The motivation for this simulation is to explore the practicalities of car sharing using autonomous electric vehicles.  Specifically, we wanted to model the public and private transport needs of Canberra and then test the ability of a fleet of autonomous electric vehicles to meet those needs.  As well as the "macro" transport goals listed above (congestion, pollution, land use...), we frame our consideration with the transport goals of typical citizens, such as these: Mary lives in Farrer and works in Barton.  Each weekday morning she drives her 3 year old son to child-care in Narrabundah, drives to work and collects him after work.  She often likes to visit the supermarket at Torrens on the way home because it stocks some of her favourite items.  Today she has a dental appointment at lunch-time in Braddon - where will she park?  Next Saturday she wants to watch her nephews play soccer, but she has to get to Amaroo by 8:30am for the kick-off. 

lives in Farrer and works in Barton.  Each weekday morning she drives her 3 year old son to child-care in Narrabundah, drives to work and collects him after work.  She often likes to visit the supermarket at Torrens on the way home because it stocks some of her favourite items.  Today she has a dental appointment at lunch-time in Braddon - where will she park?  Next Saturday she wants to watch her nephews play soccer, but she has to get to Amaroo by 8:30am for the kick-off.  Joe lives in Scullin and works stocking shelves in a supermarket in Weston three nights a week.  His shift starts at 8pm and ends at 4am.  He is also studying part-time at the Bruce CIT, and is usually in a rush to leave Bruce and get to Weston to start his shift. 

lives in Scullin and works stocking shelves in a supermarket in Weston three nights a week.  His shift starts at 8pm and ends at 4am.  He is also studying part-time at the Bruce CIT, and is usually in a rush to leave Bruce and get to Weston to start his shift.  Edith is 85 and feels she really shouldn't be driving, yet is not willing to give up her license and hence her independence.  Her arthritis makes it impossible for her to check the air in her car's tyres, and to save money, she hasn't had the car serviced for three years.  She drives her husband to medical appointments each fortnight and ferries home the bulky supplies his condition requires.  About once a month she drives to her son's house and baby-sits his children, getting home after 11pm. 

is 85 and feels she really shouldn't be driving, yet is not willing to give up her license and hence her independence.  Her arthritis makes it impossible for her to check the air in her car's tyres, and to save money, she hasn't had the car serviced for three years.  She drives her husband to medical appointments each fortnight and ferries home the bulky supplies his condition requires.  About once a month she drives to her son's house and baby-sits his children, getting home after 11pm.  Blake is out late again with his work colleagues.  It's 2am and he's in a club in Kippax, wondering whether to bother even trying to get a cab to get back to Downer, or whether he should just risk getting a lift with Jed, who really shouldn't be driving either.  But how will he collect his car from Kippax tomorrow? 

is out late again with his work colleagues.  It's 2am and he's in a club in Kippax, wondering whether to bother even trying to get a cab to get back to Downer, or whether he should just risk getting a lift with Jed, who really shouldn't be driving either.  But how will he collect his car from Kippax tomorrow?  Jamie lives in Kambah, works at Campbell Park and is studying part-time at ANU.  She often needs to work late, and also spends long nights at the Chemistry lab at the university, but has to leave before the last bus.  She often feels anxious about her safety waiting at the bus-stop and walking home in the dark, and she's wondering whether she should give up uni or buy a car. 

lives in Kambah, works at Campbell Park and is studying part-time at ANU.  She often needs to work late, and also spends long nights at the Chemistry lab at the university, but has to leave before the last bus.  She often feels anxious about her safety waiting at the bus-stop and walking home in the dark, and she's wondering whether she should give up uni or buy a car.  Henry is in year 12.  He doesn't yet have his driving license, and relies on his mum to take him to work his weekend shift at the pizza restaurant.  The nearest library is 10km from home, but he enjoys studying there, so he puts up with the travel.  His college day starts at 10am on 3 days of the week but his only option for getting to school is catching the bus at 7:30am each day.  Henry's parents are separated, and although he'd like to visit his dad more often, the return bus journey takes nearly 3 hours on the weekend. 

