Acknowledgements ASU Team Xuesong Zhou, Associate Professor - PDF document

7/11/2019 Behavioral Considerations for Integrated Modeling in an Era of Disruptive Emerging Transportation Technologies Ram M. Pendyala, Professor and Interim Director School of Sustainable Engineering and the Built Environment http://tomnet-utc.org | http://mobilityanalytics.org Acknowledgements • ASU Team – Xuesong Zhou, Associate Professor – Sara Khoeini, Assistant Research Professor – Shivam Sharda, Denise Capasso da Silva, Irfan Batur, Tassio Magassy, Taehooie Kim • Chandra Bhat, The University of Texas at Austin, and team of outstanding students 1

7/11/2019 Acknowledgements • TOMNET Team – Patricia L. Mokhtarian, Georgia Tech – Giovanni Circella, Georgia Tech and UC Davis – Deborah Salon, ASU – Michael Maness, University of South Florida – Fred Mannering, University of South Florida – Cynthia Chen, University of Washington – Daniel Abramson, University of Washington – Abdul Pinjari, Indian Institute of Science, Bangalore – and many fabulous students! What is Going On With Travel Demand? Disruption due to Socio- demographic shifts, attitudinal shifts, e- commerce, and IoT 2

7/11/2019 Percent of People Reporting ZERO TRIPS Source: McGuckin, N. (2018) 3

7/11/2019 Educational Attainment NHTS 2001 – NHTS 2017 – Generation X Millennials N=3849 N=8328 40.0 35.0 % of respondents 30.0 25.0 20.0 15.0 10.0 5.0 0.0 Less than a high High school graduate Some college or Bachelor's degree Graduate degree or school graduate or GED associates degree professional degree Household Structure NHTS 2001 – NHTS 2017 – Generation X Millennials N=3849 N=8328 50.0% % of respondents 40.0% 30.0% 20.0% 10.0% 0.0% Single adult Multiple adults, no Single parent Nuclear family 2+ adults, retired, no children children 4

7/11/2019 Frequency of Internet Use NHTS 2001 – NHTS 2017 – Generation X Millennials N=3849 N=8328 100.0 90.0 80.0 % of respondents 70.0 60.0 50.0 40.0 30.0 20.0 10.0 0.0 No internet access Daily A few times a A few times a Once a month or Never week month less Framework Geographical Period effects effects Age effects Cohort (generational) (Controlled effects 26-30 years) SOV + HOV Socio- Unexplained economic drive effects effects VMT 5

7/11/2019 Summary and Conclusions Vehicle Miles Traveled is lower for Millennials, but the size of the generation (cohort) effect is tiny (less than 0.3%). VMT differences are largely due to socio-economic and demographic characteristics. The period effect is actually greater than the generation effect. Huge UNEXPLAINED portion of person VMT variance! Source: https://www.bloomberg.com/news/articles/2019-04-25/are-u-s-malls-dead-not-if-gen-z-keeps-shopping-the-way-they-do Source: https://www.abcactionnews.com/news/national/is-the-era-of-the-shopping-mall-over-not-quite-an-unexpected- generation-is-reviving-them 6

7/11/2019 The Future of Mobility  Connected vehicles  V2V and V2I configurations  Automated vehicles  Various degrees of automation  Autonomous vehicles  Truly driverless  (Shared/Hailed) Mobility Services (TNCs)  On-demand  Electrification  No Travel – Virtual and Delivered! 14 7

7/11/2019 Technology Adoption 125 Year Span! https://www.visualcapitalist.com/rising-speed-technological-adoption/ Technology Adoption 65 Year Span! https://www.visualcapitalist.com/rising-speed-technological-adoption/ 8

7/11/2019 Waymo Now Giving Self-Driving Car Rides to the Public in Phoenix Average Joes are about to get a crack at riding in the company's autonomous minivans. http://www.thedrive.com/tech/9644/waymo-now-giving-self-driving-car-rides-to-the-public-in-phoenix AV adoption Source: http://www.pewinternet.org/2017/10/04/automation-in-everyday-life/pi_2017-10- 04_automation_3-05/ 9

7/11/2019 January 2018 May 2018 Source: https://www.autoblog.com/2018/01/24/self-driving-vehicles-survey-aaa/ https://www.usatoday.com/story/money/cars/2018/05/22/americans-more-fearful-of-self-driving-cars/35214021/ fear about riding in a fully autonomous vehicle 78 % 63 % 73 % early 2017 early 2018 may 2018 survey taken few weeks after the Uber fatal accident in Tempe, AZ Sources: https://newsroom.aaa.com/2018/05/aaa-american-trust-autonomous-vehicles-slips/ https://www.bizjournals.com/phoenix/news/2018/05/22/aaa-survey-fear-of-self-driving-cars-rises.html 10

