This paper explores the possibility to detect incidents in urban road networks by employing a density-based clustering algorithm (DBSCAN) (Ester et al., 1996).

A traffic incident algorithm based on DBSCAN, a density-based clustering algorithm, is outlined to enhance the capabilities of OTC, and shows the principal ...

To enhance the capabilities of OTC accordingly, we outline a traffic incident algorithm based on DBSCAN, a density-based clustering algorithm: In a simulated ...

Urban traffic state analysis plays an important role in the solution of traffic congestion problem. To estimate traffic state effectively is a foundational work ...

To promote this we have previously presented a density-based approach for clustering traffic flows in order to detect traffic disturbances. In this work we ...

Detecting Extended Incidents in Urban Road Networks for Organic Traffic Control Using Density-Based Clustering of Traffic Flows.

Detecting Extended Incidents in Urban Road Networks for Organic Traffic Control Using Density-Based Clustering of Traffic Flows. Chapter © 2022. Self ...

This paper proposes a deep learning method, Convolutional Neural Networks (CNN), for automatic detection of traffic incidents in urban networks by using traffic ...

Ingo Thomsen , Sven Tomforde : Detecting Extended Incidents in Urban Road Networks for Organic Traffic Control Using Density-Based Clustering of Traffic ...

For instance, self-organizing strategies like the Organic Traffic Control (OTC) system present benefits such as enhanced efficiency, resilience, and scalability ...