A blockchain and gamification approach for smart parking

City parking is increasingly complex and available parking spaces are scarce. Being able to identify a space to park their cars can lead many drivers to drive around the intended parking area several times, increasing traffic density and pollution. In this research we propose a collaborative blockchain solution with gamification for parking. Users collaborate to report free spaces and receive free parking minutes for their service to the community. In parallel, this approach can be used to collect beacon information from the parked vehicles and create a low-cost collaborative approach for managing a parking control process platform Blockchain that can handle this distributed process and the gamification platform increases users’ participation.info:eu-repo/semantics/acceptedVersio

Wide area detection system: Conceptual design study

An integrated sensor for traffic surveillance on mainline sections of urban freeways is described. Applicable imaging and processor technology is surveyed and the functional requirements for the sensors and the conceptual design of the breadboard sensors are given. Parameters measured by the sensors include lane density, speed, and volume. The freeway image is also used for incident diagnosis

WORKING GROUP ON NEPHROPS SURVEYS (WGNEPS ; outputs from 2020)

The Working Group on Nephrops Surveys (WGNEPS) is the international coordination group for Nephrops underwater television and trawl surveys within ICES. This report summarizes the na-tional contributions on the results of the surveys conducted in 2020 together with time series covering all survey years, problems encountered, data quality checks and technological improve-ments as well as the planning for survey activities for 2021.ICE

Computer Vision Algorithms for Mobile Camera Applications

Wearable and mobile sensors have found widespread use in recent years due to their ever-decreasing cost, ease of deployment and use, and ability to provide continuous monitoring as opposed to sensors installed at fixed locations. Since many smart phones are now equipped with a variety of sensors, including accelerometer, gyroscope, magnetometer, microphone and camera, it has become more feasible to develop algorithms for activity monitoring, guidance and navigation of unmanned vehicles, autonomous driving and driver assistance, by using data from one or more of these sensors. In this thesis, we focus on multiple mobile camera applications, and present lightweight algorithms suitable for embedded mobile platforms. The mobile camera scenarios presented in the thesis are: (i) activity detection and step counting from wearable cameras, (ii) door detection for indoor navigation of unmanned vehicles, and (iii) traffic sign detection from vehicle-mounted cameras. First, we present a fall detection and activity classification system developed for embedded smart camera platform CITRIC. In our system, the camera platform is worn by the subject, as opposed to static sensors installed at fixed locations in certain rooms, and, therefore, monitoring is not limited to confined areas, and extends to wherever the subject may travel including indoors and outdoors. Next, we present a real-time smart phone-based fall detection system, wherein we implement camera and accelerometer based fall-detection on Samsung Galaxy S™ 4. We fuse these two sensor modalities to have a more robust fall detection system. Then, we introduce a fall detection algorithm with autonomous thresholding using relative-entropy within the class of Ali-Silvey distance measures. As another wearable camera application, we present a footstep counting algorithm using a smart phone camera. This algorithm provides more accurate step-count compared to using only accelerometer data in smart phones and smart watches at various body locations. As a second mobile camera scenario, we study autonomous indoor navigation of unmanned vehicles. A novel approach is proposed to autonomously detect and verify doorway openings by using the Google Project Tango™ platform. The third mobile camera scenario involves vehicle-mounted cameras. More specifically, we focus on traffic sign detection from lower-resolution and noisy videos captured from vehicle-mounted cameras. We present a new method for accurate traffic sign detection, incorporating Aggregate Channel Features and Chain Code Histograms, with the goal of providing much faster training and testing, and comparable or better performance, with respect to deep neural network approaches, without requiring specialized processors. Proposed computer vision algorithms provide promising results for various useful applications despite the limited energy and processing capabilities of mobile devices

Smart streetlights: a feasibility study

The world's cities are growing. The effects of population growth and urbanisation mean that more people are living in cities than ever before, a trend set to continue. This urbanisation poses problems for the future. With a growing population comes more strain on local resources, increased traffic and congestion, and environmental decline, including more pollution, loss of green spaces, and the formation of urban heat islands. Thankfully, many of these stressors can be alleviated with better management and procedures, particularly in the context of road infrastructure. For example, with better traffic data, signalling can be smoothed to reduce congestion, parking can be made easier, and streetlights can be dimmed in real time to match real-world road usage. However, obtaining this information on a citywide scale is prohibitively expensive due to the high costs of labour and materials associated with installing sensor hardware. This study investigated the viability of a streetlight-integrated sensor system to affordably obtain traffic and environmental information. This investigation was conducted in two stages: 1) the development of a hardware prototype, and 2) evaluation of an evolved prototype system. In Stage 1 of the study, the development of the prototype sensor system was conducted over three design iterations. These iterations involved, in iteration 1, the live deployment of the prototype system in an urban setting to select and evaluate sensors for environmental monitoring, and in iterations 2 and 3, deployments on roads with live and controlled traffic to develop and test sensors for remote traffic detection. In the final iteration, which involved controlled passes of over 600 vehicle, 600 pedestrian, and 400 cyclist passes, the developed system that comprised passive-infrared motion detectors, lidar, and thermal sensors, could detect and count traffic from a streetlight-integrated configuration with 99%, 84%, and 70% accuracy, respectively. With the finalised sensor system design, Stage 1 showed that traffic and environmental sensing from a streetlight-integrated configuration was feasible and effective using on-board processing with commercially available and inexpensive components. In Stage 2, financial and social assessments of the developed sensor system were conducted to evaluate its viability and value in a community. An evaluation tool for simulating streetlight installations was created to measure the effects of implementing the smart streetlight system. The evaluation showed that the on-demand traffic-adaptive dimming enabled by the smart streetlight system was able to reduce the electrical and maintenance costs of lighting installations. As a result, a 'smart' LED streetlight system was shown to outperform conventional always-on streetlight configurations in terms of financial value within a period of five to 12 years, depending on the installation's local traffic characteristics. A survey regarding the public acceptance of smart streetlight systems was also conducted and assessed the factors that influenced support of its applications. In particular, the Australia-wide survey investigated applications around road traffic improvement, streetlight dimming, and walkability, and quantified participants' support through willingness-to-pay assessments to enable each application. Community support of smart road applications was generally found to be positive and welcomed, especially in areas with a high dependence on personal road transport, and from participants adversely affected by spill light in their homes. Overall, the findings of this study indicate that our cities, and roads in particular, can and should be made smarter. The technology currently exists and is becoming more affordable to allow communities of all sizes to implement smart streetlight systems for the betterment of city services, resource management, and civilian health and wellbeing. The sooner that these technologies are embraced, the sooner they can be adapted to the specific needs of the community and environment for a more sustainable and innovative future

