masters defense 2013

Daniel Emaasit Master of Civil Engineering Candidate

Advisor: Dr. Deo Chimba Civil and Architectural Engineering

FRAMEWORK TO IDENTIFY FACTORS ASSOCIATED WITH HIGH PEDESTRIAN

AND BICYCLE CRASH LOCATIONS USING GEOGRAPHIC INFORMATION

SYSTEM AND STATISTICAL ANALYSIS

By

OUTLINEIntroductionStatement of the ProblemObjectiveLiterature Review Study DataMethodology

GIS Geo-CodingCluster AnalysisHot Spot AnalysisStatistical Modeling

ResultsConclusions & Recommendations

INTRODUCTION• Bicyclists and pedestrians are a class of

vulnerable road users that are often over-represented in fatal or incapacitating injury crash statistics.

• While passenger car fatalities have shown sharp declines in the last decade in Tennessee, pedestrian and bike fatalities have remained relatively constant.

• A robust methodology is not currently available to identify bicycle and pedestrian high-crash locations in Tennessee.

STATEMENT OF THE PROBLEMTDOT has an extensive road safety audit program

which uses criteria based on the ratio of crashes to average daily traffic.

That program does not target locations with a high number of bike/pedestrians crashes since there are no bicycle and pedestrian counts.

A robust methodology is not currently available to identify bicycle and pedestrian high-crash locations in Tennessee.

The challenge is allocating funds, from TDOT’s Highway Safety Improvement Program (HSIP), equitably among rural and urban areas in a way that is most effective at reducing bicycle and pedestrian fatalities and incapacitating injuries.

OBJECTIVE• To develop a framework to identify factors

associated with bicycle and pedestrian high crash locations for investment prioritization of TDOT funds to maximize the reduction in state-wide severe bicycle and pedestrian crashes.

RESEARCH METHODOLOGY

Comprehensive Literature Review

Data Gathering & Preparation

GIS Cluster & Hot Spot Analysis

Statistical Modeling

Results, Conclusions & Recommendations

LITERATURE REVIEW Application of GIS in Pedestrian and Bicyclist Safety Analysis

GIS has been used by several researchers for display and cluster analysis of these type of safety studies

GIS can improve data collection, integrate crash data with roadway, traffic, demographic data and display results graphically to enhance decision-making capabilities.

Statistical Modeling of Ped/Bike Crashes Count models are recommended for modeling ped/bike crash

frequencies. Logistic regression, ordered-response, Multinomial models have

been applied for modeling ped/bike injury severities. One of the key limitations that hinder ped/bike safety analysis is

the lack of travel exposure information. Cluster analysis

Spatial statistical tools in GIS are recommended for cluster analysis.

STUDY DATA Categories of data used:-

Crash dataRoadway DataGeospatial dataDemographic and Socioeconomic data

CRASH DATAObtained from three different sources:-

TRIMS:- A total of 7,503 pedestrian crash incidents and 2,558 bicyclist crash incidents were downloaded from TRIMS.

TITAN:- Provided additional micro-level information about the crashes from TRIMS such as crash city, urban-rural designation, location highway street, location estimate, location distance type, location direction, location from intersection, etc.

Study Period:- 7-Year Crash data (2003 to 2009)

CRASH DATA-Descriptive statistics

As shown, we used 7-years of crash data which is more than the minimum of 3-years recommended in the literature and previous studies

CRASH DATA-Descriptive statisticsCounty % of Total Ped % of Total BikeShelby 33.1 26.3Davidson 20.4 16.3Hamilton 6.9 8.3Knox 6.9 8.2Montgomery 2.4 3.2Rutherford 2.2 5.8Sullivan 1.9 2.6Madison 1.6 1.2

Crash City % of Total Ped % of Total BikeMEMPHIS 32.5 23.0NASHVILLE 19.4 15.1CHATTANOOGA 5.9 6.1KNOXVILLE 5.4 7.0CLARKSVILLE 1.9 3.0JACKSON 1.4 1.0MURFREESBORO 1.2 4.7JOHNSON 1.1 1.3KINGSPORT 1.0 1.5

CRASH DATA-Descriptive statistics

CRASH DATA-Descriptive statisticsRoad Location % of Total Ped % of Total BikeAt an Intersection 59.2 62.5Along Roadway 39.5 36.8

ROADWAY GEOMETRY GEO-SPATIAL DATATDOT provided the following geospatial data files in the

form of shapefiles:-

TDOT road geometrics:- consisted of spatial data of the entire roadway network in Tennessee which contains information such as route number, begin and end log miles, codes for land use, posted speed limit, number of lanes, terrain, illumination etc.

Tennessee Road TIPS:- consisted of spatial data of the entire roadway network in Tennessee included in the geometry data but with more detailed information including the zip code, road name and the distance from the reference point such as from the intersection or known node.

DEMOGRAPHIC & SOCIOECONOMIC DATAA Tennessee Census tract shapefile was downloaded from the TIGER

webpage of the US census website:A Census tracts are the smallest geographic area for which the

Census Bureau collects and tabulates decennial census data

2010 US decennial census demographic and socioeconomic data was downloaded at census tract level from the US census website.

Demographic data consists of:-counts of population, housing, race, and age distribution

Socio-economic data consists of:-income, vehicle availability, employment, commuting to work, occupations, poverty status data.

INTEGRATING ALL STUDY DATA INTO GISCRASH DATAA key component in identifying high crash zones

involves accurately coding the location of crashes on digital maps.

This was done in a GIS environment using the “addressmatch” feature for address-type crash data and the “linear referencing” feature for the highway-type crash data. Address-type crash data:- consists of location information such

as street name, intersection name, distance from a reference point etc. Commonly used in urban areas.

Highway-type crash data:- consists of mile-post or log-mile location information used to geocode crash points along highways. Commonly used in rural areas.

Address-type crash data Highway-type crash data

CODING CRASH DATA INTO GISCRASH DATA5584 out of 7500 ped crashes (approx. 75%) were accurately mapped.1890 out of 2558 bike crashes (approx. 74%) were accurately mappedNote that some crashes had un-recognizable route numbers such as

“M0000” and “C0000” for GIS geocoding

Distribution of Pedestrian Crashes

Distribution of Bicyclist Crashes

High concentrations in Shelby, Davidson, Hamilton and Knox Counties.

CLUSTER ANALYSIS It involves finding patterns of observations within a data set.

The combination of neighborhood attributes, social-economic and demographic data are used to uncover correlated factors associated with bicycle and pedestrian crashes.

The objective To identify locations that experience a significantly higher

percentage of crashes through pattern detection technique.

To identify attributes (crash, geometrics, demographic and socio-economic attributes) associated with crash clusters for further analysis/investigation.

CLUSTER ANALYSISANSELIN LOCAL MORAN'S I STATISTIC To quantify the spatial correlation, the

ANSELIN LOCAL MORAN'S I STATISTIC was used. This tool identifies spatial clusters of features with high or low

values. To do this, the tool calculates

Local Moran's I index, Z-score, p-value, and Cluster type.

The z-scores and p-values represent the statistical significance of the computed index values.

MORAN'S I STATISTIC IN GIS

CLUSTER ANALYSISANSELIN LOCAL MORAN'S I STATISTIC The outputs of this statistic:-

The I Index value Sign of I Value Intepretation Conclusion

Positive (+)This feature has neighboring features with similarly high or low attribute values

This feature is part of a cluster

Negative (-)This feature has neighboring features with dissimilar values

This feature is an outlier

Z Value Intepretation

Z>1.96This feature has neighboring features with similarly high or low attribute values

Z<-1.96This feature has neighboring features with dissimilar values

The Z score Value

CLUSTER ANALYSIS-SHELBY• SHELBY COUNTY

As shown, pedestrian crash clusters are associated with areas with high population density of African American

Distribution of Crash Clusters per Population density of African American population

CLUSTER ANALYSIS-SHELBY• SHELBY COUNTY Distribution of Crash

Clusters per Population density of Whites population1. As shown, predominantly

whites populated areas are associated with low pedestrian crash clusters

2. However, there are some few areas shown to have pockets of pedestrian clusters which will also be further investigated for the possibility of being hot spots for TDOT considerations

CLUSTER ANALYSIS-SHELBY• SHELBY COUNTY

Distribution of Crash Clusters with Population of Seniors and with Young Population

CLUSTER ANALYSIS--SHELBY• SHELBY COUNTY

Distribution of Crash Clusters with Households that have No Vehicle and Proportion of workers who Walk to Work

CLUSTER ANALYSIS--SHELBY• SHELBY COUNTY

Distribution of Crash Clusters with Poverty Level and Unemployment rate

HOTSPOT ANALYSIS

Gi* Spatial Statistic The Gi* index was used to locate unsafe road segments and

intersections and discern cluster structures of high- or low-value concentration among local observations

A simple form of the Gi* statistic as defined by Getis and Ord(1995)

Where Gi* = statistic that describes the spatial dependency of incident J over all

n events, xj = magnitude of variable X at incident location j

wij = weight value between event i and j that represents their spatial interrelationship.

