Impact of AV in future trnasport networking system modelling
- 1. The University of Sydney Page 1 IMPACT OF AUTONOMOUS VEHICLES IN FUTURE TRANSPORT NETWORK SYSTEM (MODELLING) Presented by: Soumen Chakraborty PhD Candidate, ITLS Research Supervisor : Dr Michiel Bliemer Dr Matthew Beck D raft
- 2. The University of Sydney Page 2 Background Karl Benz 1885 1908 Henry Ford’s Model T (first mass produced automobile) 1912 Traffic Light ( The bird cage) 1935 Car’s indicator (interaction between car drivers) 1948 Cruise control 1970 Anti-lock Braking System (ABS) 1981 Computer Car (emission control system) 1992 Automated Parking System 1995 GPS 1997 Hybrid mass production 2008 On-board WI-FI 2009 Google Driverless cars 2015 Tesla’s model S 1999 Adaptive cruise control (Partial) 2006/ 2007 Adaptive cruise control (Full) 2016/ 2017 Volvo XC90, Nissan, Audi, BMW, Toyota… Fully autonomous electric shuttle bus completed on-road trial in WA (2016), Sydney (2017) Uber’s self driving car in Pittsburg and Arizona, USA (2016-2017) D raft
- 3. The University of Sydney Page 3 Level of Automation ü Level 0 – No Vehicle Automation: Driver has full control ü Level 1 – Functional-Specific Automation: Moderate level of automation for a specific function like stability control or braking that assists the driver ü Level 2 – Combined-Function Automation: Vehicle integrates detection and responses; driver’s hands on the wheel at all times ü Level 3 – Limited Self-Driving Automation: Driver takes control in emergency ü Level 4 – Full Self-Driving Automation: Driver needs only to provide destination or navigation input ü Level 5 – Automated Taxi service and cooperative vehicles capable of negotiating rights-of-way with one another (Source: NHTSA, SAE International, ADVI, VDA) D raft
- 4. The University of Sydney Page 4 Literature Review • AVs will change the travel patterns by lowering the value of travel time (Milakis et al. 2015) • AVs will increase the road capacity, depending on penetration rates • Shared AVs can reduce conventional vehicle trips (Fagnant and Kockelman 2014) • Privately owned AVs can satisfy more trips than a single car (Correia and van Arem 2016) • Trip is expected to become longer on average (Milakis et al. 2016) • Car parking cost can be reduced (Correia and van Arem 2016) • Over 90% crashes can be reduced by AVs (NHTSA) • Introduction of AVs would varry in between 2018 to 2025 and the market penetration rate would hit 50%-65% by 2050 D raft
- 5. The University of Sydney Page 5 1.2 million people are killed on the World’s road every year. 33,000 in US and 1200 killed in Australia (2015). 72% 1% 19% 4% 3% 0% 1% Passenger vehicles Motor cycles Light commercial vehicles Rigid trucks Articulated trucks Non-freight carrying trucks Buses 74% 1% 18% 4% 2%0% 1% VKT, Australia 2014 VKT, NSW 2014 For the last 10 years, Vehicle Kilometres Travelled (VKT) increased from 213 to 245 billion (about 15%) in Australia. ( 72 billion VKT in NSW 2015) Avg commuting time (28 mins) No of workers (8.33 million) = 233 million mins / day commuting in Australia 233 million mins / day 43 million mins (82 years average life expectancy) = 6 lifetimes wasted / day in Australia Other Stats…(Source: ABS, BITRE, TPA) (NSW) D raft
- 6. The University of Sydney Page 6 Shaking Statistics…..in the Future or Already?? à Car2go survey indicates : 2-5% sold their cars and 7-10% did not buy cars à Households saved $154 to $435 (USD) per month after joining a car sharing service à Lyft data indicates 25% of car trips are either to or from a public transit station. 40% of their requests are for pool service. à Car-ownership model will be changed: The end of the two car family and families will shift to a single car and a car-sharing. à Car / Ride Sharing - Lyft - GoGet - Car2go - ZipCar - Uber D raft
- 7. The University of Sydney Page 7 Transport Model System Transport Demand Transport Demand Transport Supply Transport Supply Trip Choice Destination Choice Mode Choice Departure Time Choice Departure Time Choice Route Choice Road Network Transit Services Simulation of Vehicles Simulation of Vehicles Travel time Crowding Passenger Flows Congestion Travel behaviourTravel behaviour InfrastructureInfrastructure D raft
- 8. The University of Sydney Page 8 Transport Supply Side: Test bed for AVs Source: www.astazero.com Mcity Test facility AstaZero : Full scale test facility(Sweden) (Korean Transportation Safety Authority) (Research Institutes of Sweden and Chalmers University of Technology) Source: www.astazero.com (University of Michigan) D raft
- 9. The University of Sydney Page 9 Transport Supply Side: Test bed for AVs K-City : A pilot city for AVs (Korea) (Korean Transportation Safety Authority) D raft
- 10. The University of Sydney Page 10 Future of Automated Driving City D raft
- 11. The University of Sydney Page 11 Future of Automated Driving City D raft
