Defensive driving and the external costs of accidents and travel delays

A unified model of accident and travel-delay costs describes the role that defensive driving effort plays in balancing these costs, and the costs of effort itself. This motivates a simple method for jointly estimating risk, effort, and travel-delay externalities, which exploits ordinary travel-demand modeling to directly value the congestion that generates these costs. A unique empirical setting also allows for decomposing the joint externality into its travel-delay and accident-related components, with results suggesting that together risk and effort externalities are nearly on par with travel-delay externalities. It is also demonstrated that traditional value-of-time estimates substantially reflect risk and effort costs.

Heterogeneity in Commuters' "Value of Time" with Noisy Data: A Multiple Imputation Approach

We estimate how motorists value their time savings and characterize the degree of heterogeneity in these values by observable traits. We obtain these estimates by analyzing the choices that commuters make in a real market situation, where they are offered a free-flow alternative to congested travel. We do so, however, in an empirical setting where several key observations are missing. To overcome this, we apply Rubin's Multiple Imputation Method to generate consistent estimates and valid statistical inferences. We also compare these estimates to those produced in a "single imputation” scenario to illustrate the potential hazards of single imputation methods when multiple imputation methods are warranted. Our preferred model suggests that the median commuter is willing to pay $30 to save an hour of travel time. However, taking observed heterogeneity into account, median estimates range from$7 to $65 according to varying motorist characteristics

Estimating commuters' "value of time" with noisy data: a multiple imputation approach

We estimate how motorists value their time savings and characterize the degree of heterogeneity in these values by observable traits. We obtain these estimates by analyzing the choices that commuters make in a real market situation, where they are offered a free-flow alternative to congested travel. We do so, however, in an empirical setting where several key observations are missing. To overcome this, we apply Rubin's multiple imputation method to generate consistent estimates and valid statistical inferences. We also compare these estimates to those produced in a "single imputation" scenario to illustrate the potential hazards of single imputation methods when multiple imputation methods are warranted. Our results show the importance of properly accounting for errors in the imputation process, and they also show that value of time savings varies greatly according to motorist characteristics.