Use of data from telematic devices to predict the probability of accident
Pilnik N.P.1,2
, Stankevich I.P.1, Korischenko K.N.3
1 Национальный исследовательский университет – высшая школа экономики
2 Центр перспективного финансового планирования, макроэкономического анализа и статистики финансов НИФИ
3 Российская академия народного хозяйства и государственной службы при Президенте РФ
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Journal paper
*
Volume 4, Number 3 (July-September 2017)
* Этот журнал не выпускается в Первом экономическом издательстве
Abstract:
The article analyzes the impact of driving style on the probability of getting into an accident. Driving style indicators are calculated on the basis of data from telematic devices installed in cars, accident data are provided by the insurance company. All accidents are divided into three groups according to severity, depending on the ratio of losses and the sum insured, individual models are considered both for the fact of getting into an accident and for each of the groups. We mark out the main factors that determine the accident rate of cars; they include mileage, sudden acceleration, average speed, maximum speeds at different times of the day, and a number of other factors. We draw conclusions for the state policy in the field of traffic regulation
Keywords: driving style, road traffic accident, probability of accident
JEL-classification: L62, L69
References:
Bíl M., Andrášik R., Janoška Z. (2013). Identification of hazardous road locations of traffic accidents by means of kernel density estimation and cluster significance evaluation Accident Analysis & Prevention. 55 265-273.
Durduran S.S. (2010). A decision making system to automatic recognize of traffic accidents on the basis of a GIS platform Expert Systems with Applications. 37 (12). 7729-7736.
Elliott, Mark A., Christopher J. Armitage, and Christopher J. Baughan (2003). Drivers' compliance with speed limits: an application of the theory of planned behavior Journal of Applied Psychology. 88 (5). 964.
Erdogan S. et al (2008). Geographical information systems aided traffic accident analysis system case study: city of Afyonkarahisar Accident Analysis & Prevention. 40 (1). 174-181.
French, Davina J., et al (1993). Decision-making style, driving style, and self-reported involvement in road traffic accidents Ergonomics. 36 (6). 627-644.
Kashani, Ali Tavakoli, Afshin Shariat Mohaymany (2011). Analysis of the traffic injury severity on two-lane, two-way rural roads based on classification tree models Safety Science. 49 (10). 1314-1320.
Kashani, Ali Tavakoli, Afshin Shariat-Mohaymany, and Andishe Ranjbari (2012). Analysis of factors associated with traffic injury severity on rural roads in Iran Journal of injury and violence research 4. 4 (1). 36.
Korchagin V.A., Lyapin S.A., Klyavin V.E., Sitnikov V.V. (2015). Povyshenie bezopasnosti dvizheniya avtomobiley na osnove analiza avariynosti i modelirovaniya DTP [Improving traffic safety based on emergency analysis and road accident emulation]. Fundamental research. (6-2). 251-256. (in Russian).
Kosuge, Ritsu, et al (2017). Predictors of driving outcomes including both crash involvement and driving cessation in a prospective study of Japanese older drivers Accident Analysis & Prevention. (106). 131-140.
Moiseeva O.V., Kleveko V.I. (2015). Analiz avariynyh sluchaev s uchastiem peshekhodov v g. Permi [Analysis of accidents involving pedestrians in Perm]. Vestnik Permskogo natsionalnogo issledovatelskogo politekhnicheskogo universiteta. Stroitelstvo i arkhitektura. (4). 134-143. (in Russian).
Olszewski P. et al (2015). Pedestrian fatality risk in accidents at unsignalized zebra crosswalks in Poland Accident Analysis & Prevention. 84 83-91.
Park, Ho-Chul, et al (2017). Cross-classified multilevel models for severity of commercial motor vehicle crashes considering heterogeneity among companies and regions Accident Analysis & Prevention. (106). 305-314.
Quddus M.A. (2008). Time series count data models: an empirical application to traffic accidents Accident Analysis & Prevention. 40 (5). 1732-1741.
Taubman-Ben-Ari, Orit, Mario Mikulincer, Omri Gillath (2004). The multidimensional driving style inventory—scale construct and validation Accident Analysis & Prevention. 36 (3). 323-332.
Tesema, Tibebe Beshah, Ajith Abraham, Crina Grosan (2005). Rule mining and classification of road traffic accidents using adaptive regression trees International Journal of Simulation. 6 (10-11). 80-94.
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