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21 ments had weather as an associated factor, but less than 1% of surface transportation weather observation data management truck at-fault crashes were assigned a weather-related CR. system, and to establish a partnership to create a Nationwide Juxtaposing these percentages suggests that adverse weather Surface Transportation Weather Observing and Forecasting contributes to many more crashes than it causes directly, and System. State DOTs and other road operators are the program's this was exactly the finding of a Canadian study that selected principal target users, though the program may provide prod- both primary and contributing crash causes (Gou et al. 1999). ucts and services to transport companies in the future. Weather In that study, "slick road" was deemed the primary cause of and road condition information may be provided to travelers only one of 195 truck crashes, but it was the secondary or ter- by means of navigation and route optimization services. This tiary cause of 23 crashes (12%). would make those services more dynamic and responsive to predicted conditions. Enlightened risk management requires an estimate of the relative crash risk in adverse weather or on slick roads com- pared to dry conditions. A 1980 NTSB report estimated the VEHICLE SIZE AND CONFIGURATION relative risk of fatal crashes on wet versus dry roads (for all Large trucks come in different sizes and configurations, and are vehicles) to be about four. In the LTCCS, 18% of CT crash selected for their uses primarily on the basis of productivity involvements occurred on wet roads, versus 11% of ST and practicality. A logical question is whether these sizes and involvements. A comparison of the LTCCS CT wet roads configurations are optimal from the safety perspective. Larger percentage (18%) to a naturalistic-driving wet roads expo- trucks might be safer if their use results in fewer trucks on the sure estimate of 9% (Hickman et al. 2005) suggests a relative road and, therefore, less exposure to risk. Smaller trucks might risk closer to 2. be safer if they are individually less likely to be in crashes and if their crashes are less severe because of their smaller size dif- Questions 1 and 2 of both project surveys asked respon- ferential compared with other vehicles. Answering this safety dents to select the most important (Question 1) and least question is extremely difficult, however, because of several important (Question 2) general factors affecting truck crash major variables confounding comparisons. risk. Overall, "weather and roadway surface conditions" was considered less important than driver characteristics (both A pair of questions on both the safety-manager and other- enduring and temporary) and roadway characteristics/traffic expert survey forms asked respondents to state the general conditions (e.g., road type). Only vehicle characteristics were directions of their views on larger versus smaller trucks. Both rated as less important. questions asked for ratings on a seven-point Likert scale rang- ing from -3 ("Reduces Fleet Safety") to +3 ("Improves Fleet Another question on the surveys asked about the impor- Safety"). The two questions, intentionally worded to state tance of avoiding adverse weather and slick roads. On the opposite strategies, were: seven-point Likert scale from -3 ("Reduces Fleet Safety") to +3 ("Improves Fleet Safety"), this practice was given a mean Use fewer, larger trucks (e.g., multitrailer trucks) when rating of +1.8 by safety managers, making it one of four top possible. choices. The mean rating by the other experts was +1.3, Use more, smaller trucks (e.g., single-unit trucks) when fourth among 16 driving situations and practices. possible. FHWA is developing a Road Weather Management Pro- Safety-manager respondents assigned each strategy low posi- gram that, when completed, will provide information on current tive mean ratings, suggesting perhaps that either strategy could and predicted road surface and weather conditions to highway be good for safety when used appropriately. The mean ratings users. Information sources will include fixed road sensors and were +0.6 and +0.2 for the two respective items. For other instrumented vehicles, including commercial vehicles in regu- experts, the two means were +0.4 and -0.3, respectively. lar operations. Its outputs will be "decision support systems" There were wide ranges of responses for both questions for to aid road maintainers (e.g., snow removal operations) and both respondent groups. travelers. At this writing, the project is just beginning to deter- mine what information motor carriers need and how best to The following two subsections present statistics and evi- provide that information. It is studying the economic impacts dence to help frame the issue of whether shifts in the truck of weather on motor carriers. More information on this FHWA size and configuration mix would enhance safety. They do research and development program is available at http://www. not provide definitive answers, however, because of various complexities to be discussed. The Clarus Initiative (FHWA 2007) is a joint effort of the U.S.DOT Intelligent Transportation System (ITS) Joint Pro- Single-Unit Versus Combination-Unit Trucks gram Office and the FHWA Road Weather Management Pro- gram. The word "clarus" is Latin for clear. The Clarus Initiative Large trucks are defined as those with gross vehicle weight is a multiyear effort to develop and demonstrate an integrated ratings (GVWR) of greater than 10,000 lb; 80% to 90% of

