Time and distance to first accident and driving patterns of young drivers with pay-as-you-drive insurance

Intentions and willingness to engage in risky driving behaviour among high school adolescents: evaluating the bstreetsmart road safety programme

OBJECTIVE

To investigate the impact of a road safety programme on adolescents' willingness to engage in risky behaviour as probationary drivers, adjusted for covariates of interest.

METHOD

The bstreetsmart is a road safety programme delivered to around 25 000 adolescent students annually in New South Wales. Using a smartphone-based app, student and teacher participation incentives, students were surveyed before and after programme attendance. Mixed-methods linear regression analysed pre/post-modified Behaviour of Young Novice Driver (BYNDS_M) scores.

RESULTS

2360 and 1260 students completed pre-event and post-event surveys, respectively. Post-event BYNDS_M scores were around three points lower than pre-event scores (-2.99, 95% CI -3.418 to -2.466), indicating reduced intention to engage in risky driving behaviours. Covariates associated with higher stated intentions of risky driving were exposure to risky driving as a passenger (1.21, 95% CI 0.622 to 2.011) and identifying as non-binary gender (2.48, 95% CI 1.879 to 4.085), adjusting for other predictors.

CONCLUSIONS

Trauma-informed, reality-based injury prevention programmes can be effective in changing short-term stated intentions to engage in risky driving, among a pre-independent driving student population. The adolescent novice driver age group is historically challenging to engage, and injury prevention action must be multipronged to address the many factors influencing their behaviour.

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.

Percentile charts for speeding based on telematics information

Reference charts are widely used as a graphical tool for assessing and monitoring children's growth given gender and age. Here, we propose a similar approach to the assessment of driving risk. Based on telematics data, and using quantile regression models, our methodology estimates the percentiles of the distance driven at speeds above the legal limit depending on drivers' characteristics and the journeys made. We refer to the resulting graphs as percentile charts for speeding and illustrate their use for a sample of drivers with Pay-How-You-Drive insurance policies. We find that percentiles of distance driven at excessive speeds depend mainly on total distance driven, the percentage of driving in urban areas and the driver's gender. However, the impact on the estimated percentile for these covariates is not constant. We conclude that the heterogeneity in the risk of driving long distances above the speed limit can be easily represented using reference charts and that, conversely, individual drivers can be scored by calculating an estimated percentile for their specific case. The dynamics of this risk score can be assessed by recording drivers as they accumulate driving experience and cover more kilometres. Our methodology should be useful for accident prevention and, in the context of Manage-How-You-Drive insurance, reference charts can provide real-time alerts and enhance recommendations for ensuring safety.

A Novel Vehicle Tracking ID Switches Algorithm for Driving Recording Sensors

The main task for real-time vehicle tracking is establishing associations with objects in consecutive frames. After occlusion occurs between vehicles during the tracking process, the vehicle is given a new ID when it is tracked again. In this study, a novel method to track vehicles between video frames was constructed. This method was applied on driving recorder sensors. The neural network model was trained by YOLO v3 and the system collects video of vehicles on the road using a driving data recorder (DDR). We used the modified Deep SORT algorithm with a Kalman filter to predict the position of the vehicles and to calculate the Mahalanobis, cosine, and Euclidean distances. Appearance metrics were incorporated into the cosine distances. The experiments proved that our algorithm can effectively reduce the number of ID switches by 29.95% on the model trained on the BDD100K dataset, and it can reduce the number of ID switches by 32.16% on the model trained on the COCO dataset.

Risk Assessment of Commercial dangerous -goods truck drivers using geo-location data: A case study in China

The primary objective of this study is to understand the relationship between driving risk of commercial dangerous-goods truck (CDT) and exposure factors and find a way to evaluate the risk of specific transportation environment, such as specific transportation route. Due to increasing transportation demand and potential threat to public, commercial dangerous goods transportation (CDGT) has drawn attention from decision makers and researchers within governmental and non-governmental safety organization. However, there are few studies focusing on driving risk assessment of commercial dangerous-goods truck by environmental factors. In this paper we employ survival analysis methods to analyze the impact of risk exposure factors on non-accident mileage of commercial dangerous-good truck and assess risk level of specific driving environment. Using raw location data from six transportation companies in China, we derive a set of 17 risk exposure factors that we use for model parameters estimation. The survival model and hazard model were estimated using the Weibull distribution as the baseline distribution. The results show that four factors - weather, traffic flow, travel time and average velocity have a significant impact on the non-accident mileage of driver in this company, and the assessment results of survival function and hazard function are robust to the different levels of testing data. The employment time has some effect on the results but does not result in a significant difference in most cases, and the task stability has little impact on the results. The findings of this study should be useful for decision makers and transportation companies to better risk assessment of CDT.

The public health implications of telematic technologies: An exploratory qualitative study in the UK

INTRODUCTION

Reducing motorised transport is crucial for achieving public health goals, but cars will continue to be essential for many in the medium term. The role of emerging technologies in mitigating the public health disadvantages of this private car use has been under-examined to date. Telematics are increasingly used by novice drivers in the UK to reduce insurance premiums. An exploratory study of novice drivers' experiences of telematics identified implications for public health that warrant urgent further research.

METHODS

An exploratory qualitative study, using semi-structured interviews with 12 drivers aged 17-25 in three regions of the UK (Aberdeenshire, Hertfordshire and London).

