powerpoint presentation on transit bus driver distraction

1. Comparison Of Regional And
Urban Transit Bus Driver
Distraction
Kelwyn A. D’Souza, Ph.D.
Sharad K. Maheshwari, Ph.D.
Eastern Seaboard Intermodal Transportation Applications Center (ESITAC)
USDOT Tier II UTC
Hampton University
Hampton, VA 23668, U. S. A.
1
Urban Transport 2014
The Algarve, Portugal
May 28 – 30, 2014
.

2. 2
Study was funded by the U. S. DOT
– June 2006 to December 2013 (completed)
- August 2012 to December 2014 (on-going)
- Research has generated publications and
presentations.
Disclaimer
The contents of this report reflect the views of the authors, who are responsible for the facts and the accuracy of the
information presented herein. This document is disseminated under the sponsorship of the Department of
Transportation University Transportation Centers Program, in the interest of information exchange. The U.S.
Government and partnering organizations assumes no liability for the contents or use thereof.
Acknowledgement
Kelwyn A. D’Souza, Director
ESITAC, Hampton University
Hampton, VA, U. S. A.

3. 3
1. Introduction
2. Objective
3. Analysis of Regional and Urban Bus Drivers’ Distraction
3.1. Bus Driver Distraction Data Collection
3.2. Multivariate Statistical Analysis
3.2.1. Theoretical Framework of the MLR model
3.2.2. Solving the MLR model
3.2.3. MLR model results
4. Discussion of results
5. Conclusions and Recommendations
Outline
Kelwyn A. D’Souza, Director
ESITAC, Hampton University
Hampton, VA, U. S. A.

4. 1. Introduction
4
➢ U.. S. has the highest rate of automobile ownership (779 for every
1,000 man, woman and child).
➢ In 2013, 35,200 people died in US traffic accidents, plus there were
about 3.8 million crash injuries requiring medical attention (National
Safety Council estimate).
➢ Distracted driving causes 25% of all police reported traffic accidents
(NHTSA estimate).
➢ Use of Transit is growing along with the use of advanced in-vehicle
information systems (IVIS).
➢ Transit bus drivers have higher sources of distraction.
➢ Transit bus accident reports rarely document distraction as a cause of
accident.
Kelwyn A. D’Souza, Director
ESITAC, Hampton University
Hampton, VA, U. S. A.

5. Introduction
5
The regional agency covers a larger area in and out of several
connected cities and counties.
- Lower population density (more spread out).
- Roads are wider and longer.
- Personal vehicle use is greater.
The urban agency covers a smaller compact area mostly serving
commuters and metro passengers
- High population density.
- Roads are narrower.
- Greater use of transit buses.
U. S. Studies on Driver Distraction.
▪Ranney, T. A., Driver Distraction: A Review or the Current State-of-Knowledge. National
Highway Traffic Safety Administration, Washington D. C. NHTSA/NVS-312. 2008.
International Studies On Transit Bus Driver Distraction.
▪Salmon, P. M., Young K. L. & Regan, M. A. Distraction ‘on the buses’: A novel framework of
ergonomics methods for identifying sources and effects of bus driver distraction. Applied
Ergonomics, 42, pp. 602-610, 2011.
Kelwyn A. D’Souza, Director
ESITAC, Hampton University
Hampton, VA, U. S. A.

6. 2. Objective
6
To ascertain if the nature and intensity of driver distraction and
associated distraction factors were similar for regional and urban
drivers in different areas of the Commonwealth of Virginia.
An understanding of differences is required to assess:
- the bus drivers’ training needs.
- to guide state and local governments formulate effective regulations.
Kelwyn A. D’Souza, Director
ESITAC, Hampton University
Hampton, VA, U. S. A.

7. 3. Analysis of Regional and Urban Bus
Drivers’ Distraction
3.1. Bus Driver Distraction Data Collection
- Historical accident data from regional and urban transit
agencies.
- Self-administered survey at regional and urban transit agencies.
Data Cleansing
- Missing data
- outliers
- check for normality of data
- check for homogeneity of variance
Kelwyn A. D’Souza, Director
ESITAC, Hampton University
Hampton, VA, U. S. A.

