An Interactive Visual Analytics Platform for Smart Intelligent Transportation Systems Management

The reduction of road congestion requires intuitive urban congestion-control platforms that can facilitate transport stakeholders in decision making. Interactive ITS visual analytics tools can be of significant assistance, through their real-time interactive visualizations, supported by advanced data analysis algorithms. In this paper, an interactive visual analytics platform is introduced that allows the exploration of historical data and the prediction of future traffic through a unified interactive interface. The platform is backed by several data analysis techniques, such as road behavioral visualization and clustering, anomaly detection, and traffic prediction, allowing the exploration of behavioral similarities between roads, the visual detection of unusual events, the testing of hypotheses, and the prediction of traffic flow after hypothetical incidents imposed by the human operator. The accuracy of the prediction algorithms is verified through benchmark comparisons, while the applicability of the proposed toolkit in facilitating decision making is demonstrated in a variety of use case scenarios, using real traffic and incident data sets.

[1]  Hans-Peter Kriegel,et al.  LOF: identifying density-based local outliers , 2000, SIGMOD '00.

[2]  Huan Wang,et al.  A Short-term Traffic Flow Forecasting Method Based on the Hybrid PSO-SVR , 2015, Neural Processing Letters.

[3]  Dimitrios Tzovaras,et al.  A BRPCA Based Approach for Anomaly Detection in Mobile Networks , 2015, ISCIS.

[4]  Dino Pedreschi,et al.  Visually driven analysis of movement data by progressive clustering , 2008, Inf. Vis..

[5]  Qiuchen Liu,et al.  An Improved K-nearest Neighbor Model for Short-term Traffic Flow Prediction , 2013 .

[6]  Dimitrios Tzovaras,et al.  Managing Spatial Graph Dependencies in Large Volumes of Traffic Data for Travel-Time Prediction , 2016, IEEE Transactions on Intelligent Transportation Systems.

[7]  Lionel M. Ni,et al.  Visual Analytics in Urban Computing: An Overview , 2016, IEEE Transactions on Big Data.

[8]  Jin Xin Cao,et al.  Traffic volume forecasting based on radial basis function neural network with the consideration of traffic flows at the adjacent intersections , 2014 .

[9]  Jun Wang,et al.  The Visual Causality Analyst: An Interactive Interface for Causal Reasoning , 2016, IEEE Transactions on Visualization and Computer Graphics.

[10]  Jan-Ming Ho,et al.  Travel time prediction with support vector regression , 2003, Proceedings of the 2003 IEEE International Conference on Intelligent Transportation Systems.

[11]  Hwann-Tzong Chen,et al.  Local discriminant embedding and its variants , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[12]  Xiaojun Wan,et al.  Graph-Based MultiModality Learning for Topic-Focused Multi-Document Summarization , 2009 .

[13]  Livia Mannini,et al.  On the short-term prediction of traffic state: an application on urban freeways in Rome , 2015 .

[14]  Billy M. Williams,et al.  Modeling and Forecasting Vehicular Traffic Flow as a Seasonal ARIMA Process: Theoretical Basis and Empirical Results , 2003, Journal of Transportation Engineering.

[15]  Chiou-Shann Fuh,et al.  Multiple Kernel Learning for Dimensionality Reduction , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Yiannis Kamarianakis,et al.  Space-time modeling of traffic flow , 2002, Comput. Geosci..

[17]  Rayid Ghani,et al.  Analyzing the effectiveness and applicability of co-training , 2000, CIKM '00.

[18]  Miriah D. Meyer,et al.  Visually Comparing Weather Features in Forecasts , 2016, IEEE Transactions on Visualization and Computer Graphics.

[19]  Zuduo Zheng,et al.  Short-term traffic volume forecasting : a k-nearest neighbor approach enhanced by constrained linearly sewing principle component algorithm , 2014 .

[20]  Marcin Bernaś,et al.  Segmentation of vehicle detector data for improved k-nearest neighbours-based traffic flow prediction , 2015 .

[21]  Dimitrios Tzovaras,et al.  Investigating the effect of global metrics in travel time forecasting , 2013, 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013).

[22]  Dimitrios Tzovaras,et al.  Identifying patterns under both normal and abnormal traffic conditions for short-term traffic prediction , 2017 .

[23]  Hashem R Al-Masaeid,et al.  Short-Term Prediction of Traffic Volume in Urban Arterials , 1995 .

[24]  Ye Zhao,et al.  TrajGraph: A Graph-Based Visual Analytics Approach to Studying Urban Network Centralities Using Taxi Trajectory Data , 2016, IEEE Transactions on Visualization and Computer Graphics.

[25]  Edward M. Reingold,et al.  Graph drawing by force‐directed placement , 1991, Softw. Pract. Exp..

[26]  Wanli Min,et al.  Real-time road traffic prediction with spatio-temporal correlations , 2011 .

[27]  Xiaofei He,et al.  Locality Preserving Projections , 2003, NIPS.

[28]  Alexei A. Efros,et al.  City Forensics: Using Visual Elements to Predict Non-Visual City Attributes , 2014, IEEE Transactions on Visualization and Computer Graphics.

[29]  Dino Pedreschi,et al.  Interactive visual clustering of large collections of trajectories , 2009, 2009 IEEE Symposium on Visual Analytics Science and Technology.

[30]  Stephen Lin,et al.  Graph Embedding and Extensions: A General Framework for Dimensionality Reduction , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[31]  Dimitrios Tzovaras,et al.  Multi-Objective Optimization for Multimodal Visualization , 2014, IEEE Transactions on Multimedia.

[32]  Xiaoru Yuan,et al.  Visual Traffic Jam Analysis Based on Trajectory Data , 2013, IEEE Transactions on Visualization and Computer Graphics.

[33]  Wei Wu,et al.  iVizTRANS: Interactive visual learning for home and work place detection from massive public transportation data , 2015, 2015 IEEE Conference on Visual Analytics Science and Technology (VAST).

[34]  Ye Zhao,et al.  Visualizing Hidden Themes of Taxi Movement with Semantic Transformation , 2014, 2014 IEEE Pacific Visualization Symposium.

[35]  Chang-Ho Park,et al.  Travel Time Prediction Using k Nearest Neighbor Method with Combined Data from Vehicle Detector System and Automatic Toll Collection System , 2011 .