is in year 12.  He doesn't yet have his driving license, and relies on his mum to take him to work his weekend shift at the pizza restaurant.  The nearest library is 10km from home, but he enjoys studying there, so he puts up with the travel.  His college day starts at 10am on 3 days of the week but his only option for getting to school is catching the bus at 7:30am each day.  Henry's parents are separated, and although he'd like to visit his dad more often, the return bus journey takes nearly 3 hours on the weekend.  Jayne from Higgins is a single mother of two young children.  She suffers from a spinal condition which makes walking difficult and slow.  She is unemployed, receives rent assistance and cannot afford to run a car.  The nearest bus stop is 450m from her house.  She started a hospitality course at the Reid CIT but withdrew because just getting there involved either three buses with a connection she inevitably just missed, or two buses into Civic then a 800m walk.  Just getting the kids to doctor's appointments and the shopping done is hard enough. 

from Higgins is a single mother of two young children.  She suffers from a spinal condition which makes walking difficult and slow.  She is unemployed, receives rent assistance and cannot afford to run a car.  The nearest bus stop is 450m from her house.  She started a hospitality course at the Reid CIT but withdrew because just getting there involved either three buses with a connection she inevitably just missed, or two buses into Civic then a 800m walk.  Just getting the kids to doctor's appointments and the shopping done is hard enough.  Hoang from Palmerston works in the Parliamentary Triangle.  Before pay-parking was introduced, his daily commute took 20-25 minutes each way if he left home before 7:20am or after 8:45am and left work before 4:30pm or after 6:30pm.  To save money, he now catches the bus, which takes 55 minutes in the morning and 50 minutes to get home if it runs on time.  Occasionally, he has to work late but then has to be careful when he leaves to avoid a long journey home.  Although Hoang saves money on fuel and parking, he now has 5 hours less time each week with his young family and can no longer help with shopping on the way home.  Most people would recognise these scenarios: most of us live busy and complex lives, juggling many responsibilities.  Simplistic transport options rarely meet our requirements, making us either reliant on our cars or at risk of social exclusion and disadvantage.  No-one really wants a light-rail system, or a bus system, or even a car, autonomous, electric or otherwise.  What they want is to be able to get to work, school or uni, get home safely from a party at 2am, visit the doctor, pick-up the kids from child-care, and on the weekend, take them to the soccer in the boondocks of that new suburb.  They want a way to travel safely, cheaply and quickly from door to door, whenever the need arises. 

Steve Mahan has 5% vision - like you, he'd like to pickup up his dry-cleaning,

visit a drive-thru, see his friends As Simon Corbell, ACT's Minister for the Environment and Sustainable Development said in his introduction to Transport for Canberra - Transport for a sustainable city, 2012–2031 people want "... a transport system that puts people first ... [that] will make our city a better place to live, work and do business, and a more accessible place where it is easy for everyone to get around."  Urban and transport planning occurs in a context of multiple decades.  Transport infrastructure is expensive with a long life.  Urban plans, such as Canberra's, and the associated suburbs, housing, workplaces and leisure and shopping facilities have an even longer life.  Whilst not certain, the likelihood of autonomous vehicles becoming a reality in the next 6-12 years, is very high.  Many automobile manufacturers, experienced commentators and industry experts would all have to be wrong for this not to happen.  Yet currently, Canberra citizens are debating the merits of an $800M investment in a single light-rail line, which, if it is to expand to become a viable part of Canberra's transport infrastructure, will be just the first stage of an investment which would require over $10 Billion.  Whilst no-one can feel confident that such a system will meet the needs of citizens even at such a cost, effective alternatives are not being debated.  This simulation aims to test the feasibility of an alternative. 