8. Classification of Distraction Activities
Kelwyn A. D’Souza, Director
ESITAC, Hampton University
Hampton, VA, U. S. A.
Risk
Zone
Type of
risk
Regional distracting
activities
Urban distracting activities
I
Very
High
Passengers Using Mobile
Phone, Unruly Kids,
Passengers (moving
around, standing next to
driver’s cabin and
talking
Pedestrians, Passengers
(moving around, standing
next to driver’s cabin and
talking, Other Road Users,
Unruly Kids
II High
Passengers not following
etiquette, Passengers
Trying to Talk to Driver,
Other Road Users,
Pedestrians, Ticket
Machine, Passengers with
Infants
Passengers Using Mobile
Phone, Mobile Data
Terminals, Passengers not
following etiquette (eating,
drinking, smoking, noisy),
Ticket Machine/ Fare Box
III Moderate
Food and Other Smells,
On-board rattles,
Fatigue/Sickness, Climate
Controls, Audible alerts,
General Broadcasts,
Disabled passengers,
Reading (e.g. Route
Sheet), Personal
Broadcasts
On-board rattles,
Communication with
Dispatch, Looking at
Advertisements,
Passengers Trying to Talk
to Driver,
Fatigue/Sickness, Climate
Controls, Driver’s Mobile
Phone, Disabled
Passengers, Announcing
Bus Stops, Reading ( Route
Sheet)
IV LOW
Driver’s Mobile Phone,
Looking at
Advertisements
Dispatch Broadcasts, Food
and Other Smells,
Passengers with Infants,
General Broadcasts/ Other,
Audible Alerts
FOCUS
GROUP

9. Is the rating for each distracting activity normally distributed with
equal variances and minimum outliers?
9
FOCUS
GROUP

10. Is there a significance difference in the mean ratings of the distracting
activity common to both agencies?
Kelwyn A. D’Souza, Director
ESITAC, Hampton University,
Hampton, VA, U. S. A.
FOCUS GROUP
DISTRACTING
ACTIVITIES
LEVENE’S TEST
(S/NS)
MEANS (REG, URB),
(t VALUE, df), p VALUE
CONCLUSIONS
Passengers (0.56, NS)-Equal Variance
assumed
(2.3, 2.2), (0.78, 123), p > 0.05 Fail to reject H0
Reg and Urb means are equal.
Passengers using
mobile phone
(0.57, NS)-Equal Variance
assumed
(3.2, 2.8), (2.47, 123), p < 0.05 Reject H0
Reg and Urb means are
significantly different.
Unruly kids (0.37, NS)-Equal Variance
assumed
(3.2, 2.9), (1.75, 123), p > 0.05 Fail to reject H0
Reg and Urb means are equal.
Other road users (0.001, S)-Equal Variance
not assumed
(2.5, 2.1), (2.59, 121), p < 0.05 Reject H0
Reg and Urb means are
significantly different.
Pedestrians (0.06, NS)-Equal Variance
assumed
(2.3, 2.1), (1.29, 123), p > 0.05 Fail to reject H0
Reg and Urb means are equal.

11. 11
3.2.1 Theoretical Framework of the MLR model
The multinomial linear predictor (Yij) which measures the total contribution of the
10 factors (independent variables) is expressed as:
Multilevel categorical dependent variable ((Yij)):
1 = NOT DISTRACTED, 2 = SLIGHTLY DISTRACTIED, 3 = DISTRACTED,
4 = VERY DISTRACTED.
Yij = β0 +β1SEX + β2AGE + β3EXP + β4MARITAL + β5EDU +β6DRIVING/WK + β7 LOCAT + β8DAY
+ β9PEAK + β10EQUIP
• Where,
• SEX: Gender of operator, 1 = Male, 0 = Female.
• AGE: Reported age of driver in years.
• EXP: Number of years of experience driving a bus.
• MARITAL: Marital Status*, 1 = Married, 0 = Others (Separated, Divorced, Never Married, etc).
• EDU: Educational Level*, 1 = HS or Equivalent, 0 = Others (Some College, 2,4 year degree, etc)
• DRIVING/WK: Weekly driving hours.
• LOC: Location* of HRT service area, 1 = Commuter, 0 = Others (Local, Metro etc).
• DAY: Driving Schedule, 1 = Day, 0 = Night.
• PEAK: Driving Time, 1 = Peak, 0 = Non-Peak
• EQUIP: Type of Equipment* Driven, 1 = MCI, 0 = Others (Gillig, Orion etc)
3.2. Multivariate Statistical Analysis
(Generalized Linear Models)
Kelwyn A. D’Souza, Director
ESITAC, Hampton University
Hampton, VA, U. S. A.