7/11/2019 Consumers not ready for full autonomy Source: https://www.freep.com/story/money/cars/general-motors/2018/10/16/fighting-keep-humans-not-robots-drivers/1601286002/ Consumers not ready for full autonomy Source: https://www.freep.com/story/money/cars/general-motors/2018/10/16/fighting-keep-humans-not-robots-drivers/1601286002/ 11

7/11/2019 Question: How do we control a system in which the most important agent doesn’t wish to be controlled? Evolution of Ride-hailing Frequency: Age 18-34 years Observed Heterogeneity in Evolution – Puget Sound Regional Travel Survey 18 - 24 years 25 - 34 years 90 90 80 80 70 70 Percentage (%) Percentage (%) 60 60 50 50 40 40 30 30 20 20 10 10 0 0 2015 (N: 207) 2017 (N: 343) 2015 (N: 748) 2017 (N: 1609) Puget Sound Regional Household Travel Survey, 2015 and 2017 24 12

7/11/2019 Evolution of Ride-hailing Frequency: Age (65 to 74 and ≥ 85) Observed Heterogeneity in Evolution – Puget Sound Regional Travel Survey 65 - 74 years 85 years and above 100 100 90 90 80 80 Percentage (%) Percentage (%) 70 70 60 60 50 50 40 40 30 30 20 20 10 10 0 0 2015 (N: 631) 2017 (N: 534) 2015 (N: 71) 2017 (N: 38) Puget Sound Regional Household Travel Survey, 2015 and 2017 25 Modeling Approaches 1 Electrification 2 Sharing Scenarios & Models & Fake 3 Automation Parameters Simulations Forecasts 4 Deliveries Behaviors Defined by Attitudes, Perceptions, Preferences, Values, and Evolutionary Dynamics 13

7/11/2019 How Will Emerging Technologies Impact VMT? Vehicle Ownership and So Much More! Cons Pros May displace a transit trip (not only May replace a drive-alone trip with Uber   increasing VMT, but undermining transit) + transit, or other combo (solves transit’s first- and last-mile problem) May replace one carpool trip with  multiple single-rider AV trips May eliminate a personally-owned car  (separately good), reducing unnecessary Makes travel easier, cheaper  may  trips generate new trips Neutral Time saved (e.g., for parents using  Shuddle for their children) may be used to May replace a kiss-and-ride or PNR trip  generate new trips Or replace some other drive-alone trip  On-demand vehicles cruising,  deadheading Source: Patricia L. Mokhtarian, Georgia Tech 27 The “I” Era in Transportation  Information (real-time, predictive, and personalized)  A focus on information provision and data collection  Individual  A focus on individual agents  Integrated  Addressing the built environment – travel demand – network supply nexus  Intelligent  A user responsive, adaptive, and flexible multimodal transportation system  Innovative  Big data to monitor and optimize complex adaptive system performance 14

7/11/2019 App-Empowered Connected Travelers 29 Connected, Shared, and Autonomous Agents Connectivity:  Among vehicles of all types  Among vehicles and a variety of  roadway infrastructures Among vehicles, infrastructure, and  wireless consumer devices  Enables real-time activity/trip planning (across spectrum of choices)  Integrated models for era of connectivity and real-time information 30 15

7/11/2019 A Consumer Adoption Modeling Framework MMNP Model of Smart Vehicle Options  Marginal willingness-to-pay (MWTP) computed for each attribute  Amount of money required to maintain a consumer’s current level of utility when one unit of an attribute is changed  Also compute relative importance (RI) of option based on worth of each attribute  Assuming deterministic portion of utility ( V nj ) may be divided into price-dependent component and non-price dependent component:   U x   part worth   nj jk     MWTP k RI K 100  K  x    part worth U x jk k nj j price , price k 16

7/11/2019 Level 0 Model Integration - Classic Sequential Paradigm PopGen Land-Use Model Activity-Travel Model NO YES End Convergence? Trip Information Update O-D Travel Times Dynamic Traffic Assignment Model Update Time-Dependent Shortest Path Level 4 Model Integration: Pre-trip + Enroute Traveler Choices Trips in distress Trips that arrived at their destination t = 0 min t = 1 t = 2 t = 5 t = 6 t = 11 Activity-Travel Demand Model Person(s) reached Person(s) received traffic destination and pursue Trip Record 1 Trip Record 2 congestions information Origin O 1 , activity Origin O 3 , Destination D 1 , Destination D 3 , Mode M 1 , Vehicle Mode M 3 , Vehicle Information Information Update O-D Travel Times Dynamic Traffic New Link Travel Assignment Model Times 6 second interval Update Time- Dependent A portion of trips on the network are checked on every N minutes (N = 3 mins in this figure) Shortest Path Set 1440 minutes 34 17