This simulation arose from a spreadsheet created by Warwick Cathro and Kent Fitch as part of Warwick's submission on the ACT Government's Low Emission Vehicle Strategy discussion paper.  A spreadsheet can only go so far in confidently modelling scenarios as detailed as a city-wide autonomous car infrastructure, but a simulation enables assumptions to be reified, tested and corrected.  This simulation is the work of transport amateurs.  Whilst it consciously attempts to err on the conservative side, it doubtless contains inaccurate assumptions, misunderstandings and plain old bugs.  It is being made public in the hope of reducing these errors.  The simulation is written in javascript.  All the data it uses is contained in the javascript it loads: there is no data in any database. 

The following table shows typical outcomes when the simulation is run with the six pre-configured uptake scenarios which represent an increasing passenger load on a simulated fleet, rising from a low 45,000 journeys per day (equivalent to the journeys currently provided by ACTION) to 1.1 million journeys per day: Uptake

scenario Journeys

per workday Cars in

fleet Wait time at start of journey, minutes Empty running

(transfers) km Time spent

idle Fleet energy

MWHr/day Operating surplus

(Profit/Loss)

$M/year <= 1 1-2 2-3 3-5 > 5 ACTION load 45,000 2,100 96.1% 2.2% 0.9% 0.6% 0.2% 26% 56% 172 -12 Low 120,000 4,800 97.3% 1.8% 0.5% 0.3% 0.1% 25% 51% 440 -13 Medium 300,000 10,500 98.0% 1.5% 0.3% 0.2% 0.0% 23% 47% 1,040 2 High 600,000 18,500 97.6% 1.8% 0.3% 0.2% 0.1% 23% 42% 1,970 58 Very High 750,000 23,000 98.4% 1.5% 0.1% 0.0% 0.0% 22% 43% 2,430 78 Future 1,100,000 31,500 97.8% 2.0% 0.2% 0.0% 0.0% 22% 41% 3,430 167 Future High 1,500,000 39,000 95.3% 3.3% 0.8% 0.6% 0.0% 22% 39% 4,470 313 Each simulation run will produce different results because there is a random component to both the number of journeys starting each minute and the origin and destination of each journey: the results shown above are typical.  Apart from number of journeys, cars and chargers which are specific to each uptake scenario, these simulations were run with some common assumptions: Cars : Each car costs $40,000 commissioned, residual value: $0, useful life: 36 months, financed at 10%, maintenance: 2.5 cents/km, annual registration, insurance, admin, comms: $2,000, theoretical max range: 240km (but cars only charged to 80% of range and recharged at 25% of range), travelling 6km/kWHr and with a set of complete spares sized at 5% of fleet. 

: Each car costs $40,000 commissioned, residual value: $0, useful life: 36 months, financed at 10%, maintenance: 2.5 cents/km, annual registration, insurance, admin, comms: $2,000, theoretical max range: 240km (but cars only charged to 80% of range and recharged at 25% of range), travelling 6km/kWHr and with a set of complete spares sized at 5% of fleet.  Charging : Each charging station costs: $15,000 installed, residual value: $0, useful life: 120 months, financed at 10%, annual rent & maintenance: $3,000, power delivery rate: 75kW, cost of electricity per kWH: $0.20. 

: Each charging station costs: $15,000 installed, residual value: $0, useful life: 120 months, financed at 10%, annual rent & maintenance: $3,000, power delivery rate: 75kW, cost of electricity per kWH: $0.20.  Fares : Peak period flag-fall: $0.45 and rate per km: $0.25, cost of average 13.4km trip: $3.79.  Off peak period flag-fall: $0.20 and rate per km: $0.20, cost of average 13.4km trip: $2.87. 

