Claims Frequency Modeling Using Telematics Car Driving Data

2018 ◽  
Author(s):  
Guangyuan Gao ◽  
Shengwang Meng ◽  
Mario V. Wuthrich
Keyword(s):  
Claims Frequency ◽  
Car Driving

IMPROVING AUTOMOBILE INSURANCE CLAIMS FREQUENCY PREDICTION WITH TELEMATICS CAR DRIVING DATA

2021 ◽  
pp. 1-29
Author(s):  
Shengwang Meng ◽  
He Wang ◽  
Yanlin Shi ◽  
Guangyuan Gao
Keyword(s):  
Neural Network ◽  
Risk Score ◽  
Domain Knowledge ◽  
Automobile Insurance ◽  
Average Risk ◽  
Insurance Claims ◽  
Safe Driving ◽  
Insurance Claims Data ◽  
Claims Frequency ◽  
Car Driving

Abstract Novel navigation applications provide a driving behavior score for each finished trip to promote safe driving, which is mainly based on experts’ domain knowledge. In this paper, with automobile insurance claims data and associated telematics car driving data, we propose a supervised driving risk scoring neural network model. This one-dimensional convolutional neural network takes time series of individual car driving trips as input and returns a risk score in the unit range of (0,1). By incorporating credibility average risk score of each driver, the classical Poisson generalized linear model for automobile insurance claims frequency prediction can be improved significantly. Hence, compared with non-telematics-based insurers, telematics-based insurers can discover more heterogeneity in their portfolio and attract safer drivers with premiums discounts.

Claims frequency modeling using telematics car driving data

2018 ◽  
Vol 2019 (2) ◽  
pp. 143-162 ◽  
Author(s):  
Guangyuan Gao ◽  
Shengwang Meng ◽  
Mario V. Wüthrich
Keyword(s):  
Claims Frequency ◽  
Car Driving

scholarly journals A Single-Trial P300 Detector Based on Symbolized EEG and Autoencoded-(1D)CNN to Improve ITR Performance in BCIs

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 3961
Author(s):  
Daniela De Venuto ◽  
Giovanni Mezzina
Keyword(s):  
Recognition Performance ◽  
Low Complexity ◽  
Single Trial ◽  
Eeg Signals ◽  
Speed Increase ◽  
P300 Speller ◽  
Embedded Platform ◽  
Temporal Features ◽  
High Recognition Accuracy ◽  
Car Driving

In this paper, we propose a breakthrough single-trial P300 detector that maximizes the information translate rate (ITR) of the brain–computer interface (BCI), keeping high recognition accuracy performance. The architecture, designed to improve the portability of the algorithm, demonstrated full implementability on a dedicated embedded platform. The proposed P300 detector is based on the combination of a novel pre-processing stage based on the EEG signals symbolization and an autoencoded convolutional neural network (CNN). The proposed system acquires data from only six EEG channels; thus, it treats them with a low-complexity preprocessing stage including baseline correction, windsorizing and symbolization. The symbolized EEG signals are then sent to an autoencoder model to emphasize those temporal features that can be meaningful for the following CNN stage. This latter consists of a seven-layer CNN, including a 1D convolutional layer and three dense ones. Two datasets have been analyzed to assess the algorithm performance: one from a P300 speller application in BCI competition III data and one from self-collected data during a fluid prototype car driving experiment. Experimental results on the P300 speller dataset showed that the proposed method achieves an average ITR (on two subjects) of 16.83 bits/min, outperforming by +5.75 bits/min the state-of-the-art for this parameter. Jointly with the speed increase, the recognition performance returned disruptive results in terms of the harmonic mean of precision and recall (F1-Score), which achieve 51.78 ± 6.24%. The same method used in the prototype car driving led to an ITR of ~33 bit/min with an F1-Score of 70.00% in a single-trial P300 detection context, allowing fluid usage of the BCI for driving purposes. The realized network has been validated on an STM32L4 microcontroller target, for complexity and implementation assessment. The implementation showed an overall resource occupation of 5.57% of the total available ROM, ~3% of the available RAM, requiring less than 3.5 ms to provide the classification outcome.

