Latent class analysis of accident risks in usage-based insurance: Evidence from Beijing

Critical safety management driver identification based upon temporal variation characteristics of driving behavior

Identifying critical safety management drivers with high driver-level risks is essential for traffic safety improvement. Previous studies commonly evaluated driver-level risks based upon aggregated statistical characteristics (e.g., driving exposure and driving behavior), which were obtained from long-period driving monitoring data. However, given the great advancements of the connected vehicle and in-vehicle data instrumentation technologies, there has been a notable increase in the collection of short-period driving data, which has emerged as a prominent data source for analysis. In this data environment, traditionally employed aggregated behavior characteristics are unstable due to the time-varying feature of driving behavior coupled with insufficient data sampling periods. Thus, traditional modeling methods based upon aggregated statistical characteristics are no longer feasible. Instead of utilizing such unreliable statistical information to represent driver-level risks, this study employed temporal variation characteristics of driving behavior to identify critical safety management drivers in the short-period driving data environment. Specifically, the relationships between driving behavior temporal variation characteristics and individual crash occurrence probability were developed. To eliminate the impacts of drivers' driving behavior heterogeneity on model performance, "traffic entropy" index that could quantify the abnormal degrees of driving behavior was proposed. Deep learning models including convolutional neural network (CNN) and long short-term memory (LSTM) were employed to conduct the temporal variation feature mining. Empirical analyses were conducted using data obtained from online ride-hailing services. Experiment results showed that temporal variation characteristics based models outperformed traditional aggregated statistical characteristics based models. The area under the curve (AUC) index was improved by 4.1%. And the proposed traffic entropy index further enhanced the model performance by 5.3%. The best model achieved an AUC of 0.754, comparable to existing approaches utilizing long-period driving data. Finally, applications of the proposed method in driver management program development and its further investigations have been discussed.

Safety assessment of trucks based on GPS and in-vehicle monitoring data

Increasingly, drivers are choosing to buy usage-based automobile insurance (UBI). Manage-how-you-drive (MHYD) insurance, a new type of UBI, incorporates active safety management to monitor driver behavior and issue warnings as needed. While researchers have introduced telematics data into automobile insurance pricing, the specific effect of in-vehicle active safety management on driver risk assessment has been neglected, especially for truck drivers, whose crashes have more serious consequences. This study uses telematics and in-vehicle monitoring features to examine the key factors underlying large commercial truck crashes, and quantifies the effect of these factors on crash risk. Data from 2,185 trucks in Shanghai, China, were collected for a total of 105,786 trips and 465,555 in-vehicle warnings to investigate three types of factors affecting risk: travel characteristics, driving behavior, and in-vehicle warnings. A zero-inflated Poisson (ZIP) regression model was built, and a ZIP model without the warning variables as well as a basic Poisson model with warnings were considered for comparison. It was found that the ZIP model considering in-vehicle warning information performed significantly better than the other models. The standardized regression coefficient method was used to identify the most important variables. In-vehicle yawn and smoking warnings had significantly more association with the number of crashes than did the travel characteristics and driving behavior variables, though freeway distance traveled, average freeway speed, percentage of trips on sunny days, and percentage of trips at night also correlated significantly with crash risk. These results can provide a reference for UBI insurance professionals considering in-vehicle active safety management, as well as support freight companies in drafting appropriate working regulations.

Driving Risk Assessment Using Near-Miss Events Based on Panel Poisson Regression and Panel Negative Binomial Regression

This study proposes a method for identifying and evaluating driving risk as a first step towards calculating premiums in the newly emerging context of usage-based insurance. Telematics data gathered by the Internet of Vehicles (IoV) contain a large number of near-miss events which can be regarded as an alternative for modeling claims or accidents for estimating a driving risk score for a particular vehicle and its driver. Poisson regression and negative binomial regression are applied to a summary data set of 182 vehicles with one record per vehicle and to a panel data set of daily vehicle data containing four near-miss events, i.e., counts of excess speed, high speed brake, harsh acceleration or deceleration and additional driving behavior parameters that do not result in accidents. Negative binomial regression (AICoverspeed = 997.0, BICoverspeed = 1022.7) is seen to perform better than Poisson regression (AICoverspeed = 7051.8, BICoverspeed = 7074.3). Vehicles are separately classified to five driving risk levels with a driving risk score computed from individual effects of the corresponding panel model. This study provides a research basis for actuarial insurance premium calculations, even if no accident information is available, and enables a precise supervision of dangerous driving behaviors based on driving risk scores.

