Pub Date : 2018-09-01DOI: 10.1109/ISC2.2018.8656835
Patrícia Dias, Joana Rodrigues, Ana Aguiar, G. David
Aiming to improve sustainability and life quality, urban space research is prompting an intensive use of communication and information technologies. With it, researchers are also facing more challenges regarding research data management and therefore seeking clear guidelines and tools for proper data organization, sharing and reuse. In the context of a smart cities research project, UrbanSense, held in the city of Porto, we proposed a data management plan, to support researchers from the moment they start to collect data up to the point of data publication. We also developed an ontology for the description of smart cities data, validated by UrbanSense researchers. Descriptions based on this ontology were evaluated by external parties, after the data was published in an institutional data repository.
{"title":"Planning and managing data for Smart Cities: an application profile for the UrbanSense project","authors":"Patrícia Dias, Joana Rodrigues, Ana Aguiar, G. David","doi":"10.1109/ISC2.2018.8656835","DOIUrl":"https://doi.org/10.1109/ISC2.2018.8656835","url":null,"abstract":"Aiming to improve sustainability and life quality, urban space research is prompting an intensive use of communication and information technologies. With it, researchers are also facing more challenges regarding research data management and therefore seeking clear guidelines and tools for proper data organization, sharing and reuse. In the context of a smart cities research project, UrbanSense, held in the city of Porto, we proposed a data management plan, to support researchers from the moment they start to collect data up to the point of data publication. We also developed an ontology for the description of smart cities data, validated by UrbanSense researchers. Descriptions based on this ontology were evaluated by external parties, after the data was published in an institutional data repository.","PeriodicalId":344652,"journal":{"name":"2018 IEEE International Smart Cities Conference (ISC2)","volume":"427 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123050920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-09-01DOI: 10.1109/ISC2.2018.8656717
Jérémy Petit, Rafik Zitouni, L. George
In this work, we propose a demonstration of Urban Traffic Light Control based on an IoT network (IoT-UTLC) for smart cities. We mocked up a real crossroad by integrating a 6LoWPAN Wireless Sensor Network (WSN) to control mini traffic light panels. The network’s nodes are wireless sensors and actuators interacting with an IoT Cloud Platform. MQTT Quality of Service (QoS) protocol has been implemented to manage the priority levels of exchanged data between the Cloud and WSN. Our IoT-UTLC has been found functional after verification and validation using the UPPAAL model checker.
{"title":"Prototyping of Urban Traffic-Light Control in IoT","authors":"Jérémy Petit, Rafik Zitouni, L. George","doi":"10.1109/ISC2.2018.8656717","DOIUrl":"https://doi.org/10.1109/ISC2.2018.8656717","url":null,"abstract":"In this work, we propose a demonstration of Urban Traffic Light Control based on an IoT network (IoT-UTLC) for smart cities. We mocked up a real crossroad by integrating a 6LoWPAN Wireless Sensor Network (WSN) to control mini traffic light panels. The network’s nodes are wireless sensors and actuators interacting with an IoT Cloud Platform. MQTT Quality of Service (QoS) protocol has been implemented to manage the priority levels of exchanged data between the Cloud and WSN. Our IoT-UTLC has been found functional after verification and validation using the UPPAAL model checker.","PeriodicalId":344652,"journal":{"name":"2018 IEEE International Smart Cities Conference (ISC2)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116941247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-09-01DOI: 10.1109/ISC2.2018.8656978
Joel Carneiro, R. Rossetti, D. Silva, E. Oliveira
The management and maintenance of road infrastructures demand the use of a tremendous amount of data about their maintenance history and current status. One very characteristic of this information is its geographic nature, which suggests that Geographic Information Systems (GIS) are appropriate to facilitate the way we handle it. However, the access to such information nowadays is challenging because of numerous reasons: some data is still mainly stored on paper; databases are out of date; managing scarcely existing records and creating new ones is quite a laborious and time-consuming task; field inspections require human resources and are expensive; and so forth. Thus, we need to make smarter the way management and maintenance of road infrastructures are performed. Some promising technologies appeared in the last few years to overcome a number of the identified issues. This paper presents and discusses on the most prominent work efforts aiming at more intelligent management of the city infrastructures, specially focusing on transport networks. Several of such efforts use Geographic Information Systems, Building Information Modelling (BIM), Internet of Things (IoT), and Virtual/Augmented Reality (VR/AR) technologies. Thus, this study emphasizes on the GIS-BIM-IoT and GIS-BIM-VR/AR integrations showing the possibilities and potentials when these technologies work together.