: Peak period flag-fall: $0.45 and rate per km: $0.25, cost of average 13.4km trip: $3.79.  Off peak period flag-fall: $0.20 and rate per km: $0.20, cost of average 13.4km trip: $2.87.  Miscellaneous: Fixed annual system cost: $1,000,000.  You are encouraged to configure the simulation to change these assumption and rerun the simulation for yourself.  More information is available about these assumptions and how the model uses them.  For example, a simulation based on some reasonable assumptions of using the Smart Electric Drive two-seater with a small range produces considerably more favourable results.  A graphical representation of the sensitivity of the operating surplus and wait times for the "Very high" uptake scenario to various parameters is available.  Some observations on these results: An autonomous car fleet can provide the same number of journeys as the current ACTION network at less than half the cost The simulation of the ACTION load of 45,000 journeys per day has annual costs (operating and capital) of approximately $58M, income of approximately $46M, for an annual loss of $12M. Hence, all travel could be made free for a cost to rate-payers of $58M, less than half of the rate-payer subsidy to ACTION.  Additionally, travellers would enjoy an 24x7, door-to-door, on-demand service, with 96% of journeys starting within 1 minute of being requested and over 99.5% of journeys starting within 5 minutes, even during peak periods.  Tailpipe emissions would be reduced from an estimated 92 grams of CO 2 -e per passenger km (see below) to zero.  However, traffic congestion would increase, particularly during peak periods on major roads (due to major-route buses carrying up to 100 passengers being replaced by autonomous vehicles carrying between 1 and 4 passengers), making this an undesirable scenario. [ Note that congestion can be reduced in this scenario by increasing wait times slightly by waiting for an extra minute before leaving a location if it is likely another request to a nearby destination will be received for that location.  The poorer service levels, whilst still vastly superior to a bus service, may be acceptable, and would have the side-effect of reducing the required fleet size and hence costs.]  An autonomous car fleet can service a very high load replacing most private cars journeys and generating a large annual surplus The simulation of a very high load of 750,000 journeys per day generates an annual surplus of approximately $78M. This surplus could be used to provide 75,000 free off-peak journeys per day, or provide funds for other community needs, or some combination of goals.  Over 98% of journeys start within 1 minute of being requested and 99.9% of journeys start within 2 minutes, even during peak periods.  Traffic congestion is dramatically decreased, particularly during peak periods on major roads.  For example, the average occupancy of cars arriving is Civic and Parkes is 2 passengers, compared to an estimate of 1.13 for current journeys to work.  An autonomous car fleet can service Canberra's future traffic loads using the existing road infrastructure and make a substantial contribution to government income whilst reducing transport costs for citizens The simulation of a "future" load of 1.1 million journeys per day generates an annual surplus of approximately $167M. The average occupancy of cars arriving is Civic and Parkes increases to over 2.1 passengers, so traffic increases at a slower rate than passenger journeys.  Furthermore, at such high levels, forecasters predict that traffic scheduling algorithms that take advantage of vehicle-to-vehicle communication and coordination will smooth traffic flow and decrease travel times.  At lower uptake levels, a higher car-to-journeys ratio is required to achieve acceptably short wait times With a small travelling population, requests to start a trip are more "bursty" and hard to predict.  Hence more cars need to be deployed across the city just in case a burst of trip requests are received at a location.  To see why, imagine an average of just 1 trip per minute leaves a suburb and trips for 3 consecutive minutes are delayed by the traveller.  If cars were allocated in anticipation of their arrival, all 3, or 100% of the allocated cars will be idle (hence wasted).  Conversely, if these 2 of these requests were brought forward to arrive in the same minute as the first request, then 3 cars would be required simultaneously, because it is unlikely that this small number of requests are sharing a common destination which would allow a single car to be used.  Contrast this to a large travelling population, where 10 requests leave a suburb on average.  In this case, it is relatively "cheap" to over-allocate 1 or 2 cars per minute (10% or 20% over-allocation), it is relatively unlikely that many of the trips will be delayed or brought-forward, and it is more likely that some trips will share a common destination, allowing a smaller number of cars to be used.  Over-allocation of cars creates "waste", yet under-allocation creates long wait times, and the simulations with small journey volumes struggle more to balance these undesirable outcomes.  The more unpredictable or "bursty" the requests, the larger the effect.  Given the same levels of service, higher uptake levels generate a higher surplus The size of the fleet required grows slower than the increase in the journeys it needs to service because less over-provisioning is needed to cope with demand spikes for given acceptable wait profile and the probability of car sharing rises.  Hence, each car spends less time idle (or travelling with just 1 passenger in peak periods) and more time earning revenue and defraying its fixed costs.  The simulation presents strong evidence that an on-demand, door-to-door, 24x7 public transport system based on an autonomous car fleet could be the best option for meeting Canberra's transport needs.  The simulation demonstrates that a fleet of autonomous cars can provide a service that: is at least as flexible, reliable and convenient as the personally owned car

