Urban areas get more and more congested everyday due to the increasing number of moving vehicles. This imposes the need for efficient analysis, modeling, and processing of traffic data. Moreover, the extraction of additional information about traffic conditions, optional routes and the possible prediction of troublesome situations, such as traffic jams, becomes necessary. In this work, we describe the analysis, pre-processing, modeling, and storage techniques for trajectory data that constitute a Moving Object Database (MOD). MOD is the backbone of the ΙΧΝΗΛΑΤΗΣ 1 system, which specifically focuses on extracting further information about the movement of vehicles in the Athens municipal area. Based on real-world requirements, we initially analyse the traffic data and make modeling decisions to capture these requirements in a MOD. We, then, design MOD focusing on the spatiotemporal concepts, relations and restrictions among the characteristic concepts of the system −namely, the vehicles, trajectories, and roads. Furthermore, specific, innovative preprocessing, design, and storage techniques for the trajectory data in MOD are given. Then, we present the architecture of ΙΧΝΗΛΑΤΗΣ; its core components are the characteriser, cluster finder, and associator, which are used to perform data extraction in MOD. A mining language to accommodate typical data extraction queries is presented, in terms of syntax and semantics. Answers to characteristic, complex, questions on MOD, which are based on real-world data about traffic in the Athens Metropolitan Area, show the applicability of the approach. In this paper, we describe the formatting guidelines for ACM SIG Proceedings. KeywordsData model, data mining, moving objects. MOTIVATIONAs the number of moving vehicles increases rapidly everyday, the need for analysis, modeling and processing of traffic data is vital. Moreover, the extraction of additional information about traffic conditions, optional routes and possible prediction of troublesome situations, such as traffic jams, becomes necessary.In this work, we deal with the analysis, pre-processing, modeling and storage techniques of traffic data in a Moving Object Database (MOD) for a traffic management system. Furthermore, 1 ΙΧΝΗΛΑΤΗΣ ('PATH-FINDER' in Greek): A research project funded by the Hellenic General Secretariat of Research and Technology (2002-2004).we apply data extraction techniques in MOD, assisting the prediction of difficult situations, or optional cases, such as alternative routes when traffic is congested.In order to realize MOD, based on real-world requirements about traffic in the Athens Metropolitan Area, we analyse the fundamental concept of the movement of a vehicle and register its semantics and properties in terms of a conceptual model. We organize them in a database, including vehicles, routes, trajectories (i.e., traces left behind, as vehicles move), and relations among them.Building a MOD is not a trivial issue. It consists of (i) spatial data, (i) non-spatial data and (iii) trajectories....
GPS positioning devices are becoming a commodity sensor platform with the emergence and popularity of smartphones. This abundance of GPS trajectories has fueled significant research around map-matching and related applications such as traffic assessment and prediction. Unfortunately, this research has only been used in costly and complex fleet management solutions. Our latest research endeavor addresses this issue by presenting cost-effective solutions for adapting state-of-the-art research around map-matching and live traffic assessment in the context of fleet management applications. This paper showcases various research results wrapped in a single extensible fleet management platform.
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