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Found 8 papers, 3 papers with code
Micro-Macro Spatial-Temporal Graph-based Encoder-Decoder for Map-Constrained Trajectory Recovery
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 intelligent transportation systems.
Diff-RNTraj: A Structure-aware Diffusion Model for Road Network-constrained Trajectory Generation
In this paper, we propose a new problem to meet the practical application need, \emph{i. e.}, road network-constrained trajectory (RNTraj) generation, which can directly generate trajectories on the road network with road-related information.
UVTM: Universal Vehicle Trajectory Modeling with ST Feature Domain Generation
However, most methods target only one specific task and cannot be applied universally.
A Survey on Service Route and Time Prediction in Instant Delivery: Taxonomy, Progress, and Prospects
An emerging research area within these services is service Route\&Time Prediction (RTP), which aims to estimate the future service route as well as the arrival time of a given worker.
Origin-Destination Travel Time Oracle for Map-based Services
Given an origin (O), a destination (D), and a departure time (T), an Origin-Destination (OD) travel time oracle~(ODT-Oracle) returns an estimate of the time it takes to travel from O to D when departing at T. ODT-Oracles serve important purposes in map-based services.
Pre-training General Trajectory Embeddings with Maximum Multi-view Entropy Coding
Spatio-temporal trajectories provide valuable information about movement and travel behavior, enabling various downstream tasks that in turn power real-world applications.
Spatial-Temporal Synchronous Graph Convolutional Networks: A New Framework for Spatial-Temporal Network Data Forecasting
Spatial-temporal network data forecasting is of great importance in a huge amount of applications for traffic management and urban planning.
Ranked #3 on
Traffic Prediction
on BJTaxi
Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting
The output of the three components are weighted fused to generate the final prediction results.
Ranked #14 on
Traffic Prediction
on PeMS07
