Post on 06-May-2018
transcript
• Autonomous/Automated Vehicles (AVs)
– Connected and Automated Vehicles (CAVs)
• Increased safety/reduced accidents
– Over 90% of vehicle crashes cites human error as the cause
• Enhancement of travel experience
– Do other activities instead of focusing on the road
• Reduce parking requirements
• Re-allocate road space to other users
• Improved mobility for all
AVs/CAVs
• Short, medium and long term horizons
• Traffic and transport demand modelling – based on land use, planned road network, public transport infrastructure, parking, etc
• In the ACT (Canberra Strategic Transport Model):
– Land use: Population, Employment, Retail, School and Tertiary Enrolments
– Road capacity: derived from Austroads and Highway Capacity Manual (HCM)
– Generalised Cost of Travel: Cars, Public Transport
Transport Planning –Current Practice
• AV/CAV Forecasts: Level 4 available by 2025, 40-60% fleet penetration in the 2050s (conservative estimate)
• Mobility as a Service (MaaS)/Shared Autonomy
• Electric Vehicles
• Freight (autonomous trucks and truck platooning)
Future Transport Ecosystem
• ACT medium/long term plans: 2031/2041
• Travel demand forecasting and transport modelling vehicle technology and ‘driver’/traveller behaviour will be very different
• Update transport planning assumptions and model input parameters
• Minimal to no impact in the interim; possible reduction in the early stages
• Significant capacity improvements in the longer term
• Rate of change of improvement goes much faster at 85-100% CAV penetration
Road Capacity
*Vehicle-to-vehicle (V2V) communications critical for improved road efficiency (Tientrakool, et al, 2011)
Sensitivity Testing: Arterial Road Capacity
• Improves average speed of cars
• Rate of improvement slows down as capacity increases
• At 100% increase, average speed is improved by almost 8 km/h
10% CapAR increase 0.7 km/h increase
• Gradual decrease of VHT as capacity is increased
• Cost of Travel
– Cars: Fuel cost + VOC + in-vehicle time cost + parking cost (if applicable)
– PT: PT fare + walk/wait/transfer time costs + in-vehicle time cost
– Time cost = Value of Time (VOT)
• In the current CSTM:
– Future vehicles = today’s vehicles
– Fuel cost and VOC Petrol cars
– Parking cost: increases over time
– Public transport is generally the same transit modes as today
– VOT is assumed not to change in the future
Cost of Travel (Current)
• Significant proportion of Electric Vehicles (EVs)
– Fuel cost and VOC calculations need to be revisited
• Significantly reduced parking requirements
– Parking costs could be insignificant
• Potentially new forms of public transport (personal mobility service, shared autonomy)
– Calculation of ‘PT costs’ could be considerably different
• Opportunity to engage in other activities (e.g. work, videos, sleep) while travelling by car
– VOT in AVs would be lower than VOT in non-autonomous vehicles
Cost of Travel (with AVs/CAVs)
Sensitivity Testing:Value of Time (VOT) vs Speed
• Increase of car demand on roads, hence the average speed reduction
• Average speed at 50% VOT + 100% AR capacity >Average speed at current VOT and AR capacity
10% VOT decrease 1.4 km/h decrease
• Increased convenience and accessibility additional travel, more vehicles on roads
• New forms of car (or CAV) travel
– Car trips by people who cannot drive (e.g. children, PWD, the elderly)
– CAV-induced trips (i.e. trips that would not have been made by people who can drive if CAVs were not available)
– ‘Zero-occupant’ car trips
• Additional demand could offset the capacity benefits; V/C may not change
• Increase in Vehicle Kilometres Travelled (VKT) and Vehicle Hours Travelled (VHT) people not minding longer travel distances
Trip Making
Sensitivity Testing:Value of Time (VOT) vs Trip Length
• Reducing VOT increases average car trip lengths
• 50% VOT reduction 353 metres average trip length increase
• Reduced demand for car parks
• A single Shared Autonomous Vehicle (SAV) can replace 12 privately owned vehicles and eliminate 11 parking spaces (Fagnant and Kockelman, 2014)
• Planned car parks can be re-planned and existing car parks can be re-developed
• Parking charges no longer a concern
• Parking patterns will be altered
• Moving a parking space from the CBD to a suburban area could save around $US3,000 (Fagnant and Kockelman, 2014)
Parking
Public Transport
• Road capacity increase PT mode share reduction
• 100% capacity increase 0.8% PT mode share decrease
• Reducing VOT sharper decline in PT mode share
• 50% VOT reduction 2.3% PT mode share decrease
• Driverless technology is happening sooner than what most people think
– Significant fleet penetration by 2030s or 2040s
• Preliminary sensitivity tests indicate that small/simple changes in the model (based on possible impacts of CAVs) could result in considerably different forecasts from what they are now
– Worth conducting further investigations (e.g. traffic & transport modelling)
• Governments need to take this technology into consideration when developing long term urban and transport plans
– Review current practices and investigate ways to incorporate CAVs in the planning process
• NOW is the best time/opportunity to develop planning tools and processes in preparation for a very different future transport environment
Concluding Statements
• Transport planning and modelling processes: review and modify
• Road upgrade plans: review and possibly re-prioritise
• Public transport planning: future-proofing of long term plans
• Parking: revisit current plans and guidelines
• Integrated Transport Planning: nationally recognised set of guidelines that include AVs/CAVs
• Further modelling and simulation of autonomous vehicles: to better understand the extent of network benefits (or disbenefits)
• On-road trials in the ACT?
Recommendations for Future Discussions