Total Cost of Ownership and Its Potential Consequences for the Development of the Hydrogen Fuel Cell Powered Vehicle Market in Poland

Electromobility is a growing technology for land transport, constituting an important element of the concept of sustainable economic development. The article presents selected research results concerning one of the segments of this market-vehicles powered by hydrogen fuel cells. The subject of the research was to gain extensive knowledge on the economic factors influencing the future purchasing decisions of the demand side in relation to this category of vehicles. The research was based on a numerical experiment. For this purpose, a comparative analysis of purchase prices in relation to the TCO of the vehicle after 3–5 years of use was performed. The research included selected models that are powered by both conventional and alternative fuels. The use of this method will allow to assess the real costs associated with the hydrogen vehicle. The authors emphasize the important role of economic factors in the form of the TCO index for the development of this market. The experimental approach may be helpful in understanding the essence of economic relations that affect the development of the electro-mobility market and the market demand for hydrogen fuel cell-powered vehicles in Poland.

Assessing Driving Risk Using Internet of Vehicles Data: An Analysis Based on Generalized Linear Models

With the major advances made in internet of vehicles (IoV) technology in recent years, usage-based insurance (UBI) products have emerged to meet market needs. Such products, however, critically depend on driving risk identification and driver classification. Here, ordinary least square and binary logistic regressions are used to calculate a driving risk score on short-term IoV data without accidents and claims. Specifically, the regression results reveal a positive relationship between driving speed, braking times, revolutions per minute and the position of the accelerator pedal. Different classes of risk drivers can thus be identified. This study stresses both the importance and feasibility of using sensor data for driving risk analysis and discusses the implications for traffic safety and motor insurance.

Risk factors of road accident severity and the development of a new system for prevention: New insights from China

Road accident fatalities and accident severity costs have become top priorities and concerns for Chinese policymakers. Understanding the principal factors that explain accident severity is considered to be the first step towards the adequate design of an accident prevention strategy. In this paper, we examine the contribution of various types of factors (vehicle, driver and others) in explaining accident severity in China. Unlike previous studies, the analysis gives a particular focus on fatal accidents. Using a large sample of 405,177 observations for 4-wheeled vehicles in the year 2017 and various statistical and econometrics approaches (e.g., OLS, quantile regression and extreme value theory), the results show that the factors explaining the severity of accidents differs significantly between normal and extreme severity accidents, e.g. across quantiles. Interestingly, we find that the gender factor is only significant for fatal accidents. In particular, the analysis shows that male drivers have an increased likelihood of extreme risk taking. On the basis of these empirical findings, a new ratemaking approach that aims to improve road safety and prevention is discussed and proposed.

Using telematics data to find risky driver behaviour

Usage-based insurance schemes provide new opportunities for insurers to accurately price and manage risk. These schemes have the potential to better identify risky drivers which not only allows insurance companies to better price their products but it allows drivers to modify their behaviour to make roads safer and driving more efficient. However, for Usage-based insurance products, we need to better understand how driver behaviours influence the risk of a crash or an insurance claim. In this article, we present our analysis of automotive telematics data from over 28 million trips. We use a case control methodology to study the relationship between crash drivers and crash-free drivers and introduce an innovative method for determining control (crash-free) drivers. We fit a logistic regression model to our data and found that speeding was the most important driver behaviour linking driver behaviour to crash risk.