Bus Operator Types and Driver Factors in 

Fatal Bus Crashes: 

Results from the Buses Involved in 

Fatal Accidents Survey 

 June 2009

FOREWORD 

The Buses Involved in Fatal Accidents (BIFA) project was initiated in 2000 to collect more 

detailed information about buses involved in fatal crashes. The BIFA project is supported by the 

Federal Motor Carrier Safety Administration. Previous work using BIFA data showed significant 

differences between different bus operations in terms of crash types, when and where the crashes 

occurred, the incidence of bus driver error, and previous driver record of the drivers. 

Using BIFA data for 1999–2005, the present study focuses on factors associated with driver 

errors in fatal bus crashes involving different bus operator types. Five different carrier types are 

identified: School, transit, intercity, charter/tour, and “other” bus operators. There are substantial 

differences between these carrier types that are reflected in rates of bus driver errors in crashes 

and in the previous driving record of the bus drivers. Many factors are identified that are 

associated with driver error, including bus operation type, age, sex, hours driving, trip type, 

method of compensation, and previous driving record. A logistic regression model was used to 

model the probability of driver error. Bus operation type, previous violations, and previous 

crashes were significant parameters in the model. The other factors were not significant. 

These findings may be of interest to passenger carriers as well as the agencies that regulate them. 

NOTICE 

This document is disseminated under the sponsorship of the U.S. Department of Transportation 

in the interest of information exchange. The United States Government assumes no liability for 

its contents or the use thereof. 

The contents of this Report reflect the views of the contractor, who is responsible for the 

accuracy of the data presented herein. The contents do not necessarily reflect the official policy 

of the Department of Transportation. 

This Report does not constitute a standard, specification, or regulation. 

The United States Government does not endorse products or manufacturers named herein. Trade 

or manufacturers’ names appear herein only because they are considered essential to the 

objective of this document. 

Technical Report Documentation Page 

1. Report No. 

FMCSA-RRA-09-041 

2. Government Accession No. 3. Recipient's Catalog No. 

5. Report Date 

June 2008 

4. Title and Subtitle 

Bus Operator Types and Driver Factors in Fatal Bus Crashes: Results 

from the Buses Involved in Fatal Accidents Survey 6. Performing Organization Code 

7. Authors 

Daniel Blower, Paul E. Green, Anne Matteson 

8. Performing Organization Report No. 

Special Report, Task C 

UMTRI-2008-37 

9. Performing Organization Name and Address 10. Work Unit No. 

University of Michigan 

Transportation Research Institute 

2901 Baxter Road 

Ann Arbor, MI 48109-2150 

11. Contract or Grant No. 

DTMC75-06-H-00003 

13. Type of Report and Period Covered 

Final Report 

March–June 2008 

12. Sponsoring Agency Name and Address 

Federal Motor Carrier Safety Administration 

U.S. Department of Transportation 

1200 New Jersey Avenue SE 

Washington, D.C. 20590 

14. Sponsoring Agency Code 

15. Supplementary Notes 

This program was administered through the Federal Motor Carrier Safety Administration (FMCSA). 

The FMCSA Program Manager is Betsy Benkowski. 

16. Abstract 

The Buses Involved in Fatal Accidents (BIFA) project was initiated in 2000 to collect more detailed 

information about buses involved in fatal crashes. The BIFA project is supported by the Federal Motor 

Carrier Safety Administration. 

Previous work using BIFA data showed significant differences between different bus operations in terms of 

crash types, when and where the crashes occurred, the incidence of bus driver error, and previous driver 

record of the drivers. 

Using BIFA data for 1999–2005, the present study focuses on factors associated with driver errors in fatal 

bus crashes involving different bus operator types. Five different carrier types are identified: School, transit, 

intercity, charter/tour, and “other” bus operators. There are substantial differences between these carrier 

types that are reflected in rates of bus driver errors in crashes and in the previous driving record of the bus 

drivers. 

Many factors are identified that are associated with driver error, including bus operation type, age, sex, 

hours driving, trip type, method of compensation, and previous driving record. A logistic regression model 

was used to model the probability of driver error. Bus operation type, previous violations, and previous 

crashes were significant parameters in the model. The other factors were not significant. 

17. Key Words 

Bus, motor carrier type, fatal crashes, driver 

factors 

18. Distribution Statement 

Unlimited 

19. Security Classification (of this 

report) 

Unclassified 

20. Security Classification (of this 

page) 

Unclassified 

21. No. of Pages 

33

22. Price 

N/A 

Form DOT F 1700.7 (8-72) Reproduction of completed page authorized.
SI* (MODERN METRIC) CONVERSION FACTORS 

APPROXIMATE CONVERSIONS TO SI UNITS APPROXIMATE CONVERSIONS FR

Symbol When You Know Multiply By To Find Symbol Symbol When You Know Multiply By 

LENGTH LENGTH 

in inches 25.4 millimeters mm mm millimeters 0.039 inft feet 0.305 meters m m meters 3.28 fyd yards 0.914 meters m m meters 1.09 ymi miles 1.61 kilometers km km kilometers 0.621 m

AREA AREA 

in2 square inches 645.2 square millimeters mm2 mm2 square millimeters 0.0016 sft2 square feet 0.093 square meters m2 m2 square meters 10.764 syd2 square yards 0.836 square meters m2 m2 square meters 1.195 sac acres 0.405 hectares ha ha hectares 2.47 ami2 square miles 2.59 square kilometers km2 km2 square kilometers 0.386 s

VOLUME VOLUME 

fl oz fluid ounces 29.57 milliliters ml ml milliliters 0.034 fgal gallons 3.785 liters l l liters 0.264 gft3 cubic feet 0.028 cubic meters m3 m3 cubic meters 35.71 cyd3 cubic yards 0.765 cubic meters m3 m3 cubic meters 1.307 c

MASS MASS 

oz ounces 28.35 grams g g grams 0.035 olb pounds 0.454 kilograms kg kg kilograms 2.202 pT short tons (2,000 lbs) 0.907 megagrams Mg Mg megagrams 1.103 s

lb

TEMPERATURE (EXACT) TEMPERATURE (EXAC

°F Fahrenheit 5(F-32)/9 Celsius °C °C Celsius 1.8 C + 32 F

temperature or (F-32)/1.8 temperature temperature te

ILLUMINATION ILLUMINATION 

fc foot-candles 10.76 lux lx lx lux 0.0929 ffl foot-lamberts 3.426 candela/m2 cd/m2 cd/m2 candela/m2 0.2919 f

FORCE AND PRESSURE OR STRESS FORCE AND PRESSURE OR 

lbf pound-force 4.45 newtons N N newtons 0.225 ppsi pound-force per square inch 6.89 kilopascals kPa kPa kilopascals 0.145 p

p

* SI is the symbol for the International System of Units. Appropriate rounding should be done to comply with Section 4 of ASTM E380
TABLE OF CONTENTS 

LIST OF ACRONYMS ..............................................................................................................III

INTRODUCTION..........................................................................................................................1

DATA ..............................................................................................................................................3

RESULTS .......................................................................................................................................4

DISCUSSION ...............................................................................................................................18

REFERENCES.............................................................................................................................21

