scholarly journals Snap4City Platform to Speed Up Policies

2021 ◽  
pp. 103-114
Author(s):  
Nicola Mitolo ◽  
Paolo Nesi ◽  
Gianni Pantaleo ◽  
Michela Paolucci
Keyword(s):  
Real Time ◽  
Public Transportation ◽  
Smart City ◽  
Smart Cities ◽  
Research Centers ◽  
Time Data ◽  
Aggregated Data ◽  
The Public ◽  
Starting Point ◽  
The Status

AbstractIn the development of smart cities, there is a great emphasis on setting up so-called Smart City Control Rooms, SCCR. This paper presents Snap4City as a big data smart city platform to support the city decision makers by means of SCCR dashboards and tools reporting in real time the status of several of a city’s aspects. The solution has been adopted in European cities such as Antwerp, Florence, Lonato del Garda, Pisa, Santiago, etc., and it is capable of covering extended geographical areas around the cities themselves: Belgium, Finland, Tuscany, Sardinia, etc. In this paper, a major use case is analyzed describing the workflow followed, the methodologies adopted and the SCCR as the starting point to reproduce the same results in other smart cities, industries, research centers, etc. A Living Lab working modality is promoted and organized to enhance the collaboration among municipalities and public administration, stakeholders, research centers and the citizens themselves. The Snap4City platform has been realized respecting the European Data Protection Regulation (GDPR), and it is capable of processing every day a multitude of periodic and real-time data coming from different providers and data sources. It is therefore able to semantically aggregate the data, in compliance with the Km4City multi-ontology and manage data: (i) having different access policies; and (ii) coming from traditional sources such as Open Data Portals, Web services, APIs and IoT/IoE networks. The aggregated data are the starting point for the services offered not only to the citizens but also to the public administrations and public-security service managers, enabling them to view a set of city dashboards ad hoc composed on their needs, for example, enabling them to modify and monitor public transportation strategies, offering the public services actually needed by citizens and tourists, monitor the air quality and traffic status to establish, if impose or not, traffic restrictions, etc. All the data and the new knowledge produced by the data analytics of the Snap4City platform can also be accessed, observing the permissions on each kind of data, thanks to the presence of an APIs complex system.

Cloud-Based Big Data Analysis Tools and Techniques Towards Sustainable Smart City Services

2021 ◽  
pp. 63-90
Author(s):  
Suresh P. ◽  
Keerthika P. ◽  
Sathiyamoorthi V. ◽  
Logeswaran K. ◽  
Manjula Devi R. ◽  
...  
Keyword(s):  
Big Data ◽  
Real Time ◽  
Smart City ◽  
Traffic Management ◽  
Smart Cities ◽  
Big Data Analytics ◽  
Streaming Data ◽  
City Development ◽  
Time Data ◽  
Public And Private

Cloud computing and big data analytics are the key parts of smart city development that can create reliable, secure, healthier, more informed communities while producing tremendous data to the public and private sectors. Since the various sectors of smart cities generate enormous amounts of streaming data from sensors and other devices, storing and analyzing this huge real-time data typically entail significant computing capacity. Most smart city solutions use a combination of core technologies such as computing, storage, databases, data warehouses, and advanced technologies such as analytics on big data, real-time streaming data, artificial intelligence, machine learning, and the internet of things (IoT). This chapter presents a theoretical and experimental perspective on the smart city services such as smart healthcare, water management, education, transportation and traffic management, and smart grid that are offered using big data management and cloud-based analytics services.

scholarly journals Urban Planning and Smart City Decision Management Empowered by Real-Time Data Processing Using Big Data Analytics

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2994 ◽  
Author(s):  
Bhagya Silva ◽  
Murad Khan ◽  
Changsu Jung ◽  
Jihun Seo ◽  
Diyan Muhammad ◽  
...  
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Real Time ◽  
Smart City ◽  
Data Analytics ◽  
Smart Cities ◽  
Big Data Analytics ◽  
Time Data ◽  
Real Time Data

The Internet of Things (IoT), inspired by the tremendous growth of connected heterogeneous devices, has pioneered the notion of smart city. Various components, i.e., smart transportation, smart community, smart healthcare, smart grid, etc. which are integrated within smart city architecture aims to enrich the quality of life (QoL) of urban citizens. However, real-time processing requirements and exponential data growth withhold smart city realization. Therefore, herein we propose a Big Data analytics (BDA)-embedded experimental architecture for smart cities. Two major aspects are served by the BDA-embedded smart city. Firstly, it facilitates exploitation of urban Big Data (UBD) in planning, designing, and maintaining smart cities. Secondly, it occupies BDA to manage and process voluminous UBD to enhance the quality of urban services. Three tiers of the proposed architecture are liable for data aggregation, real-time data management, and service provisioning. Moreover, offline and online data processing tasks are further expedited by integrating data normalizing and data filtering techniques to the proposed work. By analyzing authenticated datasets, we obtained the threshold values required for urban planning and city operation management. Performance metrics in terms of online and offline data processing for the proposed dual-node Hadoop cluster is obtained using aforementioned authentic datasets. Throughput and processing time analysis performed with regard to existing works guarantee the performance superiority of the proposed work. Hence, we can claim the applicability and reliability of implementing proposed BDA-embedded smart city architecture in the real world.

scholarly journals Internet of Things for the Development of Smart Cities

2019 ◽  
Vol 8 (4) ◽  
pp. 9543-9547
Keyword(s):  
Internet Of Things ◽  
Public Transportation ◽  
Traffic Congestion ◽  
Smart City ◽  
Smart Home ◽  
Smart Cities ◽  
City Development ◽  
The Public ◽  
Innovative Solution ◽  
Energy Efficient Building

Internet of things plays an important role to make smart in all the areas like smart city, smart home etc [1]. It is used in more efficient water supply, an innovative solution for traffic congestion, to make reliable public transportation, improved the public safety, energy efficient building, Vehicle smart security system etc [4]. While the average cost for basic items is going up, there is a developing concentration to include innovation to bring down those costs for smart city development. In the following chapter will discussed the few innovation for the smart city development.

scholarly journals Passenger Control in Smart Cities Using Deep Learning

2020 ◽  
Vol 5 (7) ◽  
pp. 1428-1431
Author(s):  
Delna T D ◽  
Dhanya P Pauly ◽  
Dona Johnson ◽  
Jesta Jose
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Public Transport ◽  
Smart City ◽  
Smart Cities ◽  
Average Weight ◽  
Learning Approach ◽  
The Public ◽  
Day By Day ◽  
Growing Population

In the current smart city background, people are facing a lot of accidents at the major traffic points of the business towns due to growing population and vehicles growth in smart and metropolitan cities.In this method we consider the auto taxies as well as the public transport. We know that due to the overload in the vehicles the accidents are increasing day by day so using this method the number of accidents be able to be avoided or reduced. This system is introducing the deep learning approach to find the overload in vehicles. We are considering the luggage that is taken along with the passenger and an average weight is given for the load. Then it is combined with the number of passenger and system will predict whether the vehicle is overload or not. Mainly because of using deep learning concepts we can increase the speed of the process and the efficiency. The system will analyse the number of passengers using real time videos using camera and system detect and compare with the overloading conditions to avoidaccidents.

YOUTH EXPECTATIONS OF SMART CITY LIVING: AN IMPORTANCE- PERFORMANCE ANALYSIS OF YOUNG RESIDENTS’ PERSPECTIVES OF CITY GOVERNMENT

2017 ◽  
Vol 14 (1) ◽  
pp. 118-128
Author(s):  
Jason Cohen ◽  
Judy Backhouse ◽  
Omar Ally
Keyword(s):  
Performance Analysis ◽  
Young People ◽  
Smart City ◽  
Smart Cities ◽  
City Government ◽  
Diagnostic Tools ◽  
City Management ◽  
Bottom Up ◽  
Starting Point ◽  
The City