{"title":"BIM, GIS, IoT, and AR/VR Integration for Smart Maintenance and Management of Road Networks: a Review","authors":"Joel Carneiro, R. Rossetti, D. Silva, E. Oliveira","doi":"10.1109/ISC2.2018.8656978","DOIUrl":"https://doi.org/10.1109/ISC2.2018.8656978","url":null,"abstract":"The management and maintenance of road infrastructures demand the use of a tremendous amount of data about their maintenance history and current status. One very characteristic of this information is its geographic nature, which suggests that Geographic Information Systems (GIS) are appropriate to facilitate the way we handle it. However, the access to such information nowadays is challenging because of numerous reasons: some data is still mainly stored on paper; databases are out of date; managing scarcely existing records and creating new ones is quite a laborious and time-consuming task; field inspections require human resources and are expensive; and so forth. Thus, we need to make smarter the way management and maintenance of road infrastructures are performed. Some promising technologies appeared in the last few years to overcome a number of the identified issues. This paper presents and discusses on the most prominent work efforts aiming at more intelligent management of the city infrastructures, specially focusing on transport networks. Several of such efforts use Geographic Information Systems, Building Information Modelling (BIM), Internet of Things (IoT), and Virtual/Augmented Reality (VR/AR) technologies. Thus, this study emphasizes on the GIS-BIM-IoT and GIS-BIM-VR/AR integrations showing the possibilities and potentials when these technologies work together.","PeriodicalId":344652,"journal":{"name":"2018 IEEE International Smart Cities Conference (ISC2)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125472361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-09-01DOI: 10.1109/ISC2.2018.8656801
E. J. Cedillo-Elias, Jose Antonio Orizaga Trejo, Víctor M. Larios-Rosillo, L. A. M. Arellano
Guadalajara city is in transformation towards being a Smart City, for that it has to improve its IT infrastructure. As part of the Smart City development process in Guadalajara Metropolitan Area, Jalisco State Government is improving its IT infrastructure towards a Smart Government developing more services connected to the citizens. With 8.14 million inhabitants in Jalisco State, the 61% has concentrated in de metropolitan zone; every online service must support connectivity of millions of users in peak periods with a good quality of service and user experience. As an example, each year the government has to collect taxes for the city, creating peaks of connectivity into tax offices. With the integration of Cloud Computing services, we present a study of accessibility for the citizens through to mobile platforms looking to orchestrate data flows with Private Cloud Infrastructure and Software Defined Networks (SDN). Based on open source solutions, this paper presents a collaborative experience among Government and Academia in the implementation of a private Cloud together with SDN technologies offering advantages in Cloud services to improve Smart Government services. Also through of implementation of IoT devices, the tax offices sense and monitor different environment variables to improve services.
{"title":"Smart Government infrastructure based in SDN Networks: the case of Guadalajara Metropolitan Area","authors":"E. J. Cedillo-Elias, Jose Antonio Orizaga Trejo, Víctor M. Larios-Rosillo, L. A. M. Arellano","doi":"10.1109/ISC2.2018.8656801","DOIUrl":"https://doi.org/10.1109/ISC2.2018.8656801","url":null,"abstract":"Guadalajara city is in transformation towards being a Smart City, for that it has to improve its IT infrastructure. As part of the Smart City development process in Guadalajara Metropolitan Area, Jalisco State Government is improving its IT infrastructure towards a Smart Government developing more services connected to the citizens. With 8.14 million inhabitants in Jalisco State, the 61% has concentrated in de metropolitan zone; every online service must support connectivity of millions of users in peak periods with a good quality of service and user experience. As an example, each year the government has to collect taxes for the city, creating peaks of connectivity into tax offices. With the integration of Cloud Computing services, we present a study of accessibility for the citizens through to mobile platforms looking to orchestrate data flows with Private Cloud Infrastructure and Software Defined Networks (SDN). Based on open source solutions, this paper presents a collaborative experience among Government and Academia in the implementation of a private Cloud together with SDN technologies offering advantages in Cloud services to improve Smart Government services. Also through of implementation of IoT devices, the tax offices sense and monitor different environment variables to improve services.","PeriodicalId":344652,"journal":{"name":"2018 IEEE International Smart Cities Conference (ISC2)","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125492995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-09-01DOI: 10.1109/ISC2.2018.8656935
H. Nieto-Chaupis
When Shannon’s entropy and Fisher-Snedecor statistics are working together, this can enter into a scheme of artificial intelligence to tackle social problems such as the identification of worrisome spatial points where street criminality and vehicle’s chaos is happening sharply. In this paper we construct a computational scheme to anticipate these abnormal social events. For this end we use Google-earth maps. The Fischer-Snedecor and Shannon’s entropy mathematical machinery have served to build schemes of probabilities to identify these social events. When computational simulations are done we perform matching of output ‘s simulation and official data. For the case of Lima city our modeling matches the one from real data with an accuracy of order of 85%. This result is translated as the capability of the stochastic models to analyze and measure social abnormalities such as street criminality and vehicle traffic in large cities using artificial intelligence in conjunction to stochastic formalisms.
