Managing Road Safety through the Use of Linked Data and Heat Maps

Road traffic injuries are a critical public health challenge that requires valuable efforts for effective and sustainable prevention. Worldwide, an estimated 1.2 million people are killed in road crashes each year and as many as 50 million are injured. An analysis of data provided by authoritative sources can be a valuable source for understanding which are the most critical points on the road network. The aim of this paper is to discover data about road accidents in Italy and to provide useful visualization for improving road safety. Starting from the annual report of road accidents of the Automobile Club of Italy, we transform the original data into an RDF dataset according to the Linked Open Data principles and connect it to external datasets. Then, an integration with Open Street Map allows to display the accident data on a map. Here, the final user is able to identify which road sections are most critical based on the number of deaths, injuries or accidents

Survey of smart parking systems

The large number of vehicles constantly seeking access to congested areas in cities means that finding a public parking place is often difficult and causes problems for drivers and citizens alike. In this context, strategies that guide vehicles from one point to another, looking for the most optimal path, are needed. Most contributions in the literature are routing strategies that take into account different criteria to select the optimal route required to find a parking space. This paper aims to identify the types of smart parking systems (SPS) that are available today, as well as investigate the kinds of vehicle detection techniques (VDT) they have and the algorithms or other methods they employ, in order to analyze where the development of these systems is at today. To do this, a survey of 274 publications from January 2012 to December 2019 was conducted. The survey considered four principal features: SPS types reported in the literature, the kinds of VDT used in these SPS, the algorithms or methods they implement, and the stage of development at which they are. Based on a search and extraction of results methodology, this work was able to effectively obtain the current state of the research area. In addition, the exhaustive study of the studies analyzed allowed for a discussion to be established concerning the main difficulties, as well as the gaps and open problems detected for the SPS. The results shown in this study may provide a base for future research on the subject.Fil: Diaz Ogás, Mathias Gabriel. Universidad Nacional de San Juan. Facultad de Ciencias Exactas, Físicas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; ArgentinaFil: Fabregat Gesa, Ramon. Universidad de Girona; EspañaFil: Aciar, Silvana Vanesa. Universidad Nacional de San Juan. Facultad de Ciencias Exactas, Físicas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentin

Deep Autoencoder Neural Networks for Short-Term Traffic Congestion Prediction of Transportation Networks

Traffic congestion prediction is critical for implementing intelligent transportation systems for improving the efficiency and capacity of transportation networks. However, despite its importance, traffic congestion prediction is severely less investigated compared to traffic flow prediction, which is partially due to the severe lack of large-scale high-quality traffic congestion data and advanced algorithms. This paper proposes an accessible and general workflow to acquire large-scale traffic congestion data and to create traffic congestion datasets based on image analysis. With this workflow we create a dataset named Seattle Area Traffic Congestion Status (SATCS) based on traffic congestion map snapshots from a publicly available online traffic service provider Washington State Department of Transportation. We then propose a deep autoencoder-based neural network model with symmetrical layers for the encoder and the decoder to learn temporal correlations of a transportation network and predicting traffic congestion. Our experimental results on the SATCS dataset show that the proposed DCPN model can efficiently and effectively learn temporal relationships of congestion levels of the transportation network for traffic congestion forecasting. Our method outperforms two other state-of-the-art neural network models in prediction performance, generalization capability, and computation efficiency

Breaking vendors and city locks through a semantic-enabled global interoperable Internet-of-Things system: a smart parking case

The Internet of Things (IoT) is unanimously identified as one of the main technology enablers for the development of future intelligent environments. However, the current IoT landscape is suffering from large fragmentation with many platforms and vendors competing with their own solution. This fragmented scenario is now jeopardizing the uptake of the IoT, as investments are not carried out partly because of the fear of being captured in lock-in situations. To overcome these fears, interoperability solutions are being put forward in order to guarantee that the deployed IoT infrastructure, independently of its manufacturer and/or platform, can exchange information, data and knowledge in a meaningful way. This paper presents a Global IoT Services (GIoTS) use case demonstrating how semantic interoperability among five different smart city IoT deployments can be leveraged to develop a smart urban mobility service. The application that has been developed seamlessly consumes data from them for providing parking guidance and mobility suggestions at the five locations (Santander and Barcelona in Spain and Busan, Seoul and Seongnam in South Korea) where the abovementioned IoT deployments are installed. The paper is also presenting the key aspects of the system enabling the interoperability among the three underlying heterogeneous IoT platforms.This research was funded by European Union’s H2020 Programme for research, technological development and demonstration within the projects “Worldwide Interoperability for Semantics IoT (WISE-IoT)” (under grant agreement No 723156) and “Bridging the Interoperability Gap of the Internet of Things (BIG-IoT)” (under grant agreement No. 688038) and, in part, by the Spanish Government by means of the Project ADVICE “Dynamic Provisioning of Connectivity in High Density 5G Wireless Scenarios” under Grant TEC2015-71329-C2-1-R

