Inferring Unmet Demand from Taxi Probe Data

Matching taxi supply with demand is one of the biggest challenges faced by taxi fleet operators today. One of the reasons why this problem is so hard to solve is because there are no readily available methods to infer unmet taxi demand from data. An algorithm that reliably does so would be of enormous value to fleet operators because it could be used to dispatch available taxis to areas where passenger demand greatly exceeds supply. In this paper, we formally define unmet taxi demand and develop a heuristic algorithm to quantify it. We explain how our method improves on traditional approaches and present the theoretical details which underpin our algorithm. Finally, we develop a smartphone application that uses our algorithm together with a live taxi data feed to provide real time recommendations to participating drivers and efficiently route taxis to where they are needed most.

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