Traffic Volume Prediction

Traffic volume prediction aims to forecast traffic flow, crucial for optimizing transportation systems and mitigating congestion. Current research heavily utilizes deep learning models, such as Long Short-Term Memory (LSTM) networks, Graph Neural Networks (GNNs), and ensemble methods, often incorporating spatio-temporal data from various sources (e.g., sensor networks, satellite imagery) to capture complex traffic patterns. These advancements improve the accuracy of both short-term and long-term predictions, impacting traffic management, urban planning, and the efficiency of transportation services like ride-sharing. The field is actively exploring methods to handle data heterogeneity, outliers, and uncertainties inherent in real-world traffic conditions.

Papers

August 23, 2024

Spatio-Temporal Road Traffic Prediction using Real-time Regional Knowledge
Sumin Han, Jisun An, Dongman Lee
Traffic Prediction Temporal Knowledge Temporal Traffic Traffic Volume Prediction

June 17, 2024

Deploying scalable traffic prediction models for efficient management in real-world large transportation networks during hurricane evacuations
Qinhua Jiang, Brian Yueshuai He, Changju Lee, Jiaqi Ma
High Efficiency Traffic Forecasting Traffic Network Hurricane Evacuation Traffic Speed Accurate Traffic Prediction Traffic Volume Prediction

February 8, 2024

Mesoscale Traffic Forecasting for Real-Time Bottleneck and Shockwave Prediction
Raphael Chekroun, Han Wang, Jonathan Lee, Marin Toromanoff, Sascha Hornauer, Fabien Moutarde, Maria Laura Delle Monache
Traffic Forecasting Traffic State Estimation Traffic Volume Prediction Shock Wave

January 15, 2024

Formal Logic Enabled Personalized Federated Learning Through Property Inference
Ziyan An, Taylor T. Johnson, Meiyi Ma
Multi Task Temporal Reasoning Client Model Formal Logic Property Inference Traffic Volume Prediction

November 30, 2023

An Adaptive Framework for Generalizing Network Traffic Prediction towards Uncertain Environments
Alexander Downey, Evren Tuna, Alkan Soysal
Spatio Temporal Traffic Prediction Uncertain Environment Traffic Volume Prediction Adaptive Forecasting

August 11, 2023

Phased Deep Spatio-temporal Learning for Highway Traffic Volume Prediction
Weilong Ding, Tianpu Zhang, Zhe Wang
Long Short Term Memory Spatio Temporal Spatio Temporal Learning Traffic Volume Prediction Multi Scale Traffic Traffic Estimation

July 2, 2023

STG4Traffic: A Survey and Benchmark of Spatial-Temporal Graph Neural Networks for Traffic Prediction
Xunlian Luo, Chunjiang Zhu, Detian Zhang, Qing Li
Timely Survey New Benchmark Traffic Prediction Temporal Graph Neural Network Spatial Temporal Graph Traffic Volume Prediction

March 22, 2023

Traffic Volume Prediction using Memory-Based Recurrent Neural Networks: A comparative analysis of LSTM and GRU
Lokesh Chandra Das
Comparative Study LSTM Network Traffic Prediction Traffic Datasets Traffic Flow Urban Traffic Heterogeneous Traffic Traffic Volume Prediction GRU Net

February 24, 2023

Dynamic Graph Convolutional Network with Attention Fusion for Traffic Flow Prediction
Xunlian Luo, Chunjiang Zhu, Detian Zhang, Qing Li
Dynamic Graph Traffic Datasets Traffic Flow Prediction Dynamic Graph Learning Attention Fusion Space Time Attention Traffic Volume Prediction

November 6, 2022

Efficient Traffic State Forecasting using Spatio-Temporal Network Dependencies: A Sparse Graph Neural Network Approach
Bin Lei, Shaoyi Huang, Caiwen Ding, Monika Filipovska
Graph Attention Network Temporal Dependency Traffic Prediction Traffic Network Traffic Speed Traffic Volume Prediction

May 3, 2022

Towards an Ensemble Regressor Model for Anomalous ISP Traffic Prediction
Sajal Saha, Anwar Haque, Greg Sidebottom
Outlier Detection Traffic Prediction Traffic Volume Prediction Ensemble Regression

Traffic Volume Prediction

Papers

Spatio-Temporal Road Traffic Prediction using Real-time Regional Knowledge

Deploying scalable traffic prediction models for efficient management in real-world large transportation networks during hurricane evacuations

Mesoscale Traffic Forecasting for Real-Time Bottleneck and Shockwave Prediction

Formal Logic Enabled Personalized Federated Learning Through Property Inference

An Adaptive Framework for Generalizing Network Traffic Prediction towards Uncertain Environments

Phased Deep Spatio-temporal Learning for Highway Traffic Volume Prediction

STG4Traffic: A Survey and Benchmark of Spatial-Temporal Graph Neural Networks for Traffic Prediction

Traffic Volume Prediction using Memory-Based Recurrent Neural Networks: A comparative analysis of LSTM and GRU

Dynamic Graph Convolutional Network with Attention Fusion for Traffic Flow Prediction

Efficient Traffic State Forecasting using Spatio-Temporal Network Dependencies: A Sparse Graph Neural Network Approach

Towards an Ensemble Regressor Model for Anomalous ISP Traffic Prediction