Rapid shift to passenger miles in electric cars with self driving ridesharing
The US electric car market rose to just above 1% of the broader car market in December 2016.
Going from 0.01% to 1% market penetration takes 7 doublings. The same applies to 1% going to a 100%.
GM's EV1 was released in 1996 and it took us 20 years to get to 1% (or roughly a doubling every 3 years). According to Kurzweil's theory, we need another 20 years to get to 100% new vehicles sold.
Half of U.S. demand in 2027 would add up to 9.1 million electric vehicles, based on Bloomberg New Energy Finance estimates. If those all ran on 60 kilowatt-hour lithium-ion battery packs-reportedly the standard package for the Model 3-it would require enough manufacturing capacity to produce 546 gigawatt-hours' worth of battery packs annually. And that's just for the United States market.
To put that into context, worldwide EV battery production stands at only 90 gigawatt-hour today, and there's only 270 gigawatt-hours in the planning or construction phases.
Initially, Tesla said its massive "gigafactory" in Sparks, Nevada, would produce 50 gigawatt-hours' worth of battery packs per year, but more recently raised its projection for final capacity to 150 gigawatt-hours. That means achieving Musk's goal would require companies to plan and build four to 16 additional gigafactory-scale operations just for U.S. car production, all within the next decade. Tesla's first factory is expected to be fully operational in 2020, meaning it took six years to complete from the time it was announced. The company's second gigafactory is primarily dedicated to producing solar panels, but the company said that three more factory sites could be disclosed by the end of this year, presumably devoted to batteries.
Colin McKerracher, head of transport analysis at BNEF, expects U.S. demand for electric vehicles to reach only 3.2 million by 2027, less than 18 percent of the total, though he does say a successful Model 3 launch could accelerate adoption.
Self driving and ridesharing cars accelerates the transition of passenger car miles away from oil to EV
A Columbia University study suggested that with a fleet of just 9,000 autonomous cars, Uber could replace every taxi cab in New York City - passengers would wait an average of 36 seconds for a ride that costs about $0.50 per mile. Such convenience and low cost will make car ownership inconceivable, and autonomous, on-demand taxis - the 'transportation cloud' - will quickly become dominant form of transportation - displacing far more than just car ownership, it will take the majority of users away from public transportation as well. With their $41 billion valuation, replacing all 171,000 taxis in the United States is well within the realm of feasibility - at a cost of $25,000 per car, the rollout would cost a mere $4.3 billion.
Just 5% of cars being electric, ridesharing and self driving could see 90% of passenger miles in electric vehicles by 2025.
- Car ownership has been rising with the growing global middleclass. There are 3.5 billion or so global middle and this could rise to 5.5 billion by 2030
- Self driving cars will start bending that curve of car and vehicle ownership and the growth of driving and driving related jobs
- Intel just announced they will spend $15.3 billion to buy Mobileye (a maker of self driving car components)
- Qualcomm spent $47 billion to buy NXP, the largest automotive chip supplier
- Google, Uber, Ford, Tesla, Nvidia and others are pushing hard to make self driving cars
- Regulation and laws
Self driving cars will mean that far fewer drivers and cars and trucks will be needed to fulfill many of the motor vehicle related tasks.
- Commuting
- delivery of goods
- Non-commuting passenger travel
The long term effects of the self driving car movement could potentially be staggering. Price waterhouse Coopers (PwC) predicts that the number of vehicles on the road will be reduced by 99%, estimating that the fleet will fall from 245 million to just 2.4 million vehicles. Ancillary auto related industries such as the $200 billion automobile insurance market, $100 billion automotive finance market, $100 billion parking industry, and the $300 billion automotive aftermarket will collapse as demand for their services evaporates.
Nextbigfuture thinks car ownership reduction will lag the actual fall in private vehicle usage. This is because the older generations will be reluctant to give up owning vehicles even if they shift to robotic ride sharing for commuting. There is still vehicle ownership for status in cities like Hong Kong and New York where there is excellent public transit and taxis.