Enabling technologies for urban smart mobility: Recent trends, opportunities and challenges

The increasing population across the globe makes it essential to link smart and sustainable city planning with the logistics of transporting people and goods, which will significantly contribute to how societies will face mobility in the coming years. The concept of smart mobility emerged with the popularity of smart cities and is aligned with the sustainable development goals defined by the United Nations. A reduction in traffic congestion and new route optimizations with reduced ecological footprint are some of the essential factors of smart mobility; however, other aspects must also be taken into account, such as the promotion of active mobility and inclusive mobility, encour-aging the use of other types of environmentally friendly fuels and engagement with citizens. The Internet of Things (IoT), Artificial Intelligence (AI), Blockchain and Big Data technology will serve as the main entry points and fundamental pillars to promote the rise of new innovative solutions that will change the current paradigm for cities and their citizens. Mobility‐as‐a‐service, traffic flow optimization, the optimization of logistics and autonomous vehicles are some of the services and applications that will encompass several changes in the coming years with the transition of existing cities into smart cities. This paper provides an extensive review of the current trends and solutions presented in the scope of smart mobility and enabling technologies that support it. An overview of how smart mobility fits into smart cities is provided by characterizing its main attributes and the key benefits of using smart mobility in a smart city ecosystem. Further, this paper highlights other various opportunities and challenges related to smart mobility. Lastly, the major services and applications that are expected to arise in the coming years within smart mobility are explored with the prospective future trends and scope

Health Recommender Systems Development, Usage, and Evaluation from 2010 to 2022: A Scoping Review

A health recommender system (HRS) provides a user with personalized medical information based on the user’s health profile. This scoping review aims to identify and summarize the HRS development in the most recent decade by focusing on five key aspects: health domain, user, recommended item, recommendation technology, and system evaluation. We searched PubMed, ACM Digital Library, IEEE Xplore, Web of Science, and Scopus databases for English literature published between 2010 and 2022. Our study selection and data extraction followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews. The following are the primary results: sixty-three studies met the eligibility criteria and were included in the data analysis. These studies involved twenty-four health domains, with both patients and the general public as target users and ten major recommended items. The most adopted algorithm of recommendation technologies was the knowledge-based approach. In addition, fifty-nine studies reported system evaluations, in which two types of evaluation methods and three categories of metrics were applied. However, despite existing research progress on HRSs, the health domains, recommended items, and sample size of system evaluation have been limited. In the future, HRS research shall focus on dynamic user modelling, utilizing open-source knowledge bases, and evaluating the efficacy of HRSs using a large sample size. In conclusion, this study summarized the research activities and evidence pertinent to HRSs in the most recent ten years and identified gaps in the existing research landscape. Further work shall address the gaps and continue improving the performance of HRSs to empower users in terms of healthcare decision making and self-management

Big Data Geospatial Processing for Massive Aerial LiDAR Datasets

[Abstract] For years, Light Detection and Ranging (LiDAR) technology has been considered as a challenge when it comes to developing efficient software to handle the extremely large volumes of data this surveying method is able to collect. In contexts such as this, big data technologies have been providing powerful solutions for distributed storage and computing. In this work, a big data approach on geospatial processing for massive aerial LiDAR point clouds is presented. By using Cassandra and Spark, our proposal is intended to support the execution of any kind of heavy time-consuming process; nonetheless, as an initial case of study, we have focused on fast ground-only rasters obtention to generate digital terrain models (DTMs) from massive LiDAR datasets. Filtered clouds obtained from the isolated processing of adjacent zones may exhibit errors located on the boundaries of the zones in the form of misclassified points. Usually, this type of error is corrected through manual or semi-automatic procedures. In this work, we also present an automated strategy for correcting errors of this type, improving the quality of the classification process and the DTMs obtained while minimizing user intervention. The autonomous nature of all computing stages, along with the low processing times achieved, opens the possibility of considering the system as a highly scalable service-oriented solution for on-demand DTM generation or any other geospatial process. Said solution would be a highly useful and unique service for many users in the LiDAR field, and one which could get near to real-time processing with appropriate computational resources.Xunta de Galicia; ED431C 2017/04Consolidation Programme of Competitive Research Units; R2016/037Xunta de Galicia; ED431G/01Ministerio de Economía y Competitividad; TIN2016-75845-