RESULTS

Telematics were acceptable to young drivers, and reported to mitigate some negative health consequences of driving (injury risks, over-reliance on car transport), without reducing access to determinants of health such as employment or social life. However, there were suggestions that those at higher risk were less likely to adopt telematics.

CONCLUSION

Market-based mechanisms such as telematics are potential alternatives to well-evaluated policy interventions such as Graduated Driver Licensing for reducing road injury risks for novice drivers, with a different mix of risks and benefits. However, claims to date from insurance companies about the contribution of telematics to public health outcomes should be evaluated carefully to account for biases in uptake.

The Use of Telematics Devices to Improve Automobile Insurance Rates

Most automobile insurance databases contain a large number of policyholders with zero claims. This high frequency of zeros may reflect the fact that some insureds make little use of their vehicle, or that they do not wish to make a claim for small accidents in order to avoid an increase in their premium, but it might also be because of good driving. We analyze information on exposure to risk and driving habits using telematics data from a pay-as-you-drive sample of insureds. We include distance traveled per year as part of an offset in a zero-inflated Poisson model to predict the excess of zeros. We show the existence of a learning effect for large values of distance traveled, so that longer driving should result in higher premiums, but there should be a discount for drivers who accumulate longer distances over time due to the increased proportion of zero claims. We confirm that speed limit violations and driving in urban areas increase the expected number of accident claims. We discuss how telematics information can be used to design better insurance and to improve traffic safety.

Semi-autonomous vehicles: Usage-based data evidences of what could be expected from eliminating speed limit violations

The use of advanced driver assistance systems and the transition towards semi-autonomous vehicles are expected to contribute to a lower frequency of motor accidents and to have a significant impact for the automobile insurance industry, as rating methods must be revised to ensure that risks are correctly measured. Telematics information and usage-based insurance research are analyzed to identify the effect of driving patterns on the risk of accident. This is used as a starting point for addressing risk quantification and safety for vehicles that can control speed. The effect of excess speed on the risk of accidents is estimated with a real telematics data set. Scenarios for a reduction of speed limit violations and the consequent decrease in the expected number of accident claims are shown. If excess speed could be eliminated, then the expected number of accident claims could be reduced to half of its initial value, applying the average conditions of the data used in this study. As a consequence, insurance premiums also diminish.

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

Car insurance is quickly becoming a big data industry, with usage-based insurance (UBI) poised to potentially change the business of insurance. Telematics data, which are transmitted from wireless devices in car, are widely used in UBI to obtain individual-level travel and driving characteristics. While most existing studies have introduced telematics data into car insurance pricing, the telematics-related characteristics are directly obtained from the raw data. In this study, we propose to quantify drivers' familiarity with their driving routes and develop models to quantify drivers' accident risks using the telematics data. In addition, we build a latent class model to study the heterogeneity in travel and driving styles based on the telematics data, which has not been investigated in literature. Our main results include: (1) the improvement to the model fit is statistically significant by adding telematics-related characteristics; (2) drivers' familiarity with their driving trips is critical to identify high risk drivers, and the relationship between drivers' familiarity and accident risks is non-linear; (3) the drivers can be classified into two classes, where the first class is the low risk class with 0.54% of its drivers reporting accidents, and the second class is the high risk class with 20.66% of its drivers reporting accidents; and (4) for the low risk class, drivers with high probability of reporting accidents can be identified by travel-behavior-related characteristics, while for the high risk class, they can be identified by driving-behavior-related characteristics. The driver's familiarity will affect the probability of reporting accidents for both classes.

Pathways linking car transport for young adults and the public health in Northern Ireland: a qualitative study to inform the evaluation of graduated driver licensing

Background

Novice drivers are at relatively high risk of road traffic injury. There is good evidence that Graduated Driving Licensing (GDL) schemes reduce collisions rates, by reducing exposure to risk and by extending learning periods. Legislation for a proposed scheme in Northern Ireland was passed in 2016, providing an opportunity for future evaluation of the full public health impacts of a scheme in a European context within a natural experiment. This qualitative study was designed to inform the logic model for such an evaluation, and provide baseline qualitative data on the role of private cars in health and wellbeing.

Methods

Nine group interviews with young people aged 16–23 (N = 43) and two group interviews with parents of young people (N = 8) were conducted in a range of settings in Northern Ireland in 2015. Data were analysed using thematic content analysis.

Results

Informal car-pooling within and beyond households led to routine expectations of lift provision and uptake. Experiences of risky driving situations were widespread. In rural areas, extensive use of farm vehicles for transport needs meant many learner drivers had both early driving experience and expectations that legislation may have to be locally adapted to meet social needs. Cars were used as a site for socialising, as well as essential means of transport. Alternative modes (public transport, walking and cycling) were held in low esteem, even where available. Recall of other transport-related public health messages and parents’ existing use of GDL-type restrictions suggested GDL schemes were acceptable in principle. There was growing awareness and use of in-car technologies (telematics) used by insurance companies to reward good driving.

Conclusions

Key issues to consider in evaluating the broader public health impact of GDL will include: changes in injury rates for licensed car occupants and other populations and modes; changes in exposure to risk in the licensed and general population; and impact on transport exclusion. We suggest an important pathway will be change in social norms around offering and accepting lifts and to risk-taking. The growing adoption of in-car telematics will have implications for future GDL programmes and for evaluation.