is much cheaper than either car or alternative mass transit options

comprehensively meets the city's transport-related goals as outlined in the project objectives of the Capital Metro light rail proposal: Increase the use of public transport by providing a superior alternative to the private car.  Optimise frequency and service reliability with an on-demand door-to-door service operating 24x7, utilising a decentralised fleet of thousands of autonomous vehicles less vulnerable to a single physical system failure than a single unduplicated transport corridor.  Affordable capital and operational costs with annual losses as modelled of less than 10% of the current ACTION service at low usage levels and significant operating surplus at high usage levels, which will allow for a significant community subsidy for transport for those in need; leverage and better utilise Canberra's extensive road infrastructure already built and paid for rather than constructing a duplicate in the form of rail.  Grow a more diversified Canberra economy through greater transport efficiency, and through the development of expertise and support in the deployment and management of a transport infrastructure likely to emulated in other cities.  Stimulate sustainable, urban redevelopment throughout Canberra by efficiently supporting both higher population densities and the traditional "bush capital" approach as options, and by releasing land used by car-parks to more socially and economically useful purposes.  Increase social and economic participation through increased mobility of all citizens regardless of location, age, health, physical capabilities and income.  Revitalise not just the Northbourne Avenue corridor but all Canberra's main travel routes by supporting higher population densities whilst reducing traffic congestion and travel times for all Canberrans.  Reduce carbon and other emissions across all of Canberra by using electric vehicles with "zero tailpipe emissions" and creating a very large and predictable market for renewable electrical energy. 

Furthermore, assuming that industry predictions of commercial autonomous cars availability in the 2017-2025 time-frame are correct, the simulation results show that these compelling advantages can be achieved in the medium term and without an upfront demand on public funds, as the model assumes the overwhelming majority of the infrastructure is purchased using money borrowed at commercial rates (10%), and after loan repayments and operating expenses, is cash-flow positive (for large uptake) or nearly so (for lower levels of uptake) from the first day of operation. 

Financial comparison between ACTION buses, private cars and an autonomous EV fleet