In Europe alone, the automotive (building cars) industry accounts for roughly 12 million jobs (including related jobs); in the US, more than 8 million; and in Japan, more than 5 million.
The Bureau of Labor Statistics lists that 884,000 people are employed in motor vehicles and parts manufacturing, and an additional 3.02 million in the dealer and maintenance network. Truck, bus, delivery, and taxi drivers account for nearly 6 million professional driving jobs. Virtually all of these 10 million jobs will be eliminated within 10-15 years. Globally there are 50 to 100 million driving jobs.
- eliminating the needs for car ownership will yield trillions in additional disposable income
- Having more productive commutes - enabling workers to work while commuting could potentially add 12-20% to productivity and GDP
- Supply chains and movement of goods could be vastly more efficient
- traffic jams could be eliminated
- traffic accidents, traffic injuries and deaths could be virtually eliminated
- existing road capacity could be boosted by over 5 times
- Cities could be transformed
- Many elderly people can no longer drive safely. Robotic cars will help them to be more mobile and independent
According to an AARP spokeswoman, by 2030 over 78 million boomers will be 65+, and research shows that men will outlive their driving abilities by six years and women by 10.
The Transport Workers Union is pushing to exempt bus drivers from prosecution for killing pedestrians in crosswalks is an unfortunate example of "union power" ignoring the interest of the public. The TWU bill in the City Council is supported by 16 council members, many of whom are recipients of political contributions from the TWU. Councilman I. Daneek Miller, in an op-ed in the Daily News, cited the traumatization of the bus driver who was recently arrested for hitting a 15-year-old girl in Williamsburg. There was no mention of the trauma of the young victim, who may lose a leg and be maimed by the crash.
Automation of driving has the potential to save over a million lives per year globally from reducing fatal car accidents.
Google has claimed the robotic car could :
We can reduce traffic accidents by 90%.
We can reduce wasted commute time and energy by 90%.
We can reduce the number of cars by 90%.
About 5.5 million motor vehicle accidents occurred in 2009 in the U.S., involving 9.5 million vehicles. These accidents killed 33,808 people and injured more than 2.2 million others, 240,000 of whom had to be hospitalized.
Adding up all costs related to accidents-including medical costs, property damage, loss of productivity, legal costs, travel delays and pain and lost quality of life-the American Automobile Association studied crash data in the 99 largest U.S. urban areas and estimated the total costs to be $299.5 billion.
Traffic congestion wasted 4.8 billion hours and 1.9 billion gallons of fuel a year for urban Americans. That translates to $101 billion in lost productivity and added fuel costs.
Shared Robotic cars could reduce the costs for taxis and transportation by five times
A senior who cannot drive might easily pay $4000 per year for transportation. This is usually on a fixed income. Lowering the costs by five times would be $800 per year for transportation.
Young people (under 16) who cannot drive would have mobility options other than being driven by their parents.
Society will have a lot more net benefits from robotic cars.
Transformation of cities and highways
An IEEE paper assessed the increase in highway capacity.
The increase in highway capacity when using sensors alone is about 43%.
The increase in highway capacity when using both sensors and vehicle to vehicle communication is about 273%.
Current maximum throughput is 2200 vehicles per hour per lane of highway.
Highway capacity increases was also analyzed by the California PATH program. Automation will allow shorter vehicle gaps and narrower spacing from more precise turning.
Platooning cars could get to 400% increase in highway capacity with 25% margin for merging. Longer platoons with smaller gaps enable higher capacity. The most capacity is not always needed and under most circumstances larger gaps and shorter platooning can be used. Platooning also allows the following cars to draft behind the lead vehicle in order to save on fuel.
More traffic density and Larger, More Productive City populations can boost GDP by 30%
Google told the world it has developed computer driving tech that is basically within reach of doubling (or more) the capacity of a road lane to pass cars. Pundits don't seem to realize just how big a deal this is - it could let cities be roughly twice as big, all else equal.
