Estimation of missing traffic counts using factor,genetic, neural,and regression techniques |
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| Institution: | 1. Faculty of Engineering, University of Regina, Regina, SK, Canada S4S 0A2;2. Department of Mathematics and Computing Science, Saint Mary’s University, Halifax, NS, Canada B3H 3C3;1. IBM T.J. Watson Research Center, P.O. Box 218, Yorktown Heights, NY 10598, United States;2. Department of Civil Engineering, University of Minnesota, 500 Pillsbury Drive S.E., Minneapolis, MN 55455, United States;1. Department of Civil and Environmental Engineering, University of Hawaii at Manoa, Honolulu, HI 96822, USA;2. School of Computer Science and Technology, East China Normal University, Shanghai, China;1. Department of Civil, Geological and Mining Engineering, Polytechnique Montreal, Montreal, QC H3T 1J4, Canada;2. School of Intelligent Systems Engineering, Sun Yat-Sen University, Guangzhou 510006, China;3. Department of Civil Engineering, McGill University, Montreal, QC H3A 0C3, Canada;1. OPTIMA Unit, TECNALIA, P. Tecnologico Bizkaia, Ed. 700, 48160 Derio, Spain;2. Dept. of Communications Engineering, University of the Basque Country UPV/EHU, Alameda Urquijo S/N, 48013 Bilbao, Spain;3. Basque Center for Applied Mathematics (BCAM), 48009 Bilbao, Spain |
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| Abstract: | Analyses from some of the highway agencies show that up to 50% permanent traffic counts (PTCs) have missing values. It will be difficult to eliminate such a significant portion of data from traffic analysis. Literature review indicates that the limited research uses factor or autoregressive integrated moving average (ARIMA) models for predicting missing values. Factor-based models tend to be less accurate. ARIMA models only use the historical data. In this study, genetically designed neural network and regression models, factor models, and ARIMA models were developed. It was found that genetically designed regression models based on data from before and after the failure had the most accurate results. Average errors for refined models were lower than 1% and the 95th percentile errors were below 2% for counts with stable patterns. Even for counts with relatively unstable patterns, average errors were lower than 3% in most cases. |
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