BIBLIOGRAPHY........................................................................................................................23

i 
LIST OF TABLES 

Table 1. Buses Involved in Fatal Crashes by Operator Type, 1999–2005 ......................................4 

Table 2. Percentage Distribution of Fatalities by Bus Carrier Type and Person Type, 

1999–2005........................................................................................................................5 

Table 3. Percentage Distribution of Crash Type by Bus Operator Type, BIFA 1999–2005...........7 

Table 4. Percentage of Selected Driver Errors by Carrier Type, BIFA 1999–2005........................9 

Table 5. Driver Error and Other Driver-Related Factors by Carrier Type, BIFA 1999–2005 ........9 

Table 6. Percentage of Drivers with Selected Previous Accidents or Violations by 

Carrier Type, BIFA 1999–2005 .....................................................................................10 

Table 7. Percentage of Driver Error by Trip Type, Driver Age and Sex, and 

Compensation Type, BIFA, 1999–2005 ........................................................................12 

Table 8. Coded Driver Errors by Hours Driving at the Time of the Crash...................................13 

Table 9. Factors in Initial Driver Error Model and Baseline Case ................................................14 

Table 10. Parameters, Standard Errors, and Significance of Initial Driver Model........................15 

Table 11. Final Logistic Regression Model of Driver Errors Parameters, Estimates, 

and Significance .............................................................................................................16 

Table 12. Odds Ratios for Final Driver Errors Model...................................................................16 

LIST OF FIGURES 

Figure 1. Percentage of Coded Driver Errors by Hours Driving, with Predicted and 95% 

Confidence Limits, BIFA 1999–2005............................................................................13 

ii 
LIST OF ACRONYMS 

BIFA Buses Involved in Fatal Accidents 

CI Confidence Intervals 

DWI Driving While Intoxicated 

FARS Fatality Analysis Reporting System 

FMCSA Federal Motor Carrier Safety Administration 

TIFA Trucks Involved in Fatal Accidents 

iii 

INTRODUCTION 

About 63,000 buses are involved in traffic crashes each year, including 325 with a fatal injury, 

14,000 with a non-fatal injury, and about 48,000 in crashes that result in property damage 

only.(1,2) The number of buses in crashes is small in relation to other vehicle types: About 

444,000 trucks are involved in a traffic crash each year, as are with 4.0 million light vehicles and 

6.6 million passenger cars. About 375 people are killed in bus crashes each year, including about 

50 bus occupants (including drivers), 225 occupants of other vehicles, and 100 pedestrians and 

bicyclists. These fatalities are a fraction of the roughly 5,500 deaths that occur in crashes 

involving trucks, or of the total of around 43,000 annual fatalities in all traffic accidents. 

Nevertheless, recently there has been an appropriate increase in focus on the safety of bus 

operations. 

The authors in 2000 initiated a survey called the Buses Involved in Fatal Accidents (BIFA) 

project. This crash data collection, supported by the Federal Motor Carrier Safety Administration 

(FMCSA), supplements the Fatality Analysis Reporting System (FARS) file, compiled by the 

National Highway Traffic Safety Administration. FMCSA has supported data collection on buses 

involved in fatal crashes, to better understand the buses involved and the motor carriers that 

operate them. Since the BIFA survey focuses on buses alone, it can provide a much more 

detailed description of each bus involved in a fatal crash and the carrier that operated it. 

The BIFA survey significantly improves the identification of buses and bus operators. 

Descriptions of buses in nationally representative crash data files have been relatively simple or 

lacking altogether. Until recently, the FARS file only distinguished “school,” “crosscountry/intercity,” “transit,” and “other” bus types. Common terminology for buses mixes 

physical characteristics with how they are operated, which adds to the difficulty in determining 

the scope and nature of bus safety problems. “School bus” connotes an identifiable bus type, but 

school buses often are converted to other uses. “Cross country” buses can be used by scheduled 

intercity carriers or as charter/tour buses or for private, personal transportation. In addition, both 

the vehicles used as buses and the entities that operate them are very diverse, including, along 

with the expected bus operators, hospitals and nursing homes, non-profit organizations and 

churches, shuttle services, and private companies. The BIFA survey captures this diversity. 

Enhancing the depth and detail of information on bus types and bus operators will improve our 

understanding of the different safety issues in bus transportation. 

This study focuses on driver factors in fatal bus crashes involving different bus operator types. 

Previous work with the BIFA survey data has shown that the diversity of the types of motor 

carriers that operate buses is reflected in all aspects of their crash experience, including the time 

of day in which the crashes occur, the type of area and roads, and who in the crash is at risk. The 

work also showed differences between the operator types in terms of errors by the drivers in the 

crashes, and previous driver record. In that work, school bus drivers had the lowest rates of 

coded driver errors in the crash and the best driving record, in terms of previous crashes and 

traffic violations. Drivers of intercity buses on regular routes and charter bus drivers both had 

higher rates of driver errors and worse prior driving records.(3)

Corsi et al. found that passenger carriers had excellent safety records and measures compared to 

other motor carrier types.(4) He also found that passenger carriers with more power units (larger 

1 
fleets) tended to have better safety records than those with fewer, particularly as measured by 

violations and out-of-service rates in inspections, by crash rates, and by other safety measures.. 

Corsi’s work was only able to distinguish for-hire from private passenger carriers, without any 

detail about specific types of operations. Lantz and Blevins found a significant correlation 

between the driving records of drivers and their carrier’s crash rate, out-of-service rate, and the 

various SafeStat measures (Accident, Driver, and Vehicle).(5) In a review article, Hickman found 

a number of studies showing a relationship between prior crash record and crash involvement.(6)

The present study extends the previous work on driver factors in the safety differences between 

different types of bus operators. In the previous work, only two years of BIFA crash data were 

available. This limited the number of cases available for analysis. At the present time, BIFA 

crash data is available for seven years: 1999 through 2005. The addition of five years of crash 

data provides a more robust data set for analysis. 

2 
DATA 

Data used are from the BIFA survey for 1999–2005. Modeled on the Trucks Involved in Fatal 

Accidents (TIFA) program, the BIFA survey collects detailed information on all buses involved 

in fatal traffic crashes. For the purpose of the survey, a bus is defined as a vehicle with seating 

for nine or more occupants, including the driver, not for personal use (such as a family), or for 15 

or more passengers. Buses operated by private, commercial, or non-profit organizations are 

included. Cases for the BIFA survey are selected from the FARS file, and supplement FARS 

data with a detailed description of the bus, the bus operator, type of trip, driver hours driving, 

type of driver compensation, and role of the bus in the crash. 

Throughout this paper, we classify buses by the type of “carrier” operating them. Carrier here is 

determined by the type of operations. Thus, if a bus is used to transport pupils, it is classified as a 

school bus. If a “school bus,” that is, a bus of the type commonly used by schools, is used by a 

private company to transport employees, it is classified as a private company bus. In most cases, 

the physical configuration of most school, transit, intercity, and charter buses corresponded to the 

expected type for each. The five carrier types distinguished here are defined as follows: 

 School—Any public or private school or district, or contracted carrier operating on behalf 

of the entity, providing transportation for K–12 pupils. 

