Transcript The Impact of Built Environment on Pedestrian Motor

IMPACT ASSESSMENT OF BUILT
ENVIRONMENT ON PEDESTRIAN ACCIDENTS
By
K.R. Vinodh Kumar,
Dr. Nisha Radhakrishnan*
Dr. Samson Mathew**
Department of Civil Engineering
National Institute of Technology Trichy, India.
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National Institute of Technology Tiruchirappalli, INDIA
2
Introduction
 WHO reported that over 1.2 million people die
each year on the world’s roads and 50 million
suffer non-fatal injuries.
 It also predicted that road traffic injuries will rise
to become the 5th leading cause of death by
2030.
 Pedestrians, cyclists, drivers of motorized Two –
wheelers and their passengers account for
almost half of global road traffic deaths.
3
Causes of Death
Introduction
4
Death vs Age
 Road Traffic injuries are one of the top three causes of
death for people aged between 5 and 44 years.
5
Pedestrian Safety???
 Pedestrians share a high percentage in road user
fatalities.
 They are exposed to severe consequences of
road accidents than other road users.
 Pedestrian safety is often an afterthought.
 Road facilities in urban areas are still an
important source of harm to pedestrians.
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Accident Reduction
Factors
associated
Remedial
measures
Identification
of
interventions
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Interventions
Active
Passive
Education
Enforcement
Largely involve modifications
to the Built Environment (BE)
Education and enforcement cannot be the only measures taken
to reach a sustainable road safety.
Local Environment and road infrastructure play a substantial role
in the co-occurrence of road accidents.
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Built Environment (BE)
 Built Environment refers to the structures, and
infrastructure, that are made by man.
 Transportation Built Environment - includes
road infrastructure, pedestrian infrastructure
and streetscape like crosswalk, pedestrian
signals, median, refuge island etc which has its
influence on the pedestrian activity.
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Examples of BE
Reducing Accidents
Crosswalk lighting &
signal
Road narrowing
Refuge island
10
Examples of BE
Causing Accidents
Curb parking
Flora Obstruction
11
Examples of BE
Causing Accidents
Midblock with no crosswalks, traffic calming
measures
Long walking distance – Absence of
Median/Refuge island
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Indian Scenario – Road Accidents
 A total of 4,30,654 ‘Road Accidents’ reported
during the year 2010.
 These accidents caused 1,33,938 deaths.
 5.5% increase in Accidental Deaths.
 Tamil Nadu, Andhra Pradesh and Maharashtra
have accounted for 11.5%, 10.5% and 7.1%
respectively of total ‘Road Accident’ deaths.
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Source : National Crime Records Bureau
Indian Scenario – Pedestrian Safety
• Pedestrians are more exposed to accident fatalities
caused by the motor vehicles than any other means.
• Studies have been done relating the factors like
Traffic volume, speed, etc., with the pedestrian
accidents – ignoring other factors especially BE
elements.
• Measures to identify and rectify those factors prove
to be difficult or very expensive in the field by means
of ITS implementation and monitoring.
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Objectives
 To study the impact/influence of Built Environment
on Pedestrian safety.
 To collect pedestrian accident data and to map the
accident spot in Tiruchirappalli city base map.
 To identify location of high density pedestrian
crashes using spatial analysis technique.
 To conduct Built Environment Audit along the
identified hotspots.
 To analyse the influence of the each Built
Environment elements on Pedestrian accident
occurrences by Logistic Regression modelling.
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Study Area
INDIA
Study Area
TAMILNADU
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Source: Tiruchirappalli city corporation
Methodology
Accident Data
collection
Filtering Pedestrian
Accidents & Data
analysis
Identification of
Modifiable BE elements
Preparation of BE Audit Data Sheet
Conducting BE Audit
Geo-coding all the
accident spots
Spatial Analysis of
Accident spots
7/21/2015
Land use classification of
Hotspots
Developing a statistical
model
Suggestions for
improvements
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Mode Wise Distribution of Fatalities in Trichy City
(2009 - 2011)
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Source : Traffic Control Room, Trichy
Mode Wise Percentage of Pedestrian Accidents
(2009 – 2011)
Mini bus
1%
Tempo
1%
Jeep
1%
Auto
3%
Car
16%
P.bus
6%
Unknown
30%
G.bus
8%
Lorry
13%
Share Auto
1%
Van
2%
Two wheeler
18%
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Source : Traffic Control Room, Trichy
Accident Spot
location
• Besides identifying locations of
pedestrian crashes, detecting
the high-density zones, which
refers to the number of
pedestrian crashes per unit of
road segment, is critical for an
intervention program.