n = the number of incidents

Getis-Ord Hot spot Analysis

HOTSPOT ANALYSISGIS Hot spot Analysis Tool

Pedestrian Hotspots in Davidson County

Bicycle Hotspots in Davidson County

GIS Hot spot Analysis Tool

SPECIFIC HIGH CRASH ZONES

Fatal IncapNon-Incap PDO1 Downtown Nashville Area Wide - 0.532 0 6 46 3 55 - - - - - - - - -2 Demonbreun St: 2nd Ave S to 12 Ave S Linear 5109.51 0.010 0 0 6 1 7 56 2 2 2 1 30 0 2 23 Broadway: 1st Ave N to 16th Ave N Linear 7665.01 0.025 1 1 58 5 65 92 2 2 2 1 30 0 6 64 West End: 17th Ave S to 24th Ave S Linear 5135.73 0.017 1 1 17 2 21 92 2 2 2 1 30 0 4 45 West End: 25th Ave S to 30th Ave S Linear 2722.13 0.013 0 3 14 0 17 130 2 2 2 1 30 0 6 66 Church Street: G L Davis Blvd to 21st Ave N Linear 5185.93 0.012 0 1 14 2 17 66 2 2 2 1 30 0 4 47 Eliston Pl: Louise Ave to 25th Ave N Linear 2117.84 0.005 0 2 4 0 6 66 2 2 2 1 30 0 2 28 Charlotte Ave: 14th Ave N to 22nd Ave N Linear 5365.39 0.018 1 0 15 0 16 92 2 2 2 1 40 0 4 49 21 Ave S: Scaritt PL to Wedgewood Ave Linear 3860.00 0.010 0 5 18 0 23 72 2 2 2 1 30 0 4 410 21 Ave S: Belcourt Ave W to Belcourt Ave E Linear 129.00 0.000 0 0 4 0 4 72 2 2 2 1 30 0 2 211 16 th Ave S: C Atkins Pl to Wedgewood Ave Linear 5515.71 0.011 0 0 8 1 9 56 1 2 4 1 35 0 2 212 Wedgewood Ave: 17th Ave S to 18th Ave S Linear 453.75 0.001 0 0 2 0 2 80 2 7 1 35 0 4 413 Blackmore Ave-31st Ave S: 23rd Ave S to West End Ave Linear 5437.84 0.016 0 1 8 0 9 80 2 7 1 35 0 4 414 12th Ave S: Edgehill Ave to Bate Ave Linear 4680.48 0.013 1 2 4 1 8 78 2 2 4 1 35 0 4 415 Edgehill Ave: 8th Ave S to 11th Ave S Linear 2506.47 0.005 0 1 1 1 3 56 2 2 2 1 30 15 4 416 Rosa L Parks Blvd: 10th C ir N to Cheatham Pl Linear 5374.28 0.015 3 0 12 0 15 80 2 2 2 1 35 0 4 417 J efferson St: 10th Ave N to 11th Ave N Linear 1119.38 0.002 0 0 2 0 2 46 2 2 2 1 30 0 2 218 J efferson St: 12th Ave N to Dr. Db Todd J r Blvd Linear 2829.27 0.005 0 1 4 0 5 48 2 2 4 1 30 0 2 219 J efferson St: 26th Ave N to 28th Ave N Linear 1480.62 0.003 0 0 4 0 4 60 2 2 2 1 30 0 2 220 28th Ave N: J efferson St to Albion St Linear 1660.51 0.004 0 0 6 0 6 64 2 2 4 1 30 0 4 421 Buchana St: Dr. Db Todd J r Blvd to 12th Ave N Linear 1615.05 0.003 0 1 4 0 5 54 2 2 2 1 30 0 2 222 Buchana St: Delta Ave to Rosa L Parks Blvd Linear 896.58 0.003 0 2 4 0 6 80 2 2 7 1 30 0 4 423 Spring st: Cowan St to N 1st St Linear 562.85 0.002 0 0 3 0 3 100 2 2 2 1 35 0 5 524 Spring st: Ramp at N 1st St to Ellington Pky Linear 963.63 0.004 1 1 2 0 4 110 2 2 7 1 35 0 4 425 Fairfield Ave: Robertson St to Green St Linear 1609.64 0.003 0 0 6 1 7 60 2 2 7 1 30 15 4 426 Hermitage Ave: Fairfield Ave to Decatur St Linear 723.67 0.001 0 2 1 0 3 50 2 2 2 1 40 0 2 227 Division St: 17th Ave S to 19th Ave N Linear 1315.27 0.002 0 2 3 0 5 44 2 2 2 1 30 0 2 228 Broadway: 20th Ave S to Division St Linear 355.68 0.001 0 0 2 0 2 60 2 2 2 1 30 0 3 229 Lafayette St: 7th Ave S to 2nd Ave S Linear 3952.19 0.011 0 2 11 0 13 80 2 2 2 1 30 0 6 630 Lafayette St: 1st Ave S to C laiborne St Linear 1806.60 0.005 0 2 14 0 16 80 2 2 2 1 30 15 4 4

SPEED LMT SCHOOL

LANESTHROUGH

LANESZone# ROW

DRCT ONE WAY

TERRAINLAND USE

ILLUMSPEED LMT

Zone Type Length(ft)Area

(SQ miles)Crash Injury Types Total

Crashe

Shelby County Knox County

Montgomery CountyHamilton County

STATISTICAL MODELING

A comparative crash pattern and trend was performed

Development of statistical crash models.

