Presentation on IOE Graduate Conference-2013. Finally my paper was published in Proceedings of IOE Graduate Conference-2013.

1. MODELING PEDESTRIANS’
BEHAVIOR AT ROAD CROSSINGS:
A CASE STUDY IN KATHMANDU
By:
Er. Bishnu Prasad Devkota

2. PRESENTATION OUTLINE
• Introduction
• Problem Statement
• Objectives
• Limitations
• Methodology
– Literature Review
– Selection of Model
– Descriptions of Model
– Selection of the Locations
– Data Collection
– Summary of Data
– Model Formulations
• Conclusions and Recommendations
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3. INTRODUCTION
• Over 1.2 million people die each year in the world’s roads
• 20 to 50 million people suffer non-fatal injuries
(Fef: Global Status Report on Road Safety – 2009 by WHO)
• Traffic injuries will become 5th cause of death by 2030
• while it was under 9th position in 2004
• In 068/069 among 5000 road accidents in Kathmandu
Valley;
– 150 fatalities
– 400 serious injuries
– 3300 were minor injuries (Ref: Metropolitan Traffic Police Division, Kathmandu)
• Half of those who die in road accidents are vulnerable
road users
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4. PROBLEM STATEMENT
• Drivers’ inclination to slow down and give way to
pedestrians at pedestrian crossings is very low.
• As such, pedestrians try to force their way across
hoping that drivers will slow or stop. If unsuccessful,
they will return back to the curbside and attempt again
until successful.
• This crossing diagram is likely to increase the
pedestrians’ accidents and decrease the efficiency of
road.
• In order to reduce pedestrian accidents and provide
transportation facilities, it is essential to study
behavioral aspects of pedestrians at pedestrian
crossings.
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5. OBJECTIVES
The main objective of this research is to trace out the pedestrian’s
safety in Kathmandu and thus recommend the appropriate policy
measures. Specifically objectives are postulated as:
1. To determine the significance of following parameters:
– Road surface and geometrical parameters
– Traffic parameters
– Time of day
2. To quantify significance of pedestrians’ individual behavioral
parameters
3. To establish the education level and social parameters as variables
for risky behavior of pedestrians
4. To develop a best-fit model for pedestrians’ behavior at road
crossings.
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6. LIMITATIONS
• The vehicle speed and traffic volume play
instrumental role in behavioral aspects of
pedestrians. I recommend in future to include certain
vehicular parameters during study about
pedestrians.
• This methodology for modeling pedestrians’
behavior has wide range of applicability throughout
the world, but the model was calibrated for
Kathmandu only, So it may not fit to other places.
Separate calibration shall be needed.
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7. METHODOLOGY
Literature Review
Selection of Model Survival theory: Cox Hazard Model
Description of Model
Time variable
Exogenous variable
Selection of Locations Frequency of Occurring Accidents
Data Collection Observational and Questionnaire Survey
Data Summary
Model Formulation
and Interpretation
Determination of Effect of Variables
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8. LITERATURE REVIEW
• Griffiths et al. (1984): has developed models that
describe delays at roads without a central refuge
• Himanem and Kumala (1988): studied about
encounters between drivers and pedestrians
• Varhelyi (1998): indicated that drivers’ willingness to
give the way to pedestrians at pedestrian crossings is
low.
• Mohammed M. Hamed (2000): analyzed the
pedestrian behavior at pedestrian crossing using
‘Survival Theory’.
• QIU Dongdong and XU Qing (2012): Studied about
pedestrians behavior at china
link methodology
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9. MODEL SELECTION
– Survival Model
– Theory of Planned Behavior
Link to methodology
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10. DESCRIPTION OF MODEL
I have selected Cox Proportional Hazard Model
h ( t ) 
h ( t ) e {  1 Xi 1  ...  k Xik
}
i 0 – Exponential form of equation never be zero, and in my
case, the risk during crossing the road are not equal to
zero.
– Quantification of risk are found to be easy in this
model.
– Categorical and numeric variables: separate
considerations
link methodology
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11. SURVEY LOCATIONS
Link to methodology
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12. DATA COLLECTION
• Time variable
– Waiting time (t)
• Independent Variables
– Pedestrians’ personal features
– Societal parameters
– External environmental factors
– Road surface condition
link methodology
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13. DATA SUMMARY
Continuous Variables
Variables Explanation of Variable
Freque
ncy
age Age of the sample
pedestrian
398