Seminal work by Ciccone and Hall (1996) assessed the impacts of density on productivity in the US, and found that doubling employment density, and keeping all other factors constant, increased average labor productivity by around 6%. Subsequent work by Ciccone (1999) found that in Europe, all other things being equal, doubling employment density increased productivity by 5%. A third paper (Harris and Ioannides, 2000) applies the logic directly to metropolitan areas and also finds a 6% increase in productivity with a doubling of density.
More recent work by Dan Graham (2005b, 2006) examines the relationship between increased effective density (which takes into account time travelled between business units) and increased productivity across different industries. Graham finds that across the whole economy, the urbanisation elasticity (that is, the response of productivity to changes in density) is 0.125. This means that a 10% increase in effective density, holding all other factors constant, is associated with a 1.25% increase in productivity for firms in that area. Doubling the density of an area would result in a 12.5% increase in productivity.
Economist Robin Hanson noted that doubling the population of any city requires only about an 85% increase in infrastructure, whether that be total road surface, length of electrical cables, water pipes or number of petrol stations. This systematic 15% savings happens because, in general, creating and operating the same infrastructure at higher densities is more efficient, more economically viable, and often leads to higher-quality services and solutions that are impossible in smaller places. Interestingly, there are similar savings in carbon footprints - most large, developed cities are 'greener' than their national average in terms of per capita carbon emission.
Traffic Congestion $100 billion cost in the USA
The cost to the average commuter was $713 in 2010 compared to an inflation-adjusted $301 in 1982
Sixty million Americans suffered more than 30 hours of delay in 2010
1.9 billion gallons of fuel were wasted because of traffic congestion
Traffic congestion caused aggregate delays of 4.8 billion hours.
Transport 2012.org puts a 200 billion Euro price tag on congestion in Europe (approximately 2% of GDP). Central America also has its traffic woes. Let's not forget other countries. On the weekend, Panama found that the price of congestion for business and the community was somewhere between $500 million-$2 billion annually. According to the Asian Development Bank, road congestion costs economies 2%-5% of gross domestic product every year due to lost time and higher transport costs.
If cities were densified based upon increased road capacity then it could boost GDP by 30%
Seminal work by Ciccone and Hall (1996) assessed the impacts of density on productivity in the US, and found that doubling employment density, and keeping all other factors constant, increased average labour productivity by around 6%. Subsequent work by Ciccone (1999) found that in Europe, all other things being equal, doubling employment density increased productivity by 5%. A third paper (Harris and Ioannides, 2000) applies the logic directly to metropolitan areas and also finds a 6% increase in productivity with a doubling of density.
More recent work by Dan Graham (2005b, 2006) examines the relationship between increased effective density (which takes into account time travelled between business units) and increased productivity across different industries. Graham finds that across the whole economy, the urbanisation elasticity (that is, the response of productivity to changes in density) is 0.125. This means that a 10% increase in effective density, holding all other factors constant, is associated with a 1.25% increase in productivity for firms in that area. Doubling the density of an area would result in a 12.5% increase in productivity.
Economist Robin Hanson noted that doubling the population of any city requires only about an 85% increase in infrastructure, whether that be total road surface, length of electrical cables, water pipes or number of petrol stations. This systematic 15% savings happens because, in general, creating and operating the same infrastructure at higher densities is more efficient, more economically viable, and often leads to higher-quality services and solutions that are impossible in smaller places. Interestingly, there are similar savings in carbon footprints - most large, developed cities are 'greener' than their national average in terms of per capita carbon emission.
Google told the world it has developed computer driving tech that is basically within reach of doubling (or more) the capacity of a road lane to pass cars. Pundits don't seem to realize just how big a deal this is - it could let cities be roughly twice as big, all else equal.
Road capacity could be boosted by 4 times using robotic cars. This could be another 30% boost to productivity.
