Integrating Ride App Vehicles into the SFO Curbside Traffic ModelESRI User Conference

San Diego

July 11, 2019

SFO Overview

Terminal Area

2016 TNC staging lot

Current TNC staging lots

Terminal Area

Terminal Area

International Terminal

Terminal 1

Terminal 2Terminal 3

Central Parking Garage

International Parking Garages

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

• 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

• 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…

• 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

Ride App Growth at SFO

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Year over Year Growth Rate

YOY growth, pick-ups per 100 deplanements YOY growth, drop-offs per 100 enplanements

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

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

Trip Fee Reporting Purple: drop-offsGreen: pick-ups

Curbside Features

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

ModelBuilder Model

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, "")

Heat Mapping

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

Procedural Changes

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Distance Along Curb

Cumulative Distribution of TNC Pick-Ups

Pick-ups - Sample 1 Pick-ups - Sample 2

Procedural Changes

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Terminal 3 TNC Activity, 6:00-9:59, Sample 2

CROSSWALK

DOOR

VERTICAL CIRC

DROP-OFF

PICK-UP

Visualization

Blue: Drop-OffsRed: Pick-Ups

• 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

Thank You

Chris DiPrimaAirport Planner

Christopher.DiPrima@flysfo.com(650) 821-3347