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Abstract
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Nowadays, the diffusion of in-car navigators, location-enabled smartphones and various reasons for tracking vehicles - either for insurance and recovery, fleet management or for electronic tolling - are making floating car data (FCD) a leading solution for traffic monitoring. In the next years, this solution might be much more strengthened by the introduction and diffusion of black boxes, installed on commercial or private vehicles devoted to monitor or validate new safety technologies (e.g., the automatic in-vehicle emergency call service eCall in Europe).1
More details can be found on the official website of the European Commission for Mobility and Transport, http://ec.europa.eu/transport/road_safety/specialist/knowledge/esave/esafety_measures_known_safety_effects/black_boxes_in_vehicle_data_recorders_en.htm, and of the European Parliament, http://www.europarl.europa.eu/sides/getDoc.do?pubRef=-//EP//TEXT+PV+20140225+ITEM-013+DOC+XML+V0//EN. For eCall see Directive 2010/40/EU of the European Parliament and of the Council of 7 July 2010, on the framework for the deployment of Intelligent Transport Systems in the field of road transport and for interfaces with other modes of transport, 6.8.2010, and Commission Delegated Regulation (EU) No 305/2013, of 26 November 2012, supplementing Directive 2010/40/EU of the European Parliament and of the Council with regard to the harmonised provision for an interoperable EU-wide eCall. 1 FCD, possibly integrated with data coming from infrastructure-based monitoring systems, represents a valuable platform for intelligent transport systems (ITS). Traffic monitoring based on FCD relies on a processing algorithm for aggregating the measured data into an accurate and complete traffic map. In this paper we present an experimental study on FCD processing based on a unique large amount of data in Italy, provided by heavy-duty vehicles used as probes over the Italian A4 motorway. A processing procedure is proposed for identifying the typical speed patterns, to be used as baseline for automatic anomaly detection, transport planning or traffic analysis applications. A first assessment based on real traffic-event information shows that the comparison of the probe data to previously identified historical speed patterns allows a clear detection of anomalous events. |