The Equilibrium on the Motor Insurance Market in Selected CEE Countries

Śliwiński, Adam; Kuryłowicz, Łukasz

doi:10.1007/978-3-030-32426-1_9

Cited by 3 publications

(1 citation statement)

References 14 publications

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“…With a significant increase in the number of relevant research attempts, insurance telematics is gradually becoming a better‐known area. A simultaneous decrease in technology costs creates favorable conditions for more economically and practically feasible market implementation of UBI (Śliwiński & Kuryłowicz, 2020).…”

Section: Literature Reviewmentioning

confidence: 99%

Usage‐based insurance and its acceptance: An empirical approach

Śliwiński

Kuryłowicz

2021

Risk Manage Insurance Review

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This study aims to explore the motor insurance market's attitude towards usage-based insurance (UBI), and thus its readiness for a launch in the near future. Data on client perception was collected using a structured questionnaire. On the basis of this, an initial selection of factors, other than income, that could influence the declared attitude of drivers was demonstrated showing that it is primarily dependent on certain demographic characteristics like their age, sex, or place of residence. A strong relationship was also demonstrated with respect to the intensity of the vehicle's use, to the amount of the insurance premium they have paid, and to the self-assessment of the respondents' driving skills. Clients are likely to accept the concept of UBI once implemented, but that they are not ready yet to give up the traditionally used methods of premium calculation. Their attachment to discounts granted can be very strong in particular to the no-claims bonus.

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Section: Literature Reviewmentioning

confidence: 99%

Usage‐based insurance and its acceptance: An empirical approach

Śliwiński

Kuryłowicz

2021

Risk Manage Insurance Review

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Intersection Type Classification from Connected Vehicle Data Using a Convolutional Neural Network

Saldivar-Carranza,

Gayen,

Bullock

2024

Data Sci. Transp.

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Identification of Road Network Intersection Types from Vehicle Telemetry Data Using a Convolutional Neural Network

Erramaline

Badard

Côté

et al. 2022

IJGI

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GPS trajectories collected from automotive telematics for insurance purposes go beyond being a collection of points on the map. They are in fact a powerful data source that we can use to extract map and road network properties. While the location of road junctions is readily available, the information about the traffic control element regulating the intersection is typically unknown. However, this information would be helpful, e.g., for contextualizing a driver’s behavior. Our focus is to use a map-matched GPS OBD-dongle dataset provided by a Canadian insurance company to classify intersections into three classes according to the type of traffic control element present: traffic light, stop sign, or no sign. We design a convolutional neural network (CNN) for classifying intersections. The network takes as entries, for a defined number of trips, the speed and the acceleration profiles over each segment of one meter on a window around the intersection. Our method outperforms two other competing approaches, achieving 99% overall accuracy. Furthermore, our CNN model can infer the three classes even with as few as 25 trips.

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The Equilibrium on the Motor Insurance Market in Selected CEE Countries

Cited by 3 publications

References 14 publications

Usage‐based insurance and its acceptance: An empirical approach

Usage‐based insurance and its acceptance: An empirical approach

Intersection Type Classification from Connected Vehicle Data Using a Convolutional Neural Network

Identification of Road Network Intersection Types from Vehicle Telemetry Data Using a Convolutional Neural Network

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