Short-term prediction of border crossing time and traffic volume for commercial trucks: A case study for the Ambassador Bridge |
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| Institution: | 1. University of Windsor, Cross-Border Institute, 401 Sunset Ave., Windsor, ON N9B 3P4 Canada;2. University of Windsor, Department of Civil and Environmental Engineering, 401 Sunset Ave., Windsor, ON N9B 3P4 Canada;3. University of Windsor, Department of Political Science, 401 Sunset Ave., Windsor, ON N9B 3P4 Canada;1. Department of Civil and Urban Engineering, New York University, Six Metrotech Center, 4th Floor, Brooklyn, NY 11201, USA;1. School of Management and Economics, Southeast University, Nanjing, 211189, China;2. National School of Development and Policy, Southeast University, Nanjing, 211189, China;3. Department of Geography, Oklahoma State University, Stillwater, OK, 74075, USA;4. Center for Geographic Analysis, Harvard University, Cambridge, MA, 02138, USA;5. Geo-computation Center for Social Science, Wuhan University, Hubei, 430079, China;6. Department of Landscape Architecture and Urban Planning, Texas A&M University, College Station, TX, 77840, USA;7. School of Law, Southeast University, Nanjing, 211189, China;8. Jiangsu Institute of Industrial Development, Nanjing University of Finance and Economics, Nanjing, 210023, China |
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| Abstract: | Short-term forecasting of traffic characteristics, such as traffic flow, speed, travel time, and queue length, has gained considerable attention from transportation researchers and practitioners over past three decades. While past studies primarily focused on traffic characteristics on freeways or urban arterials this study places particular emphasis on modeling the crossing time over one of the busiest US–Canada bridges, the Ambassador Bridge. Using a month-long volume data from Remote Traffic Microwave Sensors and a yearlong Global Positioning System data for crossing time two sets of ANN models are designed, trained, and validated to perform short-term predictions of (1) the volume of trucks crossing the Ambassador Bridge and (2) the time it takes for the trucks to cross the bridge from one side to the other. The prediction of crossing time is contingent on truck volume on the bridge and therefore separate ANN models were trained to predict the volume. A multilayer feedforward neural network with backpropagation approach was used to train the ANN models. Predicted crossing times from the ANNs have a high correlation with the observed values. Evaluation indicators further confirmed the high forecasting capability of the trained ANN models. The ANN models from this study could be used for short-term forecasting of crossing time that would support operations of ITS technologies. |
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| Keywords: | Short-term forecast Artificial Neural Networks Cross-border Traffic flow Crossing time Ambassador Bridge |
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