Truck-involved Crashes and Traffic Levels on Urban Freeways

Using two years of crash and average annual daily traffic data we examine the locations and conditions linked to truck-involved crashes (accidents). A binomial logit model is used to describe how the probability that a crash involves a truck is a function of the percentage of annual average daily traffic that is accounted for by trucks, time of day, day of the week, weather conditions, mix of truck types, and the absolute level of average annual daily traffic. That model can then be used to identify locations with higher or lower than expected truck involved accident rates, controlling for all of the factors that influence truck crash rates. A multinomial logit model was then estimated in order to better understand patterns of truck-involved crashes by separating crashes by type, with the main types being rear-end, lane-change, and run-off collisions. We propose that results from applications of these kinds of models, applied in a specific region, can be useful to public agencies seeking to identify and remedy problem areas either with better driver education or investments in physical or intelligent transportation system infrastructure. Golob and Regan Truck-involved Crashes and Traffic Levels on Urban Freeways Page 1 of 2 BACKGROUND One important measure of the level of safety of any road network is the number of crashes (accidents), either total crashes or crashes of a specific type, per vehicle mile of travel. Considerable effort has been spent refining the analytical methods that relate crash rates to exposure measures (1), but there has been much less work in examining the effectiveness of aggregate data, such as average annual daily traffic (AADT), as the basis of exposure measures. When focusing on crashes involving trucks, it is unclear how annual data on truck traffic can be combined with total AADT as an effective measure of exposure to the risk of various types of crashes. It is quite common for motorists who travel on routes shared with trucks to complain about the negative impacts those vehicles have on driving conditions. However, little research has examined the safety impacts of commercial vehicle operations. There are few published journal articles concerning truck crash statistics specifically, but there have been several recent relevant reports. An exception, and one that provides an excellent literature review related to studies of accident severity is found in Chang and Mannering (2). The USDOT provides an analysis of motor vehicle crash data. The most recent of these examines 2001 data. That report shows that heavy trucks are involved in about four percent of crashes and about eight percent of fatal crashes (3). A recent study performed at the University of Michigan for the AAA foundation for traffic safety examines the unsafe driver actions that leads to fatal car-truck crashes (4). Their key findings were that four factors were more likely to occur in fatal car-truck crashes than fatal car-car crashes. These were following improperly, driving with obscured vision, drowsy or fatigued driving and improper lane changing. Another recent study examined in incidence of night time truck accidents, relative to car accidents and found that truckers driving at night were not more likely to be involved in accidents, while night time drivers of passenger cars were (5). Another recent study, performed for the US Federal Motor Carrier Safety Administration (FMCSA), examined the link between driver schedules and safety (6). Their key findings were that company driving environments, economic pressures and carrier support for safe driving were the main factors influencing driver fatigue. Finally, a recent study of the impact of large trucks on interstate highway safety was conducted at the University of Kentucky in an effort to identify counter measures to be implemented in dangerous spots and sections on the interstate highways (7). Our study also identifies sections of roadway that are disproportionately the site of truck involved crashes. DATA This research uses two years of crash and average annual daily traffic (AADT) data from the Traffic Accident Surveillance and Analysis System (TASAS) database maintained by the California Department of Transportation (8). TASAS covers police-reported crashes that occur on the California State Highway System. Our study area encompasses six major freeways in Orange County, California, an urban area of about three million population located between Los Angeles and San Diego. These six routes account for a total of approximately 131 centerline miles. Of the 19,202 mainline crashes on these routes in 2000 through 2001, 1,952 or 10.2% involved trucks or tractor-trailers larger than two-axle, four-tire pickups and vans. Golob and Regan Truck-involved Crashes and Traffic Levels on Urban Freeways Page 2 of 3 The TASAS database contains information regarding the characteristics of each collision, including: (a) the postmile location of the primary collision, (b) the number of parties (usually vehicles) involved, (c) movements of each vehicle prior to collision, (d) the location of the collision involving each party, (e) the object(s) struck by each vehicle, and (f) the severity, as represented by the numbers of injured and fatally injured parties in each involved vehicle. The database also includes information regarding weather and roadway conditions and ambient lighting. No information was available to us concerning drivers or vehicle makes and models, but vehicles are categorized as to passenger cars, motorcycles, pickups and vans, trucks, and other types of specialized vehicles, such as buses and emergency vehicles. The database does not cover collisions for which there are no police reports. The TASAS database also contains AADT estimates for all freeway sections. AADT data are also available online (9). AADT is defined to be total annual traffic (from October 1 of the previous year though September 30 of the year in question), and applies to all sections of a freeway bounded by onand off-ramps and freeway-to-freeway connectors. Sample counts are performed using both portable counting instruments and permanent inductive loop detectors, and AADT is estimated using adjustments for seasonal and weekly variations. Truck traffic is available on an average annual basis (10). Truck counting is done throughout the State of California through a program of continuous truck count sampling. For freeways, this sampling includes partial day, 24-hour, and continuous vehicle classification counts, usually taken only once a year. Only selected freeway sections are sampled, and intermediate sections are interpolated. About one-sixth of the selected sections in California are counted annually, and field counts are adjusted to an estimate of truck AADT by compensating for seasonal influence and weekly variation. The stated current policy in California is to count trucks on each route at least once every six years. For 2001, truck AADT on the six Orange County freeways ranges from 4,080 to 24,300, with a mean of 14,770 and a standard deviation of 4,706. A histogram is shown in Figure 1. Truck AADT as a percentage of total AADT: ranging from 2.1% to 10.6%, with a mean of 6.4% and a standard deviation of 1.85% (Figure 2). Golob and Regan Truck-involved Crashes and Traffic Levels on Urban Freeways Page 3 of 4 24,000 20,000 16,000 12,000 8,000 4,000 20