# A Graph Signal Processing Approach For Real-Time Traffic Prediction In Transportation Networks

@article{Hasanzadeh2017AGS, title={A Graph Signal Processing Approach For Real-Time Traffic Prediction In Transportation Networks}, author={Arman Hasanzadeh and Xi Liu and Nick G. Duffield and Krishna R. Narayanan and Byron T Chigoy}, journal={arXiv: Signal Processing}, year={2017} }

Accurate real-time traffic prediction has a key role in traffic management strategies and intelligent transportation systems. [... ] Key Method First, we introduce a novel spatio-temporal clustering algorithm in order to split the large graph into multiple connected disjoint subgraphs. Then within each subgraph, we propose to use a Graph Signal Processing (GSP) approach to decouple spatial dependencies and obtain independent time series in graph frequency domain. Expand

## 3 Citations

Anomaly Detection in Smart City Traffic Based on Time Series Analysis

- Computer Science2019 International Conference on Software, Telecommunications and Computer Networks (SoftCOM)
- 2019

The proposed Occupancy based anomaly detection algorithm (OBADA) is analyzing occupancy data of the roads, by searching for subsequence of major changes in values in the occupancy's time series which reflects an inordinate behavior.

Variational Graph Recurrent Neural Networks

- Computer ScienceNeurIPS
- 2019

A novel hierarchical variational model is developed that introduces additional latent random variables to jointly model the hidden states of a graph recurrent neural network (GRNN) to capture both topology and node attribute changes in dynamic graphs.

Deep Sequence Learning with Auxiliary Information for Traffic Prediction

- Computer ScienceKDD
- 2018

This paper intends to improve traffic prediction by appropriate integration of three kinds of implicit but essential factors encoded in auxiliary information within an encoder-decoder sequence learning framework that integrates the following data: offline geographical and social attributes.

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