The effects of feedback and incentive-based insurance on driving behaviours: study approach and protocols

Design and Implementation of a Two-Wheeled Vehicle Safe Driving Evaluation System

The delivery market in Republic of Korea has experienced significant growth, leading to a surge in motorcycle-related accidents. However, there is a lack of comprehensive data collection systems for motorcycle safety management. This study focused on designing and implementing a foundational data collection system to monitor and evaluate motorcycle driving behavior. To achieve this, eleven risky behaviors were defined, identified using image-based, GIS-based, and inertial-sensor-based methods. A motorcycle-mounted sensing device was installed to assess driving, with drivers reviewing their patterns through an app and all data monitored via a web interface. The system was applied and tested using a testbed. This study is significant as it successfully conducted foundational data collection for motorcycle safety management and designed and implemented a system for monitoring and evaluation.

FEEDBACK trial - A randomised control trial to investigate the effect of personalised feedback and financial incentives on reducing the incidence of road crashes

BACKGROUND

Road crashes continue to pose a significant threat to global health. Young drivers aged between 18 and 25 are over-represented in road injury and fatality statistics, especially the first six months after obtaining their license. This study is the first multi-centre two-arm parallel-group individually randomised controlled trial (the FEEDBACK Trial) that will examine whether the delivery of personalised driver feedback plus financial incentives is superior to no feedback and no financial incentives in reducing motor vehicle crashes among young drivers (18 to 20 years) during the first year of provisional licensing.

METHODS

A total of 3,610 young drivers on their provisional licence (P1, the first-year provisional licensing) will participate in the trial over 28 weeks, including a 4-week baseline, 20-week intervention and 4-week post-intervention period. The primary outcome of the study will be police-reported crashes over the 20-week intervention period and the 4-week post-intervention period. Secondary outcomes include driving behaviours such as speeding and harsh braking that contribute to road crashes, which will be attained weekly from mobile telematics delivered to a smartphone app.

DISCUSSION

Assuming a positive finding associated with personalised driver feedback and financial incentives in reducing road crashes among young drivers, the study will provide important evidence to support policymakers in introducing the intervention(s) as a key strategy to mitigate the risks associated with the burden of road injury among this vulnerable population.

TRIAL REGISTRATION

Registered under the Australian New Zealand Clinical Trials Registry (ANZCTR) - ACTRN12623000387628p on April 17, 2023.

The Future of Road Safety: Challenges and Opportunities

Policy Points Traditional approaches to addressing motor vehicle crashes are yielding diminishing returns. A comprehensive strategy known as the Safe Systems approach shows promise in both advancing safety and equity and reducing motor vehicle crashes. In addition, a range of emerging technologies, enabled by artificial intelligence, such as automated vehicles, impairment detection and telematics hold promise to advance road safety. Ultimately, the transportation system will need to evolve to provide the safe, efficient, and equitable movement of people and goods without reliance on private vehicle ownership, towards encouraging walking, bicycling and the use of public transportation.

From Actuarial to Behavioural Valuation. The impact of telematics on motor insurance

Algorithmic predictions are used in insurance to assess the risk exposure of potential customers. This article examines the impact of digital tools on the field of motor insurance, where telematics devices produce data about policyholders’ driving styles. The individual’s resulting behavioural score is combined with their actuarial score to determine the price of the policy or additional incentives. Current experimentation is moving in the direction of proactivity: instead of waiting for a claim to arise, insurance companies engage in coaching and other interventions to mitigate risk. The article explores the potential consequences of these practices on the social function of insurance, which makes risks bearable by socialising them over a pool of insured individuals. The introduction of behavioural variables and the corresponding idea of fairness could instead isolate individuals in their exposure to risk and affect their attitude towards future initiatives.

Driving Behavior Based Relative Risk Evaluation Using a Nonparametric Optimization Method

Evaluating risks when driving is a valuable method by which to make people better understand their driving behavior, and also provides the basis for improving driving performance. In many existing risk evaluation studies, however, most of the time only the occurrence frequency of risky driving events is considered in the time dimension and fixed weights allocation is adopted when constructing a risk evaluation model. In this study, we develop a driving behavior-based relative risk evaluation model using a nonparametric optimization method, in which both the frequency and the severity level of different risky driving behaviors are taken into account, and the concept of relative risk instead of absolute risk is proposed. In the case study, based on the data from a naturalistic driving experiment, various risky driving behaviors are identified, and the proposed model is applied to assess the overall risk related to the distance travelled by an individual driver during a specific driving segment, relative to other drivers on other segments, and it is further compared with an absolute risk evaluation. The results show that the proposed model is superior in avoiding the absolute risk quantification of all kinds of risky driving behaviors, and meanwhile, a prior knowledge on the contribution of different risky driving behaviors to the overall risk is not required. Such a model has a wide range of application scenarios, and is valuable for feedback research relating to safe driving, for a personalized insurance assessment based on drivers' behavior, and for the safety evaluation of professional drivers such as ride-hailing drivers.

The effect of telematic based feedback and financial incentives on driving behaviour: A randomised trial

AIM

In-vehicle telematics monitoring systems that provide driver feedback have been identified as a promising intervention to influence driver behaviours and reduce the growing burden of road injury. The current study was undertaken to assess the effect of driver feedback alone and feedback plus financial incentives on driving behaviours (speeding, hard acceleration and hard braking).