 Transit—An entity providing passenger transportation over fixed, scheduled routes, 

within primarily urban geographical areas. 

 Intercity—A company providing for-hire, long-distance passenger transportation between 

cities over fixed routes with regular schedules. 

 Charter/tour—A company providing transportation on a for-hire basis, usually round-trip 

service for a tour group or outing. The transportation can be for a specific event or as part 

of a regular tour. 

 Other—All bus operations not included in the previous categories. Includes private 

companies providing transportation to their own employees, non-governmental 

organizations (e.g., churches or non-profit groups, non-educational units of government 

such as departments of corrections), and private individuals. These groups can be 

identified by the BIFA survey, but are combined in the present analysis. 

3 
RESULTS 

Buses involved in fatal crashes are operated by a extraordinarily diverse set of operator types. 

While the majority are operated by two well-known types, a significant number are operated by a 

varied collection of small operators, private groups, and as adjuncts to some other enterprise. 

Most buses involved in fatal crashes are either school or transit buses. Table 1 shows the 

distribution of buses involved in fatal crashes in 1999–2005 by operator type, as defined above. 

Schools buses account for about 38 percent of bus involvements and transit buses an additional 

32.5 percent. Thus, those two bus types combined account for more than 70 percent of all buses 

involved in fatal crashes. Buses operated by charter or tour companies are the next most 

common, with about 11.4 percent of the total over the period. Intercity buses on regularly 

scheduled routes account for only 3.7 percent of the involvements. 

Table 1. Buses Involved in Fatal Crashes 

by Operator Type, 1999–2005 

Carrier Type N % 

School 857 38.1 

Transit 731 32.5 

Intercity 83 3.7 

Charter/Tour 256 11.4 

Other: 

Private company 20 0.9 

Non-profit organization 62 2.8 

Government 33 1.5 

Personal 3 0.1 

Contractor for school district 40 1.8 

Other 93 4.1 

Other subtotal 251 11.1 

Unknown operator type 74 3.3 

Total 2,252 100.0 

Source: Buses Involved in Fatal Accidents (BIFA) survey. 

The “other” category encompasses a great diversity of bus operators. Buses are operated by 

companies to transport their own employees, by non-profits such as churches, by different units 

of governments, and for personal use. But these are just the types that are coded directly in the 

BIFA survey. There is even an “other” category nested within the “other” category, which 

includes shuttle operations for hotels and airports, farm labor carriers, nursing home buses to 

transport patients, and even companies that deliver new or used buses. Buses are used in an 

extraordinarily wide range of applications—from dedicated, regularly scheduled operations with 

professional drivers to occasional passenger transportation with drivers whose main job is 

something else. 

The distribution of where the fatality occurred in fatal bus involvements illustrates the 

operational differences between bus types. Table 2 shows the distribution of fatally-injured 

persons in bus crashes by the type of bus involved in the crash. The table also identifies the 

person type of each fatality for each carrier type. (Only percentages are shown in the table to 

4 
minimize the number of columns. The subtotal rows show the proportion of bus, other vehicle, 

and non-motorist fatalities for each bus type. The total number of fatally-injured persons in 

crashes for each bus type is shown in the bottom row.) 

Table 2. Percentage Distribution of Fatalities by Bus Carrier Type and Person Type, 1999–2005 

Vehicle/Occupant Type School Transit Intercity Charter Other Unk. Total 

Bus occupants 

 Driver 3.4 1.0 6.5 4.8 6.2 3.5 3.4 

 Passenger 4.8 1.7 11.2 29.5 30.6 17.4 11.4 

Bus subtotal 8.2 2.7 17.8 34.3 36.7 20.9 14.8 

Occupants of other 

vehicles 

 Drivers 56.6 39.5 36.4 33.1 30.9 33.7 43.2 

 Passengers 16.1 12.2 25.2 15.9 14.5 17.4 15.1 

 Unknown occ. Type 0.0 0.1 0.0 0.8 0.0 0.0 0.2 

Other vehicle subtotal 72.7 51.8 61.7 49.7 45.4 51.2 58.5 

Non-motorists 

 In parked vehicle 0.0 0.3 0.0 0.0 0.0 0.0 0.1 

 Pedestrian 16.6 40.5 17.8 13.9 17.0 26.7 23.7 

 Bicyclist 2.5 4.8 2.8 2.0 0.9 1.2 2.9 

Non-motorist subtotal 19.1 45.5 20.6 15.9 17.9 27.9 26.6 

Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 

Total fatalities 938 778 107 396 324 86 2,629 

Source: Buses Involved in Fatal Accidents (BIFA) survey. 

A total of 2,629 persons were killed in traffic crashes involving buses in 1999–2005, an average 

of about 376 per year. Crashes involving school buses accounted for the greatest number, with 

938, and transit buses accounted for 778. The other bus operator types had many fewer. Crashes 

involving charter or tour buses accounted for 396 fatalities during the seven year period covered 

by the data. The diverse set of bus operators aggregated as “other” accounted for the next most, 

with 324. Scheduled intercity accounted for only 107 fatalities during the period, the fewest of 

the primary types of bus operators. 

There are marked differences in where the fatalities occur between the different bus operations 

types. For most bus types, only a small proportion of the fatal injuries are to occupants of the 

bus. School and transit buses account for the greatest share of fatalities, but only a small 

proportion occurs to the occupants of the buses. Only 8.2 percent of fatalities in school bus 

crashes are to occupants of the school bus, and the on-bus proportion for transit buses is only 2.7 

percent. In contrast, 34.3 percent of fatalities in crashes involving charter buses are to bus 

occupants, and 36.7 percent of the fatalities in “other” bus crashes occur to bus passengers. 

One might expect a high proportion of pedestrian fatalities in school bus involvements, given 

that small passengers are frequently boarding, getting off, and moving around the buses. But in 

5 
fact, pedestrians and bicyclists only account for 19.1 percent of fatalities in school bus crashes, 

far less than the 45.5 percent in crashes involving transit buses. The proportion of non-motorist 

fatalities in transit bus crashes is more than twice that in any other bus operator type. 

Overall, 58.5 percent of fatalities in bus crashes occur to occupants of other vehicles, but again 

the proportions vary widely by the type of bus operations. The proportion is highest for school 

buses, with 72.7 percent. Intercity buses have the next highest proportion of fatalities to 

occupants of other vehicles with 61.7 percent, and for the other bus types (transit, charter, and 

other) the proportion is about 50 percent. 

The types of crashes also vary by bus operator type, reflecting differences in how and where the 

buses are used. Single-vehicle crashes, either through running off the road or striking an object 

(usually a pedestrian or other nonmotorist) in the road account for 22–28 percent of the crash 

involvements for school, intercity, charter, or other buses, but for more than 42 percent of transit 

bus involvements. 

Table 3 shows the different crash configurations for the different bus types. (As in Table 2, the 

table cells show percentages, and the total number of involvements for each bus type is shown at 

the bottom of the table.) Virtually all single-vehicle transit bus involvements were collisions 

with pedestrians or bicyclists. Rear-end crashes accounted for 9–16 percent of crash 

involvements for each bus type, but transit and school buses are much more likely to be struck in 

the rear than to be the striking vehicle, while the other bus types are equally likely to be striking 

or struck. Again, this is likely related to their operations. Both transit and school buses have 

pick-up and dropoff operations in which the bus stops frequently in traffic. The other types 

operate more often in a point-to-point mode without frequent intermediate stops in traffic. 

