Distributed opportunistic sensing and fusion for traffic congestion detection

Abstract

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.

Asking ‘Why’ in AI: Explainability of intelligent systems – perspectives and challenges

“…The framework described in the previous section is still largely conceptual at present, although we are building its various components in current research (Chakraborty et al, ; Nottle et al, ; Harborne, Willis, Tomsett, and Preece, ). The key stages in a roadmap for realizing the framework are: •Development of a service‐oriented approach to providing explanation mechanisms for ML systems.…”

“…The current status of a service-oriented architecture that aims to integrate multiple classification and reasoning components, together with interpretation techniques, is described in Nottle et al (2017) and Harborne et al (2017). Further details on the deep neural network based components are given in Willis, Harborne, Tomsett, and Alzantot (2017).…”

Cognitive computing for coalition situational understanding

“…This approach is exemplified in our previous [17] and current [23], [24] work, managing both the H CH attribute and also the hybrid nature of a CC system approach.…”

Advancing Opportunistic Sensing in Hydrology: A Novel Approach to Measuring Rainfall With Ordinary Surveillance Cameras

“…Compared to other crowdsourcing modalities, visual data (i.e., images or videos) from such surveillance cameras are more informative, intuitive, and achievable (B. Guo et al, ). Camera‐based opportunistic sensing has received real‐world applications in China and elsewhere, especially for traffic management (Hua, ; Notile et al, ). Some of such camera systems have been applied to collecting environmental data, including streamflow monitoring (Tauro et al, ), ocean‐water‐color sensing, and plant reflectance measurement (Cai et al, ).…”

“…Guo et al, 2017). Camera-based opportunistic sensing has received real-world applications in China and elsewhere, especially for traffic management (Hua, 2018;Notile et al, 2017). Some of such camera systems have been applied to collecting environmental data, including streamflow monitoring (Tauro et al, 2018), ocean-water-color sensing, and plant reflectance measurement (Cai et al, 2017).…”