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.

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.

scholarly journals Deep learning and Internet of Things for tourist attraction recommendations in smart cities

2022 ◽  
Author(s):  
Juan Carlos Cepeda-Pacheco ◽  
Mari Carmen Domingo
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Smart City ◽  
Recommendation System ◽  
Smart Cities ◽  
Time Of Day ◽  
Tourist Attraction ◽  
K Nearest Neighbor ◽  
Tourist Attractions ◽  
First Case

AbstractWe propose a tourist attraction IoT-enabled deep learning-based recommendation system to enhance tourist experience in a smart city. Travelers will enter details about their travels (traveling alone or with a companion, type of companion such as partner or family with kids, traveling for business or leisure, etc.) as well as user side information (age of the traveler/s, hobbies, etc.) into the smart city app/website. Our proposed deep learning-based recommendation system will process this personal set of input features to recommend the tourist activities/attractions that best fit his/her profile. Furthermore, when the tourists are in the smart city, content-based information (already visited attractions) and context-related information (location, weather, time of day, etc.) are obtained in real time using IoT devices; this information will allow our proposed deep learning-based tourist attraction recommendation system to suggest additional activities and/or attractions in real time. Our proposed multi-label deep learning classifier outperforms other models (decision tree, extra tree, k-nearest neighbor and random forest) and can successfully recommend tourist attractions for the first case [(a) searching for and planning activities before traveling] with the loss, accuracy, precision, recall and F1-score of 0.5%, 99.7%, 99.9%, 99.9% and 99.8%, respectively. It can also successfully recommend tourist attractions for the second case [(b) looking for activities within the smart city] with the loss, accuracy, precision, recall and F1-score of 3.7%, 99.5%, 99.8%, 99.7% and 99.8%, respectively.

scholarly journals A Real-time Pothole Detection Based on Deep Learning Approach

2021 ◽  
Vol 1828 (1) ◽  
pp. 012001
Author(s):  
Yeoh Keng Yik ◽  
Nurul Ezaila Alias ◽  
Yusmeeraz Yusof ◽  
Suhaila Isaak
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Learning Approach ◽  
Pothole Detection

Using a distributed deep learning algorithm for analyzing big data in smart cities

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mohammed Anouar Naoui ◽  
Brahim Lejdel ◽  
Mouloud Ayad ◽  
Abdelfattah Amamra ◽  
Okba kazar
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Data Analysis ◽  
Data Storage ◽  
Smart City ◽  
Smart Cities ◽  
Smart Environment ◽  
Data Systems ◽  
Content Type ◽  
Big Data Systems

PurposeThe purpose of this paper is to propose a distributed deep learning architecture for smart cities in big data systems.Design/methodology/approachWe have proposed an architectural multilayer to describe the distributed deep learning for smart cities in big data systems. The components of our system are Smart city layer, big data layer, and deep learning layer. The Smart city layer responsible for the question of Smart city components, its Internet of things, sensors and effectors, and its integration in the system, big data layer concerns data characteristics 10, and its distribution over the system. The deep learning layer is the model of our system. It is responsible for data analysis.FindingsWe apply our proposed architecture in a Smart environment and Smart energy. 10; In a Smart environment, we study the Toluene forecasting in Madrid Smart city. For Smart energy, we study wind energy foresting in Australia. Our proposed architecture can reduce the time of execution and improve the deep learning model, such as Long Term Short Memory10;.Research limitations/implicationsThis research needs the application of other deep learning models, such as convolution neuronal network and autoencoder.Practical implicationsFindings of the research will be helpful in Smart city architecture. It can provide a clear view into a Smart city, data storage, and data analysis. The 10; Toluene forecasting in a Smart environment can help the decision-maker to ensure environmental safety. The Smart energy of our proposed model can give a clear prediction of power generation.Originality/valueThe findings of this study are expected to contribute valuable information to decision-makers for a better understanding of the key to Smart city architecture. Its relation with data storage, processing, and data analysis.

scholarly journals Quality of Bus Services Performance: Benefits of Real Time Passenger Information Systems

2013 ◽  
Vol 14 (2) ◽  
pp. 155-166 ◽  
Author(s):  
Andres Monzon ◽  
Sara Hernandez ◽  
Rocio Cascajo
Keyword(s):  
Real Time ◽  
Public Transport ◽  
Urban Areas ◽  
Travel Behaviour ◽  
Transport Systems ◽  
Sustainable Transport ◽  
The Public ◽  
Steady Growth ◽  
Bus Services

One of the main problems in urban areas is the steady growth in car ownership and traffic levels. Therefore, the challenge of sustainability is focused on a shift of the demand for mobility from cars to collective means of transport. For this purpose, buses are a key element of the public transport systems. In this respect Real Time Passenger Information (RTPI) systems help people change their travel behaviour towards more sustainable transport modes. This paper provides an assessment methodology which evaluates how RTPI systems improve the quality of bus services performance in two European cities, Madrid and Bremerhaven. In the case of Madrid, bus punctuality has increased by 3%. Regarding the travellers perception, Madrid raised its quality of service by 6% while Bremerhaven increased by 13%. On the other hand, the users´ perception of Public Transport (PT) image increased by 14%.

scholarly journals Detecting Damage Building Using Real-Time Crowdsourced Images And Transfer Learning

2021 ◽  
Author(s):  
Gaurav Chachra ◽  
Qingkai Kong ◽  
Jim Huang ◽  
Srujay Korlakunta ◽  
Jennifer Grannen ◽  
...  
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Real Time ◽  
Transfer Learning ◽  
Learning Model ◽  
Research Community ◽  
Rescue Work ◽  
The Public ◽  
Social Media Platforms ◽  
Deep Learning Model

Abstract After significant earthquakes, we can see images posted on social media platforms by individuals and media agencies owing to the mass usage of smartphones these days. These images can be utilized to provide information about the shaking damage in the earthquake region both to the public and research community, and potentially to guide rescue work. This paper presents an automated way to extract the damaged building images after earthquakes from social media platforms such as Twitter and thus identify the particular user posts containing such images. Using transfer learning and ~6500 manually labelled images, we trained a deep learning model to recognize images with damaged buildings in the scene. The trained model achieved good performance when tested on newly acquired images of earthquakes at different locations and ran in near real-time on Twitter feed after the 2020 M7.0 earthquake in Turkey. Furthermore, to better understand how the model makes decisions, we also implemented the Grad-CAM method to visualize the important locations on the images that facilitate the decision.

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