PAYD car insurance research

(From paper abstract) This paper provides guidance for implementing Pay-As-You-Drive (PAYD) vehicle insurance, which directly incorporates mileage as a rate factor. It describes PAYD pricing options, discusses PAYD benefits and costs, describes regulatory reforms, evaluates various objections to PAYD, and provides specific recommendations for PAYD implementation. Various data sources indicate that crash costs increase with annual vehicle mileage. As a result, PAYD increases actuarial accuracy (premiums better reflect a vehicle's claim costs). PAYD pricing rewards motorists when they reduce their mileage, providing financial savings and additional benefits including increased safety, congestion reduction, road and parking facility cost savings, energy conservation, emission reductions, and increased insurance affordability.

Although there are several possible ways to implement PAYD insurance, some provide more benefits than others. Insurance regulators can maximize benefits by defining performance standards that policies must meet to be considered PAYD, as described in this paper. Critics raise various objections to PAYD pricing, but many of these are technically inaccurate or can be addressed with appropriate implementation practices.

(From paper abstract) This study compares the safety and accessibility effects of Pay-As-You-Drive (PAYD) strategies, simulated using a transportation model. PAYD is an insurance policy for car owners where, to better reflect crash risk, the insurance premium is paid per kilometer actually driven. With more advanced monitoring technologies, the PAYD insurance premium can be further differentiated to reward safe driving behavior with a lower premium.

This more differentiated concept of PAYD is currently being tested in a real life pilot. As part of this pilot a modeling study has been performed in order to assess the possible network (safety) effect of large-scale implementation of PAYD. The setup and results of this modeling study are described in this paper. Seven PAYD strategies have been investigated with different kilometer-based insurance premium differentiations (road category differentiations, time differentiations, age differentiations). The network effects appear to vary greatly depending on the design of the PAYD strategy. The most common effects found in this model study are mode shifts and trip making (elastic demand effect) and route shifts.

To improve traffic safety, the best strategy would be to differentiate premium to reflect safety, i.e. higher fees for unsafe road categories and nighttime driving, most effectively and apply it to all drivers. This way, drivers optimize towards the lowest cost and highest traffic safety. Total crash reduction is estimated to be more than 5% with the model, resulting in a reduction of 60 fatalities and over a 1000 injured by traffic each year in the Netherlands.

(From paper abstract) The Minnesota Department of Transportation (Mn/DOT) carried out a pay-as-you-drive (PAYD) demonstration simulating the replacement of the fixed costs of vehicle ownership and operation with variable costs that give drivers explicit price signals about travel decisions and alternatives. The objective was to estimate the reduction in mileage due to the mileage-based pricing scheme. The study consisted of market assessment surveys and a field experiment. The experiment is the focus of this paper.

The experimental design divided participants into three groups: a control-only group, a treatment-then-control group, and a control-then-treatment group. Participants in the treatment phase were subjected to per-mile prices, and the mileage of all participants was recorded for the entire study duration. Two types of analyses were conducted. Aggregate analyses using bootstrap methods to determine group wise changes in mileage showed that participants reduced their mileage when charged on a per-mile basis with the greatest reduction during the summer period when trips could be more discretionary in nature.

In addition, in order to better understand the variance in mileage sensitivity to per-mile prices, disaggregate analyses were performed using a matching method that matched members of the treatment group to those of the control group based on the probability of participation in the experiment and their baseline mileage. The resulting percentage change in mileage was regressed against percentage change in price and lifestyle variables. The price elasticity of peak period mileage was found to be negative. However, in both aggregate and disaggregate analyses, the price effect was statistically insignificant due to the small sample size.

(From paper abstract) A technically and economically feasible road user charging scheme should be based on quantities that are readily and accurately measurable, as well as being directly variable with the amount of road use and its impact on the environment and society. A key requirement for a pricing scheme is that the charging regime used should be easy for motorists to understand, but at the same time flexible enough for the operator to implement a wide range of policies to meet different aims. A set of Variable Road User Charging Indicators (VRUCI) are identified in this paper by considering both the associated costs of a trip and the operational requirements for a feasible road pricing scheme. These are: geographic area, road class, distance, time, pollutant emissions, driving behaviour, vehicle occupancy and traffic density. Each of these indicators is variable, measurable, and needs to be derived in-motion from actual driving conditions.

The selection of technologies and techniques to measure these variables largely depends on the accuracies required by the charge model. However, the ability to locate and track a vehicle in space and time is fundamental to charging for true road use. The focus of this paper is to identify a set of currently feasible technologies to measure these VRUCI in real-time with high accuracies. We focus on the need to accurately track vehicle movements and link these movements to geographical areas and road types. We also focus on the key pollutants CO, CO2, HC, NOx and PM, all of which have different potential effects that are in some cases dependent on location and time of emission. Other issues, such as congestion measurement are also discussed.