integrating ride app vehicles into the sfo curbside ......parking garage international parking...
TRANSCRIPT
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Integrating Ride App Vehicles into the SFO Curbside Traffic ModelESRI User Conference
San Diego
July 11, 2019
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SFO Overview
Terminal Area
2016 TNC staging lot
Current TNC staging lots
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Terminal Area
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Terminal Area
International Terminal
Terminal 1
Terminal 2Terminal 3
Central Parking Garage
International Parking Garages
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Roadways
International Terminal curbsides
Domestic curbsides
Each roadway has an inner (terminal-adjacent) and outer (island) curbside zone
During curbside study:Upper level: All drop-offs, courtesy shuttles, and ride app pick-upsLower level: All other pick-ups
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• VISSIM microsimulation models for each of the four curbside roadways
(domestic and international, upper and lower)
– Created in 2008 by SFO and HNTB
– Previously updated in 2011 for the reopening of Terminal 2
Curbside Modeling
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• Terminal 2 reopened (April 2011)
• Passenger traffic grew from 41.0 million annual passengers (MAP) to
53.1 MAP
• Transportation Network Companies (TNCs or ride apps) began
operating (licensed in September 2014)
– From 0 (2011) to 14,000 (2016) daily trips
– As of 2019, over 30,000 daily trips
• Roadway congestion increased
From 2011 to 2016…
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• Regulated by the California Public Utilities Commission (CPUC)
• Required to maintain CPUC and SFO permits
• Trip fees assessed using an in-app reporting system
– Developed in-house by SFO and licensed to AAAE
– Geofence-based
Ride Apps at SFO
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Ride App Growth at SFO
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50%
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Year over Year Growth Rate
YOY growth, pick-ups per 100 deplanements YOY growth, drop-offs per 100 enplanements
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2016-17
• In 2016, the Airport decided to perform a Curbside Congestion Study
– First time integrating ride apps into the model
• Data sources
– Traditional surveys
– Tube counts
– Commercial vehicle transponder data (cordon lines)
– Ride app trip data
Curbside Congestion Study
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Trip Fee Reporting
SFO_ID TNC_ID U_ID TXN_TYPE LICENSE_PLATE LON LATRIDE_
COUNTLOCAL_TNC_TIMESTAMP
32513 ENTRY -122.393588 37.614347 0 1/3/2018 0:00:00
32513 DROP-OFF -122.38504 37.6152 1 1/3/2018 0:00:00
32513 ENTRY -122.391947 37.614294 0 1/3/2018 0:00:00
32513 PICK-UP -122.38489 37.61512 1 1/3/2018 0:00:03
32513 EXIT -122.396512 37.6132 1 1/3/2018 0:00:04
32513 EXIT -122.397531 37.615374 1 1/3/2018 0:00:04
32513 EXIT -122.401119 37.625795 1 1/3/2018 0:00:04
32512 PICK-UP -122.3898065 37.6148366 0 1/3/2018 0:00:05
32512 PICK-UP -122.384391 37.6156674 0 1/3/2018 0:00:06
32513 ENTRY -122.40012 37.633279 0 1/3/2018 0:00:06
32512 ENTRY -122.4043913 37.6366346 0 1/3/2018 0:00:07
32513 EXIT -122.377664 37.604319 0 1/3/2018 0:00:07
32513 EXIT -122.3967 37.61463 0 1/3/2018 0:00:07
32513 EXIT -122.399456 37.632922 0 1/3/2018 0:00:07
32513 PICK-UP -122.38734 37.6177 1 1/3/2018 0:00:08
32512 ENTRY -122.3793383 37.6020836 0 1/3/2018 0:00:08
32512 ENTRY -122.3998125 37.6330114 0 1/3/2018 0:00:08
32513 PICK-UP -122.38984 37.61499 1 1/3/2018 0:00:11
32512 EXIT -122.406023 37.6431129 0 1/3/2018 0:00:14
32513 PICK-UP -122.3847 37.61524 1 1/3/2018 0:00:14
32513 ENTRY -122.393257 37.613909 0 1/3/2018 0:00:15
32513 ENTRY -122.386904 37.607229 0 1/3/2018 0:00:15
32513 ENTRY -122.390584 37.609635 1 1/3/2018 0:00:15
32513 ENTRY -122.399887 37.633373 0 1/3/2018 0:00:17
32513 PICK-UP -122.38714 37.61778 1 1/3/2018 0:00:17
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Trip Fee Reporting Purple: drop-offsGreen: pick-ups
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Curbside Features
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GIS Curbside Features, Schematic
Begin T3, 2,170 End of delineators separating traffic
merging from the outer roadway, 2,350
Door 8 and AirTrain bridge, 2,730
Door 9, 2,840
Door 9 crosswalk, 2,850
Door 11 crosswalk, 2,950
Door 11, 2,960
End T3, 3,140
2,150 2,250 2,350 2,450 2,550 2,650 2,750 2,850 2,950 3,050
Terminal 3 Schematic View
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ModelBuilder Model
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Python Script# Import arcpy module
import arcpy
import sys
# Script arguments
Raw_TNC_data_table = arcpy.GetParameterAsText(0) + "\\page$"
if Raw_TNC_data_table == '#' or Raw_TNC_data_table == "\\page$" or not Raw_TNC_data_table:
sys.exit("No file selected.")
