2020 IEEE International Smart Cities Conference (ISC2)最新文献_第5页

A Multi-Tier Sensors-based Environmental Monitoring Approach to Assess the Quality of Bike Paths in Urban Areas 基于多层传感器的城市自行车道环境监测方法

2020 IEEE International Smart Cities Conference (ISC2) Pub Date : 2020-09-28 DOI: 10.1109/ISC251055.2020.9239071

Franklin Oliveira, D. G. Costa, A. Dias

{"title":"A Multi-Tier Sensors-based Environmental Monitoring Approach to Assess the Quality of Bike Paths in Urban Areas","authors":"Franklin Oliveira, D. G. Costa, A. Dias","doi":"10.1109/ISC251055.2020.9239071","DOIUrl":"https://doi.org/10.1109/ISC251055.2020.9239071","url":null,"abstract":"Large cities are paying a lot of attention to the dramatic challenges resulted from increasing air pollution and the global warming, struggling to find new promising solutions to face them. As a result, alternative transportation in urban areas has been sought, trying to reduce urban pollution while also addressing mobility issues in crowded cities. Actually, although bikes have been considered as a promising resource for more efficient and cleaner urban mobility, there are several concerns that must be considered when using bike paths already implemented in urban areas. In fact, some environmental factors such as temperature, humidity, noise pollution, ultraviolet radiation and light intensity can bring several problems to the health of cyclists who are constantly exposed to them, thus requiring constant and historical analysis of these conditions. In this context, this paper proposes an environmental monitoring system based on low-cost mobile sensors-based devices attached to bikes, which allow the identification of the most recommended areas for cycling in relation to possible damage to the health of cyclists. For that, the electronic components chosen for the construction of those devices must be highly accessible and affordable, supporting large-scale development. Additionally, the proposed approach is based on a hybrid communication model that does not require any urban communication infrastructure, easing its practical exploitation. The proposed approach seeks then to provide better support to governments when assessing the conditions of the currently installed bike paths and also when planning the implementation of new paths, potentially bringing important contributions to the management of modern cities.","PeriodicalId":201808,"journal":{"name":"2020 IEEE International Smart Cities Conference (ISC2)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128317659","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}

引用次数: 5

Air Quality Monitoring in a Smart Campus 智能校园的空气质量监测

2020 IEEE International Smart Cities Conference (ISC2) Pub Date : 2020-09-28 DOI: 10.1109/ISC251055.2020.9239009

M. Sánchez-Meraz, Miriam Guadalupe Juárez Gutiérrez, Iván Moreno Juárez

引用次数: 2

A Safety Index for Smart Mobility using Real-Time Crowdsourced Data 使用实时众包数据的智能交通安全指数

2020 IEEE International Smart Cities Conference (ISC2) Pub Date : 2020-09-28 DOI: 10.1109/ISC251055.2020.9239007

N. A. Smith, Adriana I. Camacho, Edgar J. Escobedo, Jonatan M. Contreras, O. Mondragon, N. Villanueva-Rosales, R. Cheu, Víctor M. Larios-Rosillo

{"title":"A Safety Index for Smart Mobility using Real-Time Crowdsourced Data","authors":"N. A. Smith, Adriana I. Camacho, Edgar J. Escobedo, Jonatan M. Contreras, O. Mondragon, N. Villanueva-Rosales, R. Cheu, Víctor M. Larios-Rosillo","doi":"10.1109/ISC251055.2020.9239007","DOIUrl":"https://doi.org/10.1109/ISC251055.2020.9239007","url":null,"abstract":"Smart Mobility is an important component of Smart Cities with most of the current approaches focusing on crash incidents for safety within Smart Mobility. The Safe Community System (SCS) aims to collect and provide information to city residents about events beyond crash incidents using mobile technology. The work reported in this manuscript aims to manage and provide information to residents in a meaningful way to support their decision making. This paper describes our efforts in extending an initial proof-of-concept of the SCS by establishing a Safety Index (SI)-a derived metric that aggregates the value of resident-submitted reports to generate real-time safety levels for streets within a city considering the lifespan and verification of these reports. The SCS mobile application has been refined to provide further information about a specific incident. The SCS updated design also proposes a Safe Path Algorithm (SPA) which is a modified Dijkstra’s algorithm that uses the SI to compute a safe path for the resident. The goal of the SCS is to support resident’s decision-making when choosing a route and thus fostering safety for Smart Mobility. Efforts like the SCS contribute to converting cities to Smart Cities.","PeriodicalId":201808,"journal":{"name":"2020 IEEE International Smart Cities Conference (ISC2)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124844169","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}

