Pedestrians: The Next At- Grade Crossing Frontier Paul F. Brown, PE, PTOE Jacobs Engineering Group ITE Western District Conference June 24-27, 2012 Santa Barbara, CA 1

Outline  Background  Data Review  Key Factors  Results  Conclusion 2

Background  Pedestrian crossing evaluations on FasTracks  FRA has developed various at-grade crossing analysis tools for roadway / railroad collisions  WBAPS  GradeDec  Quiet Zone Calculator  Pedestrian treatments not considered  Some recent research and literature summaries available 3

Related Research  Light Rail Warning Systems Safety Review, Sound Transit, Seattle, WA, November 2011  Pedestrian and Bicyclist Traffic Control Device Evaluation Methods, Federal Highway Administration, May 2011  Pedestrian Safety Guide for Transit Agencies, Federal Highway Administration, February 2008  Compilation of Pedestrian Devices In Use At Grade Crossings, Federal Railroad Administration, January 2008  Additional research by FHWA, FRA, and other agencies  Illinois Commerce Commission  Nevada Department of Transportation 4

Data Overview  FRA Collision data available for download (database files)  About 224,800 at-grade crossings nationally (2008 data)  Downloaded collision data for 20-year period ( )  Total of 71,193 collisions at 42,773 crossings (about 19% of all crossings)  1906 pedestrian collisions (2.6% of total) at 1597 locations (3.7% of crossings)  Pedestrian Collisions are included  Highway User Type = K (pedestrian)  Details (suicide, pre-collision actions, etc.) coded in Narrative Description field  Narratives often not provided; no standard format when included  Inventories lack pedestrian data  Physical features not included in standard FRA crossing inventories  Google Earth, Google maps, Bing maps, local information where available 5

Data Limitations  Long data period (20 years)  Physical environment changes (sidewalks, gates, etc.)  Temporal data changes (pedestrian and train volumes vary over time)  Lack of records defining improvements installed to address pedestrian crossing issues  Time consuming data collection (15-20 minutes each)  Download and review FRA inventory; review FRA collision report database  Review aerial photography and street view for crossing conditions, compile in Excel  Initial sample set goal of 2.5% of crossings (40 crossings)  Based on subsequent analysis, sample set needs to be expanded  Current dataset reflects 3.5% of crossings (56 locations) 6

Data – Collision Frequencies  215 crossings (13%) had multiple pedestrian collisions within the 20- year analysis period  Of the top five crossings:  Three are in cities with under 100,000 population; only one is in a city with a population over 1,000,000  Three are within ¼ mile of an Amtrak station; one of these is a shared Amtrak and rail transit station  Only two have active pedestrian treatments on all four corners; one doesn’t have sidewalks Recorded Collisions Crossing Count

Key Factors – Pedestrian Facilities  Sidewalks conditions varied widely  Most crossings had complete sidewalks on both sides of the roadway (85.6%)  Some crossings had no sidewalks on either side of the roadway (3.6%)  Various intermediate cases  Complete sidewalk on one side of the crossing; nothing on the other (3.6%)  Incomplete sidewalk on one side; nothing on the other (1.8%)  Incomplete sidewalk on both sides (3.6%)  Incomplete sidewalk on one side; complete on the other (1.8%)  Hypothesis: Better pedestrian guidance (sidewalks) might lower collision rates 8

Key Factors – Pedestrian Warning Devices  Summarized into seven groups  No crossings with passive pedestrian devices encountered  As noted in the FRA Compilation, “Effective use of channelizing devices that force pedestrians to look and move in certain directions and to cross tracks at certain places can enhance safety at grade crossings …”  Hypothesis: Active devices might reduce pedestrian collision rates 9

Key Factors – Number of Tracks  Locations ranged from 1 track to 5 tracks  Multiple tracks :  Create the potential for multiple threats (second train coming)  Lengthen the distance a pedestrian must travel to cross  Some agencies use “second train coming” warning signs at multiple track crossings  Hypothesis: More tracks might increase collision rates 10

Key Factors – Exposure Factor  Common usage  Train volume x roadway volume  Used in FRA software, literature  Expressed as million entering vehicles  Does not reflect pedestrian conditions  Pedestrian count data unavailable  One measure of potential pedestrian activity  Hypothesis: Higher EF might increase collision rates 11

Key Factors – Nearby Station  There is often increased pedestrian activity near rail transit stations  Locations within ¼ mile walk distance were noted Some stations adjacent to the crossing (30.3%) Others within walking distance (25%)  Some stations serve both rail transit and Amtrak  Some crossings include transit / Amtrak station platform access  Platform access point(s) adjacent to tracks  Plat form access point(s) between tracks (requires crossing to enter/exit station)  Platform itself crosses tracks (Amtrak)  Hypothesis: Nearby transit / Amtrak stations might increase collision rates 12

Key Factors – Nearby Pedestrain Generators  Second measure of pedestrian activity due to lack of counts  Examined pedestrian generators within walking distance (1/4 mile)  Schools (K-12, colleges)  Others noted during data collection Arenas (2), Airport parking / terminal  About 1/3 of crossings (34%) had nearby pedestrian generators  Hypothesis: More / larger nearby pedestrian generators might increase collision rates 13

Key Factors – Area Type  Area types defined by  Entries in FRA database  Review of aerial photography  Three community types  Rural (lower density, small town)  Suburban (medium density)  Urban (higher density, major city)  In the downtown area (center) or outside of downtown  Hypothesis: Higher density might increase collision rates 14

Pedestrian Collision Predictions  Develop an equation to forecast pedestrian collisions  Seven independent variables  Considered linear (straight line) model; exponential (log) model  Both models failed to produce practical equations  Linear (straight line) model R 2 =  Exponential (log) model R 2 =  Reasons  Dataset limitations (sample size, assignment of independent variables)  Linear Equation Pedestrian Collision Prediction = (sidewalk) (protection type) (number of tracks) (exposure factor) (transit station) (pedestrian generator) ( area type)

Hypothesis Checks HypothesisCoefficient Sign 1 Standard Error 2 Better pedestrian guidance (sidewalks) might lower collision rates Plausible Active devices might reduce pedestrian collision rates PlausibleNot plausible More tracks might increase collision ratesPlausibleNot plausible Higher EF might increase collision ratesPlausibleNot plausible Nearby transit / Amtrak stations might increase collision rates Not plausible More / larger nearby pedestrian generators might increase collision rates Not plausible Higher density might increase collision ratesPlausibleNot plausible 1 - Does the sign of the coefficient match the expected change in the hypothesis? 2 - Does the standard error exceed the calculated coefficient value? 16

Next Steps  Review Hypotheses  Evaluate independent variables  Consider new independent variables if reasonable  Collect additional data  Would a shorter timeframe provide better results?  Collect additional data regarding new / revised independent variables  Improve sample size based on updated timeframe  Analyze new dataset within revised framework  Tap into work by NCUTCD committee, if available  Review other equation forms (polynomial, etc.) 17

Thank You Paul F. Brown, PE, PTOE Jacobs Engineering Group 18