The models examine relationships between pedestrian/bicycle crashes with respect to:-

Demographic characteristics, Population, Socio-economic characteristics, Age groups, Neighborhood and land use characteristics, Roadway geometry and features, Traffic flow, Speed characteristics.


STATA Program: Data Analysis and Statistical Software

Software Used:-STATA

List of Variables

Command

Results

STATISTICAL MODELING Criteria for Modeling Crash Frequency Poisson and negative binomial distributions are often more appropriate

for modeling discrete counts of events Poisson Regression model The probability of section i having yi crashes per year is (Cameroon and

Trivedi, 1998)

– yi = 0,1,2....– μ = the expected (mean) number of crashes

Negative Binomial Regression Model The p.m.f. of the Negative Binomial (NB) model is (Cameroon and

Trivedi, 1998) :

– mean μ = E( y) = v exp(Xβ ). – variance is Var( y) = μ +αμ2 .

Selecting Modeling Distribution

Incapacitating Pedestrian Crashes


Negative Binomial Vs Poisson

Fatal Pedestrian Crashes

PDO Pedestrian Crashes

Incap Bicycle Crashes

Non Incap Bicycle Crashes

PDO Bicycle Crashes

Injury Bicycle Crashes

Selecting Modeling DistributionNegative Binomial Vs Poisson


MODEL ESTIMATION RESULTS

Negative Binomial RegressionNumber of observations = 152

Fatal Pedestrian Crashes Coefficient Std. Err. Z-ValueTraffic Volume (AADT) 0.00002 9.05E-06 1.96Households with Income from $25000 to $49999 (%) 0.0040 0.020 0.2

Households with Income from $50000 to $74999 (%) -0.0279 0.034 -0.81Households with Income from $75000 to $99999 (%) -0.0437 0.071 -0.61Occupied housing units with no vehicle (%) 0.0348 0.015 2.27Occupied housing units with 2 vehicles (%) -0.0173 0.028 -0.63

Occupied housing units with 3 or more vehicles (%) -0.0036 0.040 -0.09POPN of 16 years and over in Civilian labor force (%) -0.0089 0.015 -0.6Households with Food Stamp benefits (%) 0.0141 0.014 1.04

Economic Factors-Pedestrian

Negative Coefficient Positive Coefficient


Economic Factors-BicyclePoisson Regression

Number of observations = 42Non-Incapacitating Crashes Coefficient Std. Err. Z-ValueTraffic Volume (AADT) 1.46E-06 1.48E-06 0.99Households with Income below $25000 (%) 0.0035 0.0156 0.22Households with Income from $25000 to $49999 (%) 0.0051 0.0211 0.24Households with Income from $50000 to $74999 (%) 4.80E-02 0.0315 1.53Households with Income from $75000 to $99999 (%) -0.0033 3.55E-02 -0.09Mean Household Income ($) -4.99E-07 0.00001 -0.05Occupied housing units with No vehicle (%) 0.0099 0.0199 0.5Occupied housing units with 1 vehicles (%) -0.0190 0.0160 -1.19POPN of 16 years and over in Civilian labor force (%) -0.0011 0.0148 -0.07Households with Food Stamp benefits (%) 0.0107 0.0180 0.6



Fatal Pedestrian Crashes Coefficient Std. Err. Z-ValueArea of Zone -64.3004 29.461 -2.18Land Use Type

Fringe 0.1538 0.5531 0.28Residential & Public parks -0.9638 0.6875 -1.4

Speed limit 30mph to 40mph 13.7433 1150 0.01

45mph 13.9741 1150 0.01Presence of School speed limit -13.6720 1005 -0.01Number of lanes 0.2609 0.1486 1.76Traffic (AADT) 0.00003 0.00001 2.52Constant -24.0749 1150 -0.02Length Exposure

Roadway Factors-Pedestrian

MODEL ESTIMATION RESULTSRoadway Factors-Bicycle


Injury Bicycle Crashes Only Coefficient Std. Err. Z-ValueRight of Way -0.0433 0.0254 -1.7Rolling terrain 2.5925 1.2541 2.07Land Use Type

Fringe 2.8718 1.3849 2.07Residential & Public parks -0.1440 0.8032 -0.18

Presence of School Speed Limit -22.034 17402 0Number of Lanes -0.0129 0.3903 -0.03Traffic (AADT) 1.13E-05 7.32E-06 1.54

MODEL ESTIMATION RESULTSAge Factors-Pedestrian


Fatal Pedestrian Crashes Coefficient Std. Err.Z-ValuePopulation under 10yrs (%) 0.0009 0.0225 0.04Population from 10 to 19yrs (%) -0.0329 0.0208 -1.58Population from 20 to 29yrs (%) -0.0205 0.0155 -1.32Population from 30 to 64yrs (%) -0.0119 0.0089 -1.34

Where; PCF=Fatal Pedestrian Crashes

P1 = Population under 10yrs (%),

P2 = Population from 10 to 19yrs (%),


P4 = Population from 30 to 64yrs (%).

