Abstract
Ever increasing traffic and consequential congestion wastes fuel and is a significant contributor to Green House Gas (GHG) emissions. Contributors here include ride-sharing services such as Uber, Lyft, and Didi, with their drivers not only transporting passengers, but also spending a considerable time in traffic searching for new ones. To mitigate their impact, this work proposes a novel algorithm to improve the efficiency the drivers' search for passengers. Our algorithm directs unassigned drivers to locations where new passengers are expected to emerge. We use a non-negative matrix factorization approach to model the time and location of passengers given historical training data. A probabilistic search strategy then guides drivers to nearby locations for which we predict new passengers. To ensure that drivers do not over subscribe to such areas, we randomize destinations and provide each driver with a home location destination when unassigned. An experimental evaluation using real-world data from Manhattan shows that our approach actually reduces the search time of drivers and the wait time of passengers compared to baseline solutions.
Please check out the video that I presented at the 7th IEEE International Conference on Data Science and Advanced Analytics (DSAA 2020)!
Please check out the video that I presented at the 7th IEEE International Conference on Data Science and Advanced Analytics (DSAA 2020)!
Source code: https://github.com/joonseok-kim/CompetitiveSearch/
Additional materials: https://sites.google.com/view/dsaa-2020
J.-S. Kim, D. Pfoser, and A. Züfle, “Vehicle Relocation for Ride-Hailing,” In Proceedings of 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), 2020, doi: 10.1109/DSAA49011.2020.00074