Traffic Prediction and Management Techniques

TopicLeading institutions, researchers & key papers

This cluster of papers focuses on the application of deep learning, neural networks, and spatio-temporal data analysis for traffic flow prediction and forecasting in urban environments. The research covers topics such as short-term forecasting, graph convolutional networks, time series analysis, and the integration of intelligent transportation systems.

Works

IDs:OpenAlex

How has Traffic Prediction and Management Techniques's publication output changed over time?

ScholarIQpublication output · 2002–2023

Output grew0% over the shown period — from 1 works in 2002 to 1 in 2023.

200220122017201820192020202120222023

What are the most-cited papers on Traffic Prediction and Management Techniques?

ScholarIQmost cited works

Graph neural network for traffic forecasting: A survey

Weiwei Jiang, Jiayun Luo

Expert Systems with Applications. 20221,259 CitationsOPEN ACCESS

Spatiotemporal Multi-Graph Convolution Network for Ride-Hailing Demand Forecasting

Geng Xu, Yaguang Li, Leye Wang, Lingyu Zhang, Qiang Yang, Jieping Ye, Yan Liu

S4210191458. 2019867 CitationsOPEN ACCESS

Automatic congestion detection system for underground platforms

Benny Lo, Sergio A. Velastín

2002273 Citations

LC-RNN: A Deep Learning Model for Traffic Speed Prediction

Zhongjian Lv, Jiajie Xu, Kai Zheng, Hongzhi Yin, Pengpeng Zhao, Xiaofang Zhou

2018260 CitationsOPEN ACCESS

Detection of flood disaster system based on IoT, big data and convolutional deep neural network

M. Anbarasan, BalaAnand Muthu, C. B. Sivaparthipan, Revathi Sundarasekar, Seifedine Kadry, Sujatha Krishnamoorthy, R. Dinesh Jackson Samuel, A. Antony Dasel

S115004586. 2019220 Citations