{"title":"Identification of the Social Duality: Street Criminality and High Vehicle Traffic in Lima City by Using Artificial Intelligence Through the Fisher-Snedecor Statistics and Shannon’s Entropy","authors":"H. Nieto-Chaupis","doi":"10.1109/ISC2.2018.8656935","DOIUrl":"https://doi.org/10.1109/ISC2.2018.8656935","url":null,"abstract":"When Shannon’s entropy and Fisher-Snedecor statistics are working together, this can enter into a scheme of artificial intelligence to tackle social problems such as the identification of worrisome spatial points where street criminality and vehicle’s chaos is happening sharply. In this paper we construct a computational scheme to anticipate these abnormal social events. For this end we use Google-earth maps. The Fischer-Snedecor and Shannon’s entropy mathematical machinery have served to build schemes of probabilities to identify these social events. When computational simulations are done we perform matching of output ‘s simulation and official data. For the case of Lima city our modeling matches the one from real data with an accuracy of order of 85%. This result is translated as the capability of the stochastic models to analyze and measure social abnormalities such as street criminality and vehicle traffic in large cities using artificial intelligence in conjunction to stochastic formalisms.","PeriodicalId":344652,"journal":{"name":"2018 IEEE International Smart Cities Conference (ISC2)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122764787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-09-01DOI: 10.1109/ISC2.2018.8656957
Saurabh Pandey, N. Chowdhury, Milan Patil, R. Raje, C. S. Shreyas, G. Mohler, J. Carter
Communities are adversely affected by heterogeneous social harm events (e.g., crime, traffic crashes, medical emergencies, drug use) and police, fire, health and social service departments are tasked with mitigating social harm through various types of interventions. Smart cities of the future will need to leverage IoT, data analytics, and government and community human resources to most effectively reduce social harm. Currently, methods for collection, analysis, and modeling of heterogeneous social harm data to identify government actions to improve quality of life are needed. In this paper we propose a system, CDASH, for synthesizing heterogeneous social harm data from multiples sources, identifying social harm risks in space and time, and communicating the risk to the relevant community resources best equipped to intervene. We discuss the design, architecture, and performance of CDASH. CDASH allows users to report live social harm events using mobile hand-held devices and web browsers and flags high risk areas for law enforcement and first responders. To validate the methodology, we run simulations on historical social harm event data in Indianapolis illustrating the advantages of CDASH over recently introduced social harm indices and existing point process methods used for predictive policing.
{"title":"CDASH: Community Data Analytics for Social Harm Prevention","authors":"Saurabh Pandey, N. Chowdhury, Milan Patil, R. Raje, C. S. Shreyas, G. Mohler, J. Carter","doi":"10.1109/ISC2.2018.8656957","DOIUrl":"https://doi.org/10.1109/ISC2.2018.8656957","url":null,"abstract":"Communities are adversely affected by heterogeneous social harm events (e.g., crime, traffic crashes, medical emergencies, drug use) and police, fire, health and social service departments are tasked with mitigating social harm through various types of interventions. Smart cities of the future will need to leverage IoT, data analytics, and government and community human resources to most effectively reduce social harm. Currently, methods for collection, analysis, and modeling of heterogeneous social harm data to identify government actions to improve quality of life are needed. In this paper we propose a system, CDASH, for synthesizing heterogeneous social harm data from multiples sources, identifying social harm risks in space and time, and communicating the risk to the relevant community resources best equipped to intervene. We discuss the design, architecture, and performance of CDASH. CDASH allows users to report live social harm events using mobile hand-held devices and web browsers and flags high risk areas for law enforcement and first responders. To validate the methodology, we run simulations on historical social harm event data in Indianapolis illustrating the advantages of CDASH over recently introduced social harm indices and existing point process methods used for predictive policing.","PeriodicalId":344652,"journal":{"name":"2018 IEEE International Smart Cities Conference (ISC2)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116809410","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-09-01DOI: 10.1109/ISC2.2018.8656960
A. A. Bona, M. Rosa, K. Fonseca, R. Lüders, N. P. Kozievitch
Based on complex network theory, metrics are proposed in this paper to identify local characteristics of public transportation networks, in special, congestion potential. As case of studies, two major Brazilian cities were chosen. Using L-space representation and geographic distances between connected bus stops as link weights at the resulting PTN model as complex network, we identified regions with high probability of vehicle and passenger congestion in both cities. We find out a type of complex network for PTNs, characterized by high degree, high number of modular communities and low clustering coefficient, differing from usual ones, characterized by bus/train terminals as network hubs. The achieved results of the complex network analysis can be applied to city planning.
