Carbin: On the way to smoother and greener roads
Carbin: On the way to smoother and greener roads
Written by Maroun Wakim
An aspiring initiative led by several research groups from different universities, such as MIT, Harvard, BZU, UMassD, and AUB, aims to add an extra layer to road maps. This initiative has developed to become Carbin, a mobile application that measures road quality. Carbin was first developed by MIT’s CSHub under the direction of Professor Franz-Josef Ulm and his Ph.D. student, Mr. Jacob Roxon. The benefits from road quality assessment are plentiful. Carbin helps drivers in choosing the best route for a safe, comfortable, and fuel-efficient ride, while highlighting to authorities which roads must be repaired.
Carbin goes through multiple steps to identify roads that minimize fuel consumption and maximize road comfort and sustainability. Compared to current expensive and bulky road quality monitoring approaches, the app uses the existing built-in accelerometer in the drivers’ phones to estimate the International Roughness Index (IRI) of the road. From this data, the researchers were able to determine the excess fuel consumed caused by driving on relatively rough terrain compared to a smooth well-maintained one. Consequently, they estimated the amount of additional CO2 emissions via the estimated data of unnecessary fuel consumption, in an attempt to highlight the effect of road maintenance on the environment. Similarly, by gathering global crowdsourced data from these streets, the app provides users with a color-coded map to help them choose the commute with the most fuel-efficient and comforting roads.
The roads are given a color based on their estimated roughness: green denotes a high quality road in terms of smoothness, yellow indicates an average quality, while orange represents a fair quality, and red marks a poor one. However, an unpleasant ride could be the result of an unmaintained road or a defect in the car. Therefore, one might ask: how could the app differentiate between the two? According to Yves Georgy Daoud, one of the project’s contributors at AUB, they were able to arrange the data in order to determine additional information about the car itself via optimization techniques and random vibration theory. Such information includes the car’s mass, damping ratios, spring stiffnesses, tire suspensions and much more.
This proposes a visionary approach that opens the door to implementing preventive maintenance features, which would warn drivers of any potentially damaged part in the car before it leads to detrimental crashes and component breakdowns. AUB has been working on Carbin for over a year, under Dr. Naseem Daher’s supervision, with the university’s work centering on the vehicle dynamics part of the project. Last year, Joy Chamoun worked on improving the fidelity of the vehicle’s dynamic representation and proposed an advanced model that gave more accurate results. In fall 2019, Yves worked on increasing the accuracy and decreasing the computational time needed per iteration of the algorithm by building on the work of Carbin and adding to it a system identification method relying on stochastic subspace identification.
Although there’s still a long way to go in order to fulfil its visionary goal, Carbin’s premise in using data from individual mobile phones to assess roads globally signifies an innovative feat that is already proving to be a successful venture.
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