Data‐driven next destination prediction and ETA improvement for urban delivery fleets

Abstract

In an urban logistics system, predictions of the next destination and estimated time of arrival (ETA) are of paramount importance for efficient resource planning of delivery fleets and for providing a satisfactory client experience. The quality of prediction is limited by the information accessible to individual logistics business entities, and further complicated by the complex urban road system. Data collection under the auspices of smart city initiatives worldwide provides exciting new opportunities to overcome these limitations. In this study, the authors identify two areas of improvement through data-driven approaches, including a next destination predictor, based on the delivery fleet's historical global positioning system trajectory data using a non-linear autoregressive neural network, and a road incident detector for real-time ETA improvement. By comparing a range of machine learning classification algorithms for incident detection, XGBoost has been found to be the most practical choice, due to its performance and efficiency. The proposed framework can be utilised by government authorities who possess such data for better urban planning and providing advanced infrastructure, so as to improve the operational efficiency of the logistics industry.