The Texxi Mission

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November 12 2007 email describing texxi mission

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The Texxi Mission

  1. 1. Fw: Texxi Mission Eric Masaba to: Michael.Butler, Trevor.Ingle, steve.meck 12/11/2007 05:31 Cc: Matthew Burden Bcc: nanbillj, pbooth, JBlundell, David.Wright, Tim.Abbott, peter.a.santoro, jonathan_havice, paras.sidapara, John.Decourcy-Bower, Lee.Taylor, mike, leslie, sgrant, mikael.jigmo, mka, Christian.Vial, graham.parkhurst Stage 1 - Prove the basic technology. Raise funds with an anchor investor (that would be Bill Johnson, legendary trader) i) Using a 5 yr old Pentium 3, a free SMS service and no small amount of "Macguyverism" in 2 days Texxi Prototype was constructed and tested ii) Used techniques from work at National Grid, study at Imperial College/Ecole Centrale, work iii) Made contact with Hammonds law firm to discuss putting company together and options agreements iv) Started to look for partners for the venture - 1 person above all came to mind, an Engineer/Technologist/Investment Banker fromYorkshire - Matthew Burden who I worked with at UBS FX Exotics Options 12 yrs ago. Achieved Nov 2004 - Mar 2005 Stage 2 - Launch a small scale trial in a target City. i) Liverpool chosen, because a) Eric is from there, b) it has the largest number of cabs per capita for a large city in the world, c) Scousers party too much and can't get cabs home ii) Driver recruitment a particularly nasty experience. Helped dramatically by the recruitment and use of 21yr old female Polish models iii) Design partners and local and national PR found iv) Patents applied for v) Trademarks acheived in UK/Europe Stage 3 - Prepare franchise licences / agreements i) Many enquiries from the world over for Franchises (Mostly US, Canada, Australia) ii) Finding and performing due diligence on potential Franchisee partners iii) Tying up legal loose ends iv) Moving UK companies to offshore entities (now due Q1 2008) Stage 4 - Refine techniques and scale to a larger city / area i) Planned for Nov 2006 - Jan 2007 in Brisbane. Unable to achieve these goals due to recalcitrant Taxi firms. ii) North Carolina ha sshown interest and this Triangle Research Area is perfect. iii) Much more comprehensive infrastructure needed in 4 countries and 3 continents (UK, France, Canada, Australia, Singapore) Stage 5 - Move to Private Jets and other vehicle types i) Partner with an Exchange / Investment Bank to work on DRT instruments ii) Partner with 5-Star Hotels and Resorts to put Booking apps within their properties iii) Partner with Hotel TV tech firms to deploy booking apps iv) Strike deals with private jet firms to help fill their "dead legs" v) Prepare an HLT alliance. ----- Forwarded by Eric Masaba/CraneDragon on 12/11/2007 12:42 ----- Chaps Greetings from China! http://www.guardian.co.uk/travel/2007/nov/01/uk.travelnews Liverpool has 22,000 cabs for 500,000 people. Each driver makes around £22,000 per year.
  2. 2. This shows that the Liverpool cab market is worth almost USD1bn by itself. London is 20 times bigger and has even more "intensity" (due to tourists and business travellers). Take the top 15 cities in the UK and you can see a multi-hundred billion dollar market just in the UK. Look at North America, Western Europe and Australia alone and we are into the USD 1 trillion range. The Mission We want nothing more than to reform the way all vehicle travel is planned and executed in every city in the world. The total market opportunity here is of the order of trillions of EUR per annum. Transport and Logistics are amongst the largest markets on the planet. We can extend those markets by applying capital market innovations to the transport space in a comprehensive and integrated manner. Freight Derivatives makes a way into this somewhat, but not in a sense that it integrates the hospitality industries with logistics and transport. We also seek to combine thought and planning in the Hospitality, Logistics and Transportation (HLT) industries as we see these three industries as being inextricably linked such that they would be more correctly considered as one industry - HLT. People travel to see a city because there are easy and well priced transport links to that city. People then want lodging when they get to that city and will stay longer or return because of the quality of the lodging / entertainment. People need to get about within a city once they are there. The city then has increased trade and needs more deliveries of food and goods. Travel to a city increases because of the amenities and ease of travel within a city and the great hospitality industry within the city. It is clear that all three functions are symbiotically linked. A Demand Responsive Transit Exchange will link all functions seamlessly - leading to increased (and more importantly long term sustainable) commerce for the city / region. The Vision A city can hedge against energy price moves with a DRT Exchange (which is both a Demand Map in the temporal Domain as well as a Demand Map in the spatial Domain). A city can fund road and infrastructure improvements related to the transport system via the DRT Exchange. Citizens can lock in their transport costs for up to a year in advance and if need be can use swap contracts as and when their situation changes (illness, new job etc). Tourists will not be able to be overcharged when visiting a city - this will lead to further tourist revenues. Hospitality Industry TV screens can act as interfaces into the TexxiLand © platform. Telecom Companies can make revenue through the DRT applications running on their networks (TexxiLand© will be an open platform). Vehicle operators can recitfy (smooth out) their cashflows