6 
Table 3. Percentage Distribution of Crash Type by Bus Operator Type, BIFA 1999–2005 

Accident type School Transit Intercity Charter Other Unknown Total 

Single vehicle 

 Ran off road 1.9 0.3 6.0 5.5 6.0 0.0 2.3

 Hit object in road 20.0 42.3 21.7 23.0 21.1 14.9 27.6

Same direction, same trafficway 

 Rear-end, bus striking 1.4 1.5 8.4 7.8 4.4 1.4 2.8

 Rear-end, bus struck 11.0 10.5 7.2 7.8 4.4 1.4 9.3

 Sideswipe, in other's lane 0.0 0.0 0.0 0.8 0.4 0.0 0.1

 Sideswipe, in bus's lane 0.5 1.0 2.4 0.8 1.2 0.0 0.8

Opposite direction, same 

trafficway 

 Head-on, in other's lane 0.6 0.0 0.0 1.6 1.2 0.0 0.5

 Head-on, in bus's lane 17.3 7.0 10.8 9.0 8.4 2.7 11.3

 Sideswipe, in other's lane 0.2 0.1 0.0 0.4 0.0 0.0 0.2

 Sideswipe, in bus's lane 6.0 1.6 2.4 4.3 7.6 1.4 4.3

Change trafficway, one vehicle 

turning 

 Bus turn across path 7.5 3.8 2.4 2.0 4.0 0.0 4.8

 Other turn across path 4.8 3.6 2.4 3.5 4.4 1.4 4.0

Intersecting paths, both going 

straight 

 Bus into side of other 9.6 9.0 3.6 7.8 8.4 0.0 8.5

 Other into side of bus 4.7 4.2 3.6 1.6 4.0 0.0 3.9

Other accident types 

 Untripped roll 0.2 0.0 0.0 0.4 3.6 0.0 0.5

 Other 11.7 10.9 22.9 19.1 20.3 5.4 13.5

 Unknown 2.9 4.1 6.0 4.7 0.8 71.6 5.6

Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0

Total Crash Involvements 857 731 83 256 251 74 2,252 

The proportions of different crash types are consistent with exposure to different risk types. For 

example, school and transit buses, which operate in stop and go travel in urban areas, have 

relatively small proportions of run off the road crash types, while (scheduled) intercity, 

charter/tour, and the diverse “other” group all have much higher proportions of run off road 

crashes. Evidently, where the operation is on low-speed roads with numerous pick-ups and dropoffs, the factors that contribute to running off the road are not strongly present. But in operations 

that likely involve (at least for the intercity and charter/tour operations) long intervals on highspeed roads, the proportion of run-offs is around three times higher. Similarly, “hit object in 

road”—typically collisions with pedestrians or other nonmotorists—account for 20–23 percent of 

the fatal involvements for most bus types, but for more than 40 percent of the fatal crash 

involvements of transit buses. Again, exposure to specific types of risks probably explains this 

result: Transit buses operate in urban areas, and inherent in their operations is exposure to foot 

7 
traffic, both in terms of the types of roads they operate on and also because their operations 

involve frequent episodes of pedestrians getting on and exiting the bus. 

Certain crash types can indicate driver error or driver contribution to the crash. In rear-end 

crashes, the error leading to the crash is much more likely to have occurred in the striking vehicle 

than in the struck. Similarly, in head-on crashes, the vehicle crossing the centerline is much more 

often “at-fault” in the crash than the other vehicle. Other crash types are not so clear-cut without 

information on right-of-way and other factors. Currently there is not sufficient sample size to do 

more than note some suggestive differences between the carrier types. 

However, the BIFA file includes information on driving errors and other conditions coded to the 

bus driver. These show significant differences among the carrier types. The following few tables 

show the distribution of driver errors and other “driver-related factors” coded for the drivers of 

the different bus carrier types. The source data are coded by analysts in the States when 

compiling the FARS file, which the BIFA survey supplements. The driver-related factors 

variables (up to four may be coded) are used to record driving errors and other driver actions or 

conditions that may have contributed to the crash. The remainder of this report explores the 

relationship between driver record and bus operation type to driver error in the crash. 

FARS analysts code the driver-related factors data from police accident reports and other 

research materials. The coded errors represent the judgment of the original crash investigators, 

not the FARS analysts. As such, while not a comprehensive evaluation of the factors that 

contribute to crashes, they represent a useful identification of driving errors and other conditions 

related to the crash by the original investigators. These data can be interpreted to show the types 

of driving errors that contribute to crashes. Analysis of the data by bus operator type shows 

significant differences, which are in turn related to safety differences. 

The primary driver errors coded are “failure to yield,” “inattention,” “driving too fast,” and “not 

in lane.” Table 4 shows the most frequent driver errors overall, and their distribution within each 

of the different carrier types. The proportions of specific types of errors differ significantly 

between the operator types. Failure to yield was a common driver error for all carrier types, 

ranging from five to twelve percent of drivers for each carrier type, with intercity carriers at the 

low end of the range and transit and school buses at the high end. About 7.2 percent of drivers of 

the “other” carrier type—typically nonprofit organizations or private companies transporting 

their own employees—were coded as inattentive, and another 3.6 percent as drowsy or asleep. In 

contrast, transit and school bus drivers are very seldom (0.1 percent) identified as drowsy or 

asleep. (It should be noted that fatigue and inattention are likely underreported, since, unlike 

alcohol or drug use, they are difficult to identify after the fact. However, difficulty of detection 

should not affect the differences between passenger carrier types.) Intercity and charter bus 

drivers were more likely to be coded as driving too fast (12.0 percent and 8.2 percent 

respectively) while both transit and school bus drivers had much lower percentages. 

8 
Table 4. Percentage of Selected Driver Errors by Carrier Type, BIFA 1999–2005 

Driver error School Transit Intercity Charter Other Unknown Total 

Failure to yield 11.6 11.2 4.8 7.0 9.6 8.1 10.3 

Inattentive 5.1 4.4 6.0 6.3 7.2 6.8 5.3 

Driving too fast 2.1 1.4 12.0 8.2 6.8 10.8 3.7 

Not in lane 2.7 1.1 2.4 3.5 7.2 2.7 2.8 

Ran off road 2.0 0.8 7.2 3.5 5.2 4.1 2.4 

Failure to obey 2.7 1.2 0.0 3.9 2.8 2.7 2.3 

Erratic/reckless 1.1 1.1 0.0 3.5 3.6 4.1 1.7 

Drowsy, asleep 0.1 0.1 4.8 2.7 3.6 2.7 1.1 

Other improper turn 0.5 1.4 0.0 0.0 2.8 0.0 0.9 

Over-correcting 0.4 0.0 2.4 0.4 4.4 1.4 0.8 

Stopping in road 0.4 0.5 1.2 0.8 0.8 2.7 0.6 

N = 857 731 83 256 251 74 2,252 

Table 5 provides insight into the overall coding of factors relating to the driver’s operation of the 

bus. For this table, the factors are aggregated either as “driver errors,” which are actions or 

omissions by the driver that in the FARS analyst’s judgment contributed to the crash, or as 

“other factors,” which are conditions or events present that may have contributed to the crash. 

