MATCHING AS SOLUTION TO TRAFFIC REDUCER RIDE-SHARING
‘Smart cities’ conference that was on November 7th 2017 was an eye opener, at least for me. One of the speaker, Samitha Samaranayake, argued that ride-sharing is not always a good thing. Without appropriate systems, instead of reducing traffic, introduction of ride-sharing to cities can actually increase traffic as it actually makes ‘private’ vehicle more accessible to the community. People who used to ride trains or public transportation because they could not afford vehicles such as cars and motorcycles, begin to utilize ride-sharing application such as Uber as their own ‘private’ transportation mode, and clearly increasing the traffic. This argument was then simulated by Samaranayake in this video: http://movie-usa.glencoesoftware.com/video/10.1073/pnas.1611675114/video-1 , which simulates the traffic condition in New York City.
Samaranayake and his colleague then introduced a new mathematical model, using matching method, to match vehicles, passengers, each passenger’s location, and each passenger’s destination to find the optimum matches among those variables such that one vehicle could match with more than one passenger, depending on the passenger’s origin point and destination, and generated the least cost. In this case, the cost is the sum of waiting time for all assigned passengers and the number of unassigned request. The scheme of the model can be illustrated from the picture below.
Picture 1 shows an example of matching between passengers and vehicles. There are four requests, denoted by orange-human, which locations show each origin point; red triangles denote destination, and yellow cars denote vehicles available. Part (B) shows the potential Request-Vehicle (RV) pairs; and part (C) shows candidate trips and vehicles which can execute them. The yellow triangle is added for requests that cannot be satisfied. Notice that here, the possibility of multi passengers are added to the scheme. Part (D) then gives the optimal assignment.
At the end, Samaranayake showed that this method is able to increase the service rate significantly, reduce the waiting time, and reduce the distance traveled by each vehicle. Using this method, ride-pooling service can provide substantial improvement in urban transportation system, instead of adding more traffic problem to cities.
Reference:
Alonso-Mora, J., Samaranayake, S., Wallar, A., Frazzoli, E., & Rus, D. (2017). On-demand high-capacity ride-sharing via dynamic trip-vehicle assignment. Proceedings of the National Academy of Sciences, 201611675.