Autonomous Urban Mobility

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1 Autonomous Urban Mobility Key Lessons from the City of Boston John Moavenzadeh Member of the Executive Committee, World Economic Forum Automated Vehicles Symposium July 11, 2018, San Francisco

2 The World Economic Forum and Boston Consulting Group have completed a three-year collaboration Consumer acceptance Customer research Go Boston 2030 AV strategy development Future modal mix research Conjoint study with 7,000 consumers in three cities City perspective City policy maker interviews AV impact study Agent-based simulation of downtown traffic AV impact study 2.0 Holistic city-wide traffic simulation with enriched trip data Mobility scenarios AV testing pilot Catalyze AV testing Scenarios for urban mobility Launch of AV testing in Boston Expanded testing area, three partners, active passenger trials 1

3 Conducted large-scale conjoint study in three cities Representative sample Boston Age 7,000 Consumers Income Berlin Geography Gender Shanghai 2

4 Respondents were given specific situations and use cases Situations along four criteria Use case examples 1 I Trip reason Commute to work alone 2 I Group context 3 I Weather Family trip to the zoo 4 I Time of day Night out with friends 3

5 and asked to chose from 8 transport modes Mass Transit Personal car Mobility on demand Bus/ subway Personal car Taxi/ ride sharing Autonomous taxi Commuter rail Autonomous personal car Autonomous shared taxi Autonomous minibus 4

6 Top 5 findings from our conjoint study Cities globally move to 30-40% mobility on demand Mass transit ridership drops in urban areas AV adoption varies across city correlated to income levels The shorter the trip, the higher the AV adoption 20-25% will use a personal car no matter what 5

7 1 Cities globally move to 30-40% mobility on demand Boston Berlin Shanghai 35% -3 32% 62% % 36% -6 30% 58% 7% Today % 30% Future % AV 87% 34% 4% Today % 26% Future % AV 95% 44% 20% Today % 40% Future % AV 72% Mass transit Personal car = Bus / subway + Commuter rail = Personal car + Autonomous personal car Mobility on demand = Taxi / ride sharing + Autonomous taxi + Autonomous shared taxi + Autonomous minibus 6

8 2 Mass transit ridership drops in urban areas Urban Boston Suburban 47% % 30% +2 32% 40% 13% % 41% % AV 88% 65% 5% % 26% % AV 87% Today Future Today Future Mass transit Personal car = Bus / subway + Commuter rail = Personal car + Autonomous personal car Mobility on demand = Taxi / ride sharing + Autonomous taxi + Autonomous shared taxi + Autonomous minibus 7

9 3 AV adoption varies across city correlated to income Boston Berlin Shanghai % AV adoption Median income $100 $149K $50 $74K K 25 49K K K Seaport and South Boston Dorchester Friedrichshain- Kreuzberg Marzahn- Hellersdorf Jiading Songjiang 8

10 4 The shorter the trip, the higher the AV adoption Boston Berlin Shanghai 29% +9% 34% 38% +13% 31% 36% 41% -3% 39% 37% 23% >40 min min <20 min >40 min min <20 min >40 min min <20 min 9

11 AV testing in Boston an example for fast scaling 4 months to launch 12 months of scaling up Sep 2016 Nov 2016 Jan 2017 Davos 2017 Jun 2017 Oct 2017 Nov 2017 Davos 2018 AV initiative announced MoU signed On-street testing started Testing area expanded 1,000 miles completed Passenger pilots launched Source: World Economic Forum, City of Boston, BCG Analysis 10

12 Testing initially allowed in small area; June 2018 expanded to entire city Testing Started in Seaport Expanded to Entire City South Boston Waterfront Source: World Economic Forum, City of Boston, BCG Analysis 11

13 Develop clear mobility vision and KPIs Identified best practices for launching an AV pilot Balance stakeholder interests in approval process Create a tiered testing plan with achievement milestones Build public awareness early, e.g. AV petting zoo/robot block party Publish regular updates on testing progress to residents Source: World Economic Forum, City of Boston, BCG Analysis 12

14 Last year: Agent-based model for downtown Boston We took a real world environment North End Haymarket and simulated traffic flows in its streets Traffic participants, autonomous and traditional Cars Buses Taxis Minibuses Pedestrians Boston Common Downtown Crossing City Hall Plaza Old State House Post Office Square Faneuil Hall Custom House Financial District Downtown Study Area BOSTON Environment and infrastructure Dynamic behaviors Traffic lights Streets Following distance Speed Traffic rules Capacity Source: World Economic Forum; BCG analysis in cooperation with MIT Media Lab 13

15 Expanded impact study across four key dimensions 1 Expanded study to cover entire City of Boston 316x larger area (142 km 2 ) 2 Added enriched trip data and commercial vehicles 11x more trips (2M/day) 3 Determined future modal mix through conjoint study with 2,400 consumers 72 modal mixes, by area & use case 4 Quantified traffic efficiency gains from AV technology 6.3% gain in throughput with 37.5% AV share 14

16 Key outputs from the city-wide impact study Today Future (Conjoint Scenario) Traffic volume on the road 1.75M -15% Vehicle distance travelled (km) 8.8M +16% Parking space needed (km 2 ) % Average travel time (min) % 15

17 Congestion increases in Downtown Boston Boston City Average Downtown Boston Allston Neighborhood Average Travel Time Modest improvement -4.3% +5.5% Worsening Large improvement -12.1% 35% -3 32% 46% % 42% -1 41% Modal Mix 58% 7% % 30% 34% 20% % 45% 42% 16% % 41% Today Future Today Future Today Future Mass transit Personal car = Bus /subway + Commuter rail = Personal car + Autonomous personal car Mobility on demand = Taxi / ride sharing + Autonomous taxi + Autonomous shared taxi + Autonomous minibus 16

18 Cities can influence outcome through policy levers City-wide travel time improvement vs. today -15.5% -10.0% -8.3% Occupancy-based pricing scheme Converting street parking Dedicated AV lanes 17

19 Thank you

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