537.
Sababa, I. “Estimation of Annual Average Daily Traffic and Missing Hourly Volume Using Artificial Intelligence”. A Thesis Presented to the Graduate School of Clemson University. (2016).
Park, B., Messer, C.J., and Urbanik, T. “Short-term Freeway Traffic Volume Forecasting Using Radial Basis Function Neural Network”. Transportation Research Record, Journal of the Transportation Research Board, Vol. 1651, No. 1, (1998). 39-47. doi:10.3141/1651-06.
Zhang, X.I., and He G.G. “Forecasting Approach for Short Term Traffic Flow Based on Principal Component Analysis and Combined Neural Network”. Systems Engineering -Theory & Practice, Vol. 27, No. 8, (2007), 167–171. doi:10.1016/S1874-8651(08)60052-6.
Doustmohammadi, M., and M. Anderson. "Developing direct demand AADT forecasting models for small and medium sized urban communities." Int. J. Traff. Transp. Eng 5, no. 2 (2016): 27-31.
Raja, P., Doustmohammadi,M., and Anderson, M. D. “Estimation of Average Daily Traffic on Low Volume Roads in Alabama”. International Journal of Traffic and Transportation Engineering (2018), 7(1): 1-6.
Khan, Sakib Mahmud, Sababa Islam, MD Zadid Khan, Kakan Dey, Mashrur Chowdhury, Nathan Huynh, and Mohammad Torkjazi. “Development of Statewide Annual Average Daily Traffic Estimation Model from Short-Term Counts: A Comparative Study for South Carolina.” Transportation Research Record: Journal of the Transportation Research Board 2672, no. 43 (November 8, 2018): 55–64. doi:10.1177/0361198118798979.
Fu, M., Kelly, J. A, and Clinch J. P. “Estimating annual average daily traffic and transport emissions for a national road network: A bottom-up methodology for both nationally-aggregated and spatially-disaggregated results”. Journal of Transport Geography 58 (2017) 186–195. doi.org/10.1016/j.jtrangeo.2016.12.002.
Kosovo Census Atlas. “Geographic and Administrative Division of Kosovo”. Kosovo Statistics Agency. (2013), Prishtina.
Alonso, B., Mouram, J. L., Ibeas, A., and Romero J. P. “Estimation of Annual Average Daily Traffic with Optimal Adjustment Factors”. Proceedings of the Institution of Civil Engineers Transport, Vol. 168, No. TR5, (2015), 406–414. doi:10.1680/tran.12.00074 Paper 1200074.
PTV AG. (2008). “How to work with TFlow Fuzzy”. Official Manual, PTV Vision Visum, Karlsruhe.
TII Publications PE-PAG-02015. (2016). “Project Appraisal Guidelines for National Roads Unit 5.1 – Construction of Transport Models”. Transport Infrastructure Ireland (TII).
PTV Visum15 User’s manual. PTV Group. (2016), Karlsruhe, Germany.
Kosovo Agency Statistic. General Statistics of Kosovo (2016), Pristine.
Feng, X., Gan, T., Wang, X., Sun, Q., and Ma, F. “Feedback Analysis of Urban Densities and Travel Mode Split”. International Journal of Simulation Modelling, Vol. 14, No. 2, (2015), 349-358. doi:10.2507/IJSIMM14(2)C09.
Rawlings,J.H., Pantula, S.G., Dickey, D.A. “Applied Regression Analysis: A Research Tool”. Second Edition. Springer. (1998).
Pelosi, M.K., and Sandifer, TH.M. Elementary Statistics from Discovery to Decision. John Willey&Sons. ISBN-13: 978-0471401421.
Kosun, C., Tayfur, G., and Celik, H.M. “Soft Computing and Regression Modelling Approaches for Link-Capacity Function”. International Journal of Non-Standard Computing and Artificial Intelligence, Vol. 2, (2016), 129–140, doi:10.14311/NNW.2016.26.007.
Pamuła, T. “Neural Networks in Transportation Research – Recent Applications”. Transport Problems, Vol. 11, No. 2, (2016), 27-36. doi:10.20858/tp.2016.11.2.3.
Yu., B., Wang Y.T., Yao J.B., and Wang J.Y. “A Comparison of the Performance of ANN and SVM for the Prediction of Traffic Accident Duration”. International Journal of Non-Standard Computing and Artificial Intelligence”. Neural Network World 3/2016, 271–287. doi:10.14311/NNW.2016.26.015.
Karim A., Adeli H., and ASCE, F. “Radial Basis Function Neural Network for Work zone Capacity and Queue Estimation”. Journal of Transportation Engineering, ASCE, Vol. 129, No. 5, 494-503. doi:10.1061/(ASCE)0733-947X (2003)129:5(494).
Saric, T., Simunovic, G., and Simunovic, K. “Use of Neural Networks and Simulation of Steel Surface Roughness”. International Journal of Simulation Modelling, Vol. 12, No. 4, (2013), 225-236. doi:10.2507/IJSIMM12(4)2.241.
Wey, C.C. “RBF Neural Networks Combined with Principal Component Analysis Applied to Quantitative Precipitation Forecast for Reservoir Watershed During Typhoon Periods”. Journal of Hydrometeorology, Vol. 13, (2012), 722-734. doi:10.1175/JHM-D-11-03.1.
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