Young people are important to cities, bringing skills and energy and contributing to economic activity. New technologies have led to the idea of a smart city as a framework for city management. Smart cities are developed from the top-down through government programmes, but also from the bottom-up by residents as technologies facilitate participation in developing new forms of city services. Young people are uniquely positioned to contribute to bottom-up smart city projects. Few diagnostic tools exist to guide city authorities on how to prioritise city service provision. A starting point is to understand how the youth value city services. This study surveys young people in Braamfontein, Johannesburg, and conducts an importance-performance analysis to identify which city services are well regarded and where the city should focus efforts and resources. The results show that Smart city initiatives that would most increase the satisfaction of youths in Braamfontein  include wireless connectivity, tools to track public transport  and  information  on city events. These  results  identify  city services that are valued by young people, highlighting services that young people could participate in providing. The importance-performance analysis can assist the city to direct effort and scarce resources effectively.

scholarly journals A Dynamic Mobility Traffic Model Based on Two Modes of Transport in Smart Cities

Smart Cities ◽  
2021 ◽  
Vol 4 (1) ◽  
pp. 253-270
Author(s):  
Mohammed Bin Hariz ◽  
Dhaou Said ◽  
Hussein T. Mouftah
Keyword(s):  
Data Exchange ◽  
Smart Cities ◽  
Time Data ◽  
School Bus ◽  
The Public ◽  
Ride Sharing ◽  
Dynamic Mobility ◽  
Proposed Model ◽  
Loading System ◽  
Suitable Combination

This paper focuses on transportation models in smart cities. We propose a new dynamic mobility traffic (DMT) scheme which combines public buses and car ride-sharing. The main objective is to improve transportation by maximizing the riders’ satisfaction based on real-time data exchange between the regional manager, the public buses, the car ride-sharing and the riders. OpenStreetMap and OMNET++ were used to implement a realistic scenario for the proposed model in a city like Ottawa. The DMT scheme was compared to a multi-loading system used for a school bus. Simulations showed that rider satisfaction was enhanced when a suitable combination of transportation modes was used. Additionally, compared to the other scheme, this DMT scheme can reduce the stress level of car ride-sharing and public buses during the day to the minimal level.

Internet of Things and the economics of smart sustainable cities

2017 ◽  
Vol 18 (1-2) ◽  
pp. 115-131 ◽  
Author(s):  
Günter Knieps
Keyword(s):  
Public Transportation ◽  
Smart City ◽  
Smart Cities ◽  
Packet Transmission ◽  
Information And Communications Technology ◽  
Global Navigation Satellite Systems ◽  
Wastewater Management ◽  
Virtual Networks ◽  
The Future

The major objective of this article is to analyze the potentials of information and communications technology (ICT) for the evolution of smart cities, with a particular focus on the challenges faced by traditional public utilities in the areas of public transportation, energy, water supply, and wastewater management due to the entry of new players originating from ICT organizations and industries. The character of virtual networks for smart cities is demonstrated based on three pillars: (1) All-IP–based real-time and adaptive broadband communication networks, (2) global navigation satellite systems and their overlay position correction networks, and (3) the interoperability of ubiquitous sensor network applications, as they form the ICT basis for a multitude of applications that are important in smart cities. The heterogeneity of virtual networks for different smart city physical network services is based on these pillars, taking into account the different requirements for the quality of service (QoS) of data packet transmission, geopositioning, and sensor networks. It can be expected that prosumer activities and resultant networked commons become increasingly relevant for the smart city of the future. However, the increasing role of prosumer activities cannot replace the role of markets in solving scarcity problems within ICT networks as well as physical networks. The role of congestion pricing and QoS differentiation for network capacities in transportation and electricity markets as well as ICT is indicated. If, due to non-rivalry in usage, efficient congestion prices are pointless, the future role of subsidies from the state is considered.

scholarly journals A Comprehensive Comparative Analysis of the Basic Theory of the Short Term Bus Passenger Flow Prediction

