Car traffic is one of the main sources of pollution in cities. In addition, the infrastructure required to absorb all these cars takes up masses of space that could be used for parks, bikes, or simply wider sidewalks. Taxis and rideshare vehicles in particular spend most of their time driving empty. Knowing that this service is essential in cities, what if we could rethink the way this service is provided to make more livable cities?
In this talk, I will show how traditional Optimization techniques can be combined with Spatial Data Science to build a powerful algorithm that reduces taxi empty driving hours while maintaining a good level of service in terms of response time. In order to build this algorithm, we start from a basic greedy algorithm with limited use of spatial information. The complexity of the algorithm is gradually increased by introducing linear optimization and the use of third-party footfall data to match supply and demand. Lastly, I will suggest further tips to improve results, as well as client and driver experience.
Intermediate and final results are visualized in vector maps, allowing data scientists and users to easily understand supply and demand patterns, and identify improvements on the designed algorithm.
Miguel is currently working as a Data Scientist at CARTO. His work focus has been on solving problems with a strong spatial component by enriching data, and applying spatial analysis and spatial statistics, combined with Machine Learning techniques. Before CARTO, he worked as an Optimization Expert and Data Scientist solving complex business problems in a wide variety of sectors from transportation & logistics, through production planning, to workforce planning.
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