• Although pedestrian safety in a
motorized urban environment
is important throughout a city,
public health interventions
prioritized at these high density
zones are paramount to make
accident reduction efforts more
effective
• Creation of Density map is
essential to identify critical
zones.
Accident
Spots
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Density analysis and density Map
• Density surfaces show where point or line
features are concentrated.
For Example
Cell Values of
Population Density
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Kernel Density
 Determine if points (events) are
exhibiting specific pattern over
study area or are they randomly
distributed.
 Estimate the intensity (density)
of how the point pattern
distributed over the study area.
 Intensity = Mean number of
events per unit area at points
defined as the limit.
 Search radius of 150m was
adopted for the analysis.
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TV koil
Toyota
showroo
m
Kernel Density
Map
Puthur 4
road
Gandhi
market
Ariyamangalam
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Airport
Spatial Autocorrelation
• This tool measures spatial autocorrelation
(feature similarity) based on both feature
locations and feature values simultaneously.
• It evaluates whether the pattern expressed is
clustered, dispersed, or random.
• The tool calculates the Moran's I Index value
and a Z score evaluating the significance of the
index value.
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Moran’s I Index
• Moran's Index value near +1.0 indicates
clustering while an index value near -1.0
indicates dispersion.
- 1.0
0
+ 1.0
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Moran’s I Index & Z Score
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List
of
Identified
Hot
Spots
List of Identified Hot Spots
1. Ariyamangalam - SIT Bus stop
2. Ariyamangalam - Rice mill
Bus stop
3. Ariyamangalam - Rail Nagar
Bus stop
4. Airport - J K Nagar
intersection
5. Airport – Wireless Rd
6. Pudhukottai Rd –
Ponmalaipati Rd intersection
7. TVS Tollgate intersection
8. Rockins – Mc Donalds Road
intersection
9. Rockins – Melapudur Rd
intersection
10. Rockins – HPO Rd
intersection
11. Lawson – Bharathidasan road
intersection
12. Reynolds – Lawson road
intersection
13. Puthur 4 road intersection
14. Gandhi market – Big bazaar
street intersection
15. Chatram Bus stand
intersection
16. Chennai bypass – Kallanai
road intersection
17. Chennai bypass –
Kodayampettai intersection
18. T V Koil intersection
19. Chennai trunk road – Kollidam
intersection
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Map
Showing
the
locations of
Hotspots
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List of Built Environment Elements
Considered
1. Curb Parking
2. Crosswalk
3. Lighting
4. Bus Stops
5. Pedestrian Signals
6. Flora Obstruction
7. Speed Humps
8. Road Width
9. Sidewalk
10.Median
11.Refuge Island
12.Instruction Signs
13.Advance Stop lines
14.Pedestrian Barriers
15.Branding Signs
16.Alcohol Serving
Establishments
17.Educational Institutions
18.Industrial areas
19.Commercial areas
20.Residential areas
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Built Environment???
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Built Environment???