Deep Learning Based Methods for Outdoor Robot Localization and Navigation

The number of elderly people is increasing around the globe. In order to support the growing of ageing society, mobile robot is one of viable choices for assisting the elders in their daily activities. These activities happen in any places, either indoor or outdoor. Although outdoor activities benefit the elders in many ways, outdoor environments contain difficulties from their unpredictable natures. Mobile robots for supporting humans in outdoor environments must automatically traverse through various difficulties in the environments using suitable navigation systems.Core components of mobile robots always include the navigation segments. Navigation system helps guiding the robot to its destination where it can perform its designated tasks. There are various tools to be chosen for navigation systems. Outdoor environments are mostly open for conventional navigation tools such as Global Positioning System (GPS) devices. In this thesis three systems for localization and navigation of mobile robots based on visual data and deep learning algorithms are proposed. The first localization system is based on landmark detection. The Faster Regional-Convolutional Neural Network (Faster R-CNN) detects landmarks and signs in the captured image. A Feed-Forward Neural Network (FFNN) is trained to determine robot location coordinates and compass orientation from detected landmarks. The dataset consists of images, geolocation data and labeled bounding boxes to train and test two proposed localization methods. Results are illustrated with absolute errors from the comparisons between localization results and reference geolocation data in the dataset. The second system is the navigation system based on visual data and a deep reinforcement learning algorithm called Deep Q Network (DQN). The employed DQN automatically guides the mobile robot with visual data in the form of images, which received from the only Universal Serial Bus (USB) camera that attached to the robot. DQN consists of a deep neural network called convolutional neural network (CNN), and a reinforcement learning algorithm named Q-Learning. It can make decisions with visual data as input, using experiences from consequences of trial-and-error attempts. Our DQN agents are trained in the simulation environments provided by a platform based on a First-Person Shooter (FPS) game named ViZDoom. Simulation is implemented for training to avoid any possible damage on the real robot during trial-and-error process. Perspective from the simulation is the same as if a camera is attached to the front of the mobile robot. There are many differences between the simulation and the real world. We applied a markerbased Augmented Reality (AR) algorithm to reduce differences between the simulation and the world by altering visual data from the camera with resources from the simulation.The second system is assigned the task of simple navigation to the robot, in which the starting location is fixed but the goal location is random in the designated zone. The robot must be able to detect and track the goal object using a USB camera as its only sensor. Once started, the robot must move from its starting location to the designated goal object. Our DQN navigation method is tested in the simulation and on the real robot. Performances of our DQN are measured quantitatively via average total scores and the number of success navigation attempts. The results show that our DQN can effectively guide a mobile robot to the goal object of the simple navigation tasks, for both the simulation and the real world.The third system employs a Transfer Learning (TL) strategy to reduce training time and resources required for the training of newly added tasks of DQN agents. The new task is the task of reaching the goal while also avoiding obstacles at the same time. Additionally, the starting and the goal locations are all random within the specified areas. The employed transfer learning strategy uses the whole network of the DQN agent trained for the first simple navigation task as the base for training the DQN agent for the second task. The training in our TL strategy decrease the exploration factor, which cause the agent to rely on the existing knowledge from the base network more than randomly selecting actions during the training. It results in the decreased training time, in which optimal solutions can be found faster than training from scratch.We evaluate performances of our TL strategy by comparing the DQN agents trained with our TL at different exploration factor values and the DQN agent trained from scratch. Additionally, agents trained from our TL are trained with the decreased number of episodes to extensively display performances of our TL agents. All DQN agents for the second navigation task are tested in the simulation to avoid any possible and uncontrollable damages from the obstacles. Performances are measured through success attempts and average total scores, same as in the first navigation task. Results show that DQN agents trained via the TL strategy can greatly outperform the agent trained from scratch, despite the lower number of training episodes.博士(工学)法政大学 (Hosei University