MATHEMATICS SHOWS JUST HOW MUCH RIDE-SHARING CAN SOLVE COMMUTING PROBLEMS

Ride-sharing has been around for a while now, and in the past, there has been a lot of research and studies dedicated to seeing how it can effectively reduce the number of vehicles on the road. There has been a positive turn to that kind of research, and we know that ride-sharing has the possibility of solving our commuting problems while reducing traffic on roads also. Either you choose to go via either of Uber or Lyft – who happen to own the biggest fleets of ride-sharing concepts – or you opt for the traditional taxi, ridesharing is the vehicle way of the future.

Even though we have been served with the fact that ride sharing would effectively cut back on the number of vehicles on the road, and reduce the problem of traffic in a very good way too, this is the first time the science of numbers has backed this fact up. According to a mathematical model proposed and used by some researchers, just how much the innovation would cut back on road vehicles can be seen. The research was published by a group of scientists from the Cornell University and Massachusetts Institute of Technology (MIT). According to the published report, demand as regards transportation could be fulfilled when you took a ride via ride-sharing, and we would just need to retain about 15% of the total taxi fleet in the New York City.

In what is certainly an impressive figure, this is a call on those in that taxi business to find ways to stay relevant in the business. Even though there might never be a time when we would be able to get rid of cabbies completely and be able to live with ourselves afterward, it is no doubt that ride-sharing is slowly but steadily reducing their impact on the transportation industry. Speaking of the mathematical approach to getting the aforementioned numbers, the authors of the research wrote “Ride-sharing services are transforming urban mobility by providing timely and convenient transportation to anybody, anywhere, and anytime. These services present enormous potential for positive societal impacts with respect to pollution, energy, consumption, congestion, etc. Current mathematical models, however, do not fully address the potential of ride-sharing.” (n.d.). Retrieved from https://www.yahoo.com/news/mathematical-study-shows-exactly-ride-172543889.html.

Using a mathematical model to predict just how well ride-sharing works has been done in the past, and the researchers of this project also acknowledge that they are not the pioneers of this line of reasoning. However, from the last line of the statement published above, the researchers have identified that the previous models did not fully address the potentials of sharing rides and carpooling. For example, the previous models were based on vehicles that are shared between only two passengers, when in fact, the same car can be shared by a whole lot of other people. The mathematical model used also took some other things into consideration. These include, but are not limited to, the capacity of the vehicle, waiting time, operational costs and travel delays. For the information pool and sample space, three million points of ride data from New York City was used.

Looking at the other figures from the study, it was determined that as much as ninety-eight percent of demand on rides could be solved by 2000 vehicles which all have the capacity to vary ten people each. With this particular type of approximation, the waiting time has been set at 2.8 minutes, and for ride delay, the mean value was taken at 3.5 minutes. Taking the number of vehicles into comparison into the current fleet of taxis in New York City, 2000 vehicles stands for about 15% of the entire fleet. To make the results even more applicable, they used the model to test cars with other capacities too.

For a vehicle that can lift up to four people at once, there would be the need for about 3000 vehicles to satisfy the current ride demands. In conclusion of their work, the researchers have concluded that this kind of data is better applicable to autonomous vehicles. This is particularly pleasing to the ears. The autonomous rides are not here yet, but when they do make their debut, there is a mathematical model on the ground that they can follow.