A Multitask Attention Network for Food Delivery Time Prediction

Abstract

Given the recent increase in the prevalence of online food ordering apps, food delivery has become an emerging service. Intelligent dispatch systems have been widely deployed by many on-demand logistics companies to maximize delivery efficiency. Predicting the food delivery time is a key module that provides critical information at the decision-making stage of order dispatch to ensure the punctual delivery service for each customer. In this study, we propose a multitask attention network for food delivery time prediction, mimicking the driver’s decision-making process during delivery. First, an attention mechanism is employed to capture mutual influences among orders and evaluate the importance of each order. Then, a multitask learning method is used to simultaneously train delivery time prediction and delivery priority prediction. Finally, a specific loss function is designed to further improve the accuracy of prediction. Extensive modeling demonstrates that our model greatly outperforms other state-of-the-art methods.