Traffic Prediction

Traffic prediction aims to forecast future traffic conditions, crucial for optimizing transportation systems and resource allocation. Current research heavily utilizes deep learning models, including graph neural networks, transformers, and recurrent neural networks like LSTMs, often incorporating techniques like transfer learning, data augmentation, and multi-modal data fusion to improve accuracy and address data limitations. These advancements are impacting various sectors, from improving urban planning and traffic management to enhancing the efficiency of telecommunication networks and autonomous driving systems. Furthermore, there's a growing emphasis on model explainability and robustness to handle outliers and uncertainties in real-world traffic data.

Papers

October 28, 2022

Measuring the Confidence of Traffic Forecasting Models: Techniques, Experimental Comparison and Guidelines towards Their Actionability
Ibai Laña, Ignacio, Olabarrieta, Javier Del Ser
Uncertainty Estimation Barzilai Borwein Technique Traffic Prediction Traffic Forecasting Traffic Data High Confidence Uncertainty Estimation Method Actionable Part Experimental Comparison

September 8, 2022

Hierarchical Graph Pooling is an Effective Citywide Traffic Condition Prediction Model
Shilin Pu, Liang Chu, Zhuoran Hou, Jincheng Hu, Yanjun Huang, Yuanjian Zhang
Traffic Prediction Traffic Forecasting Graph Pooling

August 8, 2022

A Visual Analytics System for Improving Attention-based Traffic Forecasting Models
Seungmin Jin, Hyunwook Lee, Cheonbok Park, Hyeshin Chu, Yunwon Tae, Jaegul Choo, Sungahn Ko
Deep Learning Temporal Dependency Traffic Prediction Traffic Data Visual Analytics System

July 22, 2022

July 4, 2022

NodeTrans: A Graph Transfer Learning Approach for Traffic Prediction
Xueyan Yin, Feifan Li, Yanming Shen, Heng Qi, Baocai Yin
Transfer Learning Traffic Prediction Traffic Network Traffic Pattern Temporal Traffic Graph Transfer Learning

June 19, 2022

Traffic-Twitter Transformer: A Nature Language Processing-joined Framework For Network-wide Traffic Forecasting
Meng-Ju Tsai, Zhiyong Cui, Hao Yang, Cole Kopca, Sophie Tien, Yinhai Wang
Traffic Prediction Traffic Forecasting Traffic Flow Forecasting Future Traffic Processing Framework Network Wide Traffic Long Term Traffic Forecasting

June 13, 2022

Energy-Efficient Wake-Up Signalling for Machine-Type Devices Based on Traffic-Aware Long-Short Term Memory Prediction
David E. Ruíz-Guirola, Carlos A. Rodríguez-López, Samuel Montejo-Sánchez, Richard Demo Souza, Onel L. A. López, Hirley Alves
New Machine Traffic Prediction Low Power Massive Machine Type Communication Wake Up Radio

May 30, 2022

May 10, 2022

A spatial-temporal short-term traffic flow prediction model based on dynamical-learning graph convolution mechanism
Zhijun Chen, Zhe Lu, Qiushi Chen, Hongliang Zhong, Yishi Zhang, Jie Xue, Chaozhong Wu
Graph Convolution Traffic Prediction Short Term Traffic

May 9, 2022

May 3, 2022

March 21, 2022

STCGAT: A Spatio-temporal Causal Graph Attention Network for traffic flow prediction in Intelligent Transportation Systems
Wei Zhao, Shiqi Zhang, Bing Zhou, Bei Wang
Temporal Dependency Traffic Prediction Intelligent Transportation System Traffic Flow Prediction Traffic Flow Forecasting

March 11, 2022

TrafPS: A Visual Analysis System Interpreting Traffic Prediction in Shapley
Yifan Jiang, Zezheng Feng, Hongjun Wang, Zipei Fan, Xuan Song
Shapley Value Traffic Prediction Traffic Flow Prediction Traffic Efficiency Flow Prediction

March 8, 2022

Few-Sample Traffic Prediction with Graph Networks using Locale as Relational Inductive Biases
Mingxi Li, Yihong Tang, Wei Ma
Inductive Bias Traffic Prediction Graph Network Short Term Traffic

February 18, 2022

PGCN: Progressive Graph Convolutional Networks for Spatial-Temporal Traffic Forecasting
Yuyol Shin, Yoonjin Yoon
Traffic Prediction Traffic Forecasting Traffic Flow Forecasting Temporal Traffic Progressive Graph

Traffic Prediction

Papers

Measuring the Confidence of Traffic Forecasting Models: Techniques, Experimental Comparison and Guidelines towards Their Actionability

Hierarchical Graph Pooling is an Effective Citywide Traffic Condition Prediction Model

A Visual Analytics System for Improving Attention-based Traffic Forecasting Models

Spatial-Temporal Feature Extraction and Evaluation Network for Citywide Traffic Condition Prediction

Automated Dilated Spatio-Temporal Synchronous Graph Modeling for Traffic Prediction

NodeTrans: A Graph Transfer Learning Approach for Traffic Prediction

Traffic-Twitter Transformer: A Nature Language Processing-joined Framework For Network-wide Traffic Forecasting

Energy-Efficient Wake-Up Signalling for Machine-Type Devices Based on Traffic-Aware Long-Short Term Memory Prediction

A Graph and Attentive Multi-Path Convolutional Network for Traffic Prediction

Vehicle Route Planning using Dynamically Weighted Dijkstra's Algorithm with Traffic Prediction

A spatial-temporal short-term traffic flow prediction model based on dynamical-learning graph convolution mechanism

Transfer Learning Based Efficient Traffic Prediction with Limited Training Data

Wavelet-Based Hybrid Machine Learning Model for Out-of-distribution Internet Traffic Prediction

Deep Sequence Modeling for Anomalous ISP Traffic Prediction

An Empirical Study on Internet Traffic Prediction Using Statistical Rolling Model

Towards an Ensemble Regressor Model for Anomalous ISP Traffic Prediction

STCGAT: A Spatio-temporal Causal Graph Attention Network for traffic flow prediction in Intelligent Transportation Systems

TrafPS: A Visual Analysis System Interpreting Traffic Prediction in Shapley

Few-Sample Traffic Prediction with Graph Networks using Locale as Relational Inductive Biases

PGCN: Progressive Graph Convolutional Networks for Spatial-Temporal Traffic Forecasting