MODEL ESTIMATION RESULTSAge Factors-Bicycle


Injury Bicycle Crashes Only Coefficient Std. Err. Z-ValueTraffic Volume (AADT) 3.01E-06 3.94E-06 0.76Population under 10yrs (%) 0.0721 0.0729 0.99Population from 10 to 19yrs (%) 0.0185 0.0500 0.37Population from 20 to 29yrs (%) -0.0331 0.0342 -0.97Population from 30 to 64yrs (%) -0.0470 0.0419 -1.12Population above 64yrs (%) 0.0018 0.0883 0.02

Where; BCInj= injury bicycle crashes only,

AADT = Traffic Volume,P1 = Population under 10yrs (%),




P5 = Population over 64yrs (%).

MODEL ESTIMATION RESULTSRace Factors-Pedestrian


Fatal Pedestrian Crashes Coefficient Std. Err. Z-ValueWhite Population (%) -0.0042 0.0629 -0.07Black Population (%) 0.0058 0.0629 0.09American-Indian Population (%) 0.8412 0.8326 1.01Asian Population (%) -0.0171 0.0907 -0.19Traffic volume (AADT) 0.00002 7.41E-06 2.55Constant -9.8592 6.1567 -1.6Length of Crash Zone Exposure

MODEL ESTIMATION RESULTSRace Factors-Bicycle


Injury Bicycle Crashes Only Coefficient Std. Err. Z-ValueWhite population (%) 0.0425 0.0126 3.37Black population (%) 0.0452 0.0062 7.23Asian population (%) -0.2131 0.2918 -0.73Hispanic population (%) 0.0437 0.0256 1.7Traffic Volume (AADT) 1.79E-06 3.43E-06 0.52Area of Zone Exposure

Injury Crashes Only Coefficient Std. Err. Z-Value P-ValueRight of Way -0.0433 0.0254 -1.7 0.089 -0.0931 0.0065Rolling terrain 2.5925 1.2541 2.07 0.039 0.1345 5.0506Landuse

Fringe 2.8718 1.3849 2.07 0.038 0.1575 5.5862Residential & Public parks -0.1440 0.8032 -0.18 0.858 -1.7183 1.4302

Presence of School Speed Limit -22.034 17402 0 0.999 -34129 34085Number of Lanes -0.0129 0.3903 -0.03 0.974 -0.7779 0.7520Traffic (AADT) 0.0000 7.32E-06 1.54 0.123 -3.05E-06 2.56E-05Alpha 1.2046 1.0796 0.2080 6.9774

95% Conf. IntervalLog likelihood = -29.966986

Likelihood-ratio test of alpha=0: chibar2(01) = 2.80 Prob>=chibar2 = 0.047

Negative Binomial RegressionNumber of obs = 40Wald chi2(7) = 9.47

Prob > chi2 = 0.2207


All Crashes Combined Coefficient Std. Err. Z-Value P-ValueCounty

Hamilton & Knox -0.3891 0.4643 -0.84 0.402 -1.2991 0.5209Davidson 0.4717 0.2991 1.58 0.115 -0.1146 1.0580

Shelby 0.1392 0.5122 0.27 0.786 -0.8646 1.1430Right of Way 0.0041 0.0070 0.58 0.563 -0.0097 0.0178Rolling terrain 0.1674 0.3465 0.48 0.629 -0.5118 0.8465Landuse

Fringe 0.4939 0.5351 0.92 0.356 -0.5549 1.5427Residential & Public parks 0.0624 0.2946 0.21 0.832 -0.5149 0.6397

Speed Limit35mph to 40mph 0.4565 0.2968 1.54 0.124 -0.1252 1.038245mph to 55mph 0.5311 0.4953 1.07 0.284 -0.4397 1.5020

Presence of School Speed Limit -0.7365 0.6649 -1.11 0.268 -2.0396 0.5667Number of Lanes -0.0400 0.1651 -0.24 0.808 -0.3637 0.2836Traffic Volume (AADT) 8.78E-07 1.59E-06 0.55 0.581 -2.24E-06 3.99E-06