引用次数: 2

Implementation of a Computer Vision Framework for Tracking and Visualizing Face Mask Usage in Urban Environments 在城市环境中追踪和可视化面罩使用的计算机视觉框架的实现

2020 IEEE International Smart Cities Conference (ISC2) Pub Date : 2020-09-28 DOI: 10.1109/ISC251055.2020.9239012

Gabriel T. S. Draughon, Peng Sun, J. Lynch

{"title":"Implementation of a Computer Vision Framework for Tracking and Visualizing Face Mask Usage in Urban Environments","authors":"Gabriel T. S. Draughon, Peng Sun, J. Lynch","doi":"10.1109/ISC251055.2020.9239012","DOIUrl":"https://doi.org/10.1109/ISC251055.2020.9239012","url":null,"abstract":"The COVID-19 pandemic is an evolving situation in the United States and is spreading at alarming rates. The adoption of public health-informed hygienic practices can have a large impact on community transmission of COVID-19 including the wearing of face masks in public settings. Convolutional Neural Networks (CNN) can be trained to classify people wearing face masks with impressive accuracy. However, current face mask datasets contain clear, high-resolution close-up images of individuals with face masks which is unrepresentative of the lower fidelity images of distant faces more prominent in urban camera images. This paper proposes a practical deep learning computer vision framework for detection and tracking of people in public spaces and the use of face masks. A custom 6,000 image face mask dataset curated from over 50 hours of urban surveillance camera footage is created in this work. CNN-based detectors trained using the dataset are used to perform person detection and face mask classification. Then, a multi-target tracking module extracts individual trajectories from frame-by-frame detection. By associating detected face masks with tracked individuals, overall face mask usage can be estimated. The framework is implemented on several surveillance cameras along the Detroit RiverWalk, a 5-kilometer pedestrian park connecting various greenways, plazas, pavilions, and open green spaces along the Detroit River in Detroit, Michigan. The detection of park user types is shown to have an average precision of 89% and higher for most person classes with the mask detector having an accuracy of 96%. An interactive web application visualizes the data and is used by park managers to inform management decisions and assess strategies used to increase face mask usage rates.","PeriodicalId":201808,"journal":{"name":"2020 IEEE International Smart Cities Conference (ISC2)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129447911","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}

引用次数: 19

Allocation of the inductive charging system for bus rapid transit network 快速公交网络感应收费系统的配置

2020 IEEE International Smart Cities Conference (ISC2) Pub Date : 2020-09-28 DOI: 10.1109/ISC251055.2020.9239053

A. Asaolu, S. Galloway, C. Edmunds

引用次数: 0

Exploratory Research of Social Aspects for Smart City Development in Itajubá itajub<e:1>智慧城市发展的社会层面探索性研究