A decision to replace a well-understood technology with one that is just coming of age is always difficult.  Whilst no-one would want to be stuck in an age of candles and telegrams, few envy those who take responsibility for introducing change.  Nonetheless, because of the benefits it brings, "progress" is both inevitable and welcomed, and it is our responsibility to plan for its arrival and extract the greatest benefits we can for our community.  In planning for a transport system based on autonomous electric vehicles, amongst the major risks that need to be evaluated and issues requiring community discussion are: Fully autonomous electric cars: time-frame for commercialisation, performance and costs As the 2013 KPMG white paper notes, the number of participants developing autonomous cars and related system and their rate of progress is astonishing.  It is sensible to build and maintain expertise in technical developments and to gauge the interest of major developers of the technology in participating in a large-scale deployment of their products.  Commercialisation of rapid recharging technology and its installation and operation A large fleet of autonomous vehicles requires automated recharging facilities.  Although Tesla has already deployed a large rapid charging infrastructure, and although automated (wireless) recharging for EVs has been commercialised, the combination of "rapid" and "automated" is not yet available.  Effect on the electricity grid and generation capacity The ACTION-load scenario of 175 MWHr/day is probably not significant, but the 3,400MWHr/day required to support 1.1M journeys is substantial.  By way of comparison, the Point Henry aluminium smelter near Geelong uses approximately 8,200 MWHr/day, and the current demand in the ACT averages around 7,700 MWHr/day.  EVs with extremely large batteries may only need to be charged once per day, allowing great flexibility in coordinating time of charging with generator and grid capacity, but their extra cost (even if they were commercially available) and weight may not be worth the electricity tariff savings such an arrangement may attract.  Just as large aluminium smelters attracted dedicated generation infrastructure, a large, predictable load to power EVs may encourage investment in very large scale renewable generation capacity.  Fares and subsidies Amongst the considerations: Capital and financing costs dominate, and these are largely determined by the size of the fleet needed to meet the peak loads.  Hence, it seems reasonable to charge higher fares in peak times, if not to discourage peak travel (and hence "smooth" the load and reduce fleet size) then to recoup the costs the larger fleet imposes on the community. 

Rational commuters will be enticed to use the fleet rather than private cars if the service is at least as convenient and is substantially cheaper.  As noted above, the real cost of typical private car travel excluding parking costs and owner's time costs for driving, refuelling, cleaning etc is probably between $0.55 and $1.00 per km, regardless of the number of passengers.  This simulation suggests that a peak fare of around $0.25 per km, plus a flag-fall of $0.40, and an off-peak fare of around $0.20 per km, plus a flag-fall of $0.20 is viable.  Additionally, for almost all off-peak trips and for many peak trips (except those into town centres in morning peak and "back home" in afternoon peak, that is, typical "commuter" trips), entire cars, not just a single seat, may be booked with a single fare. 

A separate flag-fall fare may be justifiable on the basis that processing a travel request, maintaining an idle car waiting to respond to that request and the movement of the idle car to the pick-up point incurs costs that are fixed regardless of the distance of the requested journey. 

A service that generates an operating surplus will not divert funds from other services provided by the government. 

