Computer Engineering / Bilgisayar Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/10
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Conference Object Making Accident Data Compatible With Its-Based Traffic Management: Turkish Case(Intelligent Transport Systems, 2010) Duvarcı, Yavuz; Geçer Sargın, Feral; Kumova, Bora İsmail; Çınar, Ali Kemal; Selvi, ÖmerOne of the most important reasons of the high rate of accidents would largely lend itself to ineffective data collection and evaluation process since the necessary information cannot be obtained effectively from the traffic accidents reports (TAR). The discord and dealing with non-relevant data may appear at four levels: (1) Country and Cultural, (2) Institutional and organizational, (3) Data collection, (4) Data analysis and Evaluation. The case findings are consistent with this knowledge put forward in the literature; there is a transparency problem in coordination between the institutions as well as the inefficient TAR data, which is open to manipulation; the problem of under-reporting and inappropriate data storage prevails before the false statistical evaluation methods. The old-fashioned data management structure causes incompatibility with the novel technologies, avoiding timely interventions in reducing accidents and alleviating the fatalities. Transmission of the data to the interest agencies for evaluation and effective operation of the ITS-based systems should be considered. The problem areas were explored through diagnoses at institutional, data collection, and evaluation steps and the solutions were determined accordingly for the case city of Izmir.Conference Object A Data Coding and Screening System for Accident Risk Patterns: A Learning System(WITPress, 2011) Geçer Sargın, Feral; Geçer Sargın, Feral; Duvarcı, Yavuz; Duvarcı, Yavuz; İnan, E.; İnan, E.; Kumova, Bora İsmail; Kumova, Bora İsmail; Atay Kaya, İlgi; Atay Kaya, İlgiAccidents on urban roads can occur for many reasons, and the contributing factors together pose some complexity in the analysis of the casualties. In order to simplify the analysis and track changes from one accident to another for comparability, an authentic data coding and category analysis methods are developed, leading to data mining rules. To deal with a huge number of parameters, first, most qualitative data are converted into categorical codes (alpha-numeric), so that computing capacity would also be increased. Second, the whole data entry per accident are turned into ID codes, meaning each crash is possibly unique in attributes, called 'accident combination', reducing the large number of similar value accident records into smaller sets of data. This genetical code technique allows us to learn accident types with its solid attributes. The learning (output averages) provides a decision support mechanism for taking necessary cautions for similar combinations. The results can be analyzed by inputs, outputs (attributes), time (years) and the space (streets). According to Izmir's case results; sampled data and its accident combinations are obtained for 3 years (2005 - 2007) and their attributes are learned. © 2011 WIT Press.