Analysis and assessment of a knowledge based smart city architecture providing service APIs

Badii, Claudio; Bellini, Pierfrancesco; Cenni, Daniele; Difino, Angelo; Nesi, Paolo; Paolucci, Michela

doi:10.1016/j.future.2017.05.001

Cited by 88 publications

(65 citation statements)

References 14 publications

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“…Note that, for each time interval we estimates the best ARIMA model according to the AIC, Akaike Information Criterion [1] . In most cases, the best predicting model has been an ARIMA [5,1,0] (it is an ARMA model), meaning that the model takes into account of 5 observations from the past, and by the difference of the last two observations. The best AICs have been obtained in the range of 10 0 0-130 0 in different time slots.…”

Section: Predicting Access Point Connectionsmentioning

confidence: 99%

“…For ARMA [5,1] we have: y t − y t −1 = ε + a r 1 ( y t −1 − y t −2 ) + a r 2 ( y t −2 − y t −3 ) + a r 3 ( y t −3 − y t −4 ) + a r 4 ( y t −4 − y t −5 ) + a r 5 ( y t −5 − y t −6 )…”

Section: Predicting Access Point Connectionsunclassified

“…Where: ar 1 , ar 2 , ar 3 , ar 4 , ar 5 are determined during the identification of the model minimising the root square error during the learning period, ɛ is an independent variable with normal distribution and zero mean.…”

Section: Predicting Access Point Connectionsmentioning

confidence: 99%

“…For the city municipality it is very important to know the movements of city users within a certain precision, and detecting where and how they are crossing the city and exploiting services by using different kinds of http://dx.doi.org/10.1016/j.jvlc.2017.08.005 1045-926X/© 2017 Elsevier Ltd. All rights reserved. transportation solutions: car, bike, walking, taxi, car sharing, buses, tram, etc., targeting services into the city [ [4] , [5] , [11] ].…”

Section: Introductionmentioning

confidence: 99%

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Wi-Fi based city users’ behaviour analysis for smart city

Bellini

Cenni

Nesi

et al. 2017

Journal of Visual Languages & Computing

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a b s t r a c tMonitoring, understanding and predicting city user behaviour (hottest places, trajectories, flows, etc.) is one the major topics in the context of Smart City management. People flow surveillance provides valuable information about city conditions, useful not only for monitoring and controlling the environmental conditions, but also to optimize the deliverying of city services (security, clean, transport,..). In this context, it is mandatory to develop methods and tools for assessing people behaviour in the city. This paper presents a methodology to instrument the city via the placement of Wi-Fi Access Points, AP, and to use them as sensors to capture and understand city user behaviour with a significant precision rate (the understanding of city user behaviour is concretized with the computing of heat-maps, origin destination matrices and predicting user density). The first issue is the positioning of Wi-Fi AP in the city, thus a comparative analyses have been conducted with respect to the real data (i.e., cab traces) of the city of San Francisco. Several different positioning methodologies of APs have been proposed and compared, to minimize the cost of AP installation with the aim of producing the best origin destination matrices. In a second phase, the methodology was adopted to select suitable AP in the city of Florence (Italy), with the aim of observing city users behaviour. The obtained instrumented Firenze Wi-Fi network collected data for 6 months. The data has been analysed with data mining techniques to infer similarity patterns in AP area and related time series. The resulting model has been validated and used for predicting the number of AP accesses that is also related to number of city users. The research work described in this paper has been conducted in the scope of the EC funded Horizon 2020 project Resolute ( http://www.resolute-eu.org ), for early warning and city resilience.

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Section: Predicting Access Point Connectionsmentioning

confidence: 99%

Section: Predicting Access Point Connectionsunclassified

Section: Predicting Access Point Connectionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Wi-Fi based city users’ behaviour analysis for smart city

Bellini

Cenni

Nesi

et al. 2017

Journal of Visual Languages & Computing

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“…A wide range of processes and data is presented [3]. Anna Luusua et al introduced a new way of designing called nonanthropocentric design to decenter the human as the focus of design while the smart city agenda is critique for its focus on technology and business led solutions [4].…”

Section: Introductionmentioning

confidence: 99%

Quantitative Assessment of City’s Smart Growth Level in China Using Fuzzy Comprehensive Evaluation

et al. 2018

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Abstract. In this paper we develop an indicator system to evaluate the Smart Growth Level based on 3E's principles and calculate the Smart Growth Level of 70 mid-scale and large cities in China using fuzzy comprehensive evaluation model. We also divide these cities into four grades: outstanding, good, normal and terrible. The results are significant for governments to design a reasonable development plan.

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