1.9 to 2.0? In this calculation, it shaves one billion from annual costs and four cents from each km. A variety of network optimizations could

be made for route, schedule and load dis- tribution across all vehicles (subject to user demand and including the distorting effects of personal preferences). Add creative ser- vice packages and behavioural nudges to increase and distribute demand and costs can be decreased to lower the effective per km cost far below 81 cents. Total employment for these first 20 bil-

lion kilometres in this 2030 scenario would not drop even though these early-adopter trips are largely replacing labour-intensive taxi and bus kilometers, becasue the ratio of human-support to robo-vehicle will be high in the beginning. For the second 20 bil- lion kilometres employment per PKT might decrease, but absolute employment would increase, since by then the declining effort of the household owner/driver is increasingly replaced by service-staff effort from the fleet operator. No matter how advanced the tech- nology, these fleets will require human staff far into the future. Certainly, by the time 75 per cent robo-vehicle penetration would be achieved in public service vehicles, aggre- gate job rates for public transportation (including taxi) will be equal to or higher than current average employment rates even as the staff-to-vehicle ratio declines.

THE PERFORMANCE OPPORTUNITY OF MASSIVE FLEETS Rather than create a new form of medal- lion system for the coming SAV fleets, local and regional governments have a remark- able opportunity to innovate a replacement for this system: performance-based fleet licenses auctioned to bidders who bid for kil- ometers of road access paralleling the way in which governments auction radio spectrum. Operators of automated fleets would bid

a per-kilometer fee (essentially a road-use fee) for access to existing roads in tranches

of 100 million kilometers. Associated with the fee set by the winning bidders would be a number of rules: Competition. No single bidder can bid for

more than 20 per cent of the available kilom- eters on auction. This preserves competition. Complexity. No bidder can bid for less

that 10 per cent of the available kilometers. This limits user confusion, unreliable bidders and undue integration complexity. High occupancy. Winning bidders pre-

agree to an average occupancy ratio (set by the government). This promotes innovation for ridesharing and rewards the bidder with lower vehicle counts. Social equity. Winners would pre-agree

to a social-equity formula. This would be an agreement to service a given fraction of low-fare customers (this might be subsi- dized) and a given fraction of less-desirable service areas. Since winners would likely be for-profit operators, they will be incented to offer higher-end services to offset their social equity commitment. They will optimize fleet turnover so as to cascade older, lower-status vehicles into lower-fare service potentially to realize targeted subsidies. Connect with transit. Winners would

pre-agree to a given fraction of connections with existing transit stations or hubs. Rewards. Winners who exceed occu-

pancy, social equity or connection targets are rewarded on ensuing bid competitions. Those failing to do so are penalized on fol- lowing bids. Attract car owners. In order to com-

pete, winners would be inclined to expand their user-base beyond existing users of taxi, transit and carsharing. They would seek to offer services that would attract car users to consider owning fewer personal vehicles. (This is a measureable effect, not a performance rule.)

WITHOUT REGION-WIDE FLEET GOVERNANCE Fleet optimization on the scale of billions

“Left to compete in the 20th century world of proprietary information stovepipes, companies and cities would continue to operate an uncoordinated world of multiple taxi, bus and carshare fleets”

of kilometers is unavailable when city fleets comprise a few thousand buses, taxis and carshare vehicles — all competing. But such optimizations are second nature to the thinking of private companies that under- stand logistics and artificial intelligence, exploit big data and telecommunications, have marketing expertise and enjoy skilled access to social platforms. There is nothing surprising in this to people

such as Uber’s Travis Kalanick, Ford’s Bill Ford, or Morgan Stanley’s Adam Jonas. In fact, they are counting on this. Digitization and auto- mation always advantages its exploiters. Transportation is no different. What has been happening to newspapers, music, retail and taxis will now happen to our municipal and personal transportation systems, threaten- ing both Big Auto and public transit. Left to compete in the 20th century world

of proprietary information stovepipes, com- panies and cities would continue to operate an uncoordinated world of multiple taxi, bus and carshare fleets targeted at different demographics or regions. When you called for a taxi in the pre-Uber world there was almost always a competitor’s taxi closer to you, but there was no way for you to know that. Uber bridged that information barrier to a limited degree. MaaS is poised to finish the job by choosing the best option from all user- acceptable suppliers at the time of demand. The current world of every driver for him-

self — the core of today’s surface transporta- tion reality — implies urban transportation systems of incomprehensible non-optimal- ity mixed with struggling transit systems. This is the world’s largest market mostly wasted in execution. According to Jonas, “… a century-old ecosystem being ogled by outside players hungry for a slice of a US$10- trillion mobility market. Many want in. It’s just beginning. And it won’t stop.” For these reasons a new governance sys-

tem for public conveyances is needed as this privatized and optimized technology pushes our existing mobility systems aside.

Bern Grush and John Niles are the founders of Grush Niles Strategic


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