Systems trying to solve mobility issues in cities, such as high levels of accidents and traffic congestion, have been developed worldwide. Intelligent Transportation Systems (ITS) services focused on urban public transport are an option contributing to solve such issues. A few intermediate cities in the Latin American context have developed some of these ITS services, which are mainly based on a tracking system for public transport vehicles. Such tracking systems have great limitations in terms of coverage, availability, and operational cost. In addition, they are commonly isolated mobility solutions, which cannot be easily integrated with other mobility services in the city because they are not based on an ITS architecture. In order to improve public transportation systems in intermediate cities, we proposed the development of an IoT-based public vehicle tracking system, using LoRa (Long Range) and ITS services. In this research, we developed the proposed system as a proof of concept. We designed and executed some experiments, in order to adjust parameters of the LoRa technology and to test its operation. This article presents the methods we followed for developing the proof-of-concept model, a description of the experiments, and their results. The results lead to conclude that the LoRa technology and an IoT-based system are adequate for implementation of a mobility service such as the one we propose, once important technical restrictions related mainly to Line of Sight (LoS) are considered. Key aspects for implementation were also identified for deploying the service (as a prototype) in the city of Popayán.

In medium-sized cities in developing countries, transit services without dedicated lanes have issues related to route compliance, schedules, speed control, and safety. An efficient way for dealing with this issue is the use of Information and Communication Technologies (ICT), to implement a Fleet Management and Control Systems (FMCS). Such implementation can be performed using Intelligent Transportation Systems (ITSs), which allow integration of services and adequate standardization. This article features: (a) a literature review, related to FMCS based on ITS and enabling technologies, (b) design of the ITS architecture of an FMCS, and (c) some advances in the development of the proposed FMCS in a Colombian city (Popayán). The results of the literature review allowed identifying the most important requirements of FMCS in order to design the ITS architecture and build a prototype featuring the suggested technologies. Finally, some experiments were performed to evaluate the operation of the developed prototype. The results showed evidence of adequate operation in sending and receiving messages from and to four prototypes developed for the vehicles, also complying with the established requirements of location, tracking, exchanged data, and security. This allows continuing the development of the proposed FMCS, with some adjustments.

Background: Availability and opportunity of epilepsy diagnostic services is a significant challenge, especially in developing countries with a low number of neurologists. The most commonly used test to diagnose epilepsy is electroencephalogram (EEG). A typical EEG recording lasts for 20 to 30 minutes; however, a specialist requires much more time to read it. Furthermore, no evidence was found in the literature on open-source systems for the cost-effective management of patient information using electronic health records (EHR) that adequately integrate EEG analysis for automatic identification of abnormal signals. Objective: To develop an integrated open-source EHR system for the management of the patients’ personal, clinical, and EEG data, and for automatic identification of abnormal EEG signals. Methods: The core of the system is an EHR and telehealth service based on the OpenMRS platform. On top of that, we developed an intelligent component to automatically detect abnormal segments of EEG tests using machine learning algorithms, as well as a service to annotate and visualize abnormal segments in EEG signals. Finally, we evaluated the intelligent component and the integrated system using precision, recall, and accuracy metrics. Results: The system allowed to manage patients’ information properly, store and manage the EEG tests recorded with a medical EEG device, and to detect abnormal segments of signals with a precision of 85.10%, a recall of 97.16%, and an accuracy of 99.92%. Conclusion: Digital health is a multidisciplinary field of research in which artificial intelligence is playing a significant role in boosting traditional health services. Notably, the developed system could significantly reduce the time a neurologist spends in the reading of an EEG for the diagnosis of epilepsy, saving approximately 65– 75% of the time consumed. It can be used in a telehealth environment. In this way, the availability and provision of diagnostic services for epilepsy management could be improved, especially in developing countries where the number of neurologists is low.

Traffic congestion has several causes, including insufficient road capacity, unrestricted demand and improper scheduling of traffic signal phases. A great variety of efforts have been made to properly program such phases. Some of them are based on traditional transportation assumptions, and others are adaptive, allowing the system to learn the control law (signal program) from data obtained from different sources. Reinforcement Learning (RL) is a technique commonly used in previous research. However, properly determining the states and the reward is key to obtain good results and to have a real chance to implement it. This paper proposes and implements a traffic signal control system (TSCS), detailing its development stages: (a) Intelligent Transportation System (ITS) architecture design for the TSCS; (b) design and development of a system prototype, including an RL algorithm to minimize the vehicle queue at intersections, and detection and calculation of such queues by adapting a computer vision algorithm; and (c) design and development of system tests to validate operation of the algorithms and the system prototype. Results include the development of the tests for each module (vehicle queue measurement and RL algorithm) and real-time integration tests. Finally, the article presents a system simulation in the context of a medium-sized city in a developing country, showing that the proposed system allowed reduction of vehicle queues by 29%, of waiting time by 50%, and of lost time by 50%, when compared to fixed phase times in traffic signals.

Long-range (LoRa) technology is a low power wide area network (LPWAN) technology that is currently being used for development of Internet of things (IoT)-based solutions. Transit transport, mainly in medium-sized cities where transit vehicles do not have exclusive lanes, is a service that can be improved with a tracking service using technology such as LoRa. Although some proposals exist, there is not enough experimental information to validate the LoRa technology as adequate. This article: (a) evaluates the operation of LoRa technology in a transit vehicle tracking service in a medium-sized city, based on an Intelligent Transportation Systems architecture and IoT; and (b) investigates optimal LoRa technology configuration parameters for the service. Experiments were performed in a semi-controlled environment using LoRa devices and a gateway, by measuring the received packets and the receive signal strength indicator (RSSI) and modifying: (a) distance; (b) number of devices; and (c) the main LoRa transmission parameters. Obtained results show the ideal values of parameters vary considerably with distance and number of devices used. There were very few settings of the experiments in which the RSSI and packet levels were adequate while distance and number of devices were both changed.