Future Radar on Self Driving Vehicles: Impact assessment on the city we want to be

Transcription

1 Future Radar on Self Driving Vehicles: Impact assessment on the city we want to be Eric de Kievit Senior Advisor Transport & Traffic Research Mobility & Public Space Manchester, Friday November 23th, 2018

2 2 After my presentation You know what future radar is Why we developed it How we use it Why you might find it useful too

3 The city we want to be

4 Context Saving money personel, parking, data Sharing More or less mobility? Sustainable E-mobility Self driving Vehicles Safety No human errors Self-parking Efficient use of space Speed traveltime

5 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% But from impact studies we know: % kilometres travelled per modality Basecase 0 (2050) Scenario1 0 Scenario2 Scenario3a Scenario3b Three scenario s based on complexity driving task (highway, corridors, everywhere in city (high/low sharing)) Bicycle Train Tram Metro Bus Car SDCar SDTaxi SDTaxi (shared) SDBus % kilometres travelled on the road

6 And Modal Shift

7 More kilometres travelled by car, more use of space in contrast of reducing # cars in city centre

8 More in-car safety devices doesn t mean safer driving Without ADAS: 14% With 4 or more ADAS: 23% equal to + 75mln repair costs

9 Clear effects, but. a lot of uncertainties Therefore actionplan on self driving vehicles : Research & Pilots Knowledge base (Community of Practice) Future radar Time critical Interventions

10 10 to summarize We are worried about impact of SDV On trips On traffic safety On modal shift On use of public space (parking) Investments in public transport or parking facilities take long time Public transport concessions/contracts last for many years So we need to know what time critical interventions we have to take now Future Radar can help

11 Future Radar What it is Liveability Strong voice of inhabitant No SDV within ringroad Hubs along ringroad Sustainablity important More space for pedestrian, cyclist and PT Competitive disadvantage What will Amsterdam (people, economy, urban planning) look like under level 5 autonomous vehicles? Individual freedom High acceptance New targetgroups Diversity of vehicle types, mobility services Chaotic traffic Creative economy Suburbanisation Efficiency of the system Sharing is popular Governance by data Crowdedness decreases Strong spatial and economic diversion smart roads regulate traffic Internet of Cars Less sharing Selfdriving wins from PT Creative economy (entertainment) A dam heart of the Metropole

12 Example Selfdriving E-taxi

13 Future Radar Example E-taxi Indicators Old Amsterdam Amsterdam Market Square Amsterdam On Demand Hub Amsterdam Attractiveness private vehicles Attractiveness of sharing Number of journeys in the city Number of vehicles in the city Impact on public transport Road safety Impact on residential and business locations (commuting) Who is leading the transition? High-medium-low High-limited-low Grow-same-decrease Grow-same-decrease More important Less important replace PT Worse-same-better Increase-same- fewer Market-government High Medium Low Very low Limited High High Very high Decreases Grows Grows Decreases Decreases Grows Grows Decreases Public transport to hubs more important Autonomous replaces public transport Autonomous replaces public transport Same Worse Worse Better Fewer commutes from and to Amsterdam Fewer commutes from and to Amsterdam Significant increase of commuting journeys Shift to selfdriving public transport Significant increase of commuting journeys Government Market Market Government

14 Future Radar Example E-taxi To conclude Impact of self driving vehicles is not always beneficial to city goals Road authorities face uncertainty about developments Future Radar is a helpful tool to discuss impact Example of E-taxi shows most likely Scenario Amsterdam On Demand Time critical interventions can be defined

15 Future Radar Thank you for your attention