PERSONALIZED TFL (Transport for London)

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Our solution is shaped by the following world-views:

  1. Personal mobility is crucial and will continue to be for the foreseeable future
  2. Personal mobility options need to evolve beyond simple ‘car ownership’ models
  3. Public transit solutions are required to supplement personal mobility options and reduce the gridlock; but for it to be truly effective, it needs to integrate more closely with the above mentioned personal mobility options
  4. To scale for the future, all of the mobility options, transit services and the infrastructure supporting them must be able to leverage real-time utilization data coupled with insights gleaned from predictive modeling to dynamically provision themselves to deliver the most efficient form of transportation flow depending on context.
  5. We need better end-user tools and applications as consumers to optimize the dynamic mix above to arrive at the optimum way of satisfying each transportation need

The 2 fundamental problems that we seek to address in this app are as follows:

  1. All routing/directions apps today provide a single-dimensional view of transportation. You can choose driving directions OR public transit options OR walking directions. None of these by themselves are truly scalable. What we need is an app that provides consumers with a blend of travel options (personal car, taxis, ride-sharing, mass transit, car and bike pools, walking) optimized for various conditions like user preferences, traffic flow, time to travel, cost of travel and overall comfort while handling all of the intermediate routing and logistics accordingly.

  2. A ‘one-size-fits-all’ approach to deal with the problems of urban gridlock. Most carrots and sticks strategies are uniformly applied across the entire affected population without taking into account their actual contribution to the congestion and gridlock. What would be a better option is to incentivize travelers based on their unique usage patterns (like observing their driving patterns using platforms like OpenXC) to emphasize and incentivize good behavior while assigning a specific, transparent and measurable cost to less than desirable behavior.

In short, we are taking a fresh look at the familiar ‘directions’ app. Users will continue to be able to intuitively request routing from A to B as they do now, except that thanks to our back-end data sources and algorithms, they will get a more optimized and more sustainable solution to their daily commute that seamlessly stitches together all of the details to reduce the ‘impedance mismatch’ at each junction point. Along the way, their transportation ‘usage’ patterns are monitored to present them with a ‘score’ and we recommend that sufficiently high scores are rewarded with tangible benefits by the city to drive behavior modification.

As mentioned above, we believe that for a TRULY scalable solution, cities need to apply data to create ‘smart’ infrastructure like roadways that dynamically switch directions between inbound and outbound depending on time of day and traffic lights that stay coordinated and make their switching patterns available through an open-data mechanism so that navigation systems can advise drivers to maintain a steady speed to optimize a series of greens thus minimizing the waste associated with stop and start patterns. Our app would be scalable to such advances in the infrastructure and would take advantage of these data-points in its routing guidance.

To summarize, our app provides the following functionality:

A ‘mixed-model’ routing guidance system that takes all transit options into consideration for optimum routing with ‘intelligent junction management’ as under: a) A ‘parking finder and reservation’ system to find parking at junction points where you switch from your car to an alternate mode of transport. It will aggregate available spots from all parking options like traditional Multi-storeys, open lots and newer modalities like ‘ParkatmyHouse.com’. b) A ‘venue map’ of the parking structure that shows your spot and how to get there. c) An ‘augmented reality’ display of the next connection in your route along with timing and stop information

and

A ‘transport score’ that is generated from a combination of factors including driving patterns (using a mechanism like OpenXC), commute patterns through the day (peak vs. off-peak), combination of travel-modalities etc. A holistic score such as this can help cities approach decongestion schemes more granularly by rewarding people who exhibit desirable tendencies and identifying segments of the population where it needs to focus its efforts better.

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