Modeling Users' Performance: Predictive Analytics in an IoT Cloud Monitoring System

We exploit the feasibility of predictive modeling combined with the support given by a suitably defined IoT Cloud Infrastructure in the attempt of assessing and reporting relative performances for user-specific settings during a bike trial. The matter is addressed by introducing a suitable dynamical system whose state variables are the so-called origin-destination (OD) flow deviations obtained from prior estimates based on historical data recorded by means of mobile sensors directly installed in each bike through a fast real-time processing of big traffic data. We then use the Kalman filter theory in order to dynamically update an assignment matrix in such a context and gain information about usual routes and distances. This leads us to a dynamical ranking system for the users of the bike trial community making the award procedure more transparent.

[1]  R. E. Kalman,et al.  A New Approach to Linear Filtering and Prediction Problems , 2002 .

[2]  J. Scott Armstrong,et al.  Principles of forecasting , 2001 .

[3]  Majed Kamel Al-Azzam,et al.  Smart City and Smart-Health Framework, Challenges and Opportunities , 2019, International Journal of Advanced Computer Science and Applications.

[4]  Susan Grant-Muller,et al.  Using Non-Parametric Tests to Evaluate Traffic Forecasting Performance. , 2002 .

[5]  Rudolf Giffinger,et al.  Smart cities ranking: an effective instrument for the positioning of cities? , 2009, 5th International Conference Virtual City and Territory, Barcelona, 2,3 and 4 June 2009.

[6]  Ennio Cascetta,et al.  Dynamic Estimators of Origin-Destination Matrices Using Traffic Counts , 1993, Transp. Sci..

[7]  Ivan Ganchev,et al.  A generic IoT architecture for smart cities , 2014 .

[8]  Athanasios V. Vasilakos,et al.  Characterizing the role of vehicular cloud computing in road traffic management , 2017, Int. J. Distributed Sens. Networks.

[9]  Susan Grant-Muller,et al.  Use of sequential learning for short-term traffic flow forecasting , 2001 .

[10]  Majid Sarvi,et al.  Using Kalman filter algorithm for short-term traffic flow prediction in a connected vehicle environment , 2019, Journal of Modern Transportation.

[11]  Imad Mahgoub,et al.  Big vehicular traffic Data mining: Towards accident and congestion prevention , 2016, 2016 International Wireless Communications and Mobile Computing Conference (IWCMC).

[12]  Guoqiang Peter Zhang,et al.  Time series forecasting using a hybrid ARIMA and neural network model , 2003, Neurocomputing.

[13]  Maria Fazio,et al.  An IoT Cloud System for Traffic Monitoring and Vehicular Accidents Prevention Based on Mobile Sensor Data Processing , 2018, IEEE Sensors Journal.

[14]  Maria Fazio,et al.  An Innovative Osmotic Computing Framework for Self Adapting City Traffic in Autonomous Vehicle Environment , 2018, 2018 IEEE Symposium on Computers and Communications (ISCC).

[15]  Matthew G. Karlaftis,et al.  A multivariate state space approach for urban traffic flow modeling and prediction , 2003 .

[16]  J.L. Martins de Carvalho,et al.  Towards the development of intelligent transportation systems , 2001, ITSC 2001. 2001 IEEE Intelligent Transportation Systems. Proceedings (Cat. No.01TH8585).

[17]  M Di Gangi,et al.  Combining simulative and statistical approach for short time flow forecasting , 2005 .

[18]  Eleni I. Vlahogianni,et al.  Short‐term traffic forecasting: Overview of objectives and methods , 2004 .

[19]  Antonio Puliafito,et al.  The Need of a Hybrid Storage Approach for IoT in PaaS Cloud Federation , 2014, 2014 28th International Conference on Advanced Information Networking and Applications Workshops.

[20]  Massimo Di Gangi,et al.  Modeling Evacuation of a Transport System: Application of a Multimodal Mesoscopic Dynamic Traffic Assignment Model , 2011, IEEE Transactions on Intelligent Transportation Systems.

[21]  Nicola Ivan Giannoccaro,et al.  MODELING AND DESIGNING A FULL BEAMFORMER FOR ACOUSTIC SENSING AND MEASUREMENT , 2017 .

[22]  J. Scott Armstrong,et al.  Principles of forecasting : a handbook for researchers and practitioners , 2001 .