Deep learning for trajectory data management and mining: A survey and beyond

W Chen, Y Liang, Y Zhu, Y Chang, K Luo… - arXiv preprint arXiv …, 2024 - arxiv.org
Trajectory computing is a pivotal domain encompassing trajectory data management and
mining, garnering widespread attention due to its crucial role in various practical …

Lightpath: Lightweight and scalable path representation learning

SB Yang, J Hu, C Guo, B Yang, CS Jensen - Proceedings of the 29th …, 2023 - dl.acm.org
Movement paths are used widely in intelligent transportation and smart city applications. To
serve such applications, path representation learning aims to provide compact …

iETA: A Robust and Scalable Incremental Learning Framework for Time-of-Arrival Estimation

J Han, H Liu, S Liu, X Chen, N Tan, H Chai… - Proceedings of the 29th …, 2023 - dl.acm.org
Time-of-arrival estimation or Estimated Time of Arrival (ETA) has become an indispensable
building block of modern intelligent transportation systems. While many efforts have been …

Uncertainty-aware probabilistic travel time prediction for on-demand ride-hailing at didi

H Liu, W Jiang, S Liu, X Chen - Proceedings of the 29th ACM SIGKDD …, 2023 - dl.acm.org
Travel Time Estimation (TTE) aims to accurately forecast the expected trip duration from an
origin to a destination. As one of the world's largest ride-hailing platforms, DiDi answers …

Micro-Macro Spatial-Temporal Graph-Based Encoder-Decoder for Map-Constrained Trajectory Recovery

T Wei, Y Lin, Y Lin, S Guo, L Zhang… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Recovering intermediate missing GPS points in a sparse trajectory, while adhering to the
constraints of the road network, could offer deep insights into users' moving behaviors in …

Travel time distribution estimation by learning representations over temporal attributed graphs

W Zhou, X Xiao, YJ Gong, J Chen… - IEEE Transactions …, 2023 - ieeexplore.ieee.org
Travel time estimation is a crucial task in practical transportation applications, while
providing the reliability of estimation is important in many working scenarios. Most existing …

PreCLN: Pretrained-based contrastive learning network for vehicle trajectory prediction

B Yan, G Zhao, L Song, Y Yu, J Dong - World Wide Web, 2023 - Springer
Trajectory prediction of vehicles is of great importance to various smart city applications
ranging from transportation scheduling, vehicle navigation, to location-based …

Graphmm: Graph-based vehicular map matching by leveraging trajectory and road correlations

Y Liu, Q Ge, W Luo, Q Huang, L Zou… - … on Knowledge and …, 2023 - ieeexplore.ieee.org
Map matching of sparse vehicle trajectories is a fundamental problem in location-based
services, such as traffic flow analysis and vehicle routing. Existing literature mainly relies on …

Cross-View Location Alignment Enhanced Spatial-Topological Aware Dual Transformer for Travel Time Estimation

H Zhang, X Zhang, Q Jiang, L Li… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Accurately estimating route travel time is crucial for intelligent transportation systems. Urban
road networks and routes can be viewed from spatial and topological perspectives while …

Multi-Faceted Route Representation Learning for Travel Time Estimation

T Liao, L Han, Y Xu, T Zhu, L Sun… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Travel time estimation (TTE) is a fundamental and challenging problem for navigation and
travel planning. Though many efforts have been devoted to this task, most of the previous …