12. Multinomial logistic regression (MLR) was applied as an extension of binary logistic regression
to model distracting activities in Risk Zone I.
Each response variable level is compared to a reference level providing three binary logistic
regression models.
Logit model comparing odds of Slightly Distracted with Not Distracted is stated as:
Log [Pr(Y=Slightly Distracted)] = Yij
Pr(Y=Not Distracted)
Yij = β0 +β1SEX + β2AGE + β3EXP + β4MARITAL + β5EDU +β6DRIVING/WK + β7 LOCAT +
β8DAY + β9PEAK + β10EQUIP
Logit model comparing odds of Distracted with Not Distracted is stated as:
Log [Pr(Y=Distracted)] = Yij
Pr(Y=Not Distracted)
Logit model comparing odds of Very Distracted with Not Distracted is stated as:
Log [Pr(Y=Very Distracted)] = Yij
Pr(Y=Not Distracted)
3.2.2 Solving the MLR Model
12
Kelwyn A. D’Souza, Director
ESITAC, Hampton University
Hampton, VA, U. S. A.

13. 13
4. Results (Passenger Related Distractions)
Kelwyn A. D’Souza, Director
ESITAC, Hampton University
Hampton, VA, U. S. A.
DISTRAC. FACTORS
PASS MOB PH
SD
(χ2
= 31.71, p >
0.10)
(χ2
= 52.15, p <
0.01)
PASSENGERS SD
(χ2
= 49.12, p <
0.05)
(χ2
= 42.81, p <
0.01)
PASSENGERS
D/VD (χ2
= 49.12,
p < 0.05)
UNRULY
KIDS SD (χ2
=
31.92, p >0.10)
(χ2
= 31.95, p <
0.05)
REG URB REG URB REG URB REG URB
INTERCEPT/CONSTANT 29.74 101.35 3.56 8.87 2.22 7.25 -6.75 17.23
AGE - - 0.205 - - - - -
DRIVE HRS/WK - -1.97 - - - - - -
DRIVE EXPERIENCE - 1.91 - -1.36 - - - -
GENDER -3.81 - 6.24 - 5.79 -24.6 - -
EDUCAT STATUS - - 3.53 - 3.47 4.44 - -
MARITAL STATUS - - -6.58 - - - - -
LOCATION - -223 -4.49 - -4.45 - -
DRIVE SCHEDULE 4.69 - 6.57 - 5.92 -8.07 4.13 -6.67
DRIVE TIME - - - -5.01 - -8.06 - -
TYPE OF EQUIP - - 26.4 - - - - -
AGE OF EQUIP YRS - - - - - - 0.73

14. 14
• The highest risk (Zone I) distracting activities were mostly passenger-related
(Passengers, Passengers Using Mobile Phones, Unruly Kids).
• Some of the distracting factors were significant at both agencies
(Passenger).
4. Discussion of Results (Passengers)
Kelwyn A. D’Souza, Director
ESITAC, Hampton University
Hampton, VA, U. S. A.
FACTORS REGIONAL URBAN INTERPRETATION
Route Location Significant (-ve) Significant (-ve) Northside (1) and Commuter (1) drivers less distracted
Driving Schedule Significant (+ve) Significant (-ve) Regional Day (1) drivers more likely to get distracted.
Urban Day (1) less likely to get distracted
Gender Significant (+ve) Significant (-ve) Regional Male (1) drivers more distracted. Urban
Male (1) drivers less distracted
Education Significant (+ve) Significant (+ve) HS or EQU (1) drivers are more likely to get distracted
Age Significant (-ve) Not Significant No comparison
Martial Status Significant (-ve) Not Significant No Comparison
Driving Experience Not Significant Significant (-ve) No Comparison

15. Results Discussion - LOCATION
Regional (-4.49, -4.45)
Southside Drivers (code 0)
are more likely to get
Distracted.
Northside Drivers (code 1) are
less likely to get Distracted.
15
NON-PREVENTABLE
PREVENTABLE
TOTAL ACCIDENTS
0
200
400
600
800
1000
1200
1400
1600
1800
A
c
c
i
d
e
n
t
s
Type of Accident
Accident at Different Locations
(Regional)
NORTHSIDE
SOUTHSIDE

16. Results Discussion – EDU LEVEL
Regional (3.53, 3.47)
HS or Equ (code 1) Drivers are
more likely to get Distracted.
Urban (4.44)
HS or Equ (code 1) Drivers are
more likely to get Distracted.
16
31%
41%
19%
9%
Education Level
(Regional)
HIGH SCHOOL
SOME COLLEGE
2 YR COLLEGE
4 YE COLLEGE OR
HIGHER
46%
20%
20%
14%
Education Level
(Urban)
UPTO HIGH
SCHOOL OR EQU
SOME COLLEGE
2 YR COLLEGE
4 YR COLLEGE OR
HIGHER
Simulation

17. Results Discussion – DRIVING EXP
Regional (N/S)
Urban (-1.36)
Drivers with more driving
experience are less likely to get
Distracted
17
0
5
10
15
20
0 - 5
YRS
6 - 10
YRS
11 - 15
YRS
16 - 20
YRS
21 - 25
YRS
26 - 30
YRS
> 30
YRS
NumberOfAccidents
Experience
Annual regional transit bus
accidents
(per million riders)
TOTAL
ACCIDENTS
0.00
2.00
4.00
6.00
8.00
10.00
12.00
0-5 Years 6-10 Years 11-15
Years
NumberOfAccidents
Experience
Annual urban transit bus accidents
(per million riders)
Total Accidents
Non
Preventable
Simulation

18. 18
Study has produced research opportunities
(Total 21 publications)
DRIVER DISTRACTION
• D’Souza, Kelwyn A, Maheshwari Sharad K, and Zbigniew Banaszak (2013).
“Research Framework for Studying Driver Distraction on Polish City
Highways.” Management and Production Engineering Review. Vol. 4, No. 2,
pp. 12-24.
• D’Souza, K. A., Siegfeldt, D., and A. Hollinshead (2013). ”A Conceptual
Analysis of Cognitive Distraction for Transit Bus Drivers.” Management and
Production Engineering Review. Vol. 4, No. 1, pp.10-19.
• D’Souza, K. A. & Maheshwari, S. K (2012). “Multivariate Statistical Analysis
of Public Transit Bus Driver Distraction.” Journal of Public Transportation:
Special Edition: Rural and Intercity Bus, Vol. 15, No. 3, pp. 1-23.
Results
Kelwyn A. D’Souza, Director
ESITAC, Hampton University
Hampton, VA, U. S. A.

19. 19
Study has produced research opportunities
(Total 32 presentations)
DRIVER DISTRACTION
• D’Souza, K. A. & S. K. Maheshwari (2014). Comparison of Regional and Urban Transit Bus Driver
Distraction. Accepted for Presentation and Publication in the Proceedings of the Urban Transport
2014 Conference, Algarve, Portugal, May 28-30, 2014.
• D’Souza, K. A. & S. K. Maheshwari (2013). Research Framework for Studying Public Transit Bus
Driver Distraction. Proceedings of the Urban Transport 2013 Conference, Kos, Greece, May 29-31,
2013. Editor: C.A. Brebbia. Wessex Institute of Technology Press, Southampton, U. K, pp. 137-148.
• D’Souza, K. A., Siegfeldt, D., and A. Hollinshead (2012). A Conceptual Analysis of Cognitive
Distraction for Transit Bus Drivers. Proceedings of the National Conference on Intermodal
Transportation (NCIT), Hampton University, Hampton, VA 23668. October 11 – 12, 2012.
• D’Souza, K. A, Maheshwari S. K, and Z. Banaszak (2012). Research Framework for Studying Driver
Distraction on Polish City Highways. Workshop on Multimodal Networks Modeling and Design.
Warsaw University of Technology, Warsaw, Poland. June 5, 2012.
• D’Souza, K. A. & S. K. Maheshwari (2012). Improving Performance of Public Transit Buses by
Minimizing Driver Distraction. Proceedings of the Urban Transport 2012 Conference, A Coruňa,
Spain, May 13-16, 2012. Editors: J.W.S. Longhurst and C.A. Brebbia. Wessex Institute of Technology
Press, Southampton, U. K. Pages 281-293.
• D’Souza, Kelwyn A. & S. K. Maheshwari (2012). An Analysis of Transit Bus Driver Distraction
Using Multinomial Logistic Regression Models. On-line Proceedings of the Journal of the
Transportation Research Forum, Tampa, FL, March 15-17, 2012.
Results
Kelwyn A. D’Souza, Director
ESITAC, Hampton University
Hampton, VA, U. S. A.

20. 20
• The highest risk (Zone I) distracting activities were mostly passenger-
related.
• Some of the distracting factors were significant at both agencies.
• Training and regulations need to differ at Regional and Urban agencies.
• Sample size is smaller than size suggested by the Rule of 10.
– 17 covariates will need minimum 170 sample size
CONCLUSIONS
Kelwyn A. D’Souza, Director
ESITAC, Hampton University
Hampton, VA, U. S. A.

21. Thank You
21
Kelwyn A. D’Souza, Director
ESITAC, Hampton University
Hampton, VA, U. S. A.