Distributed opportunistic sensing and fusion for traffic congestion detection

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.

[1]  Md Khalilur Rhaman,et al.  An efficient algorithm for detecting traffic congestion and a framework for smart traffic control system , 2016, 2016 18th International Conference on Advanced Communication Technology (ICACT).

[2]  F. Porikli,et al.  Traffic congestion estimation using HMM models without vehicle tracking , 2004, IEEE Intelligent Vehicles Symposium, 2004.

[3]  Alun D. Preece,et al.  Deep learning for situational understanding , 2017, 2017 20th International Conference on Information Fusion (Fusion).

[4]  Changwei Yuan,et al.  Evaluation, Classification, and Influential Factors Analysis of Traffic Congestion in Chinese Cities Using the Online Map Data , 2016 .

[5]  N. Rakesh,et al.  Flow based traffic congestion prediction and intelligent signalling using Markov decision process , 2016, 2016 International Conference on Inventive Computation Technologies (ICICT).

[6]  Bonghee Hong,et al.  A Prediction Model of Traffic Congestion Using Weather Data , 2015, 2015 IEEE International Conference on Data Science and Data Intensive Systems.

[7]  Veselin Rakocevic,et al.  Distributed road traffic congestion quantification using cooperative VANETs , 2014, 2014 13th Annual Mediterranean Ad Hoc Networking Workshop (MED-HOC-NET).

[8]  Alok Bhushan Mukherjee,et al.  Assessment of network traffic congestion through Traffic Congestability Value (TCV): a new index , 2015 .

[9]  Murat Sensoy,et al.  Human-in-the-loop situational understanding via subjective Bayesian networks , 2017 .

[10]  K. Ramachandra Rao,et al.  Identification Of Traffic Congestion On Urban Arterials For Heterogeneous Traffic , 2016 .

[11]  Hicham Medromi,et al.  Multiagent based model for urban traffic congestion measuring , 2015, 2015 5th World Congress on Information and Communication Technologies (WICT).

[12]  Jianguo Zhang,et al.  The PASCAL Visual Object Classes Challenge , 2006 .

[13]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Shengwu Xiong,et al.  A Campus Traffic Congestion Detecting Method Based on BP Neural Network , 2015, 2015 2nd International Symposium on Dependable Computing and Internet of Things (DCIT).

[15]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[16]  Fei-Yue Wang,et al.  Long short-term memory model for traffic congestion prediction with online open data , 2016, 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC).

[17]  Fang Chen,et al.  Discovering Congestion Propagation Patterns in Spatio-Temporal Traffic Data , 2017, IEEE Transactions on Big Data.

[18]  Zhoujun Li,et al.  Estimating Urban Traffic Congestions with Multi-sourced Data , 2016, 2016 17th IEEE International Conference on Mobile Data Management (MDM).

[19]  Twittie Senivongse,et al.  Twitter analysis of road traffic congestion severity estimation , 2016, 2016 13th International Joint Conference on Computer Science and Software Engineering (JCSSE).

[20]  Tien Pham,et al.  Controlled English to facilitate human/machine analytical processing , 2013, Defense, Security, and Sensing.

[21]  Yuan Yu,et al.  TensorFlow: A system for large-scale machine learning , 2016, OSDI.

[22]  Waqas Ahmed,et al.  Real-Time Vehicle Recognition and Improved Traffic Congestion Resolution , 2015, 2015 13th International Conference on Frontiers of Information Technology (FIT).

[23]  Hai Le Vu,et al.  Traffic COngestion pattern classification using multi-class SVM , 2016, 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC).

[24]  Milind R. Naphade,et al.  Smarter Cities and Their Innovation Challenges , 2011, Computer.

[25]  Veselin Rakocevic,et al.  Short paper: Distributed vehicular traffic congestion detection algorithm for urban environments , 2013, 2013 IEEE Vehicular Networking Conference.