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

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Outline Background

Data Review

Key Factors

Results

Conclusion

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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

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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

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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

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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)

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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

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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

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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

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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

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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

)

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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

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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

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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

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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

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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?

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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.)

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Thank YouPaul F. Brown, PE, PTOE

Jacobs Engineering Group

Paul.Brown@Jacobs.com

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