| A | B | C | D | E | F | G | H | I | J | |
|---|---|---|---|---|---|---|---|---|---|---|
1 | No | Title | Authors | Journal | Year | Type of analysis | Type of data | No obs | Models used | Main conclusions |
2 | 1 | Classification of scale-sensitive telematic observables for riskindividual pricing | W. Weidner, F. W. G. Transchel, R. Weidner | European Actuarial Journal | 2016 | Recognition of driving behaviors in different time scales | ?? | ?? | Discrete Fourier transform and ??? | "we show how maneuvers, trips and trip sections as well as the total insurance period can be analyzed to individually or collectively gain significantly scoreable insights into individual driving behavior" |
3 | 2 | Covariate selection from telematics car driving data | Mario V. Wuthrich | European Actuarial Journal | 2017 | Clustering of drivers | GPS location by second | 1753 drivers, 200 trips for each | K-means on v-a heatmaps | "we describe how driving styles can be categorized so that they can be used for a regression analysis in car insurance pricing" |
4 | 3 | Telematic driving profile classification in car insurance pricing | Wiltrud Weidner; Fabian W.G. Transchel; Robert Weidner | Annals of Actuarial Science | 2015 | Categorisation of the vehicle trips into six driving styles | Random waypoint (RWP) vehicle model + velocity & acceleration data | 91,716 vehicle trips from 110 vehicles | Ward's min variance model with Lance-William algorithm | "Specific driving styles can be derived from driving profiles based on validated velocity and acceleration parameters even under consideration of severe privacy protection issues" "driving styles based on empirical data correspond well with the driving styles deduced from simulated driving profiles" |
5 | 4 | Unraveling the Predictive Power of Telematics Data in Car Insurance Pricing | Verbelen Roel; Katrien Antonio; Gerda Claeskens | ??? | 2017 | Feature creation for pricing models | Nº of kms driven in 5 time slots & 4 road types + nº of claims | 10 406 unique policyholders; 1481 claims | Generalized additive models & compositional predictors | "We find that such variables increase the predictive power and render the use of gender as a discriminating rating variable redundant" |
6 | 6 | Telematics and Gender Discrimination: Some Usage-Based Evidence on Whether Men’s Risk of Accidents Differs from Women’s | Mercedes Ayuso; Montserrat Guillen; Ana María Pérez-Marín | Risks | 2016 | Survival analysis on time to first accident | average kms travelled, % of nighttime and urban, % in excess limit; nº claims | 8198 young drivers | Weibull regression model | "gender differences in the risk of accidents are, to a large extent, attributable to the fact that men drive more often than women"; "no gender discrimination is necessary if telematics provides enough information on driving habits" |
7 | 7 | Relationships Between Crash Involvement and Temporal–Spatial Driving Behavior Activity Patterns | Jungwook Jun; Jennifer Ogle; Randall Guensler | Transportation Research Record: Journal of the Transportation Research Board | 2007 | Evaluation of driving exposure & performance differences between drivers who were involved in crashes versus drivers who were not involved in crashes | GPS location by second --> mileages and speeds filtered by the modified Kalman filter; crash info by survey algorithm | ?? | Wilks’ lambda test to verify differences in the means of behavior activity metrics between the two driver groups. | "mileage exposure, speed, and acceleration patterns of drivers who were involved in crashes were significantly different from those of drivers who were not involved in crashes. Further, crash-involved drivers accumulated more mileage, consistently traveled at higher speeds, and engaged more frequently in hard deceleration events than their no-crash counterparts did" |
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