Symmetry ◽  
2018 ◽  
Vol 10 (9) ◽  
pp. 369 ◽  
Author(s):  
Huawei Zhai ◽  
Licheng Cui ◽  
Yu Nie ◽  
Xiaowei Xu ◽  
Weishi Zhang
Keyword(s):  
Real Time ◽  
Public Transportation ◽  
Transportation Systems ◽  
Training Data ◽  
Time Data ◽  
Short Term ◽  
Passenger Flow ◽  
Modeling Process ◽  
Automatic Passenger Counters ◽  
Public Transportation Systems

In order to meet the real-time public travel demands, the bus operators need to adjust the timetables in time. Therefore, it is necessary to predict the variations of the short-term passenger flow. Under the help of the advanced public transportation systems, a large amount of real-time data about passenger flow is collected from the automatic passenger counters, automatic fare collection systems, etc. Using these data, different kinds of methods are proposed to predict future variations of the short-term bus passenger flow. Based on the properties and background knowledge, these methods are classified into three categories: linear, nonlinear and combined methods. Their performances are evaluated in detail in the major aspects of the prediction accuracy, the complexity of training data structure and modeling process. For comparison, some long-term prediction methods are also analyzed simply. At last, it points that, with the help of automatic technology, a large amount of data about passenger flow will be collected, and using the big data technology to speed up the data preprocessing and modeling process may be one of the directions worthy of study in the future.

scholarly journals Semantic Traffic Sensor Data: The TRAFAIR Experience

2020 ◽  
Vol 10 (17) ◽  
pp. 5882
Author(s):  
Federico Desimoni ◽  
Sergio Ilarri ◽  
Laura Po ◽  
Federica Rollo ◽  
Raquel Trillo-Lado
Keyword(s):  
Air Quality ◽  
Real Time ◽  
Smart Cities ◽  
Low Cost ◽  
Semantic Annotation ◽  
Open Data ◽  
Transportation Systems ◽  
Sensor Data ◽  
Time Data ◽  
Future Evolution

Modern cities face pressing problems with transportation systems including, but not limited to, traffic congestion, safety, health, and pollution. To tackle them, public administrations have implemented roadside infrastructures such as cameras and sensors to collect data about environmental and traffic conditions. In the case of traffic sensor data not only the real-time data are essential, but also historical values need to be preserved and published. When real-time and historical data of smart cities become available, everyone can join an evidence-based debate on the city’s future evolution. The TRAFAIR (Understanding Traffic Flows to Improve Air Quality) project seeks to understand how traffic affects urban air quality. The project develops a platform to provide real-time and predicted values on air quality in several cities in Europe, encompassing tasks such as the deployment of low-cost air quality sensors, data collection and integration, modeling and prediction, the publication of open data, and the development of applications for end-users and public administrations. This paper explicitly focuses on the modeling and semantic annotation of traffic data. We present the tools and techniques used in the project and validate our strategies for data modeling and its semantic enrichment over two cities: Modena (Italy) and Zaragoza (Spain). An experimental evaluation shows that our approach to publish Linked Data is effective.

scholarly journals Alerts and Notifications for Smart Citizens

2019 ◽  
Vol 29 ◽  
pp. 03001
Author(s):  
Silviu Vert ◽  
Radu Vasiu
Keyword(s):  
Public Transportation ◽  
Smart City ◽  
Smart Cities ◽  
Notification Data ◽  
Future Plans

Smart cities function on the premises of efficiency and transparency. One of the key requests of smart citizens is to be informed, through modern, digital and personalized means, of issues that might affect them: utility cut-offs, changes in public transportation, exceeding levels of pollutants and so on. In this research, we implement an extension — to an existing smart city notification platform — that consists of a notification and alert module build as a chatbot for Facebook Messenger. We describe the sources of notification data, how we designed the chatbot and current possibilities for users to interact with it and be notified. We also relate on future plans on improving the chatbot.

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