Analyzing Spatial Patterns, Statistics Based on FAST Act Safety Performance Measures
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1 Analyzing Spatial Patterns, Statistics Based on FAST Act Safety Performance Measures Abhishek Bhargava, PhD April 13, 2017
2 Agenda Objective Highway Safety Improvement Program (HSIP) & FAST Act Performance Measures Safety Countermeasures (NHTSA): Non-Infrastructure Projects Spatial Statistics Presence of Clustering: Spatial Autocorrelation (Global Statistic: Moran s I) Cluster Analysis: Hotspot Analysis (Local Statistic: Getis-Ord Gi*) Summary Spatial Analysis Parameters Conceptualization of Spatial Relationships Aggregation Feature: 1. Counties 2. Polygon Grid 3. Traffic Analysis Zones
3 FAST Act: Highway Safety Improvement Program Goal: To achieve reduction in fatalities and serious injuries on all public roads Performance Measures: Crash Count, Crash Rate (Crash Count / year / million vehicles or VMT); Fatal & Serious Injuries Rate (Count / year / million vehicles or VMT) Two Types of Projects can be funded Infrastructure Improvement Projects: Intersection Design, Pedestrian Crossings, Retrofits to reduce wildlife/vehicle collisions, Highway signage and pavement markings, pedestrian/bicycle safety Non-Infrastructure Projects: Education, Enforcement and Emergency Services Programs, Safety Planning and Data Collection, Road safety audits
4 NHTSA Countermeasures
5 NHTSA Countermeasures: Distracted Driving Crashes
6 Methodology: Spatial Data Processing 1. Get crash data (for e.g. distracted driving) & Project to NAD83 Texas Statewide (EPSG: 3081)
7 Methodology: Spatial Data Processing 1. Get crash data (for e.g. distracted driving) & Project to NAD83 Texas Statewide (EPSG: 3081) Texting Using a cell phone or smartphone Eating and drinking Talking to passengers Grooming Reading, including maps Using a navigation system Watching a video Adjusting a radio, CD player, or MP3 player
8 Methodology: Crash Data Processing 2. Aggregate Crashes County Polygon Grid TAZ
9 Methodology: Spatial Data Processing 3. Aggregate AADT for Crash Rate calculation (Crash Count / year / million vehicles) County Polygon Grid TAZ
10 Methodology: Analyzing Spatial Randomness Styling does not Establish Statistical Significance
11 Methodology: Analyzing Spatial Randomness
12 Methodology: Conceptualization of Spatial Relationships 4. Incremental Spatial Autocorrelation to select distance band
13 Distance (Meters) Methodology: Presence of Cluster 4. Incremental Spatial Autocorrelation to select distance band that is associated with most clustering in the study area Crash Frequency Crash Rate County Selected Distance Band Polygon Grid TAZ Crash Frequency 56,000 M Crash Frequency 25,000 M Crash Frequency 21,500 M Crash Rate 80,000 M Crash Rate 80,000 M Crash Rate 40,000 M Crash Rate No Clustering
14 Distance (Meters) Methodology: Presence of Cluster 5. Ensure sufficient number of neighbors for each feature using Calculate Distance Band from Neighborhood Count Tool. Preferred 1-8 neighbors. 100,000 90,000 80,000 70,000 60,000 50,000 40,000 30,000 20,000 10,000 0 County Selected Distance Band 56,000 M 80,000 M ,000 25,000 20,000 15,000 10,000 5,000 0 Polygon Grid Selected Distance Band 25,000 M 40,000 M ,500 4,000 3,500 3,000 2,500 2,000 1,500 1, TAZ Selected Distance Band: 21,500 M None Number of Neighbors Number of Neighbors Number of Neighbors
15 Methodology: Presence of Cluster 6. Presence of Clustering: Calculate Global Moran s I Crash frequency Crash Rate County Crash M Polygon Grid Crash M TAZ Crash M
16 Methodology: Presence of Cluster 6. Presence of Clustering: Calculate Global Moran s I Crash frequency Crash Rate No Clustering County Crash Polygon Grid Crash TAZ Crash Rate
17 Methodology: Cluster Analysis 7. Clustering Analysis: Calculate Local spatial statistics (Getis ord Gi*) for hotspot / cold spot identification Crash frequency Crash Rate
18 Crash Frequency Based Hot Spot Analysis Crash Frequency (Crash Count / year) Distance Band: 75,000 M Crash Count Distribution
19 Crash Frequency Based Hot Spot Analysis Crash Frequency (Crash Count / year) Distance Band: 75,000 M Crash Frequency (Crash Count / year) Distance Band: 56,000 M
20 Hot Spot Analysis using Crash Frequency Crash Frequency (Crash Count / year) Distance Band: 56,000 Crash Count Distribution
21 Crash Rate Based Hot Spot Analysis Crash Rate (Crash Count / year / million vehicles) Distance Band: 80,000 Crash Count Distribution
22 Crash Rate vs. Crash Frequency based Hotspot Analysis Crash Rate (Crash Count / year / million vehicles) Distance Band: Crash Frequency (Crash Count / year) Distance Band: AgileAssets Inc. All Rights Reserved Confidential
23 Polygon Grid
24 Cluster Analysis: Crash Rate vs. Crash Frequency Crash Rate (Crash Count / year / million vehicles) Crash Frequency (Crash Count / year)
25 Cluster Analysis: Crash Rate vs. Fatal/Serious Injury Rate Polygon Grid Crash Rate (Crash Count / year / million vehicles) County Crash Rate (Crash Count / year / million vehicles)
26 Cluster Analysis: Crash Rate vs. Fatal/Serious Injury Rate Crash Rate (Crash Count / year / million vehicles) Fatal / Serious Injuries (Injuries / year / 100M VMT)
27 Traffic Analysis Zone (TAZ)
28 Hotspot Analysis TAZ, Crash Rate vs. Crash Frequency Crash Frequency (Crash Count / year) Crash Rate (Crash Count / year / million vehicles) 2017 AgileAssets Inc. All Rights Reserved Confidential
29 Hotspot Analysis Polygon Grid vs. TAZ, Crash Frequency TAZ Crash Frequency (Crash Count / year) Polygon Grid Crash Frequency (Crash Count / year)
30 Hotspot Analysis TAZ vs. County, Crash Frequency TAZ Crash Frequency (Crash Count / year) County Crash Frequency (Crash Count / year)
31 Summary Establishing statistical significance is important Similar spatial distribution of crashes may not repeat itself resulting in in-effective programs The hotspot location is significantly different from the study area Analysis scale plays an important role in identifying clustering & hotspots County vs. Polygon Grid vs. TAZ vs County and Polygon Grid results similar. TAZs had some clustering but no hotspots Distance band used in calculation of test-statistics influences hotspot identification Performance measure selection depends on goals Crash frequency: If goal is to minimize number of crashes, irrespective of VMT Crash rate: If goal is to minimize crashes per unit VMT Fatal/Severe Injury rate: If goal is to achieve Zero Injuries/Deaths
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