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22 large-truck crashes involve heavy trucks with GVWRs of monetized value of all crash injuries and damage), however, is greater than 26,000 lb. The two major large truck configu- considerably higher. From the crash rate and average per-crash rations are combination-unit trucks (typically tractor- costs, one can calculate average crash costs per mile for each semitrailers) and single-unit trucks (also called straight vehicle type. As it turns out, the opposite-direction differences trucks). CTs are typically in long-haul service, whereas in crash rate and crash severity cancel each other out almost most STs are short-haul. STs are more numerous (6.8M ver- exactly. Derived crash costs per truck-mile are almost equal for sus 2.2M U.S. registrations in 2008), but on average they are the two vehicle types based on these sources. driven only about one-fifth the mileage of CTs (12,362 mi per ST versus 64,764 mi per CT; FHWA 2010). As predom- An earlier study (Wang et al. 1999) used 5 years (1989 inantly long-haul vehicles, CTs are more likely to be driven 1993) of NHTSA General Estimates System crash statistics, on Interstate highways and in rural areas between cities. FHWA mileage statistics, and the same approach to crash Statistics for 2008 from the FHWA VM-1 vehicle mileage severity estimates to derive comparisons very similar to those table (FHWA 2010) indicate that 49% of CT mileage was on presented previously. In the Wang study, the ST crash rate Interstates, versus 21% for STs. The percentage of miles on was 28% higher than the CT rate, but the CT average crash rural roads (of all types) was 55% for CTs versus 43% for STs. severity was 24% higher than that of STs. Average crash costs per VMT were 9.7 for CTs and 10.0 for STs. For both Most CTs are operated across state lines. This makes them, analyses, these costs represent the harm to all parties involved their drivers, and their carriers subject to the Federal Motor in all crashes, regardless of fault. They do not represent Carrier Safety Regulations (FMCSRs). Many STs are used financial losses to carriers, which are much lower. intrastate and thus are subject only to state regulations. Many CTs are employed in multiday trips, whereas most STs are day- These crash costs-per-mile derivations might suggest that use vehicles, with their drivers returning home at the end of hauling freight by means of CTs has less overall risk, because each shift. ST driving is more likely to involve regular physical CTs have much greater capacities. The same total freight activities other than driving; indeed, many STs are primarily could be hauled by fewer vehicles. However, the CT and ST work-support vehicles rather than cargo-delivery vehicles. risk statistics are based on quite different road risk exposures, These are among the operational differences making CT-ST because CTs are used much more on lower-risk roads. There- comparisons problematic (Knipling and Bocanegra 2008). fore, no generalized conclusions may be drawn. Fleets may want to consider the factors in developing their risk avoid- One might automatically assume that CTs would have ance strategies, but the decision likely comes down to their higher crash risks than STs because they are much larger, own individual operational needs. articulated (making them vulnerable to jackknifes and more vulnerable to rollovers), and permit less visibility around the truck. Actually, overall CT crash rates are considerably lower Higher-Productivity Vehicles than those of STs, probably the result of the differences in road type exposure cited earlier. However, when CT crashes The previous questions extend to the use of trucks larger than occur, they are generally more severe. Table 5 presents a com- standard CTs. Higher-productivity vehicles (HPVs) are those posite analysis of CT and ST crash rates, severities, and "bot- with GVWRs of more than 80,000 lb, the maximum size of tom line" crash costs per mile based on three sources. Mileage standard tractor-semitrailers. This report does not address data are from FHWA VM-1 statistics, vehicles in crashes the many policy and regulatory issues surrounding the use of from NHTSA (2010), and mean crash severities (expressed as HPVs, but will address the question from the perspective of car- cost) from Zaloshnja and Miller (2007). Because the compo- riers deciding how best to haul cargo within current regulations. nent source statistics are from disparate sources, the derived statistics need to be considered rough estimates. Longer combination vehicles (LCVs) are HPVs with more than one trailer. LCV tractors may pull two or three trailers with Table 5 shows the CT crash rate per VMT to be consider- different configurations, subject to different restrictions. Spe- ably lower than that of STs. CT average crash severity (average cific LCV configurations include six-axle tractor-semitrailers, TABLE 5 COMPOSITE ANALYSIS OF ST AND CT CRASH RATES AND COSTS Metric Truck-Crash Crash Trucks in Involvements Cost Per Costs Per Truck Type VMT1 Crashes2 Per 100M VMT Crash3 Truck Mile ST 83,951 M 190,000 226.3 $56,296 12.7 CT 143,507 M 190,000 132.4 $97,574 12.9 Sources: 1FHWA VM-1 Statistics; 2NHTSA (2010); 3Zaloshnja and Miller (2007). Other statistics derived.