{"title":"Congestion Potential – A New Way to Analyze Public Transportation based on Complex Networks","authors":"A. A. Bona, M. Rosa, K. Fonseca, R. Lüders, N. P. Kozievitch","doi":"10.1109/ISC2.2018.8656960","DOIUrl":"https://doi.org/10.1109/ISC2.2018.8656960","url":null,"abstract":"Based on complex network theory, metrics are proposed in this paper to identify local characteristics of public transportation networks, in special, congestion potential. As case of studies, two major Brazilian cities were chosen. Using L-space representation and geographic distances between connected bus stops as link weights at the resulting PTN model as complex network, we identified regions with high probability of vehicle and passenger congestion in both cities. We find out a type of complex network for PTNs, characterized by high degree, high number of modular communities and low clustering coefficient, differing from usual ones, characterized by bus/train terminals as network hubs. The achieved results of the complex network analysis can be applied to city planning.","PeriodicalId":344652,"journal":{"name":"2018 IEEE International Smart Cities Conference (ISC2)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128511908","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-09-01DOI: 10.1109/ISC2.2018.8656985
Wangtong Ding, Wei Fang
With the development of economy and the increase of population mobility, the public environment becomes more and more complex. Monitoring system has become an indispensable part of smart city. Target tracking is a key part of monitoring system, tracking is essentially an optimization process, so that, it can be solved by evolutionary algorithms. As evolutionary algorithms with high accuracy and fast convergence, which have attracted increasing attention, particle swarm optimization (PSO) as well as Quantum-behaved Particle Swarm Optimization (QPSO) have been widely used in tracking problem. However, lots of studies have shown that PSO and QPSO all have inherent shortcomings. Falling into local optimum and time consuming make them limited in dealing with tracking applications. For these reason we apply a new random drift particle swarm optimization algorithm (RDPSO) to target tracking. Compared with PSO and QPSO, RDPSO has better global convergence and it is more efficient. Based on traditional PSO-based tracking framework, we propose a sequential RDPSO tracking algorithm. To further improve the performance of the proposed tracking algorithm, we change the particle initialization method, combine the resampling measures in particle filter (PF), and use the Gaussian mixture model to evaluate fitness value. A large number of experimental results show the effectiveness and efficiency of our algorithm, especially for the cases that the background changes greatly, the target is deformed or moves quickly and the camera shakes.