Poisson RegressionNumber of obs = 40

Wald chi2(12) = 113.41Prob > chi2 = 0


Injury Crashes Only Coefficient Std. Err. Z-Value P-ValueRight of Way -0.0433 0.0254 -1.7 0.089 -0.0931 0.0065Rolling terrain 2.5925 1.2541 2.07 0.039 0.1345 5.0506Landuse

Fringe 2.8718 1.3849 2.07 0.038 0.1575 5.5862Residential & Public parks -0.1440 0.8032 -0.18 0.858 -1.7183 1.4302

Presence of School Speed Limit -22.034 17402 0 0.999 -34129 34085Number of Lanes -0.0129 0.3903 -0.03 0.974 -0.7779 0.7520Traffic (AADT) 0.0000 7.32E-06 1.54 0.123 -3.05E-06 2.56E-05Alpha 1.2046 1.0796 0.2080 6.9774



Negative Binomial RegressionNumber of obs = 40Wald chi2(7) = 9.47

Prob > chi2 = 0.2207

Property Damage Only Coefficient Std. Err. Z-Value P-ValueRight of Way -0.0036 0.0146 -0.24 0.807 -0.0323 0.0251Rolling terrain 0.9013 0.6131 1.47 0.142 -0.3003 2.1029Landuse


Presence of School Speed Limit -15.478 2207 -0.01 0.994 -4341 4310Number of Lanes 0.3008 0.2836 1.06 0.289 -0.2550 0.8566Traffic Volume (AADT) 2.35E-06 3.98E-06 0.59 0.554 -5.44E-06 0.00001Constant -2.4546 0.9424 -2.6 0.009 -4.3016 -0.6076Alpha 7.57E-23 . . .

95% Conf. Interval


Pseudo R2 = 0.1318Prob > chi2 = 0.2341

LR chi2(10) =9.27Number of obs = 40

Negative Binomial Regression

Log likelihood = -30.51258Non-Incapacitating Crashes Coefficient Std. Err. Z-Value P-ValueCounty

Hamilton & Knox -0.8152 0.6328 -1.29 0.198 -2.0554 0.4250Davidson 0.6370 0.3489 1.83 0.068 -0.0469 1.3209

Shelby 0.0517 0.6219 0.08 0.934 -1.1671 1.2706Area of Zone 16.593 25.657 0.65 0.518 -33.694 66.881Right of Way 0.0109 0.0086 1.26 0.207 -0.0060 0.0277Rolling terrain -0.4144 0.4706 -0.88 0.379 -1.3367 0.5079Landuse


Speed Limit35mph to 40mph 0.6719 0.3557 1.89 0.059 -0.0253 1.369045mph to 55mph 0.7332 0.6810 1.08 0.282 -0.6015 2.0679

Presence of School Speed Limit 0.1938 0.6988 0.28 0.782 -1.1758 1.5634Number of Lanes -2.82E-01 2.01E-01 -1.4 0.161 -6.77E-01 0.1126Traffic Volume(AADT) 2.06E-07 1.95E-06 0.11 0.916 -3.62E-06 4.03E-06

Log likelihood = -54.622295% Conf. Interval


Wald chi2(13) = 39.63Prob > chi2 = 0.0002

Injury Crashes Only Coefficient Std. Err. Z-Value P-ValueWhite population (%) 0.0425 0.0126 3.37 0.001 0.0178 0.0672Black population (%) 0.0452 0.0062 7.23 0 0.0329 0.0574Asian population (%) -0.2131 0.2918 -0.73 0.465 -0.7850 0.3588Hispanic population (%) 0.0437 0.0256 1.7 0.089 -0.0066 0.0939Traffic Volume (AADT) 1.79E-06 3.43E-06 0.52 0.602 -4.94E-06 8.52E-06Area of ZoneAlpha 0.5840 0.9122 0.0274 12.4718

Prob > chi2 = 0

Exposure

95% Conf. Interval


Log likelihood = -32.055339

Negative Binomial RegressionNumber of obs = 42

Wald chi2(5) = 192.53

Injury Crashes Only Coefficient Std. Err. Z-Value P-ValueTraffic Volume (AADT) 3.01E-06 3.94E-06 0.76 0.446 -4.72E-06 1E-05Population under 10yrs (%) 0.0721 0.0729 0.99 0.322 -0.0707 0.2149Population from 10 to 19yrs (%) 0.0185 0.0500 0.37 0.711 -0.0794 0.1165Population from 20 to 29yrs (%) -0.0331 0.0342 -0.97 0.334 -0.1002 0.0340Population from 30 to 64yrs (%) -0.0470 0.0419 -1.12 0.262 -0.1290 0.0351Population above 65yrs (%) 0.0018 0.0883 0.02 0.983 -0.1712 0.1749Alpha 1.9899 1.4888 0.4592 8.6236