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Format of Environment Audit Sheet
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Findings of Environment Audit Survey
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Hotspot Linear Buffering
30m Linear Buffer
Pedestrian
Accidents
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Land Use Classification
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Kollidam
TV Koil
10000
Kodayampatti
Kallanai Road
Chatram
RailNagar
Ricemill
Gmarket-Bigbazar
Puthur 4 Road
Lawson_Bharathidasan
Reynolds-Lawson
Rockins_Hpo
Rockins_Melapudur
Mc Donalds_Rockins
TVS TollGate
Pudhukotai_Ponmalaipatti
Airport-Wireless RD
Airport _ JK Nagar
SIT
Area in sq. m
Land Use Classification 10m Buffer
Residential
14000
Commercial
12000
Educational
Public
8000
6000
4000
2000
0
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Kollidam
TV Koil
20000
Kodayampatti
Kallanai Road
Chatram
RailNagar
Ricemill
Gmarket-Bigbazar
Puthur 4 Road
Lawson_Bharathidasan
Reynolds-Lawson
Rockins_Hpo
Rockins_Melapudur
Mc Donalds_Rockins
TVS TollGate
Pudhukotai_Ponmalaipatti
Airport-Wireless RD
Airport _ JK Nagar
SIT
Area in Sq. m
Land Use Classification 20m Buffer
22000
Residential
18000
Commercial
16000
Educational
Public
14000
12000
10000
8000
6000
4000
2000
0
37
18000
38
Kollidam
20000
TV Koil
Kodayampatti
Kallanai Road
Chatram
RailNagar
Ricemill
Gmarket-Bigbazar
Puthur 4 Road
Lawson_Bharathidasan
Reynolds-Lawson
Rockins_Hpo
Rockins_Melapudur
Mc Donalds_Rockins
TVS TollGate
Pudhukotai_Ponmalaipat
ti
Airport-Wireless RD
Airport _ JK Nagar
SIT
Area in Sq. m
Land Use Classification 30m Buffer
28000
26000
Residential
24000
Commercial
22000
Educational
Public
16000
14000
12000
10000
8000
6000
4000
2000
0
Change in Residential area
14000
10 M
20 M
30 M
Area in Sq. m
12000
10000
8000
6000
4000
2000
0
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Change in Commercial area
30000
10 M
20 M
30 M
Area in Sq. m
25000
20000
15000
10000
5000
0
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Logistic Regression in SPSS 20
 Used to analyze relationships between a
dichotomous dependent variable and metric or
dichotomous independent variables.
 Combines the independent variables to estimate the
probability that a particular event will occur or not.
 Finds the impact of each independent variable on
dependent variable.
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Independent Variables
Indicator
1. Curb Parking
2. Crosswalk
3. Lighting
4. Bus Stops
5. Pedestrian Signals
6. Flora Obstruction
7. Speed Humps
8. Road Type
9. Sidewalk
10.Median
11.Refuge Island
12.Instruction Signs
13.Advance Stop lines
14.Pedestrian Barriers and Fences
15.Branding Signs
16.Alcohol Serving Establishments
Continuous
1.
2.
3.
4.
Educational areas
Public areas
Commercial areas
Residential areas
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Logistic Regression in SPSS
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Significance of Variables
Score
Variables CURB PARKING(1)
df
Sig.
.010
1
.919
CROSSWALK(1)
21.430
1
.002
LIGHT(1)
15.130
1
.000
BUS STOP(1)
19.952
1
.003
PEDESTRIAN SIGNAL(1)
23.673
1
.000
LONGBLOCKS(1)
1.513
1
.219
ROAD TYPE(1)
4.821
1
.004
SIDEWALK(1)
30.045
1
.001
MEDIAN(1)
12.033
1
.002
REFUGE ISLAND(1)
.952
1
.329
INSTRUCTION SIGN(1)
.159
1
.690
ADVANCE STOPLINE(1)
8.143
1
.003
31.857
1
.001
BRNDING SIGN(1)
2.010
1
.919
ALCOHOL SHOP(1)
44.540
1
.000
RESIDENTIAL AREA
17.140
1
.004
COMMERCIAL AREA
24.000
1
.003
EDUCATIONAL AREA
1.341
1
.559
PUBLIC AREA
3.129
1
.719
PEDESTRIAN BARRIERS(1)
Variables are
removed due
to
insignificance
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Block 1 - Final Model
Variables in the Equationa
Step 1Variables
B
S.E
CROSSWALK(1)
0.234
0.032
LIGHT(1)
0.113
df
Sig.