{"title":"Target Tracking by Sequential Random Draft Particle Swarm Optimization Algorithm","authors":"Wangtong Ding, Wei Fang","doi":"10.1109/ISC2.2018.8656985","DOIUrl":"https://doi.org/10.1109/ISC2.2018.8656985","url":null,"abstract":"With the development of economy and the increase of population mobility, the public environment becomes more and more complex. Monitoring system has become an indispensable part of smart city. Target tracking is a key part of monitoring system, tracking is essentially an optimization process, so that, it can be solved by evolutionary algorithms. As evolutionary algorithms with high accuracy and fast convergence, which have attracted increasing attention, particle swarm optimization (PSO) as well as Quantum-behaved Particle Swarm Optimization (QPSO) have been widely used in tracking problem. However, lots of studies have shown that PSO and QPSO all have inherent shortcomings. Falling into local optimum and time consuming make them limited in dealing with tracking applications. For these reason we apply a new random drift particle swarm optimization algorithm (RDPSO) to target tracking. Compared with PSO and QPSO, RDPSO has better global convergence and it is more efficient. Based on traditional PSO-based tracking framework, we propose a sequential RDPSO tracking algorithm. To further improve the performance of the proposed tracking algorithm, we change the particle initialization method, combine the resampling measures in particle filter (PF), and use the Gaussian mixture model to evaluate fitness value. A large number of experimental results show the effectiveness and efficiency of our algorithm, especially for the cases that the background changes greatly, the target is deformed or moves quickly and the camera shakes.","PeriodicalId":344652,"journal":{"name":"2018 IEEE International Smart Cities Conference (ISC2)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128355763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-09-01DOI: 10.1109/ISC2.2018.8656941
Lama Alfaseeh, Shadi Djavadian, B. Farooq
The impact of distributed dynamic routing with different market penetration rates (MPRs) of connected autonomous vehicles (CAVs) and congestion levels has been investigated on urban streets. Downtown Toronto network is studied in an agent-based traffic simulation. The higher the MPRs of CAVs–especially in the case of highly congested urban networks–the higher the average speed, the lower the mean travel time, and the higher the throughput.
{"title":"Impact of Distributed Routing of Intelligent Vehicles on Urban Traffic","authors":"Lama Alfaseeh, Shadi Djavadian, B. Farooq","doi":"10.1109/ISC2.2018.8656941","DOIUrl":"https://doi.org/10.1109/ISC2.2018.8656941","url":null,"abstract":"The impact of distributed dynamic routing with different market penetration rates (MPRs) of connected autonomous vehicles (CAVs) and congestion levels has been investigated on urban streets. Downtown Toronto network is studied in an agent-based traffic simulation. The higher the MPRs of CAVs–especially in the case of highly congested urban networks–the higher the average speed, the lower the mean travel time, and the higher the throughput.","PeriodicalId":344652,"journal":{"name":"2018 IEEE International Smart Cities Conference (ISC2)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129334453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-09-01DOI: 10.1109/ISC2.2018.8656937
Yussef Parcianello, N. P. Kozievitch, K. Fonseca, M. Rosa, T. Gadda, Francisco C. Malucelli
The increasing urban population sets new demands for mobility solutions. The impacts of traffic congestions or inefficient transit connectivity directly affect public health (emissions, stress, for example) and the city economy (deaths in road accidents, productivity, commuting, etc). In parallel, the advance of technology has made it easier to obtain data about the systems which make up the city information systems. The result of this scenario is a large amount of data, growing every day and requiring effective handling in order to be transformed into integrated and useful information. This article aims to analyze the urban public transportation from the perspective of open data and data science. We focus on data integration challenges for smart city applications and present an use case of data usage to speed limit enforcement. We also present an initial comparative analysis of New York and Curitiba data collection and processing approaches. The results unveil challenges to overcome regarding file formats, reference systems, precision, accuracy and data quality, among others, that still need effective approaches to easy open data exploitation for new services. We discuss data characteristics that can possibly be used to optimize public transportation systems aiming at standards for transportation data worldwide.
{"title":"Transportation: An Overview from Open Data Approach","authors":"Yussef Parcianello, N. P. Kozievitch, K. Fonseca, M. Rosa, T. Gadda, Francisco C. Malucelli","doi":"10.1109/ISC2.2018.8656937","DOIUrl":"https://doi.org/10.1109/ISC2.2018.8656937","url":null,"abstract":"The increasing urban population sets new demands for mobility solutions. The impacts of traffic congestions or inefficient transit connectivity directly affect public health (emissions, stress, for example) and the city economy (deaths in road accidents, productivity, commuting, etc). In parallel, the advance of technology has made it easier to obtain data about the systems which make up the city information systems. The result of this scenario is a large amount of data, growing every day and requiring effective handling in order to be transformed into integrated and useful information. This article aims to analyze the urban public transportation from the perspective of open data and data science. We focus on data integration challenges for smart city applications and present an use case of data usage to speed limit enforcement. We also present an initial comparative analysis of New York and Curitiba data collection and processing approaches. The results unveil challenges to overcome regarding file formats, reference systems, precision, accuracy and data quality, among others, that still need effective approaches to easy open data exploitation for new services. We discuss data characteristics that can possibly be used to optimize public transportation systems aiming at standards for transportation data worldwide.","PeriodicalId":344652,"journal":{"name":"2018 IEEE International Smart Cities Conference (ISC2)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124632522","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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