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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. Outline. Background Data Review Key Factors Results Conclusion. Background. - PowerPoint PPT Presentation
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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 (1991-2011)
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
CrossingCount
10 2
9 1
6 2
5 4
4 10
3 31
2 165
1 1382
7
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
app. road gates cross
walk14%
app. road gates cross walk; ped gates with flashers on
exit12%
None30%
Ped flashers & bells
10%
Ped gates, flashers, bells
30%
Ped gates, flashers, bells, escape path
2%
Ped gates, flashers, bells, exit gates
2%
Ped Protection Devices
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
120%
254%
318%
45%
54%
Number of Tracks
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
0
0.5
1
1.5
2
2.5
3
3.5
Exposure Factor
Crossing
(Mill
ions
)
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
Nearby Pedestrian Generators
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
Rural2% Rural Center
9%
Suburban13%
Suburban Center38%
Urban25%
Urban Center14%
Area Types
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 R2 = 0.0594
Exponential (log) model R2 = 0.0558
Reasons Dataset limitations (sample size,
assignment of independent variables)
Linear EquationPedestrian Collision Prediction =
- 0.011 (sidewalk)
- 0.023 (protection type)
+ 0.005 (number of tracks)
+ 0.015 (exposure factor)
- 0.0001 (transit station)
- 0.003 (pedestrian generator)
+ 0.007( area type)
+ 0.195
15
Hypothesis ChecksHypothesis Coefficient Sign1 Standard Error2
Better pedestrian guidance (sidewalks) might lower collision rates
Plausible Plausible
Active devices might reduce pedestrian collision rates
Plausible Not plausible
More tracks might increase collision rates Plausible Not plausible
Higher EF might increase collision rates Plausible Not plausible
Nearby transit / Amtrak stations might increase collision rates
Not plausible Not plausible
More / larger nearby pedestrian generators might increase collision rates
Not plausible Not plausible
Higher density might increase collision rates Plausible Not 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