Distributed opportunistic sensing and fusion for traffic congestion detection

Abstract

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