delays and performance: king county metro rapidride c & d lines
DESCRIPTION
Delays and Performance: King County METRO RapidRide C & D Lines. University of Washington URBDP 422 Geospatial Analysis, Winter 2014 Debmalya Sinha, Austin Bell, Riley Smith, Andrew Brick. Overview. Primary task: identify delays Where When Magnitude Secondary tasks: - PowerPoint PPT PresentationTRANSCRIPT
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Delays and Performance:King County METRORapidRide C & D Lines
University of WashingtonURBDP 422 Geospatial Analysis, Winter 2014Debmalya Sinha, Austin Bell, Riley Smith, Andrew Brick
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Overview• Primary task: identify delays• Where• When• Magnitude
• Secondary tasks:• Identify priorities for remediation• Recommend delay reduction strategies
• Future research:• Relationship between delays and socioeconomic status
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Data• Onboard System (OBS) for October 2013 (245,826
entries)• Records real-time information of bus activity• No weekend data was included in data file
• General Transit Feed Specification (GTFS)• Provides scheduled arrival times for all routes
• Shapefiles• C & D Line stop locations (point)• C & D Line routes, manually segmented (line)
• Field Data• Physical attributes of stops and route segments
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Methods• Raw OBS and GTFS data imported into R• All times converted to seconds after midnight where
required• Trips categorized by start time:• 0000 – 0600: pre-peak• 0600 – 0900: am-peak• 0900 – 1500: midday• 1500 – 1800: pm-peak• 1800 – 0000: post-peak
Data Preparation
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Methods• Delays• scheduled arrival time – actual arrival time (in seconds after
midnight)• Stop performance• “Marginal” doors open time: number of seconds it takes for
each passenger to board or alight (over the amount of time it takes only one passenger to do so)• Averaged for each stop
• Segment performance• Seconds per foot: number of seconds between sequential
stops divided by the segment length, converted to speed• Averaged for each segment
Computations
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Methods• Raw OBS data imported into GIS• X,Y data extracted from GPS entries (generated point
shapefile)• Data screen: retained only those stops which did not
occur at bus stops (retained only entries where STOP_ID = 0)• Computed kernel density with DWELL_SEC as value field• Reclassified output raster from 1 to 9, with 1
representing shortest stops / lowest number of stops
Unplanned Stops
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• Worst Delays• Southbound in West Seattle• Southbound and
Northbound Downtown
ResultsDelays
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Results
• Marginal on/off time consistently higher in D than C• Correlated with
passengers embarking and alighting• Off board payment
generally unused
Relative Stop Performance
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Results
• Worst performance:• Northern and
Southern endpoints of Rapid Ride• Downtown
segments• Alaska Junction
Relative Segment Performance
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Results• Averaged data reveals differences by time of day
and by ridership
Stops & Segments
0 3 6 9 12 15 18 21 24 27 30 33 36 39 42 45 480
1
2
3
4
5
6
7
8
0
2000
4000
6000
8000
10000
12000
14000
Number of “Doors Open” Seconds per Passenger by Ridership
Per-passenger Doors Open Time Observations (Secondary Axis)
Number of Passengers Boarding and Alighting
Seco
nds
Obs
erva
tions
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Conclusions and Questions• No correlation between physical attributes of stops and
performance• Ridership explains only 26% of doors open time• More complex phenomena (traffic flows, signals)
account for most variation
• Why does C Southbound accumulate large delays in West Seattle?
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Questions
University of WashingtonURBDP 422 Geospatial Analysis, Winter 2014Debmalya Sinha, Austin Bell, Riley Smith, Andrew Brick