ncrosspd Number of road cross per
day by the sample
pedestrian
393
edu Education level of the
sample pedestrian
394
locn Location of survey
400
ncattem
pt
Number of crossing
attempts before the
successful crossing
340
Surface Surface Condition of the
road
400
Categorical Variable Coding
Variables Categories Frequency Coding
gendera 0=Female 133 0
1=Male 236 1
maritala 0=Un-Married 146 0
1=Married 223 1
childrena 0=No, donot have 167 0
1=yes, have children 202 1
Priveha 0=No, do not have 280 0
1=yes, have private vehicle 89 1
involvea 0=No, not involved/witness 234 0
1=Yes, involved/witness 135 1
desta 0=No, other than work 310 0
1=Yes, destination to work 59 1
carrya 0=Nothing 202 0
1=Something 167 1
flowa 0=Not in group 216 0
1=In group 153 1
Divideda 1=Divided 155 1
0=Undivided 245 0
link methodology
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14. MODEL FORMULATION, INTERPRETATION
• ‘Cox Proportional Hazard Model’ is calibrated
by ‘Maximum Likely Ratio’ test
• Maximization of ‘Maximum Likelihood
Estimates’ are done by SPSS.
• The likelihood ratio (LR) test is used to
determine the overall goodness of fit of the
model.
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15. RESULT: Undivided Street
Table 5, Pedestrian Risk of Ceasing Waiting Time at Undivided Street
Variable
Coefficie
nt (B)
Significa
nce(P)
Exp(B)
95% Confidence for
Exp(B)
Lower Upper
age -.006 .265 .994 .982 1.005
gender .123 .294 1.131 .899 1.423
marital * .560 .006 1.750 1.176 2.606
children -.377 .096 .686 .440 1.069
priveh -.234 .074 .792 .613 1.023
involve√ -.375 .001 .687 .554 .852
edu √ .102 .044 1.107 1.003 1.223
time -.202 .064 .817 .660 1.012
Number of Observations 369
-2Log-likelihood at convergence model 3147.768
Chi-square model 29.943
Degree of freedom 8
Significance level of the model 0.0000
√ = Variables under significant level
• who were never been
involved/witness of
pedestrians accidents
bear less Risk
• pedestrians who have
children and/or access
to private vehicle take
less risk
• waiting time at peak
hour is more than that
for off-hour
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16. RESULT: Curbside of a Divided Street
Table 6, Pedestrian Risk of Ceasing Waiting Time at Curbside of a Divided Street
Variable
Coefficie
nt (B)
Significa
nce(P)
Exp(B)
95% Confidence
for Exp(B)
Lower Upper
age -.005 .450 .995 .981 1.009
gender .162 .405 1.176 .803 1.720
priveh * .235 .230 1.264 .862 1.855
involve √ -.367 .046 .693 .483 .994
dest * -.373 .166 .688 .406 1.168
Edu √ .221 .008 1.247 1.061 1.466
flow √ .370 .034 1.447 1.027 2.038
Surface √ .421 .003 1.524 1.160 2.003
Number of Observations 369
-2Log-likelihood at convergence model 1322.953
Chi-square model 37.870
Degree of freedom 8
Significance level of the model 0.000
√ = Variables under significant level
• those pedestrians who
cross the road with
group have lesser
waiting time
• Similar to undivided
street; the pedestrians
who involved or witness
of pedestrians accidents
have lesser risk of
ceasing their waiting
time
• If the road is not in good
condition, the risk by
pedestrians seem high
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17. RESULT: Central Refuge on Divided Street
Table 7, Pedestrian Risk of Ceasing Waiting Time at Central Refuge on Divided Street
Variable
Coeffici
ent (B)
Significa
nce (P)
Exp(B)
95% Confidence
for Exp(B)
Lower Upper
age √ -.019 .046 .981 .963 1.000
gender √ .407 .049 1.503 1.001 2.255
Children * .366 .171 1.442 .854 2.433
dest .344 .204 1.411 .830 2.398
edu .097 .231 1.102 .940 1.292
flow .270 .133 1.309 .921 1.861
Surface .215 .106 1.240 .955 1.610
Number of Observations 369
-2Log-likelihood at convergence model 1194.897
Chi-square model 11.641
Degree of freedom 7
Significance level of the model 0.133
√ = Variables under significant level
• Result shows that two
explanatory variables age
and gender lies in
significant region however
the outcome is similar to
the Table -5 and 6.
• The risk of ceasing waiting
time for male pedestrians
is more than female
• The hazard ratio between
male and female is 1.50:
male takes risk 50% more
risk than female one
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18. RESULT: Ordinal Varible-1, Age
1.600
1.400
1.200
1.000
0.800
0.600
0.400
0.200
0.000
Hazard Ratio for Undivided Street
Hazard Ratio for Divided Street at Curbside
Hazard Ratio for Divided Street at Central Refuge
0 20 40 60 80
Figure 2, Hazard Ratios for Different Age
of Pedestrians
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Hazard Ratio
age in years
• the average age of pedestrian
has made to unity.
pedestrians below mean age
take high risk (more than
unity) and the pedestrians
with the age greater than
average age take low risk (less
than unity)
• In the case of divided street
the line for risk plotted for
central refuge is steeper than
the risk line plotted at
curbside. This indicates that
the pedestrians seem more
hurry when he/she reaches to
the central refuge.