Typical “other factors” coded include vision obstructed by inclement weather or by parts of the 

vehicle, or swerving to avoid a vehicle in the road. The overall incidence of driver and other 

factors is about the same for both school and transit bus drivers. Overall, about 70 percent of 

school and transit bus drivers were not considered to have committed any driving error or had 

any other factor associated in connection with the crash. The percentages of drivers with no 

coded factors for intercity and charter/tour bus drivers were lower. About 63 percent of intercity 

drivers did not have any factor coded, and only 64.8 percent of charter/tour bus drivers had no 

driver factor coded. Only 55.8 percent of “other” bus drivers had no factor coded. (Up to four 

driver-related factors may be coded, so the proportions do not sum to the “any factor coded” cell 

because both a driving error and another factor can be coded for the same driver.) 

Table 5. Driver Error and Other Driver-Related Factors by Carrier Type, BIFA 1999–2005 

Driver factor coded School Transit Intercity Charter Other Unknown Total 

None 69.1 71.4 62.7 64.8 55.8* 64.9 67.5 

 Driver error 24.1 20.9 31.3 31.0* 38.7* 29.7 25.5 

 Other factor coded 9.4 9.5 8.4 9.9 11.3 9.5 9.5 

Any factor coded 29.6 26.9 37.3 34.1 43.6* 35.1 32.5 

N = 857 731 83 256 251 74 2,252 

* Statistically different from school bus proportions at 0.05 level. 

A driver error was coded for 24.1 percent of school bus drivers, and some other related factor 

was coded for 9.4 percent. For transit bus drivers, the proportions were 20.9 percent and 9.5 

percent. In contrast, both scheduled intercity and charter/tour bus drivers had higher proportions 

of driver errors coded; charter bus drivers also had a slightly higher proportion of other factors 

9 
coded. “Other” bus drivers had the highest rate, with 38.7 percent coded with a driver error and 

11.3 percent with another related factor. 

Statistical tests were performed to determine the reliability of the differences, given sample sizes, 

at the 0.05 level. School buses were taken as the baseline case, to which the other carrier types 

were compared, because they had the smallest proportion of factors coded and because of the 

relatively large sample of school bus fatal involvements. Only charter/tour and “other” carrier 

type drivers differed by a statistically significant amount from school bus drivers in the 

proportion of driver and other factors coded. About 31.0 percent of charter/tour bus drivers were 

coded with a driving error, compared with 24.1 percent of school bus drivers and only 20.9 

percent of transit bus drivers. Almost 39 percent of the “other” carrier type drivers were coded 

with a driving error that contributed to the crash. 

There are also significant differences by carrier type with respect to the previous driving record 

of the drivers. Table 6 shows the incidence in the three years prior to the crash of accidents, 

suspensions, driving while intoxicated (DWI) convictions, speeding, and other moving 

violations. As with driver errors, school bus drivers were defined as the baseline case for 

comparison purposes, and statistical tests were performed to determine the reliability of the 

differences. 

Table 6. Percentage of Drivers with Selected Previous Accidents or Violations 

by Carrier Type, BIFA 1999–2005 

Driver history School Transit Intercity Charter Other 

All 

Buses 

Accidents 15.9 29.9* 11.0 21.9 15.2 21.0 

Suspensions 3.1 3.3 0.0 6.6* 8.5* 4.2 

DWI 0.0 0.7* 0.0 0.0 0.0 0.2 

Speeding 8.8 11.3 10.3 17.2* 12.6 11.1 

Other moving violations 9.2 14.8* 12.8 18.9* 15.4* 13.3 

All violations 18.0 24.2* 21.8 31.2* 27.9* 23.3 

Any violation or accident 29.7 46.3* 32.4 44.0* 39.6* 38.5 

N = 805 689 73 237 231 2,102 

* Statistically different from school bus proportions at 0.05 level. 

School bus drivers have the lowest incidence of previous accidents or violations, compared with 

other passenger carrier types. School bus drivers also have among the lowest percentages when 

the individual violation types are considered, such as license suspensions, speeding violations, or 

other moving violations. Only some of the differences are statistically significant in these data, 

however. Less than one percent of transit drivers had a DWI conviction in the prior three years, 

but no bus driver for the other carrier types had such a conviction. Transit bus drivers also had 

higher proportions than school bus drivers of previous accidents, speeding and other moving 

violations. The differences are statistically significant for the percentages of previous crashes and 

of other moving violations. In addition, when all the violation types are aggregated to measure 

any previous moving violation or any violation or accident, transit bus drivers had significantly 

worse driving records than school bus drivers. 

10 
Scheduled intercity bus drivers also had driving records with higher proportions of violations and 

of either a violation or a crash, but the sample size for intercity drivers is not large enough to 

attain statistical significance. However, charter/tour bus drivers had higher proportions on each 

measure except for DWI, and the differences were statistically significant at the 0.05 level for 

suspensions, speeding, other moving violations, and any violation or accident. The proportions of 

charter bus drivers with speeding violations or other moving violations within the last three years 

were each around twice that of school bus drivers. Almost 45 percent of charter bus drivers had 

either a traffic violation or crash within the last three years, compared with about 30 percent of 

school bus drivers. 

The diverse “other” bus group also shows higher proportions of traffic violations on their driving 

records, though the proportion of prior crashes is about the same as the baseline school bus 

drivers. “Other” bus drivers had higher proportions of suspensions, speeding and other moving 

violations, and the combination of either a prior traffic violation or crash. Most of the differences 

were statistically significant at the stringent 0.05 percent level. 

Both the incidence of driver errors in the current crash and the prior driving record of the bus 

drivers vary significantly by bus operation type. Previous work(3,4)

 has shown that driver record 

is predictive of crash involvement, and related to carrier safety. So, are the differences between 

carrier types in terms of driver errors in the crash related to differences in the prior driving record 

of the drivers, or are they related to other factors? Evidence has been presented here to show that 

carriers vary by their operations and that this may also be reflected in exposure to crash risk. In 

addition, other factors can be associated with driver error in crashes, and these may also vary for 

different carrier types. For example, the population of school bus drivers is disproportionately 

female compared with other bus operator types. Both transit and school bus drivers operate 

primarily on local trips, while intercity and charter/tour bus more often are involved in over-theroad trips. School bus and transit bus operations are predictable and have a daily routine, while 

charter/tour buses can have more lengthy schedules. There are also differences in terms of the 

types of compensation the drivers receive, whether hourly, on salary, or some other method.1 

All of these factors may affect driver error in crashes, in addition to the previous driving record. 