# Local variables:
Layer_Name_or_Table_View = "page$_Layer"
Temp_Zones = "Data.gdb\\Temp_Segments"
Temp_Zones__2_ = Temp_Zones
Zones = "Data.gdb\\Upper_road_sections"
Output_folder = "Distribution\\"
# Process: Make XY Event Layer
print("Making XY layer...")
arcpy.MakeXYEventLayer_management(Raw_TNC_data_table, "lon", "lat", Layer_Name_or_Table_View, "GEOGCS['GCS_WGS_1984',DATUM['D_WGS_1984',SPHEROID['WGS_1984',6378137.0,298.257223563]],PRIMEM['Greenwich',0.0],UNIT['Degre
# Process: Feature To Point
print("Feature to point...")
arcpy.FeatureToPoint_management(Layer_Name_or_Table_View, Temp_Zones, "CENTROID")
# Process: Near
print("Near...")
…
arcpy.Near_analysis(Temp_Zones, "Upper_road_sections", "120 Feet", "NO_LOCATION", "NO_ANGLE", "PLANAR")
# Process: Add Join
#arcpy.AddJoin_management(Temp_Zones, "NEAR_FID", "Zones", "OBJECTID", "KEEP_ALL")
# Process: Table to Table
print("Exporting table...")
arcpy.TableToTable_conversion(Temp_Zones, Output_folder, "Output" + str(time.time()) + ".csv", "", "LOCAL_TNC_TIMEST \"LOCAL_TNC_TIMEST\" true true
# Process: Delete
print("Cleaning up...")
arcpy.Delete_management(Temp_Zones, "")
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Heat Mapping
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Drop-off Data Analysis
TNC drop-offs versus first
Southwest door
TNC drop-offs versus first Delta
door
TNC drop-offs versus first United
door
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Confirms, Confirmed by Firsthand Observations
• Complemented traditional curbside validation process (camera and in-
person observation and discussions with Airport Duty Managers and
Police Service Aides)
• Transaction locations aligned with stakeholders’ impressions of
curbside “hot spots”
• For drop-offs, the transactions patterns were also applied to taxis and
private vehicles
Data Analysis
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Procedural Changes
0%
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Distance Along Curb
Cumulative Distribution of TNC Pick-Ups
Pick-ups - Sample 1 Pick-ups - Sample 2
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Procedural Changes
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Terminal 3 TNC Activity, 6:00-9:59, Sample 2
CROSSWALK
DOOR
VERTICAL CIRC
DROP-OFF
PICK-UP
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Visualization
Blue: Drop-OffsRed: Pick-Ups
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• Using GIS tools to perform analysis of TNC activity allowed the Airport
to update its VISSIM model with unprecedented accuracy for:
– Volume
– Desirability of curb segments
• GIS analysis, and subsequent visualizations, allowed the Airport to
make procedural changes to improve traffic flow
• Changes in pick-up or drop-off policy, or in-app behavior, can be
analyzed immediately
Findings