Transport is a public good, and society benefits by subsiding travel for those in need.  Hence it seems desirable to set fares such that an autonomous fleet can generate a surplus largely from commuter fares (that are never-the-less much lower than commuters would otherwise pay) and then apply at least some of that surplus as subsidies for those in need, and whose travel, whether promoting access to education and health services, employment, or just facilitating independence and enjoyment of life, not only directly benefits the recipient and their families but also benefits their community.  Assumptions in this simulation's model: tease-out and justify or amend; explore sensitivities to assumptions The model enumerates known limitations, speculates on "known unknowns", attempts to justify default but configurable settings and hard-coded settings.  A rudimentary sensitivity analysis of operating surplus and waiting times to various parameter settings has also been undertaken.  However, an audience with a wider experience in transport will be able to identify errors and omissions in these assumptions, some of which may have a material effect on the outcomes.  The model almost certainly contains bugs, some of which may be significant.  The output can surely be improved to convey its implications.  Community acceptance: trust in self-driving cars Surveys into views towards autonomous cars report generally positive attitudes, especially amongst younger people and when motivated by lower insurance costs (See Insurance.com, Cisco, J.D. Power and Associates).  Community acceptance: public transport, car sharing For some people, their car is an extension of their lounge-room, and they are initially unlikely to want to use public transport, regardless of convenience or cost.  Even in cities where private car transport is extremely expensive and discouraged, some people place such a high value on private travel that they are prepared to pay for what they perceive as added convenience (just as a very few are prepared to pay for private jets, or more commonly have corporate shareholders pay for them).  However, it seems likely that for most people, the practical convenience and economics of a shared fleet will dominate their choice, particularly over time as comfort with the concept grows.  Travellers will be able to avoid sharing in peak periods by booking a car for 4 travellers, but they will pay 4 times the per-km cost, but perhaps only one flag-fall.  Is this to be encouraged, as a way of subsidising costs for "sharers", or discouraged as not reducing congestion?  Could non-sharers be allocated cars only after sharers, meaning that their wait times would sometimes be greater?  Should female travellers be able to stipulate "I will only share with another female"?  Many people will continue to use their own vehicles, for example, trades-people transporting their tools in the back of the ute and people travelling to locations "off the grid".  Sharing of roads by autonomous cars and human-driven vehicles It is likely that autonomous cars will coordinate their activities with the aim of increasing safety and optimising system-wide travel times and energy efficiency.  Regardless of commuter and other private traveller uptake, autonomous cars will have to share the roads with human-driven vehicles (concrete-mixers, semi-trailers, tradies-utes, emergency services etc) for the forseeable future.  Coexsistence with human-driven vehicles at all levels of autonomous vehicle uptake is vital.  Suitability for special needs transport, including wheel-chair accessible models Transport often presents challenges for people living with disabilities.  It would benefit the whole community if the autonomous vehicle fleet were made as accessible as possible to all citizens, some of whom may require specialised facilities to be available in part of the fleet, such as vehicles that allow people in wheelchairs to board without assistance, and vehicles with dedicated staff to facilitate travel for people with special needs.  Privacy The community will need to determine a policy regarding privacy of travellers, dealing with retention and access of details of journeys and in-car video surveillance.  Children travelling alone The community will need to determine a policy regarding unaccompanied children using the service.  Economic disruption to petrol stations, car repairers, car manufacturers and retailers, bus drivers, taxi owners and drivers, and car-park operators The introduction of autonomous electric vehicles will adversely affect many businesses dedicated to serving the current transport infrastructure, especially those unable or unwilling to adapt.  Such changes are inevitable, as advances in technology continually disrupt the status-quo.  Many automotive industry skills will be readily transferable from a fleet based on the internal combustion engine to one based on electric batteries and motors, as many mechanical aspects of vehicles are largely unchanged.  However, the community will need to determine policies which ease the transition and encourage retraining for those facing declining demand for their skills.  Regardless of decisions on fleet ownership and management, it may be desirable to decentralise fleet maintenance to the existing commercial mechanical workshops across Canberra.  Operators of car-parks are very likely to face declining demand with the introduction of autonomous cars, regardless of whether they are operated as a shared fleet.  A shared fleet will require a significant number of charging stations (ranging from a few hundred for the ACTION-level uptake to 3000 for the highest levels of uptake with small capacity batteries as modelled with the Smart ED).  These charging stations should be distributed across Canberra and, given the default modelled cost of $15,000 to purchase and installation and $3000 per annum for rental and maintenance, at least some of the reduced demand for undercover car-parks could be taken up by charging stations and associated cleaning and admin facilities.  Government revenue from licensing, registration and fines According to the ACT Government's 2013-14 Revenue and Forward Estimates, motor vehicle registration and duties were estimated as raising $141M, parking fees and fines $31M, traffic fines $24M and driving licenses $10M in 2014-15.  This represents a total revenue "at risk" of just over $200M (or if you prefer, a wealth transfer of $200M from citizens to government) if all private vehicle ownership were abandoned.  However, many households will retain cars for convenience of distance travel, and many vehicles (utes, light commercial, trucks) are not replaceable anyway by a shared fleet of autonomous electric vehicles.  Even if the private vehicle fleet were halved in size, the reduction in annual revenue of approximately $100M would be more than compensated for by the direct reduction in ACTION subsidy (over $120M) and substantial benefits arising from reassignment of land from car-parks to higher rateable uses, reduction in health costs due to morbidity and mortality caused by motor vehicle pollution and productivity improvements to the local economy supported by less congested travel and greater mobility.  Government revenue from GST Widespread use of a fleet of autonomous vehicles would reduce total community spending on transport, and GST revenue would decrease accordingly.  Money saved on transport would either be diverted to other spending and hence attract GST (unless spent outside Australia or on GST-exempt items), or saved.  As a result, it is almost certain that total GST revenue would fall.  Ownership Should a fleet of autonomous vehicles providing public transport be owned and operated by: The government, as a monopoly, along the lines of ACTION. 