2020 IEEE International Smart Cities Conference (ISC2) Pub Date : 2020-09-28 DOI: 10.1109/ISC251055.2020.9239032

Rafael S. Salles, A. C. Z. Souza, P. Ribeiro

引用次数: 1

A Multi-Criteria Framework for Smart Parking Recommender System 智能停车推荐系统的多准则框架

2020 IEEE International Smart Cities Conference (ISC2) Pub Date : 2020-09-28 DOI: 10.1109/ISC251055.2020.9239098

G. Baranwal, D. Kumar, D. P. Vidyarthi

{"title":"A Multi-Criteria Framework for Smart Parking Recommender System","authors":"G. Baranwal, D. Kumar, D. P. Vidyarthi","doi":"10.1109/ISC251055.2020.9239098","DOIUrl":"https://doi.org/10.1109/ISC251055.2020.9239098","url":null,"abstract":"Parking has become a real challenge in cities, especially in metros, due to exponential increase in number of vehicles. A significant amount of time wasted in locating the parking space results in traffic congestion, pollution and fuel consumption. Recommending parking spot is an important service towards intelligent transportation system. Evolution in Internet of Things (IoT), Fog and Cloud Computing, and Sensor technologies can be better utilized to explore parking details such as parking occupancy, traffic congestion in parking path etc. in real time and an efficient and effective Parking Recommender System (PRS) can be designed. Parkers may have different expectations from PRS such as walking distance between the destination and the parking spot, pricing, safety etc. Therefore, a personalized recommender system is warranted in which a parker specifies its preference to various quality parameters related to parking. Considering parkers as human being, uncertainty in decision making over the preference cannot be ruled out. This work proposes a framework for multiple quality parameters/criteria based smart parking spot recommender system. It also provides various quality parameters, related to parking, to help parkers to express their need which helps in recommendation. As the boundaries between the parametric values are not crisp, fuzzy logic is utilized in parking recommender method to handle the uncertainty in human decision making. A case study, along with sensitivity analysis, demonstrates the effectiveness of the proposed model.","PeriodicalId":201808,"journal":{"name":"2020 IEEE International Smart Cities Conference (ISC2)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127880172","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}

引用次数: 2

Optimizing City’s Service Routes for Road Repairs 优化城市道路维修服务路线

2020 IEEE International Smart Cities Conference (ISC2) Pub Date : 2020-09-28 DOI: 10.1109/ISC251055.2020.9239021

Sami Alshammari, Haymanot Gebre-Amlak, Kaushik Ayinala, Sejun Song, Baek-Young Choi

{"title":"Optimizing City’s Service Routes for Road Repairs","authors":"Sami Alshammari, Haymanot Gebre-Amlak, Kaushik Ayinala, Sejun Song, Baek-Young Choi","doi":"10.1109/ISC251055.2020.9239021","DOIUrl":"https://doi.org/10.1109/ISC251055.2020.9239021","url":null,"abstract":"Reoccurring freeze and thaw cycles and damp conditions from rain, ice, and snow damage roadways and result in potholes throughout the city area. Auto damages caused by the potholes can add up to thousands of dollars per vehicle. Besides, pothole resolution is one of the most expensive street maintenance strategies. Most of the cities have established social data networks (i.e., Open Data KC 311 in Kansas City) for residents to report potholes to mitigate the problem. Although rudimentary patching policies are defined by the road condition’s volume and significance in many cities, it does not provide the optimized resolution routes. In this paper, we propose a practical framework for optimizing the resolution route schedule using open data, including the pothole locations, traffic situations, weather conditions, type of patch or other repair needed, crew availability, etc. We have analyzed the past 13 years of pothole data from the Open Data KC 311 in Kansas City. According to our analysis, we have recognized spatiotemporal pothole characteristics in the density and designed a cluster-based heuristic algorithm named Traveling Pothole Crew (TPC) by enhancing an NP-hard Traveling Salesperson Problem (TSP) algorithm. TPC classifies potholes into layers of clusters. TPC traverses the shortest possible pothole route within a cluster. Furthermore, it identifies the starting and ending potholes in each cluster group to optimize the distance among clusters. This proposed solution has shown effective optimization in terms of traveling distance and computation time. Our analysis indicates that the TPC algorithm reduces the traversing distance and is faster in computation time than typical TSP algorithms for daily resolution scheduling.","PeriodicalId":201808,"journal":{"name":"2020 IEEE International Smart Cities Conference (ISC2)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121484046","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}

引用次数: 0

DetectSignal: A Cloud-Based Traffic Signal Notification System for the Blind and Visually Impaired 探测器信号:一个基于云的交通信号通知系统为盲人和视障人士

2020 IEEE International Smart Cities Conference (ISC2) Pub Date : 2020-09-28 DOI: 10.1109/ISC251055.2020.9239004

Chikadibia Ihejimba, R. Wenkstern

引用次数: 4

An IoT-based Eco-Parking System for Smart Cities 智慧城市基于物联网的生态停车系统

2020 IEEE International Smart Cities Conference (ISC2) Pub Date : 2020-09-28 DOI: 10.1109/ISC251055.2020.9239041

Ibrahim Tamam, Shen Wang, S. Djahel

引用次数: 4