Fuzz sensoring

Treball desenvolupat en el marc del programa "European Project Semester".Traffic congestion is a significant problem which affects smoothness in transportation in many cities around the world. It is unavoidable due to increasing numbers of vehicles and overuse of roads in large and growing metropolises. Although, there are several policies that are implemented to reduce traffic congestion, such as improvement of public transport, car and motorcycle restriction on several roads, and an even-odd license plate policy, the major problem involves getting data in order to predict and avoid traffic. Information can be collected from many sources such as: city sensors, GPS, as well as, from many application programming interfaces (API) provided by different companies. The project involves gathering sources and information about traffic congestion in order to create guidelines which can be essential in creating a traffic map of Vilanova i la Geltrú in the future. Eventually, the guidelines to the city of Vilanova i la Geltrú are provided, consisting of analysis of traffic inside the city, IoT management, choices of APIs, effective selection of sensors, and cost analysis to vastly improve traffic flow.Incomin

Modeling Clemson Football Traffic: New Techniques for Small Communities

Many communities host planned special events that generate several times the communties\u27 AADT around the event period (e.g. pro and college football games). Larger metropolises benefit from ITS to collect data from, model, plan for, and analyze potential solutions to event-caused congestion. The smaller communities, which do not have the resources for traffic management centers, could benefit from more cost-appropriate methodologies. This thesis presents a cost-effective methodology for traffic data collection before and after these events. Modelers can then use this data in a microsimulation package, such as VISSIM, to model how the transportation network performs during this period, to model treatments, and to obtain MOEs useful for making planning decisions. Furthermore, because these events cause networks to be severely over-saturated, collected data can underestimate the level of demand, as it is restricted by capacity. This thesis also presents a methodology to account for this as well. Researchers collected traffic data with these methods from games in 2014-16, developed models for base and treatment scenarios, and proposed changes to the traffic plan starting in 2015. In addition to the methodology, travel-time results from these models are provided as measures of effectiveness. The author\u27s uses his experience with this project to demonstrate that these methods can be used to microsimulate a severely-oversaturated network and predict treatment effectiveness

Overview of the work carried out in CleanAtlantic on improving marine litter monitoring: • WP 5.2.1. – Improving methods for marine litter monitoring in the Atlantic Area: seabed, floating and coastal litter • WP 5.2.2. – New tools for the monitoring of marine litter

This report collates the main results delivered in the frame of the CleanAtlantic project, Work package 5.2. Monitoring the presence of marine litter in the marine environment. With this purpose, an overview of new and improved marine litter monitoring methods for seabed, water surface and coastal compartments in the Atlantic Area is presented. Main findings, gaps on monitoring and research as well as potential improvements and recommendations are highlighted. For some of the topics addressed partners produced fully-dedicated reports. In these cases, links to the original reports are included in the reference section for further information

Exploring informants’ perspectives on the role of crowdsourced active travel data

Peer reviewedPostprin

The Response of Beef Cattle to Disturbances from Unmanned Aerial Vehicles (UAVs)

Unmanned aerial vehicles (UAVs) are increasingly becoming common in animal agriculture. However, research regarding the impact of UAV disturbance on animal wellbeing is lacking or limited. The goal of this study was to investigate the effect of UAV flights on beef cattle by measuring cattle heart and movement rate when introduced to single or multiple UAV flights. A total of 16 -18 crossbred beef heifers were introduced to different flights patterns at between 5 and 9 m above ground level (AGL) at approximately 1 to 2 m/s horizontal velocity for 4 weeks with flights repeated 3 days per week. Results from the study showed that single UAV flights conducted in (i) circular and (ii) grid pattern flights had no significant effect on heifer heart and movement rate. However, multiple (i) circular pattern and (ii) approach style flights increased heifer heart rate when first introduced to UAVs, but repeated flights resulted in habituation. Moreover, heifers first introduced to circular pattern flights were likely to flee but became habituated after repeated flights. However, heifers introduced to approach style flights showed more fleeing behavior even after repeated flights. The findings of this study will provide information for safely using UAVs in cattle health and behavior monitoring