53.473
1.000
0.000
1.264
0.023
24.138
1.000
0.001
1.120
-1.025
0.560
3.350
1.000
0.030
0.359
PEDESTRIAN SIGNAL(1)
1.236
0.163
57.499
1.000
0.015
3.442
ROAD TYPE(1)
0.089
0.080
1.238
1.000
0.022
1.093
SIDEWALK(1)
2.453
0.350
49.120
1.000
0.000
11.623
MEDIAN(1)
0.897
0.321
7.799
1.000
0.003
2.452
ADVANCE STOP LINE(1)
0.234
0.143
2.678
1.000
0.014
1.264
PEDESTRIAN BARRIERS(1)
2.621
0.285
84.575
1.000
0.028
13.749
ALCOHOL SHOP(1)
-1.831
0.186
96.906
1.000
0.018
0.160
RESIDENTIAL AREA
-0.021
0.004
27.563
1.000
0.000
0.979
COMMERCIAL AREA
-0.037
0.006
38.028
1.000
0.004
0.964
1.342
0.387
12.025
1.000
0.021
3.827
BUS STOP(1)
Constant
Wald
Exp(B)
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Important Findings
Variables
Increases*
Pedestrian Barriers
13 times
Sidewalk
11 times
Pedestrian Signal
3 times
Decreases*
Alcohol shop
84%
Bus Stop
65%
Residential Areas
2.1%
Commercial Areas
3.6%
* Chances of not having Pedestrian Hotspots
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Accuracy of the test
Model Summary
Step
-2 Log likelihood
Cox & Snell
R Square
Nagelkerke
R Square
0
69.941
0.501
0.485
1
55.448
0.709
0.822
-2Log likelihood is a measure of error associated with the model in predicting the dependent
variable and its value should be as low as possible.
Cox & Snell R square and Negelkerke R square are the two pseudo R squares used to
measure the fitness of model in Logistic Regression.
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Model Validation
Classification Table
Predicted
Observed
ACCID
0
Step 1
Percentage
Correct
1
ACCIDENTS < 3
0
8
3
72.2
ACCIDENTS > 3
1
3
16
84.2
Overall Percentage
85.0
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For Puthur 4 Road
 Total No. of Accidents occured in Puthur – 5
 Y=1.342+0.234*(CRSSWLK)+0.113*(LIGHT)+1.025*(BST
OP)+1.236*(PEDSIG)+0.089*(ROADW)+2.453*(SIDEW)+
0.897*(MED)+0.234*(ASTOPL)+2.621*(BANDF)1.831*(ALCSHP)-0.021*(RESI)-0.037*(COMM).
 Y=1.342+0.234*(1)+0.113*(1)+1.025*(1)+1.236*(1)+0.0
89*(1)+2.453*(1)+0.897*(1)+0.234*(1)+2.621*(1)1.831*(1)-0.021*(0)-0.037*(3780).
 Y= -0.5481
 P(X)=1/(1+e(P-Y))
= 0.98 (Prob. having of less than 3 accident occurrence)
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Conclusions

Integrated Geospatial-Statistical analysis employed to
analyse the pedestrian fatalities with respect to Built
Environment elements along Tiruchirappalli road network.
 The analysis proved to be effective in providing the following
information
• 86% of pedestrian fatalities
were
observed in
intersections and rest of them in mid blocks.
• Examination of Built Environment elements in the hotspot
showed that they had lack of pedestrian infrastructure.
• The residential area increases as we move away from the
road.
• The commercial area decreases as we move away from the
road.
• The commercial area is having higher impact on pedestrian
activity than any other.
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Conclusions
(Contd….)
•
Pedestrian Barriers, sidewalk and pedestrian signals are
having higher impact on the accident reduction.
• Alcohol Shops and bus stops are increasing the chances
of accidents to greater extent.
• Absence of speed calming measures has been observed
to have a negative influence on pedestrian safety
 The study
• helps in identifying the effective Built Environment in
reducing the accident occurrence
• provides information to Improve the hotspots in terms of
modifying the Built Environment which would seem to be
effective and easy in implementation.
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Suggestions for Improvement
• Constructing Barriers can prove to be more effective in
avoiding uncontrolled pedestrian crossings.
• Paving the Sidewalk will reduce the pedestrian vehicle
interaction. It avoids the pedestrian to walk on the road.
• Avoiding the Bus stops near the intersections. It should be
located at least 150 m away from the intersection so that the
intersection traffic will not much affect the pedestrian
movement.
• Avoiding Alcohol serving establishments around the
intersection.
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