A Fundamental Model with Stable Interpretability for Traffic Forecasting

We propose a definition of fundamental models with stable interpretability. In the paper, we first showcase existing attention-based interpretable models in ...

In the paper, we first showcase existing attention-based interpretable models in traffic prediction and analysis. Subsequently, we introduce and ...

A Fundamental Model with Stable Interpretability for Traffic Forecasting

However, attacks on the sensor networks that traffic prediction relies on can introduce severe disturbances and uncertainties in the interpretability of models, ...

A Fundamental Model with Stable Interpretability for Traffic Forecasting

Deep learning models have been widely applied in traffic prediction and analysis. Notably, attention-based models like Graph Attention. Network ...

A Fundamental Model with Stable Interpretability for Traffic Forecasting

A Fundamental Model with Stable Interpretability for Traffic Forecasting ... To read the full-text of this research, you can request a copy directly from the ...

A Fundamental Model with Stable Interpretability for Traffic Forecasting

Abrar M Alajlan . 2022 . Multi-step detection of simplex and duplex wormhole attacks over wireless sensor networks . Computers, Materials & Continua 70 , 3 ( ...

A Fundamental Model with Stable Interpretability for Traffic Forecasting

A Fundamental Model with Stable Interpretability for Traffic Forecasting. Xiaochuan Gou 1. ,. Lijie Hu 2. ,. Jinhui Xu 2. ,. XIANGLIANG ZHANG 3. Show full list: ...

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Title: A Fundamental Model with Stable Interpretability for Traffic Forecasting. Authors: Xiaochuan Gou, Lijie Hu, Di Wang 0015, Xiangliang Zhang 0001 ...

‪Xiaochuan Gou‬ - ‪„Google“ mokslinčius‬

A Fundamental Model with Stable Interpretability for Traffic Forecasting. X Gou, L Hu, D Wang, X Zhang. Proceedings of the 1st ACM SIGSPATIAL International ...

Explainable Traffic Flow Prediction with Large Language Models

Empirically, TF-LLM shows competitive accuracy compared with deep learning baselines, while providing intuitive and interpretable predictions. We discuss the ...

Dynamic traffic prediction for urban road network with the ...

A Fundamental Model with Stable Interpretability for Traffic Forecasting ... models with stable interpretability is proposed, promising that the model will ...

Deep Learning Algorithms for Traffic Forecasting: A Comprehensive ...

Finally, the study elucidates the merits and drawbacks of proposed models through applied research in traffic forecasting. Findings indicate ...

Granger Causal Inference for Interpretable Traffic Prediction

Granger causality is a classical statistical concept of causality that is based on prediction [3]. By explicitly modeling the Granger causal relationship ...

Dynamic traffic prediction for urban road network with the ...

First of all, we study the temporal characteristics model of traffic through the Markov chain. Secondly, combining the Expectation-Maximization algorithm and ...

An interpretable model for short term traffic flow prediction

In this paper, we propose a deep polynomial neural network combined with a seasonal autoregressive integrated moving average model. The new model has superior ...

Towards Explainable Traffic Flow Prediction with Large Language ...

This paper contributes to advancing explainable traffic prediction models and lays a foundation for future exploration of LLM applications in transportation. To ...

Multistep traffic forecasting by dynamic graph convolution

Many of these deep learning models show promising predictive performance, but inherently suffer from a lack of interpretability. This difficulty largely ...

A Survey on Deep Learning for Cellular Traffic Prediction

In this manner, it is possible to predict cellular traffic by learning a model that maps historical traffic data with fixed time slots to future traffic with ...

Full Bayesian Significance Testing for Neural Networks in Traffic ...

First, we approach traffic forecasting through Bayesian modeling and employ a Bayesian neural network to capture complicated traffic relationships. Then, the ...

Beyond Short-term Traffic Forecasting Models - Tecnalia

However, published per- formance improvements are narrow. This Thesis conducts first a literature review on short-term traffic forecasting ...