Retail Suburbanization, Modernization, and Growth in Sydney during Australia’s Postwar Boom

2021 ◽  
pp. 009614422110236
Author(s):  
Matthew Bailey
Keyword(s):  
Capital City ◽  
Growth Strategies ◽  
Capital Cities ◽  
Form And Function ◽  
Urban Core ◽  
Socially Constructed ◽  
And Function ◽  
Retail Formats ◽  
Car Driving

This article uses Sydney as a case study to examine the process of retail decentralization during Australia’s postwar boom, showing how the form and function of capital city retailing changed completely in just a couple of decades. Suburban migration, the emergence of mobile car-driving consumers, socially constructed gender roles, the ongoing importance of public transport networks, planning regimes that sought to concentrate development in designated zones, and business growth strategies that deployed retail formats developed in America all played a role in shaping the form and function of Australian retailing during the postwar boom. In the process, the retail geographies of Australia’s capital cities were transformed from highly centralized distribution structures dominated by the urban core, to decentralized landscapes of retail clusters featuring modern retail forms like the supermarket and shopping center that would come to define Australian retailing for the remainder of the century.

Examining drivers’ socio-demographic variables and perceptions towards sanction mechanisms on speeding behaviour on highways: targeting appropriate prevention

2021 ◽  
pp. 1-12
Author(s):  
Fitri Trapsilawati ◽  
Nadhiya Ulhaq Priatna ◽  
Titis Wijayanto ◽  
Ari Widyanti ◽  
Utami Dyah Syafitri ◽  
...  
Keyword(s):  
Traffic Accident ◽  
Online Survey ◽  
Demographic Variables ◽  
Targeted Prevention ◽  
Material Loss ◽  
Legal Sanction ◽  
Study Results ◽  
Legal Sanctions ◽  
Prevention Programmes ◽  
Car Driving

Abstract Investigating the underlying predictors of speeding behaviour deserves the full attention of research. This study aims to examine the effects of demographic variables on the perceived deterrent mechanisms and to predict speeding behaviour to target appropriate prevention programmes. In this study, 212 randomly selected drivers having a valid car driving licence participated in an online survey. The results revealed that demographic variables influenced drivers’ perceptions towards social and legal sanctions as well as material loss. The model revealed that two sanction-related constructs, that is, legal sanction (b = −0⋅227, P = 0⋅007) and material loss (b = −0⋅218, P = 0⋅005), as well as lax perception towards traffic accident (b = −0⋅176, P = 0⋅025), were the significant predictors of speeding behaviour. These findings suggested that prevention programmes should prioritise young and single drivers. The most effective targeted prevention programmes are highlighted accordingly based on the study results.

Self-directed Robot for Car Driving using Genetic Algorithm

2021 ◽  
Author(s):  
Harivansh Prasad Sharma ◽  
Reshu Agarwal ◽  
Manisha Pant ◽  
Shylaja VinayKumar Karatangi
Keyword(s):  
Genetic Algorithm ◽  
Car Driving

Simulated car driving in fMRI—Cerebral activation patterns driving an unfamiliar and a familiar route

2009 ◽  
Vol 464 (3) ◽  
pp. 222-227 ◽  
Author(s):  
Mandy Mader ◽  
André Bresges ◽  
Reyhan Topal ◽  
Alexander Busse ◽  
Michael Forsting ◽  
...  
Keyword(s):  
Cerebral Activation ◽  
Activation Patterns ◽  
Car Driving

Motorcycling in a Repair Society: A Maintenance and Repair–Centered Approach to Automobility

2020 ◽  
pp. 088832542095080
Author(s):  
Gabriel Jderu
Keyword(s):  
Additional Material ◽  
Maintenance And Repair ◽  
Mass Tourism ◽  
Administrative Personnel ◽  
Lower Ranking ◽  
The Creation ◽  
Material Engagement ◽  
The 1950S ◽  
The Aesthetic ◽  
Car Driving

In a departure from car-centered analyses of the automobility systems, this article highlights the importance of motorcycles and motorcycling in the mobility practices of socialist countries. For at least half of the existence of socialist mobility systems, and especially during the 1950s and 1960s, there were more motorcycles on the roads than cars. Motorcycling was important in commuting, for the mobility of lower-ranking administrative personnel in the countryside, and for mass tourism and leisure. Although in that era maintenance and repair practices were equally central to motorcycling and car-driving, the distinction between user-owner and mechanic was much more fluid in the case of motorcyclists. As a result, the centrality of maintenance and repair to socialist-era motorcycling offers an ideal opportunity to enrich current interdisciplinary conversations about breakdown, maintenance, and repair. Building on the car-centered research into maintenance and repair activities, I add additional material on the nature, types, and complexity of such practices for motorcycling. I outline nine forms of material engagement with motorcycles that reference, but transcend, the current dichotomies between necessity and pleasure, the formal and the informal, the technical and the aesthetic, and the repair of existing objects and the creation of new ones.

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