The BIFA data set includes data on driver age and sex, driver compensation, trip type, and 

compensation. Table 7 shows that these characteristics of the driver and trip are associated with 

differences in the proportion of driver error. Drivers on over-the-road trips (longer than 50 miles) 

are more likely to be coded with a driver error than those on local trips. Males are somewhat 

more likely to be coded with a driving error than females; and younger (≤30 yrs) and older (>60 

years) drivers are both more likely to be coded with an error than those in middle age. The BIFA 

file includes detailed information on how the driver was compensated, but the two most common 

methods for bus drivers are hourly compensation and salary. Drivers compensated by the hour 

have lower rates of coded driver errors than others, while the difference between salary and all 

other types of compensation is small. On the other hand, the group of drivers compensated by all 

other means (mileage, tips, and even no compensation) have the highest rates of coded driver 

errors. 

 

1

 See, for example, Buses Involved in Fatal Accidents Factbook, 2004. Center for National Truck and Bus Statistics, University of Michigan 

Transportation Research Institute, Ann Arbor. Tables 4-3, 4-4, 4-9, 4-11. 

11 
Table 7. Percentage of Driver Error by Trip Type, Driver Age and Sex, and 

Compensation Type, BIFA, 1999–2005 

Driver/Trip 

Characteristic 

Characteristic 

Variable N 

Percent Driver 

Error 

Trip type Local 1,616 23.5 

Trip type Over-the-road 337 33.2 

Driver sex Male 1,473 27.2 

Driver sex Female 725 22.5 

Driver age ≤30 211 29.4 

Driver age 30–60 1,683 24.0 

Driver age 60+ 298 32.6 

Driver compensation Hourly: Yes 1,438 23.5 

Driver compensation Hourly: No 380 29.2 

Driver compensation Salary: Yes 147 26.5 

Driver compensation Salary: No 1,672 24.6 

Driver compensation All other: Yes 369 29.0 

Driver compensation All other: No 1,452 23.7 

Table 8 shows number of coded driver errors by hours driving at the time of the crash. The 

number of hours driving varies from 1 to 10 hours. A logistic regression model can be fit to these 

data to estimate the probabilities of coded driver errors at each of the ten levels of hours driving. 

The model takes the form 

10,,1 1 log 

   













 ixp

p

i

i

i 

where  and  are parameters to be estimated, and represents hours of driving at the time of 

the crash. The are the probabilities of coded driver errors. Statistical software was used to fit 

this model to the data shown in 

i x

pi

Table 8. After fitting the model, the above formula can be solved 

to give the estimated probabilities of coded driver error. 

12 
13 

Table 8. Coded Driver Errors by Hours Driving 

at the Time of the Crash 

Hours 

Driving 

( ) i x

Coded 

Driver 

Errors: 

YES 

Coded 

Driver 

Errors: 

NO 

Total 

1 99 370 469 

2 66 234 300 

3 66 147 213 

4 39 115 154 

5 32 102 134 

6 32 84 116 

7 13 32 45 

8 11 31 42 

9 8 15 23 

10 3 8 11 

Figure 1 shows a general trend to higher percentages of coded driver error the longer the driver 

has been at the wheel since the driver’s last 8-hour period of rest. The figure shows the observed 

percentage of driver error, along with the fitted logistic regression line and a 95 percent 

confidence band. The observed percentages can be reproduced from Table 8 by dividing the 

number of coded driver errors “Yes” by the total in each row. Most of the observed percentages 

fall within the 95 percent confidence interval. Note that the interval widens significantly for 

cases with greater than 6 hours of driving. As shown by Table 8, sample sizes drop precipitously 

in that region, so that there were only 23 and 11 cases respectively for hours 9 and 10. 

Figure 1. Percentage of Coded Driver Errors by Hours Driving, 

with Predicted and 95% Confidence Limits, BIFA 1999–2005 

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

1 hrs 2 hrs 3 hrs 4 hrs 5 hrs 6 hrs 7 hrs 8 hrs 9 hrs 10 hrs

Hours driving at time of crash

Percent Driver Errors

Observed

Predicted

Lower bound

Upper bound
A number of factors have been identified that are associated with the probability of errors coded 

to the bus driver in fatal crash involvements. The factors are related to characteristics of the 

driver (age, sex, method of payment, and previous driving record), or of the type of bus 

operations (operation type and trip type). Given the large number of factors that are apparently 

associated with differences in the probability of a driving error coded to the bus driver, statistical 

models were fitted to the data to estimate the effect of each factor, holding the other factors 

constant. 

The logistic regression model that was described and used above to model the relationship 

between coded driver errors and number of hours driving at the time of the crash can be extended 

to include multiple factors. The method is to include various variables in a model and to estimate 

the associated parameters simultaneously in order to judge their effects on the likelihood of 

coded driver error. Certain parameters in the model have interpretations as odds ratios on the log 

scale. The models estimate the effects of various factors on the odds of driver error, relative to a 

baseline case. Some of the levels of the variables were combined for the sake of interpretation. 

The parameters estimate the increase (or decrease) in driver error probability when the particular 

factor is varied from the baseline case. Table 9 represents the baseline cases for the initial driver 

error model. 

Table 9. Factors in Initial Driver Error Model 

and Baseline Case 

Factor Baseline case 

Bus type Transit 

Trip type Local 

Compensation Hourly/Mileage 

Age ≤30 years 

Sex Male 

Previous suspensions None 

Previous DWI None 

Previous speeding None 

Previous moving 

violations None 

Previous crashes None 

Initially, the variable hours driving was incorporated in the model, but since the value is 

unknown for about 30.1 percent of the involvements, this was not practical. Cases with missing 

data on any of the predictor variables are deleted from the model. Including hours driving 

resulted in almost half of the cases being excluded from the model, which was not viable. So, 

while the results shown in Figure 1 suggest a relationship, it was not possible to test the 

relationship in combination with the other factors that are also apparently associated with coded 

driver error. 

Table 10 shows the parameter estimates, standard errors of the parameters, and the statistical 

significance of the parameters. The initial model incorporates all the factors available that were 

identified above as associated with the probability of a bus driver coded with a driving error in a 

fatal crash. Negative parameters indicate that the factor reduces the probability of a driver error, 

14 
15 

relative to the baseline and positive parameters indicate that the factor increases the probability 

of a driver error relative to the baseline. 

Table 10. Parameters, Standard Errors, and Significance of Initial Driver Model 

Parameter Level Parameter 

Estimate 

Standard 

Error 

p value 

Intercept -1.3807 0.2183 <.0001 

Bus School 0.2352 0.1513 0.1200 

Bus Intercity 0.4190 0.3434 0.2225 

Bus Charter 0.3303 0.2457 0.1789 

Bus Other 0.8039 0.1998 <.0001 

Trip type Over the road 0.2746 0.1976 0.1646 

Driver sex Female -0.1177 0.1369 0.3899 

Driver age 1 31–60 -0.2055 0.1989 0.3014 

Driver age 2 61+ 0.1749 0.2382 0.4628 

Compensation Hourly or mileage 0.0511 0.1483 0.7303 

Previous suspensions 1 or more 0.6726 0.2814 0.0168 

Previous DWI 1 or more -12.4198 434.600 0.9772 

Previous speeding 1 or more -0.2412 0.1968 0.2203 

Previous moving violations 1 or more 0.4306 0.1690 0.0108 

Previous crashes 1 or more 0.1698 0.1416 0.2304 

Many of the factors in Table 10 do not achieve statistical significance, using a p value of 0.05 as 

a rule of thumb for significance. Trip type, driver sex, driver age, and compensation type all fail, 

in these data, based on the usual test of statistical significance. Even though the tables above 

indicated a relationship, when the factors were considered one at a time, in a multi-factor model, 

their effect on driver error was not large enough to be reliable. The effects of some of the 

specific violation types were significant: for example, previous suspensions and previous moving 

violations. There were very few cases where a driver had a previous DWI, which probably 

accounts for the very high p value. In sum, the model incorporating all the factors previously 

identified as associated with driver error may be too ambitious for the data under investigation. 

