An exploratory analysis of traffic accidents and vehicle ownership decisions using a random parameters logit model with heterogeneity in means
| Autor: | Mohammad M. Hamed, Basel M. Al-Eideh |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
050210 logistics & transportation
Choice set 05 social sciences Human factors and ergonomics Poison control Transportation Loss and damage Logistic regression Insurance policy 0502 economics and business Injury prevention Econometrics 0501 psychology and cognitive sciences Business Renewal theory human activities Safety Research 050107 human factors |
| Zdroj: | Analytic Methods in Accident Research. 25:100116 |
| ISSN: | 2213-6657 |
| DOI: | 10.1016/j.amar.2020.100116 |
| Popis: | The interaction between traffic accidents and household vehicle ownership decisions has not been thoroughly explored in literature. This interaction is worthy of investigation as the outcome of traffic accidents involving property damage significantly affects the household members’ overall mobility levels and car fleet utilization and renewal process. In this study, we present a methodology that addresses a driver’s decisions pertaining to a damaged car following a traffic accident. The choice set includes three alternatives: to replace the damaged car with a new one, replace the damaged car with a used one, and repair the damaged car. A random parameters (mixed) logit model with heterogeneity in the means is specified and estimated to gain more insight into the driver’s decision-making process following a traffic accident. The estimation results of the random parameters (mixed) logit model with heterogeneity in the means indicate that a wide range of explanatory variables affect the driver’s decision to replace a damaged car with a new or used one. For example, as the repair cost to the car value ratio increases, the likelihood of the driver replacing the damaged car with a new one increases. The marginal effect of not repairing a damaged car is twice that of replacing a damaged car with a new car. Additionally, traffic accidents causing critical injuries to drivers decrease the likelihood of replacing a damaged car with a used car relative to accidents that do not generate critical injuries. Other explanatory variables that are likely to increase the propensity of drivers to replace damaged cars with new ones include the car age, type of insurance policy, total household annual income, driver age, and number of workers at the household. Clearly, a driver’s decision to repair or replace a damaged car should not be viewed as a discrete or isolated decision. The stochastic component of traffic accidents (driver decisions pertaining to damaged cars) should be accounted for when formulating changes in household vehicle ownership levels and utilization decisions. |
| Databáze: | OpenAIRE |
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