Mining Twitter and Taxi Data for Predicting Taxi Pickup Hotspots

Abstract

In recent times, people regularly discuss about poor travel experience due to various road closure incidents in the social networking sites. One of the fallouts of these road blocking incidents is the dynamic shift in regular taxi pickup locations. Although traffic monitoring from social media content has lately gained widespread interest, however, none of the recent works has tried to understand this relocation of taxi pickup hotspots during any road closure activity. In this work, we have tried to predict the taxi pickup hotspots, during various road closure incidents, using their past taxi pickup trend. We have proposed a two-step methodology. First, we identify and extract road closure information from social network posts. Second, leveraging the inferred knowledge, prediction of taxi pickup hotspot is done near the activity location with an average accuracy of ~ 86.04%, where the predicted locations are within an average radius of only 0.011 mile from the original hotspots.