Abstract
A type of spatial dependence might be suspected in the vehicle inspection data because it has similar characteristics with spatial data. This study aims to contribute to the establishment of a traffic operation order by revealing the spatial autocorrelation and by identifying regional characteristics that influence vehicle inspection failure rates. Based on the estimation of spatial econometric models, spatial dependence was found with a value of 0.37(Moran's I index), indicating that vehicle inspection data are spatially correlated. With respect to regional characteristics affecting vehicle inspection failure rates, five significant factors were identified: average vehicle age, average temperature, percentage of private inspection stations, percentage of diesel vehicles, and amount of precipitation. The results showed that differentiated vehicle inspections according to the characteristics of each region, such as strengthening automobile fuel filter inspections in areas with low average temperatures and strengthening emission inspections in regions with a high proportion of diesel vehicles, should be conducted.
| Original language | English |
|---|---|
| Pages (from-to) | 19-27 |
| Number of pages | 9 |
| Journal | Transactions of the Korean Society of Automotive Engineers |
| Volume | 30 |
| Issue number | 1 |
| DOIs | |
| State | Published - 2022 |
Keywords
- Failure
- Spatial autocorrelation
- Spatial regression
- Vehicle inspection