Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

Distributed opportunistic sensing and fusion for traffic congestion detection

Nottle, Alistair, Harborne, Daniel, Braines, David, Alzantot, Moustafa, Quintana-Amate, Santiago, Tomsett, Richard, Kaplan, Lance, Srivastava, Mani, Chakraborty, Supriyo and Preece, Alun David 2017. Distributed opportunistic sensing and fusion for traffic congestion detection. Presented at: IEEE Smart World Congress 2017 Workshop: DAIS 2017 - Workshop on Distributed Analytics InfraStructure and Algorithms for Multi-Organization Federations, San Francisco, CA, USA, 7-8 August 2017.

[img]
Preview
PDF - Accepted Post-Print Version
Download (2MB) | Preview

Abstract

Our particular research in the Distributed Analytics and Information Science International Technology Alliance (DAIS ITA) is focused on ”Anticipatory Situational Understanding for Coalitions”. This paper takes the concrete example of detecting and predicting traffic congestion in the UK road transport network from existing generic sensing sources, such as real-time CCTV imagery and video, which are publicly available for this purpose. This scenario has been chosen carefully as we believe that in a typical city, all data relevant to transport network congestion information is not generally available from a single unified source, and that different organizations in the city (e.g. the weather office, the police force, the general public, etc.) have their own different sensors which can provide information potentially relevant to the traffic congestion problem. In this paper we are looking at the problem of (a) identifying congestion using cameras that, for example, the police department may have access to, and (b) fusing that with other data from other agencies in order to (c) augment any base data provided by the official transportation department feeds. By taking this coalition approach this requires using standard cameras to do different supplementary tasks like car counting, and in this paper we examine how well those tasks can be done with RNN/CNN, and other distributed machine learning processes. In this paper we provide details of an initial four-layer architecture and potential tooling to enable rapid formation of human/machine hybrid teams in this setting, with a focus on opportunistic and distributed processing of the data at the edge of the network. In future work we plan to integrate additional data-sources to further augment the core imagery data.

Item Type: Conference or Workshop Item (Paper)
Date Type: Completion
Status: Unpublished
Schools: Computer Science & Informatics
Crime and Security Research Institute (CSURI)
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Related URLs:
Last Modified: 30 Sep 2017 07:01
URI: http://orca.cf.ac.uk/id/eprint/101501

Actions (repository staff only)

Edit Item Edit Item

Full Text Downloads from ORCA for this publication

Top Downloads of this item by Country

Monthly Full Text Downloads of this item

More statistics for this item...