95% Conf. Interval

Negative Binomial RegressionNumber of obs = 42Wald chi2(6) = 9.04Prob > chi2 = 0.1716


Property Damage Only Coefficient Std. Err. Z-Value P-ValuePopulation under 10yrs (%) -0.0155 0.0468183 -0.33 0.74 -0.1073 0.0762Population from 10 to 19yrs (%) 0.0261 0.0393262 0.66 0.508 -0.0510 0.1031Population from 20 to 29yrs (%) 0.0654 0.0142064 4.61 0 0.0376 0.0933Population from 30 to 64yrs (%) 0.0791 0.0182419 4.34 0 0.0433 0.1148Population above 65yrs (%) -0.0609 0.0706128 -0.86 0.389 -0.1993 0.0775Area of ZoneAlpha 9.38E-07 0.0017372 0 .

Likelihood-ratio test of alpha=0: chibar2(01) = 0.0e+00 Prob>=chibar2 = 0.500

Exposure


Wald chi2(5) = 422.19Prob > chi2 = 0

Log likelihood = -36.1397895% Conf. Interval

Nonincapacitating Crashes Coefficient Std. Err. Z-Value P-ValuePopulation under 10yrs (%) 0.0119 0.0350 0.34 0.735 -0.0568 0.0805Population from 10 to 19yrs (%) -0.0076 0.0359 -0.21 0.833 -0.0780 0.0628Population from 20 to 29yrs (%) -0.0041 0.0276 -0.15 0.882 -0.0581 0.0500Population from 30 to 64yrs (%) -0.0100 0.0356 -0.28 0.779 -0.0798 0.0598Constant 1.0244 2.8298 0.36 0.717 -4.5219 6.5707

95% Conf. Interval


LR chi2(4) = 0.73Prob > chi2 = 0.9471Pseudo R2 = 0.0055


Incapacitating Crashes Coefficient Std. Err. Z-Value P-ValuePopulation under 10yrs (%) 0.0566 0.0705 0.8 0.422 -0.0817 0.1949Population from 10 to 19yrs (%) 0.0162 0.0481 0.34 0.737 -0.0781 0.1104Population from 20 to 29yrs (%) -0.0196 0.0265 -0.74 0.459 -0.0715 0.0323Population from 30 to 64yrs (%) -0.0326 0.0385 -0.85 0.397 -0.1082 0.0429Population above 65yrs (%) -0.0218 0.0870 -0.25 0.802 -0.1923 0.1487Alpha 1.8868 1.4909 0.4010 8.8780


95% Conf. Interval

Negative Binomial RegressionNumber of obs = 42Wald chi2(5) = 9.63Prob > chi2 = 0.0865


Injury Crashes Only Coefficient Std. Err. Z-Value P-ValueTraffic Volume (Average AADT) 1.77E-06 3.07E-06 0.58 0.565 -4.25E-06 7.78E-06Households with Income & Benefits below $25000 (%) 0.0243 0.0395 0.6200 0.5380 -0.0531 0.1016Households with Income & Benefits from $25000 to $49999 (%) 0.0671 0.0404 1.6600 0.0970 -0.0121 0.1464Households with Income & Benefits from $50000 to $74999 (%) 0.1833 0.0646 2.8400 0.0050 0.0567 0.3099Households with Income & Benefits from $75000 to $99999 (%) -0.1185 0.0865 -1.3700 0.1710 -0.2881 0.0510POP of 16 years and over in Civilian labor force (%) -0.0246 0.0322 -0.7600 0.4460 -0.0877 0.0386Households with Food Stamp benefits (%) 0.0017 0.0411 0.0400 0.9660 -0.0788 0.0823Families below poverty level (%) 0.0207 0.0483 0.4300 0.6690 -0.0741 0.1154Area of ZoneAlpha 0.0508 0.6009 4.26E-12 6.05E+08


Exposure



Wald chi2(8) = 338.34Prob > chi2 = 0

Pedestrian Crashes

Fatal

Low Income, No Vehicle, Food

Stamps, Young Age, Fringe, Speed, AADT,

Black POPN,

Non-Incap

Rolling terrain, Fringe & Residential, Speed, School Zone, AADT,

Injury Only

AADT, Lanes,

PDO

Rolling terrain, Speed, School Zone,

AADT,

MODEL ESTIMATION RESULTSSummary of Factors with +ve Correlation-Pedestrian

Bicycle Crashes

Incap

Low to Middle Income, Young & Teens, White & Black & Hispanic,

Rolling terrain, Fringe & Residential, Speed,

AADT

Non-Incap

Low to Middle Income, No Vehicle, Food

Stamps, AADT, Low Employment rate, Young, Fringe &

Residential, Speed, AADT

Injury Only

Low to Middle Income, Poverty Level, AADT, Low Employment rate, Food Stamp, Young &

Teens & Seniors, White & Black &

Hispanic

PDO

Low Income, Low Employment rate,

Fringe & Residential, Speed,

AADT

MODEL ESTIMATION RESULTSSummary of Factors with +ve Correlation-Bicycle

CONCLUSIONS The objective of the research was:-

To develop a framework to identify factors associated with bicycle and pedestrian high crash locations.