METHOD

A pragmatic randomised trial was undertaken over a 28-week observational period. Drivers were recruited and randomly allocated to one of three groups namely, driver feedback, driver feedback plus incentives and a control group. The feedback group received a weekly summary of their driving performance via SMS text message and access to more detailed feedback via an online dashboard or smartphone application. The feedback plus financial incentive group received the feedback but lost financial incentives for risky driving behaviour above a threshold.

RESULTS

A total of 174 drivers completed at least one driving trip during the study period; 18,082 trip days completed by these 174 drivers during the study period provided the sample for analysis. For the primary outcomes of probability of speeding, hard acceleration and hard braking on any given trip, neither feedback alone nor feedback plus incentives delivered statistically significant improvements in driving behaviour relative to the controls. Treatment effects for feedback plus incentives were, however, consistently in the expected direction and large enough to warrant further investigation. For the secondary composite measure of risky driving, namely the DriveScore™, a statistically significant improvement was observed for the feedback and incentive group compared to the control group (TE = 2.6 points on a 0-100 scale, p < 0.05).

DISCUSSION

This study adds to our understanding of the potential effects of feedback and financial incentives. Findings suggest that, while feedback alone may be insufficient to motivate behaviour change, combining feedback with financial incentives can deliver potentially important and statistically significant reductions in risky driving behaviours.

Post-trip safety interventions: State-of-the-art, challenges, and practical implications

INTRODUCTION

Currently, risky driving behaviour is a major contributor to road crashes and as a result, wide array of tools have been developed in order to record and improve driving behaviour. Within that group of tools, interventions have been indicated to significantly enhance driving behaviour and road safety. This study critically reviews monitoring technologies that provide post-trip interventions, such as retrospective visual feedback, gamification, rewards or penalties, in order to inform an appropriate driver mentoring strategy delivered after each trip.

METHOD

The work presented here is part of the European Commission H2020 i-DREAMS project. The reviewed platform characteristics were obtained through commercially available solutions as well as a comprehensive literature search in popular scientific databases, such as Scopus and Google Scholar. Focus was given on state-of-the-art-technologies for post-trip interventions utilized in four different transport modes (i.e. car, truck, bus and rail) associated with risk prevention and mitigation.

RESULTS

The synthesized results revealed that smartphone applications and web-based platforms are the most accepted, frequently and easiest to use tools in cars, buses and trucks across all papers considered, while limited evidence of post-trip interventions in -rail was found. The majority of smartphone applications detected mobile phone use and harsh events and provided individual performance scores, while in-vehicle systems provided delayed visual reports through a web-based platform.

CONCLUSIONS

Gamification and appropriate rewards appeared to be effective solutions, as it was found that they keep drivers motivated in improving their driving skills, but it was clear that these cannot be performed in isolation and a combination with other strategies (i.e. driver coaching and support) might be beneficial. Nevertheless, as there is no holistic and cross-modal post-trip intervention solution developed in real-world environments, challenges associated with post-trip feedback provision and suggestions on practical implementation are also provided.

An Acceptance Approach for Novel Technologies in Car Insurance

Background: Unlike other financial services, technology-driven changes in the insurance industry have not been a vastly explored topic in scholarly literature. Incumbent insurance companies have hitherto been holding their positions using the complexity of the product, heavy regulation, and gigantic balance sheets as paramount factors for a relatively slow digitalization and technological transformation. However, new technologies such as car telematic devices have been creating a new insurance ecosystem. The aim of this study is to assess the telematics technology acceptance for insurance purposes. Methods: The study is based on the Unified Theory of Acceptance and Use of Technology (UTAUT). By interviewing 502 new car buyers, we tested the factors that affect the potential usage of telematic devices for insurance purposes. Results: The results indicate that facilitating conditions are the main predictor of telematics use. Moreover, privacy concerns related to the potential abuse of driving behavior data play an important role in technology acceptance. Conclusions: Although novel insurance technologies are mainly presented as user-driven, users (drivers and insurance buyers) are often neglected as an active party in the development of such technologies.

The effect of 'smart' financial incentives on driving behaviour of novice drivers

Recent studies have demonstrated that financial incentives can improve driving behaviour but high-value incentives are unlikely to be cost-effective and attempts to amplify the impact of low-value incentives have so far proven disappointing. The present study provides experimental evidence to inform the design of 'smart' and potentially more cost-effective incentives for safe driving in novice drivers. Study participants (n = 78) were randomised to one of four financial incentives: high-value penalty; low-value penalty; high-value reward; low-value reward; allowing us to compare high-value versus low-value incentives, penalties versus rewards, and to test specific hypotheses regarding motivational crowding out and gain/loss asymmetry. Results suggest that (i) penalties may be more effective than rewards of equal value, (ii) even low-value incentives can deliver net reductions in risky driving behaviours and, (iii) increasing the dollar-value of incentives may not increase their effectiveness. These design principles are currently being used to optimise the design of financial incentives embedded within PAYD insurance, with their impact on the driving behaviour of novice drivers to be evaluated in on-road trials.