Taxi Demand Prediction Using Parallel Multi-Task Learning Model

Accurate and real-time taxi demand prediction can help managers pre-allocate taxi resources in cities, which assists drivers quickly finding passengers and reduce passengers' waiting time. Most of the existing studies focus on mining spatial-temporal characteristics of taxi demand distributions, while lacking in modeling the correlations between taxi pick-up demand and the drop-off demand from the perspective of multi-task learning. In this article, we propose a multi-task learning model containing three parallel LSTM layers to co-predict taxi pick-up and drop-off demands, and compare the performance of single demand prediction methodology and that of two demands' co-prediction methodology. Experimental results on real-world datasets demonstrate that the pick-up demand and the drop-off demand do depend on each other, and the effectiveness of the proposed co-prediction methods.

Automated summary

This article investigates the importance of accurate and real-time taxi demand prediction for pre-allocating taxi resources in cities, and proposes a multi-task learning model to co-predict taxi pick-up and drop-off demands. Experimental results demonstrate the effectiveness of the proposed model.