19. RESULT: Ordinal Variable-2, SDI
3.000
2.500
2.000
1.500
1.000
0.500
0.000
Hazard Ratio for Divided Street at Curbside
Hazard Ratio for Divided Street at Central Refuge
0 1 2 3 4 5
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Hazard Ratio
Surface Condition (SDI-Value)
0=Excellent; 1=Good; 2=Fair; 3=Poor; 4=very poor
Figure 3, Hazard Ratios for Different Road
Surface Conditions
• the hazard ratio for mean
surface condition is made to
unity
• The pedestrian in poor road
surface takes more risk of
ceasing their waiting time as
compared with the good
surface roads.
• The speed of vehicle in roads with poor
surface condition is generally less as
compared roads with good surface. The
pedestrians shall be taking the advantage
of this in order to make his/her successful
crossings.

20. RESULT: Ordinal Variable-3, Education
1.800
1.600
1.400
1.200
1.000
0.800
0.600
0.400
0.200
0.000
Hazard Ratio for Undivided Street
Hazard Ratio for Divided Street at Curbside
Hazard Ratio for Divided Street at Central Refuge
0 1 2 3 4 5 6
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Hazard Ratio
Education Level of Pedestrians
1=Uneducated; 2=Below SLC;
3=Intermediate Level; 4=Bachelor Level;
5=Masters Level and above
• In Figure 4, the hazard
ratios for mean
education level of
pedestrians has
expressed to unity. The
result is interesting, the
well educated
pedestrians takes more
risk of ceasing their
waiting time as
compared with the
pedestrians having
education level less than
mean education level.
Figure 4, Hazard Ratios for Different
Education Levels of Pedestrians

21. CONCLUSIONS
The safety conditions of pedestrians in Kathmandu at road
crossing has traced out and it can be concluded that not enough
is being done to meet the needs of these pedestrians. The
conclusions of this research can be postulated as:
1. The individual behavior of pedestrians plays as exogenous variable.
2. Education level and societal parameters of pedestrians are also found
significant for risky behavior of pedestrians.
3. The existence of medians, as a road parameter and the time of day was
found as significant in modeling the pedestrians behavior at road
crossings.
4. Finally ,The Cox Proportional Hazard Model was successfully calibrated
for Kathmandu.
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22. RECOMMENDATIONS
Two phases of policy implications that follow these findings are:
1. Pedestrian oriented compulsory training courses directed
towards the drivers (of public vehicles) are recommended
2. Educational and public information awareness campaigns for
Pedestrians in Kathmandu is recommended.
Along with these policy implications, I recommend following measures:
– A small refuge area at the central part of two-way traffic road is
recommend to construct.
– The Installations of PHB at various mid-block pedestrian crossings
– The installations of curbside railings
– Road surface should be maintained
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23. THANK YOU
Any Questions ???