A systematic procedure was used to eliminate factors that were not significant one at a time to 

search for a statistically significant model. In addition, a new variable was created that captured 

whether the driver had any previous traffic violations, replacing the individual violation types. 

As a result of this stepwise procedure, a much simplified model was estimated, incorporating 

many fewer factors, but resulting in a much better model fit. This final model includes only the 

bus operation type, prior traffic violations, and prior crashes. Table 11 shows the parameter 

estimates, the standard error of the estimates, and the significance values. While not all the 

estimates are significant at the 0.05 level, all but one are significant at better than 0.10 which is a 

reasonable level for non-experimental, observational data. (The bus operation type variable has 

five levels; it is not useful to exclude individual levels.) 
16 

Table 11. Final Logistic Regression Model of Driver Errors 

Parameters, Estimates, and Significance 

Parameter Level Parameter 

Estimate 

Standard 

Error 

p value 

Intercept -1.4991 0.1082 <0.0001 

Bus School 0.2371 0.1288 0.0657 

Bus Intercity 0.6404 0.2712 0.0182 

Bus Charter 0.5177 0.1728 0.0027 

Bus Other 0.9483 0.1678 <0.0001 

Previous violation 1 or more 0.2433 0.1210 0.0444 

Previous crash 1 or more 0.2403 0.1274 0.0592 

The parameter estimates shown in Table 11 indicate that the primary factors affecting the 

probability of bus driver error in fatal crashes is the operation type of the bus, and the driver’s 

previous record with respect to traffic violations and crashes. The size of the parameter for 

school bus is relatively small, and fails the 0.05 test of significance, reflecting the fact that the 

differences in proportion of driver error between school bus and transit bus drivers is not large. 

However, the parameters for the other bus operation types is larger, with the parameter for the 

diverse “other” type the largest in the model. All are statistically significant. A record of 

previous violations or previous crashes also is associated with a higher probability of driver 

error. 

Table 12 shows the odds ratios associated with the factors, along with 95 percent confidence 

intervals (CI) for the odds ratios. Odds ratios are a way of expressing how much the factor 

increases the chance (odds) of the event modeled. Factors with ratios greater than 1 increase the 

odds, and those with ratios less than 1 decrease the odds. If the CI excludes 1, then the factor is 

statistically significant, in this case at the 5 percent level. If the CI includes 1, then it is not 

significant at that level. 

Table 12. Odds Ratios for Final Driver Errors Model 

Factor Factor Level vs. Baseline 

Odds 

Ratio 

Estimate 

95% CI: 

Lower 

95% CI: 

Upper 

Bus School vs. transit 1.268 0.985 1.631 

Bus Intercity vs. transit 1.897 1.115 3.228 

Bus Charter vs. transit 1.678 1.196 2.355 

Bus Other vs. transit 2.581 1.858 3.587 

Previous violation Previous violation vs. no previous 1.275 1.006 1.617 

Previous crash Previous crash vs. no previous 1.272 0.991 1.632 

The model parameters show that both operations type and driver record have a substantial effect 

on the probability that a bus driver in a fatal crash will be coded with a driving error. School 

buses have the lowest odds ratio compared with the baseline, and note that the 95 percent 

confidence interval includes 1. Intercity, charter, and other all have odds ratios substantially 

higher, and their 95 percent confidence intervals do not include 1. For intercity, the odds increase 

by 1.9 times relative to transit buses, with the 95 percent confidence interval ranging from 1.1 to 
17 

3.2. The odds ratio for charter (including tour) buses is about 1.7 times higher than transit, while 

the odds ratio for the diverse “other” bus operations is about 2.6 times. The 95 percent 

confidence interval ranges from 1.9 to 3.6. The “other” type includes buses operated by 

companies to transport their own employees, by non-profits such as churches, by different units 

of governments, shuttle operations for hotels and airports, farm labor carriers, nursing home 

buses to transport patients, and even companies that deliver new or used buses. 

Previous driving record also is significantly related to the probability of being coded with a 

driving error in the current crash. The variables record violations or crashes within three years 

preceding the crash. Both previous violations and previous crashes increase the odds of a driving 

error by about 1.3 times. The parameter for previous crashes just fails the usual 0.05 significance 

test, but such a test is likely too rigid for observational data. It is reasonable to conclude that the 

driver’s record within the past three years is associated with the probability of a driver error 

being recorded. Bus drivers with marks on their driving record are about 27 percent more likely 

to commit a driving error in a fatal crash than those without. 
18 

DISCUSSION 

Passenger carrier type has a significant effect on virtually all aspects of the experience of buses 

in fatal traffic accidents. Suggestive differences were found, in this and previous studies, 

between the four primary carrier types defined here—school, transit, scheduled intercity, and 

charter/tour—in virtually every dimension examined. These differences are reflected in the time 

of the crashes, the area and roads on which the crashes occurred, and who in the crash is at 

greatest risk of fatal injury. BIFA project data allow bus operators to be identified at a level of 

detail significantly greater than in any other crash data. 

The passenger carrier types also differ in the percentages at which drivers involved in fatal 

accidents are coded with a driving error in the crash. The driver errors here are specifically 

identified on the original police accident report or other crash investigation. While not a 

comprehensive determination of contribution to the crash, they may be regarded as the judgment 

of the investigators as to the driver’s role in precipitating the crash. In this way, they reflect on 

the safety performance of the different bus operator types. 

In terms of previous driver record and driver errors in the crash, significant differences were 

found among the passenger carrier types examined. Some of these differences were great enough 

to be statistically significant, even given the limited data available. School bus drivers had the 

best driving records and were coded with relatively few driving errors in the crash, compared 

with the other bus carrier types. Both intercity and charter/tour bus drivers had much higher 

percentages than school bus drivers on most of the measures. Statistical significance could not be 

established for intercity drivers because of the number of cases available, but the differences for 

charter bus drivers were both large and statistically significant. Fully 44 percent of charter bus 

drivers had a conviction, suspension, or crash in the three years prior to the crash, compared with 

only about 30 percent of school bus drivers. And 31 percent of charter bus drivers were coded 

with a driving error in the current crash, compared with 24.1 percent of school bus drivers. This 

difference was statistically significant at the 0.05 level. 

The bus operations types identified here differ in a number of respects, any of which are 

arguably related to safety and tend to influence the likelihood of driver error. Transit and school 

buses operate typically on predictable, regular schedules, and usually in urban areas on low 

speed roads. Scheduled intercity buses of course are scheduled, but have longer hauls and travel 

more on roads that may have lower traffic density, but higher speeds. Charter/tour buses may 

also have long hauls, but less predictable schedules. School buses have a higher proportion of 

female drivers, while the collection of “other” buses may include very inexperienced drivers 

whose main occupation is something other than driving. A wide variety of factors were 

considered, including age, sex, trip type (local or over-the-road), type of driver compensation, 

and hours driving, in addition to driver record and bus operations type. 

