Travel Time Estimation
13 papers with code • 1 benchmarks • 1 datasets
Evaluation of the time required to travel between two points.
TTE-A&O
Most implemented papers
DeepIST: Deep Image-based Spatio-Temporal Network for Travel Time Estimation
Estimating the travel time for a given path is a fundamental problem in many urban transportation systems.
Unsupervised Path Representation Learning with Curriculum Negative Sampling
Sean-Bin-Yang/Path-InfoMax
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In the global view, PIM distinguishes the representations of the input paths from those of the negative paths.
Multi View Spatial-Temporal Model for Travel Time Estimation
775269512/sigspatial-2021-giscup-4th-solution
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Specifically, we use graph2vec to model the spatial view, dual-channel temporal module to model the trajectory view, and structural embedding to model traffic semantics.
Fine-Grained Trajectory-based Travel Time Estimation for Multi-city Scenarios Based on Deep Meta-Learning
To tackle these challenges, we propose a meta learning based framework, MetaTTE, to continuously provide accurate travel time estimation over time by leveraging well-designed deep neural network model called DED, which consists of Data preprocessing module and Encoder-Decoder network module.
Weakly-supervised Temporal Path Representation Learning with Contrastive Curriculum Learning -- Extended Version
In this setting, it is essential to learn generic temporal path representations(TPRs) that consider spatial and temporal correlations simultaneously and that can be used in different applications, i. e., downstream tasks.
Logistics, Graphs, and Transformers: Towards improving Travel Time Estimation
The problem of travel time estimation is widely considered as the fundamental challenge of modern logistics.
Jointly Contrastive Representation Learning on Road Network and Trajectory
mzy94/jclrnt
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Unlike the existing cross-scale contrastive learning methods on graphs that only contrast a graph and its belonging nodes, the contrast between road segment and trajectory is elaborately tailored via novel positive sampling and adaptive weighting strategies.
Similarity-based Feature Extraction for Large-scale Sparse Traffic Forecasting
c-lyu/traffic4cast2022-tse
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Short-term traffic forecasting is an extensively studied topic in the field of intelligent transportation system.
RNTrajRec: Road Network Enhanced Trajectory Recovery with Spatial-Temporal Transformer
However, many real-life trajectories are collected with low sample rate due to energy concern or other constraints. We study the task of trajectory recovery in this paper as a means for increasing the sample rate of low sample trajectories.
GCT-TTE: Graph Convolutional Transformer for Travel Time Estimation
eighonet/gct-tte
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This paper introduces a new transformer-based model for the problem of travel time estimation.
