GIS Based Non-Signalized Intersection Data Inventory Tool To Improve Traffic Safety

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1 GIS Based Non-Signalized Intersection Data Inventory Tool To Improve Traffic Safety UNIVERSITY OF ALABAMA JENNA SIMANDL JENNY BLEIHOLDER ANDREW GRAETTINGER TIM BARNETT LUKE TAYLOR RANDY SMITH

2 Introduction According to the National Highway Traffic Safety Administration, in 2011: 9,412,000 total crashes across the US 47.6% of those crashes were at intersections or intersection related Improve roadway safety Better understand intersection characteristics Analyze data inventory for intersection statistics Correlate existing crash data to intersection roadway parameters 2

3 Project Goal and Objectives Goal: Collect geo-referenced roadway data associated with non-signalized intersections along state routes in Alabama Develop: o o o o a data collection methodology an online GIS tool pilot study for statistical representation level of effort for statewide implementation Application: Correlations with existing crash data 3

4 Project Approach Non-signalized intersections along state routes in Alabama Roughly 30,000 intersections along state routes 4 Signalized intersections are being handled in a separate project Limited to 3-leg and 4-leg intersections Utilize GIS link-node linear referencing system and remote sensing from existing aerial and street view imagery

5 Intersections of Interest 9 Non-Signalized Intersection Types: Number of Legs lanes, 3 legs 2 lanes, 4 legs Rural Number of Lanes 2 lanes Multilane And Crossroad Ramp Terminals Collect data from 30 random intersections of each type Totaling 270 intersections 5 Multilane, 3 legs Multilane, 4 legs Urban Number of Lanes 2 lanes Multilane 2 lanes, 3 legs 2 lanes, 4 legs Multilane, 3 legs Multilane, 4 legs

6 270 Random Intersections 6 Spatial Analysis of Municipalities Census Data Rural: population < 5,000 Urban: population > 5,000 Randomly selected 135 rural and 135 urban Covering 42 Counties and 90 Municipalities

7 Developing a Data Collection Methodology 7 Two levels of data Intersection level Leg level Standardizing a leg numbering convention Major road first, followed by minor road North to South, West to East Example: Node 7606, Rural Hale, Hale County and Node 138, Greensboro, Hale County 4

8 Intersection Attributes Intersection ID Node ID Intersection Category Area Type Number of Legs Traffic Control Type Lighting Milepost Latitude Longitude County City Ecoregion Terrain Skew Angle Offset & Offset Distance Distance to Next Public Intersection Data Parameters Leg Attributes 8 Leg ID SWLinkID Link ID Leg Number Intersection ID Leg Type Leg Route Type Leg Speed Leg Width Number of Lanes Number of Merge Lanes Pavement Type Orientation Median Type Median Width Leg Traffic Control Type Number of Left Turn Lanes Left Turn Lane Width Number of Right Turn Lanes Right Turn Lane Width Channelized Right Turn Right Turn Lane Movement Control Pedestrian Crossing Control One Way Turn Prohibitions Limited Sight Distance AADT PHV Ramp Terminal Attributes Intersection ID Ramp Speed Approaches within 250 ft Distance to Adjacent Ramp

9 Prototype Prototyped collection methodology on Desktop with Excel Collection Method: Pull from existing datasets Record descriptive data Measure Investigate using Street View 9

10 Intersection Skew Angle 10 1) Draw a triangle with legs of a representative length, following imagery of the road 2) Measure lengths of triangle legs 3) Calculate intersection angle using Law of Cosines 4) Record skew to nearest whole degree Example: Skew Angle = 35 Θ Example: Node 12, McMullen, Pickens County Θ=55

11 Intersection Offset and Offset Distance 1) Draw centerlines out from the middle of offset lanes 2) Draw a line to measure their distance apart 3) Use the measure tool to record the offset distance Example: 11 Offset Distance= 19 feet Example: Node 180, Fayette, Fayette County

12 Limited Sight Distance 12 Record left and right view obscurity for each possible turn maneuver Leg 3, Left, Not Limited Leg 4, Right, Limited Node 715, Cullman Co, Cullman Node 306, Jefferson Co, Leeds

13 Database Model Table Schema 13

14 GIS-Based Intersection Data Inventory Web Portal 14 Download to shapefile

15 GIS-Based Intersection Data Inventory Web Portal 15

16 Intersection Level Data 16

17 Draw and Measure Tools 17

18 Leg Level Data 18

19 Google Street View 19

20 Data Inventory Summary intersections of each type 270 intersections total 955 legs 20 minutes per intersection Data can be used for: Intersection statistics Correlations to crash data to identify possible locations for remediation

21 Data Inventory Statistics Frequency of Intersection Skew Angle in Ranges >=41 8% 9% 13% 16% 54% >=41 Presence of Lighting at Intersection and Area Type Number of Intersections % 73% 75% 25% 0 Rural Urban Lighting Present No Lighting 98 34

22 Data Inventory Statistics 22 Painted Lines 3% Grass 13% 2-way Left-turn Lane 6% Frequency of Median Types Other Divided 2% Undivided 76% Undivided Grass Painted Lines 2-way Leftturn Lane Other Divided Median Widths Std. Average Dev. Undivided 0 ft 0 ft Grass 43 ft 23 ft Painted Lines 15 ft 7 ft 2-way Leftturn Lane 15 ft 2 ft Other Divided 14 ft 5 ft

23 CARE Crash Dataset CARE: Critical Analysis Reporting Environment Center for Advanced Public Safety (CAPS) Dataset from Along mileposted routes (state, federal, interstate) 286,475 crashes Of those: 60.2% occur at intersection or are intersection related 23

24 Crash Data With a 600 foot buffer: 2,127 crash events near investigated intersections Spatial join to the 270 intersections Normalized number of crashes at each intersection by AADT Number of Crashes/1,000,000 vehicles 24

25 Number of Normalized Crashes/1,000,000 vehicles Skew Angle Analysis 25 Effect of Skew Angle on Crash Frequency Skew Angle

26 Number of Normalized Crashes/1,000,000 vehicles per Intersection Turn Lane Analysis No Turn Lanes Right Turn Lane Only Left Turn Lane Only Both Right and Left Turn Lanes Normalized Crashes Effect of Turn Lanes on Crash Frequency per Intersection

27 Conclusion 270 intersections in the data inventory 30 replicas of each intersection type for statistical representation Statewide Implementation level of effort is large Systematic approach for additional data 27 Starting with 100 worst intersections along state routes in the state Early stages of correlations with crash events We are seeing relationships with intersection characteristics and crashes Online GIS Based Intersection Data Inventory Web Portal Extensible to other states with transportation linear referencing systems