Two methods were proposed to examine these factors:- GIS-Cluster Analysis Statistical Analysis

Major findings include:- Low Income, Poverty Level, Food Stamp Benefits, No vehicle ownership, Young & Senior Population, Black Populated areas, Traffic Volume, Fringe neighborhoods Narrow ROW

Increase Crash Frequency

RECOMMENDATIONS Injury severity Modeling should be performed.

To identify design mitigation issues, such as design of crosswalks and intersections that influence the outcomes of pedestrian/Bike crashes.

To provide additional insight into pedestrian behavior (e.g. impairment by alcohol or drugs) that contributes to the likelihood of a fatality in a crash.

Other factors should be considered:-Education level, Intersection studies,Time of day, e.t.c.

REFERENCES

1. Harkey, D. (1999). Development of a GIS-Based Crash Referencing and Analysis System, Proc., Enhancing Transportation Safety in the 21st Century, ITE International Conference.

2. Bicycle and Pedestrian Data: Sources, Needs, and Gaps. BTS00-02. (2000). U.S. Department of Transportation, Bureau of Transportation and Statistics.

3. Levine, N., K. Kim, and L. Nitz. (1995). Spatial Analysis of Honolulu Motor Vehicle Crashes: I. Spatial Patterns, Accident Analysis and Prevention, Vol. 27, No. 5, pp. 663-674.

4. Kim. K., D. Takeyama, and L. Nitz. (1994). Moped Safety in Honolulu Hawaii. Journal of Safety Research, Vol. 26, No. 3, 1195, pp. 177-185.

5. Hanks Mohle and Associates. (1996). GIS for small Municipalities. Presentation Material. OTS Summit.

6. Pele. A., Hja-Yehia, and A.S. Hakkert. (1996). Arch Info-Based Geographical Information System for Road safety Analysis and Improvement.

7. Pulugurtha, S.S., Krishnakumar, K.V., and Nambisan, S.S. (2007). New methods to identify and rank high pedestrian crash zones: An illustration. Accident Analysis and Prevention 39, 800–811.

8. Chu. Y., M. Azer, F. Catalonotto, H. Ungar, and L. Goodnman. (1999). Safety/GIS Models reviewed and Related to Long Island Arterial Needs Study. Proc., Enhancing Transportation Safety in the 21 st Century. ITE International Conference.

9. Miller. J. S. (2000).The Unique Analytical Capabilities Geographic Information Systems Can Offer the Traffic Safety Community. Presented at the 79th Annual Meeting of the Transportation Research Board, Washington. D.C.

10. Braddock. M., G. Lapidus, E. Comley, R. Cromley, G. Burke, and L. Banco. (1994). Using a Geographic Information System to Understand Child Pedestrian Injury. American Journal of Public Health, Vol. 84, No. 7, pp. 1158-1161.

11. McMahon. P. A. (1999). Quantitative and Qualitative Analysis of the Factors Contributing to Collisions between Pedestrians and Vehicles along Roadway Segments. Master’s project. University of North Carolina at Chapel Hill.

12. Pedestrian and Bicycle Safety Analysis Tools. (2000). North Carolina Center for Geographic Information and Analysis (NC CGIA).

13. Cameron, A.C. And Trivedi, P.K. Regression Analysis of Count Data. Cambridge University Press, 1998.

14. Ord, J. K. and Getis, A. Local Spatial Autocorrelation Statistics: Distributional Issues and an Application. Geographical Analysis, Vol. 27, 1995, 286-306.

CONFERENCE PRESENTATIONSEmaasit, D., Chimba, D., Cherry, C., Kutela, B., Wilson, J. “Methodology to Identify Factors Associated with Pedestrian High-Crash Clusters Using GIS-Based Local Spatial Autocorrelation”. Accepted for presentation at the Transportation Research Board 92nd Annual Meeting, (TRB), Washington, DC, January 15th, 2013.

Emaasit, D., Chimba, D. “Methodology to Identify Factors Associated with Pedestrian High-Crash Clusters Using GIS-Based Local Spatial Autocorrelation”. Presented at the 35th Tennessee State University-Wide Research Symposium, Nashville, April 4th, 2013.

CONFERENCE PRESENTATIONS

THANK YOU

QUESTIONS

masters defense 2013

Data & Analytics

socioeconomic data crash

years of crash data

demographic data

year crash data

study data categories

geometry data

data collection

decennial census data