RESIST 2018 Abstracts

Full Papers Short Papers
Full Papers
Paper Nr: 1
Title:

Development of the Web Platform for Management of Smart Charging Stations for Electric Vehicles
Authors:

Vitalijs Komasilovs, Aleksejs Zacepins, Armands Kviesis, Corneliu Marinescu and Ioan Serban
Abstract: Shortage of fossil fuels and ecological thinking leads to shift in technologies for vehicle production. In the future only electric vehicles (EVs) would be produced. This will lead to huge increase in number of EVs worldwide, so it would be crucial to provide a broad public charging infrastructure. This paper exactly concentrates on the essential role of infrastructure in the mass implementation of electric vehicles. A focus is placed on sharing the residential infrastructure for public usage. Paper describes authors developed Web platform for sharing the information about privately owned charging stations, describing the additional option to link station hardware with software for real-time charging data and station availability updates. Developed platform brings together drivers of EVs and owners of the infrastructure. Developed platform is built like an interactive map, based on Google Maps service. Together with software part, authors developed also hardware, which is one Microgrid based on renewable energy sources with EV charging station functionality.
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Paper Nr: 2
Title:

Smart Parking Tools Suitability for Open Parking Lots: A Review
Authors:

Vijay Paidi, Hasan Fleyeh, Johan Håkansson and Roger G. Nyberg
Abstract: Parking a vehicle in traffic dense environments is a common issue in many parts of the world which often leads to congestion and environmental pollution. Lack of guidance information to vacant parking spaces is one of the reasons for inefficient parking behaviour. Smart parking sensors and technologies facilitate guidance of drivers to free parking spaces thereby improving parking efficiency. Currently, no such sensors or technologies are in use for the common open parking lot. This paper reviews the literature on the usage of smart parking sensors, technologies, applications and evaluate their suitability to open parking lots. Suitability was made in terms of expenditure and reliability. Magnetometers, ultrasonic sensors and machine vision were few of the widely used sensors and technologies used in closed parking lots. However, this paper suggests a combination of machine vision, fuzzy logic or multi-agent systems suitable for open parking lots due to less expenditure and resistance to varied environmental conditions. No application provided real time parking occupancy information of open parking lots, which is a necessity to guide them along the shortest route to free space. To develop smart parking applications for open parking lots, further research is needed in the fields of deep learning.
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Paper Nr: 3
Title:

Enhancing with EV Charging Station Functions a Residential RES based Network
Authors:

Corneliu Marinescu, Luminita Barote, Daniel Munteanu, Vitalijs Komasilovs, Aleksejs Zacepins and Armands Kviesis
Abstract: The emergence of Electric Vehicles is creating a possible congestion of the electric grid. The switch in transportation, especially in cities (future Smart Cities are considered) is asking for the utilization of Renewable Energy Sources, RES, to decrease pollution. To address these two demands the paper proposes a solution based on a Residential Charging station architecture for Urban Electric Vehicles. The theoretical structure is presented and then the practical solution, as Smart Residential MicroGrid based on RES, is shown. In order to make an implementation more economically and technically affordable and be able to address in the very near future the growing need of EV Charging stations, the presented solution starts from the existing equipment used in millions of homes, mainly for solar energy.
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Paper Nr: 5
Title:

Evaluating the Predictability of Future Energy Consumption - Application of Statistical Classification Models to Data from EV Charging Points
Authors:

Pietro Faes Belgrado, Luboš Buzna, Federica Foiadelli and Michela Longo
Abstract: The overall purpose of our study has been to evaluate the predictability of future energy consumption analysing the electric mobility in the Netherlands. The climate and energy framework, the European energy production and main developments, as well as the European targets and policy objectives to reduce the current CO2 emissions were first assessed. Then, a deeper look was taken at electric mobility and at Electric Vehicles (EVs). The adoption and development of EVs in the European Union and charging infrastructure were taken into account. The Dutch energy production and emissions, as well as, the mobility in the country and its infrastructure were investigated. Previous studies about electric vehicles and charging points have addressed the predictability of future energy consumption in larger areas to only very limited extent, so our research work has concentrated on this gap. A large real-world dataset was used as a basis to create statistical models, in order to study the users’ behaviour within the charging points infrastructure and to evaluate the predictability of future energy consumption of the charging points in selected regions of the Netherlands. Results vary across different regions with the number of charging points, but suggest that statistical models could be useful in the management of energy production to optimize the dispatch of energy sources.
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Paper Nr: 6
Title:

Dynamic Cloud-based Vehicle Apps - Information Logistics in Disaster Response
Authors:

Oleg Gusikhin, Ayush Shah, Omar Makke, Alexander Smirnov and Nikolay Shilov
Abstract: The efficient management of transportation networks during disruptions caused by manmade accidents or natural disasters is a major attribute of the Resilient Smart City Transportation. There have been extensive research and development towards intelligent automatic disaster response systems. The majority of the proposed systems provide information logistics to the response team. In general, motorists caught in the disaster area typically tend to “go with the flow” or operate in an unorganized manner that may hamper the emergency response efforts. Connected vehicle technology and interactive vehicle applications enable the possibility to provide personalized information to individual motorists. This paper proposes the concept of dynamic vehicle applications integrated with cloud-based intelligent disaster response command and control system to facilitate evacuation, personalized routing, volunteering, and information gathering. The intelligent back end extends the knowledge based disaster response system for professional responders to automatically generate the guidance for the individual participant. The proposed dynamic vehicle applications leverage open source SmartDeviceLink interface and Node.js.
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Paper Nr: 7
Title:

Dynamic Beaconing using Probability Density Functions in Cooperative Vehicular Networks
Authors:

Sandy Bolufé, Cesar A. Azurdia-Meza, Sandra Céspedes, Samuel Montejo-Sánchez, Richard Demo Souza, Evelio M. G. Fernandez and Claudio Estevez
Abstract: Vehicular networks comprise vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications based on wireless radio access technologies. These networks require the periodic exchange of beacon messages between neighboring vehicles, to support cooperative road safety applications. The regular broadcast of beacon in the common control channel (CCH) using the IEEE 802.11p standard can lead to interference and recurrent packet collisions. This issue impacts negatively in the quality and freshness of the beaconing information which is essential to detect and mitigate potentially dangerous traffic situations on time. In this paper, we evaluate the performance of a dynamic beaconing strategy where both beacon rate and transmit power are assigned by means of probability density functions (PDFs). The idea is to know which PDF is more convenient to increase the system’s performance according to vehicular scenario characteristics. We investigate four types of PDFs, attending to four different performance metrics, in four distinct vehicular scenarios, using the well established Veins (Vehicles in network simulation) framework. The simulation results show that a beaconing strategy based on uniform PDF is convenient in scenarios with high vehicle density and low relative speed, whereas a beaconing strategy based on normal PDF is suitable in scenarios with high relative speed and low vehicle density.
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Paper Nr: 8
Title:

Bus Arrival Time Prediction with Limited Data Set using Regression Models
Authors:

Armands Kviesis, Aleksejs Zacepins, Vitalijs Komasilovs and Marcela Munizaga
Abstract: The increase of population has intensified everyday rush. Traffic congestions are still a problem in cities and are one of the main cause for public transport delays. City residents and visitors have experienced time loss by using public transport buses, because of waiting at the bus stops and not knowing if the bus is delayed or already serviced the stop. Therefore it is valuable for people to know at what time the bus should arrive (or is it already missed) at specific bus stop. Real-time public bus tracking and management system development has been the focus of many researchers, and many studies have been done in this area. This paper focuses on bus travel time prediction comparison between linear regression and support vector regression models (SVR), when using limited data set. Data were limited in a way that only historical GPS (Global Positioning System) coordinates of bus location (recorded each 30 seconds) and driven distance were used, there were no information about arrival/departure times, delays or dwell times. Distance between stops and delay (assumed values based on route observations by authors) were used as inputs for both models. It was concluded that SVR algorithm showed better results, but the difference was not significantly large.
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Paper Nr: 9
Title:

Towards Intelligent Tuning of Frequency and Transmission Power Adjustment in Beacon-based Ad-Hoc Networks
Authors:

Javier Rubio-Loyola, Hiram Galeana-Zapien, Francisco Aguirre-Gracia, Christian Aguilar-Fuster, Sandy Bolufé, Cesar A. Azurdia-Meza and Samuel Montejo-Sánchez
Abstract: This paper presents a genetic-based approach to determine optimal values of frequency and transmission power in beacon-based ad-hoc networks. The approach has been evaluated through simulations, and it has demonstrated to be more efficient than a dynamic control of frequency and transmission power, with reduction of up to 73% in packet collisions and with reduction of packet losses of up to 63% in an urban scenario. The approach and the results presented in this paper represent our initial efforts towards a more efficient control of beacon frequency and transmission power, which can exploit the benefits of a genetic-based approach but that can be applied in runtime in practical scenarios.
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Short Papers
Paper Nr: 10
Title:

Traffic Monitoring System Development in Jelgava City, Latvia
Authors:

Vitalijs Komasilovs, Aleksejs Zacepins, Armands Kviesis, Eliecer Peña, Felipe Tejada-Estay and Claudio Estevez
Abstract: Smart traffic management and monitoring is one of the key aspects of the modern smart city. Traffic flow estimation is crucial for sustainable traffic planning in the city. A requirement for successful planning and optimization of traffic is vehicle counting on the streets. Surveillance video is a suitable data source for precise vehicle counting. A solution for real-time vehicle traffic monitoring, tracking and counting is proposed in Jelgava city, Latvia. It is based on motion detection using background modeling, which is enhanced by statistical analysis. Two-phase assessment is utilized: motion blobs are detected and tracked using custom state machine implementation, then tracking results are passed through number of statistical filters to eliminate false positive detections. The system demonstrates good performance and acceptable accuracy on given test cases (about 97% accuracy for regular traffic conditions).
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Paper Nr: 11
Title:

Implementation of Smart Parking Solution by Image Analysis
Authors:

Aleksejs Zacepins, Vitalijs Komasilovs and Armands Kviesis
Abstract: Modern smart city concept implies various smart aspects including smart parking management. Searching for a free parking lot can be a challenging task, especially during major events, therefore automatic system, which will help drivers to find a free parking is very valuable. There are many intrusive and non-intrusive technologies available for smart parking development, but authors of this paper developed a system based on video processing and analysis. Authors developed Python application for real-time parking lot monitoring based on video analysis of public video stream. Five classifier models (Logistic Regression, Linear Support Vector Machine, Radial Basis Function Support Vector Machine, Decision Tree and Random Forest) were compared for parking lot occupancy detection. Logistic regression classifier showed better results and was chosen for real-time parking monitoring application. System shows good performance and correctly predicted parking lot occupancy almost in all test cases.
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