with such an innovation. The finance industry can now make use of a whole new type of financial instrument - DRT Options, DRT Futures, DRT Bonds , DRT Swaps and other DRT Derivatives. Origins While working for Xaraf LLC (Part of Paloma Partners and founded by Lauren Rose formerly of Amaranth Advisers) in Greenwich, Connecticut in 2003 – 2004, I was introduced to Capital Structure Arbitrage, Convertible Bonds and Credit Default Swaps. What I noticed was that there were obviously times when a company defaulting in one sector had a noticeable impact on others in its sector or on others it did enough business with. I also realised that shock events like environmental catastrophes and interest rate shocks could change the basis of a company’s short term and long term viability almost immediately. The latency of the information in the effects on the credit rating (and hence the price of Credit Default Swaps) of other entities “downstream” of the company was in some cases, enormous – of the region of 3 months or even longer. This was not dissimilar to 2 neighbours who don’t realise they work in the same office block and
  3. 3. commute to work at around the same time each day – in separate cars. Thus as I was preparing the mathematical foundation for this model in November 2003 (derived independently from work done by Zhang – see citations), I realised it most resembled a Demand Responsive Transit Broker who aggregates trips for customers in real time. At this time, I was also paying $40 per day for transit from North White Plains station to the Paloma offices, despite several other people I knew working in the same vicinity and going to the same destination at the same time. I realised there was an arbitrage opportunity here (by increasing vehicle load factors) and as this realisation grew, it crystallised my thoughts on the design of a system to make use of unheeded correlative effects. The Database of Transit Intentions Fundamentally if one could collect all the intentions of travellers in a city as well as a collection of all the capabilities of the suppliers of transit (taxi drivers, train operators, bus and coach companies, private jet and charter aircraft operators), we could effect an efficient exchange activity in much the same way as on any other capital market with exchange traded activities eases commerce by matching buyers and sellers. This activity then yields dynamic "Demand Maps" in both the temporal and spatial domains. From these yield management techniques (as beloved of the hotel and airline industries) a toolbox of solutions can be brought to bear on the problems. (Hotels, Airlines and Credit Card companies already collaborate with their Airmiles rewards programs - the TexxiLand© platform takes this a couple of stages further) Texxi Feeder Material Byzantine Generals Problem1. NP-Hard Problems (Bin Packing problem, Travelling Salesman, Generic Vehicle Routing with2. Time Variant Windows) This class of problems allows one to calculate the shortest path between a set of points, visiting each one once. They also allow us to find the minimum number of vessels with certain capacities in which to store a finite number of items most efficiently (think music files to fit on a 1GB pen drive - we may have 200 files, amounting to 3GB, what combination of files packs the largest number of files into 1GB) Genetic Algorithms3. A method for solving NP-hard problems. Borrows heavily from biology and can give us a 99% solution within a tiny fraction of the time brute force methods take. Structrual Dynamics and relevant matrix methods4. Once we have modelled connections in individuals' transit behaviour we can represent these in the same way as structural engineers map connectivity in building structures to calculate how a particular structure will behave under stress conditions. There is a massive toolbox of tricks available to applied mathematicians related to matrices. Groupware Databases and related relational database concepts5. This is how we will collect the "intention" material - via a set of distributed databases. This work is very similar to building trading systems for investment banks / hedge funds. Complex Analysis (Joukowksi Transformations, Laplace Transforms)6. We can map one problem set which seems to have no solutions to another which has well understood solutions. The Kutta-Joukowski transformation is a good specific example of Complex Analysis applied to wing design and inviscid (non-viscous) flows. Basic Set Theory (Routes and Routing, Connectedness, Venn Diagrams, Euler Diagrams,7. Karnaugh Maps). This is the core of the work. Applied with Matrix methods, our Demand Maps become powerful tools indeed. This can also be applied to Credit Contagion Mapping to mitigate
  4. 4. credit cascade risk. Transitive Trust Relationships (Domains, SSL Certificates, Certificate Revocation Lists, Trust8. applied to the Reputational Credit System) Any credit system is based on trust networks. The internet system has been dealing with these problems since its inception. So have computer file access systems. Many of these innovations (from DNS, MX, IPv6, Kerberos, Active Directory, VAX/VMS privileges, UNIX Kernel) can be brought to bear on the Credit Ratings / TexxiGroups Trust systems. Theory of Markets (Exchanges, Brokers, Liquidity, Margin Calls, Options, Futures and Other9. Derivatives) Essential background for the corollaries on the DRT Exchange. Capital Structure Arbitrage (Convertible Bonds, Guarantees, Default Contagion, Reduced10. Form Models, Copulas) The womb from which Texxi was finally born. I needed to see Cap Struct Arb in action (Equity, Bonds, Credit Default Swaps) before I saw the connection to transit.

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