Deep learning for cross-domain data fusion in urban computing: Taxonomy, advances, and outlook

X Zou, Y Yan, X Hao, Y Hu, H Wen, E Liu, J Zhang… - Information …, 2025 - Elsevier
As cities continue to burgeon, Urban Computing emerges as a pivotal discipline for
sustainable development by harnessing the power of cross-domain data fusion from diverse …

A survey on service route and time prediction in instant delivery: Taxonomy, progress, and prospects

H Wen, Y Lin, L Wu, X Mao, T Cai, Y Hou… - … on Knowledge and …, 2024 - ieeexplore.ieee.org
Instant delivery services, such as food delivery and package delivery, have achieved
explosive growth in recent years by providing customers with daily-life convenience. An …

A predictive framework for last-mile delivery routes considering couriers' behavior heterogeneity

A Pegado-Bardayo, A Lorenzo-Espejo… - Computers & Industrial …, 2024 - Elsevier
Last-mile route prediction is a powerful tool for freight delivery companies that can be
essential in the development of new features such as arrival time prediction or accurate …

Package Arrival Time Prediction via Knowledge Distillation Graph Neural Network

L Zhang, Y Liu, Z Zeng, Y Cao, X Wu, Y Xu… - ACM Transactions on …, 2024 - dl.acm.org
Accurately estimating packages' arrival time in e-commerce can enhance users' shop**
experience and improve the placement rate of products. This problem is often formalized as …

Spatial Meta Learning With Comprehensive Prior Knowledge Injection for Service Time Prediction

S Wang, Q Yang, S Ruan, C Long… - … on Knowledge and …, 2024 - ieeexplore.ieee.org
Intelligent logistics relies on accurately predicting the service time, which is a part of time
cost in the last-mile delivery. However, service time prediction (STP) is non-trivial given …

A Hybrid Model Integrating Fuzzy Systems and Convolutional Factorization Machine for Delivery Time Prediction in Intelligent Logistics

D Zhu, Z Han, X Du, D Zuo, L Cai… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Logistics distribution plays a crucial role in smart logistics systems, with delivery time
prediction being a key issue. Accurately predicting the delivery time of parcels positively …

Adaptive Cross-platform Transportation Time Prediction for Logistics

S Zhong, W Lyu, Z Hong, G Yang, W Zuo… - Proceedings of the 33rd …, 2024 - dl.acm.org
Accurate prediction of order transportation time is essential for customer satisfaction in
logistics. Existing methods based on origin-destination (OD) pairs do not consider the …

A Momentum Contrastive Learning Framework for Query-POI Matching

Y Qiang, J Zheng, L Wu, H Wen, J Lou… - … Conference on Data …, 2024 - ieeexplore.ieee.org
The query-POI matching task involves interpreting noisy textual addresses to retrieve
corresponding Points-of-Interest (POIs), which is crucial for location-based service providers …

Balancing Efficiency and Experience: A Predictive Cyber Physical System (CPS) for Urban Logistics

S Zhong - 2024 - search.proquest.com
Abstract Cyber-Physical Systems (CPS) integrate physical entities with information systems,
enabling sensing, decision-making, and control actions, which has driven the development …

Dynamic Time-Window Updates in Last-Mile Delivery:'Follow Your Parcel'

R Mahes, MAA Boon, A Kuiper, M Mandjes… - Available at SSRN … - papers.ssrn.com
Accurate delivery time windows are crucial for customer satisfaction in last-mile logistics,
presenting a challenging task influenced by factors such as route optimization and driver …