User Engagement Engine for Smart City Strategies

The new challenges in the smart city context are mainly related to the stimulation of the city users towards taking more sustainable behaviors, in mobility and energy. The state of the art in this case is mainly focused on classical smart city solution for informing the city users and or for engaging them with specific wired rules toward virtuous models. And not using flexible languages and predictive models, pushing them towards a larger range of virtuous habits. On this regards, the main problems are the computation of user behavior via data analytic (semantic computing, machine learning), as well as the formalization of strategies via simple and well formalized language for producing engagements to the city users, which can be understood by city operators. In this paper, a solution for city users engagement is studied and implemented for Sii-Mobility Smart city national project in Italy has been presented. The solution has been implemented thanks to the exploitation of Km4City model and semantic computing. The paper also presents the validation of results about the effective usage of the solution by providing some statistical evidence about the efficient assessment of user behavior and of engagement rules acceptance rate.

[1]  Thad Starner,et al.  Using GPS to learn significant locations and predict movement across multiple users , 2003, Personal and Ubiquitous Computing.

[2]  Reza Curtmola,et al.  Fostering participaction in smart cities: a geo-social crowdsensing platform , 2013, IEEE Communications Magazine.

[3]  Dino Pedreschi,et al.  Mobility, Data Mining and Privacy - Geographic Knowledge Discovery , 2008, Mobility, Data Mining and Privacy.

[4]  Elaine Toms,et al.  What is user engagement? A conceptual framework for defining user engagement with technology , 2008, J. Assoc. Inf. Sci. Technol..

[5]  Derry O'Sullivan,et al.  Understanding Case Based Recommendation: A Similarity Knowledge Perspective , 2005, Int. J. Artif. Intell. Tools.

[6]  Pierfrancesco Bellini,et al.  Km4City Smart City API: An Integrated Support for Mobility Services , 2016, 2016 IEEE International Conference on Smart Computing (SMARTCOMP).

[7]  Zoubin Ghahramani,et al.  An Introduction to Hidden Markov Models and Bayesian Networks , 2001, Int. J. Pattern Recognit. Artif. Intell..

[8]  Gabriella Kazai,et al.  Towards a science of user engagement. , 2011 .

[9]  David Banister,et al.  The sustainable mobility paradigm , 2008 .

[10]  Nicole Novielli,et al.  HMM modeling of user engagement in advice-giving dialogues , 2010, Journal on Multimodal User Interfaces.

[11]  Wei-Ying Ma,et al.  Recommending friends and locations based on individual location history , 2011, ACM Trans. Web.

[12]  Pierfrancesco Bellini,et al.  AP Positioning for Estimating People Flow as Origin Destination Matrix for Smart Cities , 2016, DMS.

[13]  Mounia Lalmas,et al.  Models of user engagement , 2012, UMAP.

[14]  Pierfrancesco Bellini,et al.  Optimization of information retrieval for cross media contents in a best practice network , 2014, International Journal of Multimedia Information Retrieval.

[15]  Abdelghani Chibani,et al.  Rule-Based Context Assessment in Smart Cities , 2012, RR.

[16]  Laura Díaz,et al.  Mobile Application for Noise Pollution Monitoring through Gamification Techniques , 2012, ICEC.

[17]  D. Hoang.  FLOW: The Psychology of Optimal Experience , 2018 .