Survey on traffic prediction in smart cities

By Attila Matyas Nagy, Vilmos Simon. Pervasive and Mobile Computing, 2018

299 citations recorded by the source corpus.

Abstract The rapid development in machine learning and in the emergence of new data sources makes it possible to examine and predict traffic conditions in smart cities more accurately than ever. This can help to optimize the design and management of transport services in a future automated city. In this paper, we provide a detailed presentation of the traffic prediction methods for such intelligent cities, also giving an overview of the existing data sources and prediction models.

Subfields: Building and Construction, Transportation

View Paper: https://www.semanticscholar.org/paper/e11fcade45a8c6d7b15c7825947a58f90cb4f416

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Building and Construction

In Transportation

Citations in the corpus

A Survey on Modern Deep Neural Network for Traffic Prediction: Trends, Methods and Challenges (2020, background): “A recent paper by Nagy and Simon [10] is a more upto-date survey on traffic prediction.”
Traffic Flow Prediction via Spatial Temporal Graph Neural Network (2020, background): “Traffic prediction [30] is more challenging since the number of vehicles is continuously growing with the rapid urbanization process all over the world.”
Deep Learning on Traffic Prediction: Methods, Analysis, and Future Directions (2021, background): “[4] provided sources of trafﬁc data acquisition, and mainly focused on traditional machine learning methods.”
Spatial-Temporal Graph ODE Networks for Traffic Flow Forecasting (2021, background): “1 Traffic Flow Forecasting In recent years a large body of research has been conducted on traffic flow forecasting, which has always been a critical problem in intelligent transportation systems(ITS)[23].”