A multi level clustering approach for forecasting taxi travel demand
In this paper, we use time series modeling to forecast taxi travel demand, in the context of a mobile application based taxi hailing service. In particular, we model the passenger demand density at various locations in the city of Bengaluru, India. Using the data, we first shortlist time series models that suit our application. We then analyse the performance of these models by using Mean Absolute Percentage Error (MAPE) as the performance metric. In order to improve the model performance, we employ a multi level clustering technique where we aggregate demand over neighboring cells/geohashes. We observe that the improved model based on clustering leads to a forecast accuracy of 80% per km2. In addition, our technique obtains an accuracy of 89% per km2 for the most frequently occurring use case.
2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC)