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How Big Data Can Bring Big Changes To Our Commutes With Small Suggestions [Forbes]

October 24, 2019

Traffic congestion is one of the most pernicious factors in our experience as human beings in urban environments. It is arguably the face of what ails us in our private automobile-centric societies. It is also the phenomenon that compels us to find palatable ways to solve the broad transportation crisis that we are seeing in our cities.

We’ve seen a number of trial balloons floated for solutions that have questionable utility. Tolling during rush hour to reduce congestion is effectively a payroll tax, as most people who drive during peak periods are going to or from work. City core charges in London resulted in a sharp drop in private automobiles in city center, but the reduction in congestion didn’t take hold—private cars were replaced by ride hailing vehicles and delivery vans. An arguably much more thoughtful and effective approach didn’t involve fees or tolls at all: local school boards in Connecticut staggered bell times for elementary school, middle school and high school so that congestion dropped and volume on buses was more evened out.

It’s also accepted that a major cause of congestion and traffic jams is poor driving. Adjusting driver behaviour can have a profound effect on local traffic. William Beaty, the inquisitive and perceptive mind behind has been studying the problems of traffic for years. While not all experts agree with Mr. Beaty’s conclusions, some researchers have found his methods are sound in some circumstances, labelling it “jam-absorption driving”. This is a nod to how a single driver in the right scenario can make decisions that, while uninituitive, can dramatically improve the flow of traffic in the immediate area.

Cambridge Mobile Telematics (CMT) is in this business, and has been examining the problem of distracted driving and poor driving habits since 2010. Hari Balakrishnan, co-founder and CTO of the organization, highlighted the company’s raison d’être: “We start from the mission of making the world’s roads and drivers safer. [We do that by] coming up with good measurements for driving quality. It’s really important that whatever we do to measure how well somebody is driving is correlated with how a certain type of driving causes crashes to happen. […] [We also want to] engage users, so that we can provide incentives for people to become better drivers.”

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