- 12. The University of Sydney Page 12 Influence of AVs on Travel Behaviours B1: Privately Own (AV)A1: Privately Own (Non- AV) A2: Non-AV, PT A3: Shared car, PT A4: Shared car, PT, Active transport An: multiple combinations based on destination, purpose, choice and cost B2: AVs, PT B3: Active transport, APT B4: Shared AVs, PT, Active transport Bn: multiple combinations based on destination, purpose, choice and cost D raft
- 13. The University of Sydney Page 13 Influence of AVs on Travel Behaviours Before After D raft
- 14. The University of Sydney Page 14 Influence of AVs on Travel Behaviours Tomorrow Today Ability to use travel time more meaningfully - reduces the ‘disutility’ (inconvenience, generalised costs) of travel More Travel!! More Trips!! D raft
- 15. The University of Sydney Page 15 Influence of AVs on Travel behaviours à Residential location choice à Work location choice à Car ownership choice à Activity choice à Trip choice à Destination choice à Mode choice à Departure time choice à Route choice à Lane choice à Speed Choice Long term decision Medium term decision Short term decision D raft
- 16. The University of Sydney Page 16 Travel behaviour research for AVs Revealed Preference Data (RP) Travel Survey Stated Preference Data (SP) Travel Choice Simulation Laboratory (TRACSLab) at Sydney University (ITLS) D raft
- 17. The University of Sydney Page 17 Travel behaviour research for AVs Travel Choice Simulation Laboratory (TRACSLab) at Sydney University (ITLS) Insert Video D raft
- 18. The University of Sydney Page 18 Proto-type model for AVs Walk (access /egress) Public transport Car (AV) Conventional Car Multimodal Super Network Transport Demand Transport supply Network Level Integrated approach D raft
- 19. The University of Sydney Page 19 Scenario Testing Scenario I Scenario II Scenario III Scenario IV Privately owned AVs Shared ownership or shared ride Influence of car parking policies / infrastructure Automated shuttle PT service Scenario V Road capacity enhancement by AVs D raft
- 20. The University of Sydney Page 20 Reference lists Arnaout, G. and Bowling, S. 2011, “Towards reducing traffic congestion using cooperative adaptive cruise control on a freeway with a ramp”, Journal of Industrial Engineering and Management, 4(4), pp. 699–717. Brands, T. 2015, “Multi-objective optimisation of multimodal passenger transportation networks”, The Netherland Trail Research School, Delft University Press, The Netherlands, Trail Thesis Series no. T2015/15. Fagnant, D. J., and Kockelman, K.M. 2014, “The travel and environmental implications of shared autonomous vehicles, using agent-based model scenarios”, Transportation Research Part C, 40, pp. 1-13. Fiorenzo - Catalano, M. S. 2007, “Choice Set Generation in Multimodal Transportation Networks”, The Netherland Trail Research School, Delft University Press, The Netherlands, Trail Thesis Series no. T2007/6. International Transport Forum (ITF) 2015, Urban Mobility System Upgrade: How shared self-driving cars could change city. Kyriakidis, M., Happee, R. and de Winter, J. 2015, ‘Public opinion on automated driving: Results of an international questionnaire among 5000 respondents’. Transportation Research Part F: Traffic Psychology and Behaviour, 32, pp. 127-140. Lanser, S. 2005, “Modelling travel behaviour in multimodal networks”, The Netherland Trail Research School, Delft University Press, The Netherlands, Trail Thesis Series no. T2005/4. Automated Vehicles Symposium 2016 and 2017. Milakis, D., Snelder, M., van Arem, B., van Wee, B., and Correia, G. 2016, ‘Development and transport implications of automated vehicles in the Netherlands: scenarios for 2030 and 2050’, European Journal of Transport and Infrastructure Research. Milakis, D., van Arem, B. and van Wee, B. 2015, ‘Policy and society related implications of automated driving: a review of literature and directions for future research’, Paper under review by the Journal of Intelligent Transportation Systems. National Highway Traffic Safety Administration (NHTSA) 2013, Preliminary statement of policy and concerning Automated Vehicles. Shladover, S. E., Su, D., and Lu, X.-Y. (2012), “Impacts of Cooperative Adaptive Cruise Control on Freeway Traffic Flow”, Transportation Research Record: Journal of the Transportation Research Board, 2324, pp. 63–70. Spieser, K., Treleaven, K., Zhang, R., Frazzoli, E., Morton, D. and Pavone, M. 2014, “Toward a Systematic Approach to the Design and Evaluation of Automated Mobility-on-Demand Systems: A Case Study in Singapore”, Road Vehicle Automation SE – 20, Springer International Publishing, Cambridge, Massachusetts, pp. 229 – 245. SAE International, 2014, “Taxonomy and Definitions for Terms Related to On-Road Motor Vehicle Automated Driving Systems”. van Nes, R.2002, “Design of multimodal transport network – A hierarchical approach”, The Netherland Trail Research School, Delft University Press, The Netherlands, Trail Thesis Series no. T2002. D raft
- 21. The University of Sydney Page 21 Thank you for listening…… AVs are closer than you think……….. D raft