Team Leader :


Air pollution, on the scale to which it exists today, has major adverse environmental and health impacts. Of all the contributing factors to air pollution, carbon emissions are particularly concerning as they constitute approx 80% of all greenhouse gas emissions in the US. Of these emissions, transportation related carbon emissions account for close to 30% of the total. Therefore, targeting the transportation sector is an easy, yet impactful way to address pollution issues.

Inspiration is drawn from the many cities in India which display a countdown timer at their traffic stops. This allow the vehicle driver to switch off their engine while waiting for a green light instead of idling. This simple timer reduced the air pollution at traffic signals by a huge amount, to say nothing of the gas savings, noise, etc.

In terms of carbon emissions, while the industrial sector is, and continues to be, the major contributor, providing solutions to local communities is a strong idea because they are more accessible and reachable. Solutions can be implemented to whatever degree local governance is open to. With varying levels of openness, and with positive results, faster change can be brought about. Lastly, even a small scale implementation brings a big local impact (as seen in the Indian traffic light model.) Smaller scale solutions allow local governance and communities to take the lead in implementing these solutions without the need for top down orders.

The Transportation Problem is a Linear Programming problem and therefore, relatively easy to process. Traditionally, the transportation problem covers all vehicle costs and logistics. We can reframe the constraints to include the idling time and emissions at junctions. We structure the issue as an optimization transport problem with the objective variable here being the emissions on certain routes. It follows that roads/routes and intersections with the highest bottlenecks and traffic jams will see the highest carbon emissions. Different weighting factors based on the existing pollution levels in the locality, proximity to residential areas, schools, hospitals, etc. Additional weighting factors based on the frequency of the types of vehicles.

Reformulating the standard transport problem into one that penalizes idling and, in particular, idling from heavy vehicles could be key to building a sustainable and flexible model that can be used in standard situations. This evolving transport model would use terms related to routes and vehicle types, rather than the standard source-destination.

A physical model would involve placement of carbon dioxide sensors at select junctions. The prototype model would likely be a greater investment of time, rather than money. The sensors mounted on the traffic lights would not largely effect their structural integrity, while being near invisible and at an elevation above commuter traffic. While carbon dioxide sensors are generally low cost, placement of these sensors is key here. There is also an allowance for their positions to be changed as their traffic models evolve and develop. Real time sensing speeds allow the model to be recalculated and updated when needed.
Proposing an additional constraint to the transport problem allows us to take planning to another level.

References:
- Environmental and Health Impacts of Air Pollution: A Review: Manisalidis et all
- https://www.eia.gov/energyexplained/energy-and-the-environment/where-greenhouse-gases-come-from.php

Inspiration

Displaying a countdown timer at traffic junctions is a seemingly minor, yet impactful, way to drastically reduce local carbon emissions resulting from vehicle idling. We use the insights reaped from this practice to influence traffic planning models, in particular, the standard transportation model to make cities and locales cleaner and healthier.

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