Big Data for Automated Driving Technology, Transportation Planning, - PowerPoint PPT Presentation

2018 SF Bay Area ITE/ITS CA Joint Transportation Workshop Big Data for Automated Driving Technology, Transportation Planning, and Engineering Big Data Sources and Methodologies Gary Carlin, PE, PMP, PTP

Thousands Use INRIX Real-time Traffic and Analytics INRIX powers more country, state & city agencies than any other company Public ic Sector or Customers omers & Pa Partner ers A Histor ory y of ITS Public ic Sector or Firsts ts • Fusion of private and roadside sensor data on a country-wide basis • Country-wide traffic services based exclusively on GPS probe data • Innovative traffic analytics to understand origin and destination • Corridor-wide multi-state traffic monitoring web site • Pay-for-performance contract with payments tied to data • Exclusive sourcing deals and industry partnerships 2

Mining Data On The Road We use a connected network of sensors, devices, car and drivers to develop robust insights 3

Global Scale and Impact Powered by global relationships and coverage, INRIX takes on the big transportation and population movement challenges 100B+ 5M+ 60+ 60+ 1PB+ Real-time data Miles of road Countries we Data analyzed points aggregated, we cover in 50 are live in every day processed and countries delivered each month 350M+ 15M+ 450+ 450+ 29M 29M Real-time vehicles Connected cars in B2B/B2G Parking spots and connected the world powered customers we cover devices we by INRIX services we serve crowdsource 4 4

Movement Today & Tomorrow Technology is fundamentally reinventing transportation, creating a unique opportunity Transformation of Automotive Industry Use of Big Data for Decision Making Smarter Transportation The convergence of Autonomous Shared Connected IoT Electric Analytics the connected car Urbanization Sustainability and smart cities

Industry Inflection Point: The ACES A utonomous S hared E lectric C onnected

Industry Inflection Point: The ACES A utonomous S hared E lectric C onnected

The Promise of Big Data • Improved Intelligence • More Data ( every day…) • Better Data/Relational/Location Based Databases • Better Spatial Granularity and Coverage • Achilles Heel • DRIP (Data Rich Information Poor) • Drowning in Data • Don’t have the Staff/Resources/Tools to Effectively Store/Analyze/Communicate the Data One day y worth th of Origin ins s and Desti tina nati tion ons in Seattl ttle

WHAT IS BIG DATA?

You Can’t Handle th the Dat ata! a!!! !! Big g Da Da  ta ta : /bi ɡ / / ˈ dad ə /: noun. 1. Too big to fit in Excel

Growth in Connected Vehicles Steady growth in global auto sales Rapid rise in car connectivity (new connected car (units in Millions) penetration rate in %) 120� � 104� � 102� � 100� � 97� � 94� � 100� � 90� � 86� � 83� � 80� � 76� � 73� � 80� � 51%� 60� � 42%� 35%� 40� � 26%� 19%� 20� � 13%� 9%� 7%� 5%� 4%� 3%� 0� � 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 Actual Forecast Source: IHS Automotive and internal sources

Connected Cars Require a Lot of Software INRIX Confidential 12

Connected Cars Use a Lot of Data Current AV’s generate 2 GIGs/secon econd! 13 INRIX Confidential

Data Mining the Connected Car Machine Learning Self-tuning Raw Data Contextual Services Temperature LiDAR Sensors Mirror Sensors Fuel Level Wipers Status Engine Diagnostics Camera Traction Control Fog Lights Tire Pressure Speed Location

There are More Mobile Devices Than People

By 2020, More People Will Have Mobile Phones Than Electricity

Data Privacy • Changes (like winter) are coming… • Who owns what data? • Impact of recent events/legislation • Numerous private sector data breaches • Russian hacking • Facebook Congressional hearings • Europe’s GDPR (General Data Protection Regulation) e tc., etc… • Benef nefit its s of Shared red Data Data Privacy cy

Traditional Transportation Data Sources • Speed/Travel Time Data • Lane by lane • Volume Data (ADT/AADT) • Origin-Destination Data/Trip Purpose Full Modal Split/Occupancy Data • • Incidents • Construction • Weather • Events etc. •

New/Expanding/Non-Traditional Data Sources • CV/AV Data • Numerous Safety Applications: Windshield Wipers, ABS, Air Bags, etc. • User Generated Information (UGI) • Socio-Economic Data Land Use Data • • Location Based Services (LBS) Data Provides Context/Trip Purpose • • Snow Plow Data • etc.

Data “Layer Cake” Speed d Data ta Delay y Impa mpacts cts “Cut Through” Data Layers Volume/A ume/ADT/ T/AAD ADT Socio-Eco Economi nomic c Data Land Use Data Trip Purp urpose se Transit sit Service ice Data Const st./I /Inci ncident dent/W /Wea eathe ther Origin in-Destin Destinat ation on Data Toll Fea easi sibi bility lity Modal al Data Study udy

The Power of Future Multiple Data Future Source Mode Source Split Data Sets Volume Data Future Future Source Source Land-Use CV/AV Freight Data Data Data Future Source Future Source Future Origin- Future Speed Data Source Socio- Destination Source Economic Data Data

Impact of the Digital Economy: NYC Freight Data Sample - Selection Area Selected all trips the start, end or pass • through the box • Only selected fleet data and only freight profiles (i.e., no taxis) • Selected all weight classes

OSM Map Layer Only

One Day of Freight Data in New York City

One Week of Freight Data in New York City

One Week of Freight Data in New York City – Zoomed Detail

One month of Freight Data in New York City

San Francisco Water Authority Problem blem: Water Main • Breaks Throughout the City • Ap Approa oach ch: Assess Impacts of Heavy Trucks on Water Main Breaks • Data Used: ed: Combine Freight O-D Data with Water Main Locations and Break Locations Water Mains ----- --- Freight Waypoints

Return to Normal Analyses for Incident Management Programs • Important for TSM&O/ICM Applications • New Performance Measurement • Important for Toll Road Operators • Possible Insurance Claim for Insured Toll Authorities for Revenue Loss Image Source: Press Democrat

SH 183 Accident Near MOPAC – Saturday, November 11, 2017 • November 11, 2017 • On NB SH 183 near MOPAC • 3:30 pm Jeep jumps center median into SB lanes Two dead at scene •

SH 183 Accident Near MOPAC – November 11, 2017 Return to normal ~10:30 pm Accident occurs 3:30 pm

Georgia Dome Origin-Destination Assessment • Looked at December 2017 due to Atlanta Falcons Home Schedule • Three Home Games • December 3, 7 and 31 • Vikings, Saints, Panthers

Georgia Dome December 2017 Waypoint Data 33

Georgia Dome December 2017 Waypoint Data

Oroville Dam Mandatory Evacuation Approximately 70 miles north of Sacramento • • Approximately 180,000 people evacuated • Impacted three counties Butte, Sutter and Yuba Mandatory evacuation lasted three days • 35

NB CA 99/149 and SB CA 70 Exiting Oroville – Sunday, February 12, 2017 Manda dator ory y evacu cuati tion on order der given en at 4:58 8 pm 2/12/1 2/17 36

Questions? Gary Carlin, PE, PMP, PTP gary.carlin@inrix.com 425-495-5476 37