IMPACT ASSESSMENT OF BUILT IMPACT ASSESSMENT OF BUILT ENVIRONMENT ON PEDESTRIAN ACCIDENTS ENVIRONMENT ON PEDESTRIAN ACCIDENTS

ByK.R. Vinodh Kumar,

Dr. Nisha Radhakrishnan* Dr. Samson Mathew**

Department of Civil EngineeringNational Institute of Technology Trichy, India.

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National Institute of Technology Tiruchirappalli, INDIA

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.

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Road Traffic injuries are one of the top three causes of death for people aged between 5 and 44 years.

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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

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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.

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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Crosswalk lighting & signal

Refuge islandRoad narrowing

Reducing Accidents

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Examples of BE

Curb parking Flora Obstruction

Causing Accidents

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Examples of BE

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Midblock with no crosswalks, traffic calming measures

Causing Accidents

Examples of BE Examples of BE

Long walking distance – Absence of Median/Refuge island

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.

Source : National Crime Records Bureau13

• 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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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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Source: Tiruchirappalli city corporation16

Study Area

INDIA

TAMILNADU

Accident Data collection

Identification of Modifiable BE elements

Filtering Pedestrian Accidents & Data

analysis

Preparation of BE Audit Data Sheet

Conducting BE AuditGeo-coding all the

accident spots

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Methodology

Spatial Analysis of Accident spots

Land use classification of Hotspots

Developing a statistical model

Source : Traffic Control Room, Trichy18

Mode Wise Distribution of Fatalities in Trichy City (2009 - 2011)

19Source : Traffic Control Room, Trichy

Mode Wise Percentage of Pedestrian Accidents (2009 – 2011)

Accident Spots

• 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. 20

Accident Spot location

• Density surfaces show where point or line features are concentrated.

Cell Values of Population Density

For Example

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Density analysis and density Map

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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Kernel Density

Kernel Density Map

Airport

Ariyamangalam

Puthur 4 road

Gandhi market

Toyota showroo

m

TV koil

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• 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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Spatial Autocorrelation

• Moran's Index value near +1.0 indicates clustering while an index value near -1.0 indicates dispersion.

- 1.0 + 1.00

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Moran’s I Index

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Moran’s I Index & Z Score

List of Identified Hot Spots1. Ariyamangalam - SIT Bus stop2. Ariyamangalam - Rice mill Bus

stop3. Ariyamangalam - Rail Nagar

Bus stop4. Airport - J K Nagar

intersection5. Airport – Wireless Rd6. Pudhukottai Rd –

Ponmalaipati Rd intersection7. TVS Tollgate intersection8. Rockins – Mc Donalds Road

intersection9. Rockins – Melapudur Rd

intersection10. Rockins – HPO Rd intersection

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11. Lawson – Bharathidasan road intersection

12. Reynolds – Lawson road intersection

13. Puthur 4 road intersection14. Gandhi market – Big bazaar

street intersection15. Chatram Bus stand

intersection16. Chennai bypass – Kallanai

road intersection17. Chennai bypass –

Kodayampettai intersection18. T V Koil intersection19. Chennai trunk road – Kollidam

intersection

List of Identified Hot Spots

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List of Built Environment Elements Considered

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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30m Linear Buffer

Pedestrian Accidents

Hotspot Linear Buffering

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Land Use Classification

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Land Use Classification 10m Buffer

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Land Use Classification 20m Buffer

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Land Use Classification 30m Buffer

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Change in Residential area

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Change in Commercial area

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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Logistic Regression in SPSS 20

Indicator1. Curb Parking2. Crosswalk3. 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 Signs13.Advance Stop lines 14.Pedestrian Barriers and Fences 15.Branding Signs16.Alcohol Serving Establishments

Continuous1. Educational areas 2. Public areas 3. Commercial areas 4. Residential areas

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Independent Variables

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Logistic Regression in SPSS

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Score df Sig.

Variables CURB PARKING(1) .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

PEDESTRIAN BARRIERS(1) 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

Variables are removed due to insignificance

Significance of Variables

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B S.E Wald df Sig. Exp(B)

Step 1

Variables CROSSWALK(1) 0.234 0.032 53.473 1.000 0.000 1.264

LIGHT(1) 0.113 0.023 24.138 1.000 0.001 1.120

BUS STOP(1) -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

Constant 1.342 0.387 12.025 1.000 0.021 3.827

Variables in the Equationa

Block 1 - Final Model

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Variables Increases* Decreases*

Pedestrian Barriers 13 times

Sidewalk 11 times

Pedestrian Signal 3 times

Alcohol shop 84%

Bus Stop 65%

Residential Areas 2.1%

Commercial Areas 3.6% * Chances of not having Pedestrian Hotspots

Important Findings

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Accuracy of the test

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

Model Summary

-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.

Classification Table

Observed

Predicted

ACCIDPercentage

Correct0 1

Step 1

ACCIDENTS < 3

ACCIDENTS > 3

0 8 3 72.2

1 3 16 84.2

Overall Percentage 85.0

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Model Validation

Total No. of Accidents occured in Puthur – 5 Y=1.342+0.234*(CRSSWLK)+0.113*(LIGHT)

+1.025*(BSTOP)+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)+0.089*(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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For Puthur 4 Road

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

• 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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Conclusions (Contd….)

• 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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Suggestions for Improvement

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