Multi-Source Data-Driven Route Prediction for Instant Delivery

Compared with conventional delivery services, instant delivery usually provides a stricter constraint on delivery time (e.g., 30 minutes). To guarantee the quality of time constraint service, precisely predicting the courier’s actual route plays an important role in order dispatching. Most of the existing studies on route prediction are based on single-source data-set such as GPS trajectories or order waybills information, and are not significant to accurately predict the courier’s route. This paper focuses on fully leveraging multi-source data to improve the accuracy of route prediction, including the encounter data, active site report data and GPS trajectories. To achieve this, we propose a multi-source data fusion framework for route prediction. It consists of (i) a multi-source features extracting and fusion module to address the challenge of the heterogeneity of multisource data; (ii) a prediction module taking full advantage of features with different aspects of information containing noise. We evaluate our approach with real-world data collected from one of the largest instant delivery companies in China, i.e., Eleme. Experimental results show that the performance of our multisource data fusion-based prediction model outperforms other state-of-the-art baselines, and achieves a precision of 83.08% for route prediction.