Always Green Traffic Control

Nick Musachio, local inventor in Minnesota, has just been issued a patent (No. 8,711,005) for his Always Green Traffic Control System. (Since this is transportation, we will abbreviate this AGTCS)

Imagine you have an isolated signalized intersection, operating near but below capacity. If vehicles were able to travel at the correct speed when approaching the intersection for a significant distance, they should be able to  travel through the intersection without hitting a red light or being delayed by standing queues. If at 45 MPH they would hit a red light, but at 35 MPH would get a green, they should be informed to reduce speed to 35 MPH. This not only reduces driver delay, but should decrease crashes and decrease emissions, both of which are exacerbated by intersection control and braking and acceleration.

How would drivers know which speed to travel? An upstream Variable Message Sign with Dynamic Speed Limits (tied into the traffic signal controller cabinet, or with the pre-programmed traffic signal timings) would tell them the best speed to avoid stopping. If only the first car in a platoon does this (on a 1 lane road), all following cars are controlled by default.

Audi has a similar in-vehicle system. That is only useful if the traffic agencies produce live feeds of traffic signal timings. Comment: it is appalling that such a traffic signal timing live feed doesn’t generally exist (even transit agencies, not historically known for their cutting edge research) have GTFS.

AGTCS is infrastructure based, and works for all vehicles anywhere an agency wants to set it up.

Some videos and simulations below.

Transit Exchange

Usually, when someone spams your comments, you delete it. But I got an interesting spam comment the other day which is a potentially good idea. There is a company called Texxi which is arguing there is a market in which people post where they are going to enable dynamic ride-matching/taxi services, which they call a Transit Exchange.

The message in full

The answer, David, is a Demand Responsive Transit Exchange which borrows heavily from the I.T. network transit exchange (that, incidentally, also allows for largescale peer-peer bandwidth sharing and trading, killing centralised monopolies and NSA snoopers in one go –, finance, aeronautics and biology.

RoadSpaceTime is virtualised into a tradable commodity and made available to people via mobile devices (XML, SMPP, SMS, Smartphone App, Web Page or Email) to connect to a futures exchange and “database of transit intentions” which store itineraries and price searches.

A credit contagion solver working through the social network graph applies genetic algorithms to suggest “solutions” to customers in realtime. In 2013, this is now laughably trivial, but even 5 years ago, the cost of the computing power would have required millions in VC money.

Take a look at:

“The Market Opportunity for Dynamic Ridesharing” |
Or download (26Mb pdf) The Market Opportunity –

“Transport – A New Beginning” |
Or download (21.70Mb pdf) –

“The underpinnings of the Transit Exchange” |
Or download (19.53Mb pdf) –

Certainly they are not the only ones in this space, and the idea has been around for a quite long time (Mel Webber noted the idea of Dynamic Ridesharing decades ago) but they filed for a patent as early as 2006. They propose to be the market maker here, rather than relying on peer-to-peer matching. They thus would guarantee the seller and the buyer that they will be fulfilled at a given price (and get a vig).

They say:

By combining Futures Exchanges + Mobile + Social + Big Data + Genetic Algorithms any vehicle with spare capacity becomes your private vehicle for just when you need it.

Whatever your needs, Texxi can make any participating local vehicle fleet able to offer an easy and tailored transport solution for you
Patent Pending (July 2006)

They also have a really weird video

The website is worth visiting if you are interested in possible future scenarios for The New Mobility.

The Week: Big Data Demands Define Pricing

Today on The Week: Big Data Demands Define Pricing

The Week: Crowd-Sourcing As a Transportation Tool

The Week: Crowd-Sourcing As a Transportation Tool

where Stephen Goldsmith talks about smart apps.

The traveler costs of unplanned transport network disruptions: An activity-based modeling approach

Recent working paper:

In this paper we introduce an activity-based modeling approach for evaluating the traveler costs of transport network disruptions. The model handles several important aspects of such events: increases in travel time may be very long in relation to the normal day-to-day fluctuations; the impact of delay may depend on the flexibility to reschedule activities; lack of information and uncertainty about travel conditions may lead to under- or over-adjustment of the daily schedule in response to the delay; delays on more than one trip may restrict the gain from rescheduling activities. We derive properties such as the value of time and schedule costs analytically. Numerical calculations show that the average cost per hour delay increases with the delay duration, so that every additional minute of delay comes with a higher cost. The cost varies depending on adjustment behavior (less adjustment, loosely speaking, giving higher cost) and scheduling flexibility (greater flexibility giving lower cost). The results indicate that existing evaluations of real network disruptions have underestimated the societal costs of the events.