A logistic regression model was used to estimate the effect of the different factors on the 

probability of bus driver error in the crash. This technique allows us to account for the separate 

effects of the different factors. A stepwise procedure was used, in which all the factors were 

included in the model initially; then nonsignificant parameters were eliminated until a good fit 

was achieved with significant parameters. 
19 

In the final model, driver record captured as violations and crashes within the previous three 

years, and bus operation types were the only factors with statistically significant parameters. 

Even though trip type, compensation, and even driver sex (i.e., the high proportion of female 

school bus drivers) are related to passenger bus operation types, none significantly contributed to 

the fit of the model—the ability of the model to predict driver error—and so were dropped. Prior 

driver record, captured as previous violations and previous crashes, both increase the probability 

that a driver will be coded with an error in the crash. Put another way, drivers with a record of 

driving violations, or who have been involved in a crash, are more likely to have contributed to 

the current crash than other drivers. 

The type of bus operations is also a significant predictor of coded driver error in the current 

crash. Both transit and school bus drivers are the least likely to have contributed to the crash. 

Intercity operations are associated with an increase in the odds by 1.9 times, with a 95 percent 

confidence interval from 1.1 to 3.2 times. Both charter and the “other” bus operations were 

associated with significantly higher odds of driver error. The odds ratio for charter/tour bus 

operations was 1.7 (range of 1.2–2.4), while for “other” buses it was 2.6 (1.9–3.6). 

The differences uncovered in this analysis have implications for safety improvements and 

validate the approach taken in the BIFA survey. Motor carrier type plays a major role in fatal bus 

crash involvements and, even at the level undertaken in this study, point to quite different safety 

interventions, depending on the operation type. Pedestrian/bicyclist crashes are, of course, a 

problem for school buses and improved driver vision around the bus remains an issue. Driver 

vision around the bus is clearly a major issue for transit bus drivers, in light of the very high 

proportion of pedestrian/bicyclist collisions. The high proportion of rear-end crashes in which 

the bus is struck suggests that conspicuity and awareness that the bus is stopped may need to be 

addressed. Driver issues are more of a focus for intercity, charter/tour bus, and the diverse 

“other” bus operations, although the small sample size for intercity involvements make 

conclusions tentative. Finally, there are safety differences associated with the operations 

themselves, beyond the type of drivers employed. 

Given the significantly higher rate of driver error in the “other” bus group, future research might 

be focused on better defining this group and understanding the diversity of operations 

encompassed. The group ranges from airport shuttle buses, to companies transporting workers to 

the job, to nonprofits such as churches transporting members to social functions. Future work 

could explore the group to determine which subpopulations are driving the overall record. For 

example, airport shuttle operations bear some resemblance to how transit buses are operated, 

while church buses driven by volunteers likely have a very different experience. Charter/tour bus 

operations also warrant future research to determine the factors that contribute to their 

performance relative to the other bus types. Many charter/tour bus companies are small 

operations which may affect their safety performance. While passenger carriers as a whole do 

not contribute disproportionately to fatal crashes, there are clearly opportunities to understand 

the factors that contribute to poor performance and to address them. 

21 

REFERENCES 

1. Traffic Safety Facts, 2005. (n.d.) National Highway Traffic Safety Administration, 

National Center for Statistics and Analysis, U.S. Department of Transportation, 

Washington, DC. Available at www-nrd.nhtsa.dot.gov/Pubs/TSF2005.PDF 

2. National Automotive Sampling System General Estimates System. Crash data files for 

1999–2005 inclusive. Compiled by National Highway Traffic Safety Administration, 

National Center for Statistics and Analysis, U.S. Department of Transportation, 

Washington DC. Available at ftp://ftp.nhtsa.dot.gov/GES 

3. Blower, D., & Matteson, A. (2004). Motor Carrier Type and Factors Associated with 

Fatal Bus Crashes 1999 and 2000. Michigan University, Ann Arbor, Transportation 

Research Institute, Center for National Truck Statistics. 19 p. Report No. UMTRI-2004–

20. Available online: www.umtri.umich.edu/content/UMTRI_2004_20.pdf 

4. Corsi, T. M., Newhouse, M. L., Shukla, A., & Chandler, P. (2002). Passenger motor 

carriers: a safety performance profile. Maryland University, College Park, School of 

Business, Supply Chain Management Center/ Federal Motor Carrier Safety 

Administration, Commercial Passenger Carrier Safety Division, Washington, D.C. 26 p. 

Zacharia, Z. G., ed. Proceedings: International Truck and Bus Safety Research and Policy 

Symposium. Knoxville, TN, p. 523–548. 

5. Lantz, B. M., & Blevins, M. W. (2002). An Analysis of Commercial Vehicle Driver 

Traffic Conviction Data to Identify High Safety Risk Motor Carriers. In Zacharia, Z. G., 

ed. Proceedings: International Truck and Bus Safety Research and Policy Symposium. 

Knoxville, TN, p. 549–556. 

6. Hickman, J. S. (2006) High-Risk Commercial Motor Vehicle Drivers and Differential 

Crash Risk: Future Directions. Future Truck and Bus Safety Research Opportunities, 

Conference Proceedings 38. Transportation Research Board, Washington DC. Available 

at http://trb.org/news/blurb_detail.asp?ID=5742 

23 

BIBLIOGRAPHY 

Ellis, D. R., & Hanley, P. (2002). Characteristics of camioneta van operations in Texas. 

Texas Transportation Institute, College Station/ Federal Motor Carrier Safety Administration, 

Washington, DC. In Zacharia, Z. G., ed. Proceedings: International Truck and Bus Safety 

Research and Policy Symposium. Knoxville, TN, p. 511–521. 

Foreman, C., Rey, J. R., & DeAnnuntis, C. (2002). National transit bus accident data 

collection and analysis. South Florida University, National Center for Transit Research, 

Tampa. Sponsor: Transportation Department, Research and Special Programs 

Administration, Washington, DC.; Florida Department of Transportation, Tallahassee. 

Report No. NCTR-416–13. 

Hughes, R. G., & Rodgman, E. A. (2000). Commercial bus crashes in North Carolina 1995–

1999. North Carolina University, Chapel Hill, Highway Safety Research Center. Sponsor: 

North Carolina State Department of Motor Vehicles, Raleigh. 

Jovanis, P. P., & Schofer, J. L., Prevedouros, P., & Tsunokawa, K. (1989). Analysis of Bus 

Transit Accidents: Empirical, Methodological, and Policy Issues. Northwestern Univ., 

Evanston, IL. Transportation Center. Sponsor: Urban Mass Transportation. Administration, 

Washington, DC. Univ. Research and Training Program. 

Putcha, D. (2001). Bus accidents in the United States, 1995–1999. Michigan University, Ann 

Arbor, Transportation Research Institute, Systems Analysis Division. Sponsor: American 

Bus Association, Washington, DC. Report No. UMTRI-2001–17.