Title: Data chain management for planning in city logistics

Authors: Jan Fabian Ehmke, Stephan Meisel, Stefan Engelmann, Dirk Christian Mattfeld

Addresses: Decision Support Group, Business Information Systems, Carl-Friedrich Gauß Department, University of Braunschweig, Muhlenpfordtstraße 23, Braunschweig 38106, Germany. ' Decision Support Group, Business Information Systems, Carl-Friedrich Gauß Department, University of Braunschweig, Muhlenpfordtstraße 23, Braunschweig 38106, Germany. ' Decision Support Group, Business Information Systems, Carl-Friedrich Gauß Department, University of Braunschweig, Muhlenpfordtstraße 23, Braunschweig 38106, Germany. ' Decision Support Group, Business Information Systems, Carl-Friedrich Gauß Department, University of Braunschweig, Muhlenpfordtstraße 23, Braunschweig 38106, Germany

Abstract: This contribution is about data chain management enabling route planning in city logistics. The transformation of raw data into reliable decisions requires effective data chain management. The data chain closes the gap between empirical collection of raw traffic data and decision-making in terms of route planning. We define the data chain for the support of route planning in city logistics. The data chain transforms raw empirical traffic data into planning data by first and second level aggregation. The single elements of the data chain are investigated in detail. We discuss basic issues of telematics-based data collection, data cleaning and data integration. The key element of the data chain is the aggregation by cluster analysis. Aggregated data is evaluated by explorative data analysis. Finally, the efficient application of aggregated data for route planning is illustrated.

Keywords: data chain management; data mining; city logistics; cluster analysis; route planning; floating car data; FCD; efficient decision making; time-dependent; telematics; information systems; traffic data; data collection; data cleaning; data integration.

DOI: 10.1504/IJDMMM.2009.029030

International Journal of Data Mining, Modelling and Management, 2009 Vol.1 No.4, pp.335 - 356

Published online: 29 Oct 2009 *

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