As a community cooperative. 

As a private-for-profit or private competing providers.  Legislative/Legal Would a fleet of autonomous vehicles providing public transport be materially different from a fleet of mini driver-less ACTION buses?  Who is responsible for loss of life, injury or damage following an accident?  It is the vehicle supplier, the fleet operator, or someone else?  Who is responsible for choices made by an autonomous vehicles, such as the often-raised ethical dilemma of weighing costs and benefits when an accident cannot be avoided?  Is this something determined by the community/government, the vehicle supplier, or do passengers specify their preferences in advance, which are then used by the vehicles carrying them? ( For example, "act as a selfish driver: preserve my life at all costs", "weight the worth of my life as 80% of the average life; weight the worth of people under 18 as 200% of the average life", ...) System infrastructure Amongst the issues requiring investigation are: The system must be robust and able to cope with degraded capabilities of the system infrastructure (IT, power, mobile network).  Failing gracefully is of the utmost importance. 

The system must be designed to withstand attacks on its IT infrastructure

The public interface to the system must be easy to use by the entire community.  It must accommodate frequent and casual users (such as tourists).  Risk of overselling the benefits and applicability A fleet of autonomous vehicles is not a complete replacement for all private vehicles (even cars) and existing public transport.  Special needs transport services would need to be retained, perhaps incorporated as extra services supplied by the autonomous vehicle fleet.  Large families may not want to divide into multiple cars: they may choose to keep their "people movers".  Vehicle characteristics Although most of exploration of this model has been based on a vehicle holding 4 passengers, simulations using a 2 passenger vehicle such as the SMART ED show that despite a smaller range, using reasonable cost assumptions it provides at least as good a service with a higher operating surplus.  However, it is not clear whether its smaller size compensates for increased numbers required (and hence possibly congestion) during peak hour.  Perhaps a promising direction is a mix of 4 passenger vehicles used as much as possible from the highest population density areas in peak hours which can be rested or recharged off-peak when the more diffuse traffic is carried by 2 seaters.  This exploration is beyond the current capabilities of this model.  Siting of recharging stations Recharging infrastructure must be sited to allow efficient connection to high capacity power and to minimise travel time for recharging.  At least some recharging stations may be co-located with cleaning stations (allowing cars to be cleaned whilst being recharged) and maintenance facilities/workshops.  Predicting uptake Planning the introduction of fleet capacity so that expected levels of service are achieved whilst developing the capability to maintain the fleet and associated systems.  Estimating minute-by-minute demand Accurate prediction of demand for cars will help to optimise the transfer of idle cars to where they will be needed and will help to minimise waiting times and overheads.  Demand patterns will be affected by many factors such as seasons, holidays, weather and special events.  Prediction will always be imperfect.  Servicing large but predictable demands Some demands will be large but predictable: 2,000 people leaving concert in Civic, 20,000 people leaving Bruce Stadium at the end of a football game, 50,000 people leaving a fireworks display at the lake.  What's the best way to organise fleets of autonomous cars to transport such large numbers of people leaving from a relatively small area over a short period of time?  Building and maintaining expertise in developing and operating a large autonomous car fleet KPMG, Morgan Stanley, Accenture, The Boston Consulting Group, The Conference Board of Canada and others predict that autonomous cars will have an enormous economic and social impact in the decades ahead.  The current public transport problem facing Canberra could be transformed into an economic opportunity by attracting people to a city with an unsurpassed transport system and encouraging development of locally-based businesses to develop and support the required technical innovations.