Smart Transport : Low Barrier Brownfield Projects For Smart Cities

One of 31 factors in the European smart cities model is “Sustainable, innovative and safe transport systems” with 3 indicators. It is definitive that to sustain the economy of any city, transport becomes a critical backbone. And it is surprising that projects are taken to build Greenfield smart cities, that to work on existing megacities improving their efficiency, since it is easy to build from scratch that to retrofit a city. But urban planners, miss the point of breakeven with infrastructure, they don’t consider it critical, as if citizen money is their property to plan in any way required. They also have to consider that if the existing cities are made efficient and attractive to businesses, then there will be orders of magnitude more growth than in a city built from scratch, just due to the availability of profitable consumer base. To start I would provide the concept of a Brownfield project, that will start giving returns immediately in smart transport. let check our expectations on Transport. basically as a commuter, I should be able to reach from my source to destination in the same time and same cost 24X7, and should potentially be shortest for that distance. This is closely represented by point to point airline networks across the globe. If you feel that such road planning is hard, please use Physarum ( algae ), modelling for natural optimization of roads and mass transit networks which can be used to model with the constraints of existing cities while unique constraints of the planned new city. The next factor is sustainability. The basic policy requirement, that any private automobile should be zero emission should suffice for the city, as a hub for clean technology like fuel cell, or high density batteries for electric transport. so we essentially did a small policy change for reducing the co2 footprint of the city. When also highlighted the need of road and mass transit optimization. Good to ask but hard to solve. How can we do it? There are few steps that can help do it while adding intelligence to the transport system for traffic control and predictive analytics for further planning. Cities are about people. They first question we should ask is what are the real commute demographics compared to one we think we know. The best attempt made so far is by MIT human dynamics lab by mining Mobile Call Log with the respective locations that the mobile were in different part of day in weeks. They efficiently found true communities as represented in the book social physics. This is the first step in planning transport, since communities have specific transport needs. E.g. By profession. How do we then mine their needs, by time spent at a location over a period of 6 months. These two factors needs continuous monitoring to update the public hotspots in the  city, while drilling down for community hotspots. This can be used for creating a hub and spoke road/massive transport model, with spokes under 10 min walkable distances. The road and massive transit will be linked tightly together, then the choice for a massive transit hub vs a road hub is basically a function of headcount commuting between points.

 

So let recap the discussion above with few more insights.

 

1.      Change policy for only allowing zero emission private vehicles in the city, and plan for Zero emission public vehicles.

2.      Mine Call log for Real city demographics.

3.      Identify public and community hotspots.

4.      Use the identified public and community hotspots, and filter by forecasted commuting density over next 10 years.

5.      Use physarum to model both mass transit and road networks, one unconstrained and then by iteratively enforcing them by the effort to overcome it in ascending order, since we know, no constraints is optimal for building these infra, but adding constraints we want to reach a point, where the total cost of development with constraints is significantly exceed greenfield development.

 

Then we think about traffic control, which needs to be equally efficient, and enable safety. With sensors getting smaller every day, a smart number plate is what I think about for every vehicle in the transport ecosystem, what do we need in there, few from my wish list :

 

1.       GPS with Glonass.

2.       Dual Camera at the length ends of the plates for 3D vision.

3.       Quad Core Digital signal processor for different tasks.

4.       Accelerometer.

5.       Gyroscope.

6.       Magnetometer.

7.       3G Connection for data transfer.

8.       Weight sensor.

9.       Auto Emotive for Driver.

 

How does this help.

 

1.       GPS with Glonass will give :

a.       Precise position of the vehicles, directing the change in traffic signs.

b.      Precise speed information for Law enforcement.

2.       Positioning with 3D vision will allow Law enforcement to be semi-automated for certain tasks like :

a.       Pollution ( if you choose not to enforce zero emission vehicles )

b.      Speeding.

c.       Signal Violation.

d.      Using the trajectory to infer offender in accidents.

e.      To plan for Incident response in critical traffic blocks, and penalize drivers, e.g. if there is a fight between drivers blocking a major road in peak hours.

3.       The weight sensor is used to cut ignition if the vehicle is taking more than recommended load, similar to stopping lifts with alarms.

 

The Sensor information will augment the Demographic information, for improved planning. And the accelerometer, gyroscope, magnetometer and auto emotive with the Positioning data can used to create accurate pay per mile insurance models, with insurance proportional to the driving quality. Do let me know your thoughts.

 

Disclaimer : All rights reserved for the ideas. Anybody trying to copy these should credit the blog post and discuss the impact with me.