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Found 14 papers, 7 papers with code
Context-aware knowledge graph framework for traffic speed forecasting using graph neural network
This study proposes a novel context-aware knowledge graph (CKG) framework to enhance traffic speed forecasting by effectively modeling spatial and temporal contexts.
Counterfactual Explanations for Deep Learning-Based Traffic Forecasting
The results showcase the effectiveness of counterfactual explanations in revealing traffic patterns learned by deep learning models, showing its potential for interpreting black-box deep learning models used for spatiotemporal predictions in general.
A causal intervention framework for synthesizing mobility data and evaluating predictive neural networks
Deep neural networks are increasingly utilized in mobility prediction tasks, yet their intricate internal workings pose challenges for interpretability, especially in comprehending how various aspects of mobility behavior affect predictions.
Where you go is who you are -- A study on machine learning based semantic privacy attacks
Concerns about data privacy are omnipresent, given the increasing usage of digital applications and their underlying business model that includes selling user data.
Uncertainty Quantification for Image-based Traffic Prediction across Cities
We compare two epistemic and two aleatoric UQ methods on both temporal and spatio-temporal transfer tasks, and find that meaningful uncertainty estimates can be recovered.
Evaluating geospatial context information for travel mode detection
While studies have acknowledged the benefits of incorporating geospatial context information into travel mode detection models, few have summarized context modeling approaches and analyzed the significance of these context features, hindering the development of an efficient model.
Spatially-Aware Car-Sharing Demand Prediction
In particular, we compare the spatially-implicit Random Forest model with spatially-aware methods for predicting average monthly per-station demand.
Context-aware multi-head self-attentional neural network model for next location prediction
Accurate activity location prediction is a crucial component of many mobility applications and is particularly required to develop personalized, sustainable transportation systems.
Vision Paper: Causal Inference for Interpretable and Robust Machine Learning in Mobility Analysis
Particularly, the transportation sector would benefit from the progress in AI and advance the development of intelligent transportation systems.
National-scale bi-directional EV fleet control for ancillary service provision
Deploying real-time control on large-scale fleets of electric vehicles (EVs) is becoming pivotal as the share of EVs over internal combustion engine vehicles increases.
How do you go where? Improving next location prediction by learning travel mode information using transformers
Predicting the next visited location of an individual is a key problem in human mobility analysis, as it is required for the personalization and optimization of sustainable transport options.
Traffic4cast at NeurIPS 2021 -- Temporal and Spatial Few-Shot Transfer Learning in Gridded Geo-Spatial Processes
The IARAI Traffic4cast competitions at NeurIPS 2019 and 2020 showed that neural networks can successfully predict future traffic conditions 1 hour into the future on simply aggregated GPS probe data in time and space bins.
Traffic Forecasting on Traffic Moving Snippets
With the performance on the traffic4cast test data and further experiments on a validation set it is shown that patch-wise prediction indeed improves accuracy.
Applications of deep learning in traffic congestion detection, prediction and alleviation: A survey
In this survey, we present the current state of deep learning applications in the tasks related to detection, prediction, and alleviation of congestion.
