Xinrui Zhang(张心睿)

Xinrui Zhang(张心睿)

Ph.D. student, School of Computer Science and Engineering，focusing on causal inference, graph neural network.

Publications

PATR: Periodicity-Aware Trajectory Recovery for Express System via Seq2Seq Model

Published in IEEE Globecom, 2022

The analysis of express trajectory is essential for delivery service optimization and courier management. Express trajectory consists of a large amount of community paths which is different from the trajectory of transportation tools such as taxis which run in the main road of a city. Due to the complexity of the community road networks and the energy constraint of devices, trajectories are collected under a low sampling rate and accuracy. These trajectories need to be recovered and mapped to the road networks for further analysis. Current recovery approaches utilize two-stage or end-to-end models to generate the upsampled trajectories. These methods fail to fully exploit the periodicity relation in historical data, which leads to accuracy losses for complex community road networks. In this paper, we design PATR, a periodicity-aware Seq2Seq model, to recover the couriers’ trajectories by leveraging the periodical patterns of the historical data. We conduct experimental evaluations based on real-world GPS data of JD logistics consisting of 836,959 points and 25,726 road segments, and our model outperforms the state-of-the-art baselines by reducing recovery error by 2.4%-21.9%.

Recommended citation: Zhang, X., Liang, X., Wang, H., Wang, S., & He, T. (2022, December). PATR: Periodicity-Aware Trajectory Recovery for Express System via Seq2Seq Model. In GLOBECOM 2022-2022 IEEE Global Communications Conference (pp. 486-491). IEEE. https://ieeexplore.ieee.org/abstract/document/10001476

Multi-scale Temporal Feature Fusion for Time-Limited Order Prediction

Published in China Conference on Wireless Sensor Networks, 2022

The time-limited order is a new type of real-time delivery service that platforms need to complete the order delivery with different time granularity (e.g. one day, two days, or three days). Predicting the number of time-limited orders plays an important role for real-time order delivery allocation and anomaly detection in logistics IoT scenarios. However, the impact between orders of different time granularity is complex and the contribution of the static order features is unknown. Previous order predicting methods are not suitable for time-limited orders because they do not fully consider the dependencies among orders with different time granularity. In this paper, we propose a spatial-temporal framework based on stacked long short-term memory networks (LSTM) and deep & cross network (DCN) to take the dependencies of multi-scale temporal features into account and fuse cross-domain static features effectively. In addition, we utilize a multi-head attention mechanism to model the heterogeneous strengths of different dependencies. We evaluate our model on a real-world dataset with about 400,000 orders from one of the largest logistics companies in China. The evaluation results show that the Mean Absolute Error and R2 score of our method achieve 9.407 and 0.948, outperforming state-of-the-art solutions. Download paper here

Recommended citation: Wang, J., Zhou, X., Liu, Y., Zhang, X., & Wang, S. (2022, November). Multi-scale Temporal Feature Fusion for Time-Limited Order Prediction. In China Conference on Wireless Sensor Networks (pp. 132-144). Singapore: Springer Nature Singapore. https://link.springer.com/chapter/10.1007/978-981-19-8350-4_11