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The effects of the 2013 floods on Germany’s freight traffic
Institution:1. DIW Berlin, Mohrenstraße 58, 10117 Berlin, Germany;2. Humboldt Universität zu Berlin, Unter den Linden 6, 10099 Berlin, Germany;1. School of Civil Engineering, College of Engineering, University of Tehran, Iran;2. Civil Engineering Department, Faculty of Engineering, Bu-Ali Sina University, Hamedan, Iran;3. Department of Urban Management, Kyoto University, Japan;1. Economic Development and Social Sustainability Research Group, University Institute of Maritime Studies, Department of Economic Analysis and Business Administration, Faculty of Economy and Business, University of A Coruña, Spain;2. University Institute of Maritime Studies, University of A Coruña, Spain;3. Faculty of Economics and Business Administration, “Constantin Brancusi” University of Targu Jiu, Romania;1. School of Economics and Management, Shanghai Maritime University, Shanghai 201306, China;2. Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong SAR, China;3. Logistics Engineering College, Shanghai Maritime University, Shanghai 201306, China;4. Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong SAR, China;1. School of Urban and Regional Planning, Florida Atlantic University, Building 44, Room 284, 777 Glades Road, Boca Raton, FL 33431, United States;2. Department of Civil and Environmental Engineering, Louisiana State University, 3330C Patrick F. Taylor Hall, Baton Rouge, LA 70803, United States;3. Glenn Department of Civil Engineering, Clemson University, Lowry Hall, Clemson, SC 29634, United States;4. Department of Civil and Environmental Engineering, California Polytechnic State University, San Luis Obispo, Office 12-212, San Luis Obispo, CA 93407, United States
Abstract:This article analyzes the spatio-temporal effect of the 2013 floods on freight traffic in Germany by using automatic traffic counter data. The methodology uses a proven time-series outlier detection and identification technique to endogenously determine if a counter was affected during the flood and estimate the magnitude and duration of the change in the number of vehicles passing through it. This is the first paper able to quantify climate-related variations in traffic across all the counters of a national network. Results show variations on 10% of all counters and 23% of all main roads. Results allow us to trace the configuration of disrupted and detour routes, recovery times, and the total effect on the network. Our findings serve as an input to other studies on the impact of exogenous events on the transport system and contribute towards the formulation of public policies to improve road resilience.
Keywords:Natural disasters  Flooding  Road network  Freight traffic  Automatic traffic counters  Outliers in time series
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