The Transit Exchange was designed to firstly make the best use of the carrying capacity of vehicles on a roadway and then make the best use of the carrying capacity of the roadway itself.
An added feature was social network matching to enable people to find suitable travel partners to share rides with (either because they had either or both similar itineraries (start and end points and times) and a similar social profile (which today can be some test for non-contagiousness).
Where Uber/Didi/Lyft and all the others went wrong was a failure to realise that by only focusing on getting people into vehicles, without changing the way road access was calculated, congestion would increase. The data received from many cities and regions over the 10 years Uber has been pretending to be an innovator has showed how wrong this approach was and revealed just what they had not invented.
The main thrust of Texxi – which was designed for predicting “credit contagion”, was to make the trading instruments (forwards, futures and options) valuable so value could be stored – much as credit default swaps have a value and are indeed part of many financial portfolios. Every citizen with a right to access to the roadspace could either use it or trade it.
Thus we can store value as a savings resource in the Transit Exchange and since roads are always used (and insurance contracts can be entered into for disruptions), the saver can rest assured that he/she is protected.
In order to create a whole new type of road pricing system that takes into account both the needs of reforming road transport revenue collection while adhering to privacy, we can make use of several features that Texxi pioneered and talked about as far back as 2005.
By using geolocation of social networks, both suitable travel partners and correct pricing can be calculated while maintaining the privacy of the subscribers. This is where cryptographic methods and blockchain (which is about 40 years old in its current form) comes in. The use of technology for collecting road tolls while not betraying user location is now well advanced, but even as far back as 2003, work had been completed on privacy homormorphisms.
WHAT PRIVACY HOMOMORPHISMS ARE
As strange as it sounds, there is a way to do a sum like adding A + B to get C without knowing what A, B or C are; just the fact that the sum is correct. This is what a homomorphism is.
There is a way to know that a message I send to you is correct and unaltered without knowing the message content or its length. It is called a oneway hash and is the basis of both asymmetric and symmetric cryptography.
If I am a military commander and I want to make sure my subordinates know that my orders are authentic, I have to be able to send you a message in the open (which my enemies can see), but I can SIGN that message in a way that makes the receiver sure that the message content is both unaltered (non repudiated) from when I wrote the message and authentic (it really was me).
A one way cryptographic hash of a message allows the receiver to know that the message received is authentic and unaltered. The best way to guarantee this is to send the one way has separately from the original message, but then this defeats the object of communicating without a trusted courier. Enter asymmetric encryption which allowed sender and receiver to communicate in the open, exchange keys to lock messages in the open, send their messages in the open and be able to be sure that their messages were remained secure (confidential) and unaltered (non-repudiated) and really from who it was supposed to be from (authentic). These three conditions laid the foundations for blockchain.
Click the orange play button below to listen to episode 2 of the Mobility Paradox Podcast in which Eric Masaba discusses how the above scenarios are not quite the product of “blue sky” thinking as they might at first appear.
To contact Eric directly: