Factors Contributing to the Relationship between Driving Mileage and Crash Frequency of Older Drivers

Abstract

As a characteristic of senior drivers aged 65 +, the low-mileage bias has been reported in previous studies. While it is thought to be a well-known phenomenon caused by aging, the characteristics of urban environments create more opportunities for crashes. This calls for investigating the low-mileage bias and scrutinizing whether it has the same impact on other age groups, such as young and middle-aged drivers. We use a crash database from the Ohio Department of Public Safety from 2006 to 2011 and adopt a macro approach using Negative Binomial models and Conditional Autoregressive (CAR) models to deal with a spatial autocorrelation issue. Aside from the low-mileage bias issue, we examine the association between the number of crashes and the built environment and socio-economic and demographic factors. We confirm that the number of crashes is associated with vehicle miles traveled, which suggests that more accumulated driving miles result in a lower likelihood of being involved in a crash. This implies that drivers in the low mileage group are involved in crashes more often, regardless of the driver’s age. The results also confirm that more complex urban environments have a higher number of crashes than rural environments.

City-Level China Traffic Safety Analysis via Multi-Output and Clustering-Based Regression Models

“…Since drivers in China break traffic law more often than those in developed countries [2], we are interested in factors related to traffic enforcement, such as traffic police numbers and the number of road cameras. The relation between traffic safety and a lot more factors, such as roadway infrastructure [48,49], advanced control schemes [50], vehicle mileage traveled [51], and vehicle market predictions [52] are also worthy of being explored. Without a city-level dataset on the aforementioned variables, we may adopt statistical data fusion approaches, such as density ratio fusion [53], to obtain a comprehensive traffic safety dataset with the aforementioned factors.…”

Analysis of Vehicle Breakdown Frequency: A Case Study of New South Wales, Australia

“…However, there have been several studies in the recent past on macro-level safety models, where spatially aggregated accidents are modeled against area-wide variables. These studies have employed various aggregation levels, such as census tracts, traffic analysis zones, counties, cities, states, and countries [39,40]. Attributes of these spatial aggregations, such as population, density, income, land use characteristics, environmental variables, traffic characteristics, trip generation rates, road density, etc., are typically used to model crashes.…”

Analysis and Prediction of Vehicle Kilometers Traveled: A Case Study in Spain

“…In addition, ITV records can be used to search for mobility patterns, in relation to kilometers traveled and vehicle age [34], or the differences between travel patterns, depending on rural and urban areas and the dependence on vehicle age [35]. It is even possible to establish relationships between the kilometers traveled and the frequency of accidents involving drivers of different ages [36].…”

“…As a result of the literature review, it was identified that the survey method has been used to obtain data [36,[38][39][40][41]46,[51][52][53][54][55][56] and is potentially subject to bias [35,49]. Its massive application to road safety studies in practice becomes impossible and economically unfeasible, limited by the volume and geographical origin of the same [34].…”