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Abstract: The expected growth of electric vehicle (EV) usage will not only increase the energy demand but also bring the requirement to provide the necessary electrical infrastructure to handle the load. While charging infrastructure is becoming increasingly present in urban areas, special attention is required for transit traffic, not just for passengers but also for freight transport. Differences in the nature of battery charging compared to that of classical refueling require careful planning in order to provide a resilient electrical infrastructure that will supply enough energy at critical locations during peak hours. This paper presents a hybrid simulation model for analyzing fast-charging demand based on traffic flow, projected EV adoption, battery characteristics, and environmental conditions. The model integrates a probabilistic model for evaluating the charging requirements based on traffic flows with a discrete-event simulation (DES) framework to analyze charger utilization, waiting queues, and energy demand. The presented case of traffic flow on Slovenian motorways explored the expected power demands at various seasonal traffic intensities. The findings provide valuable insight for planning the charging infrastructure, the electrical grid, and also the layout by anticipating the number of vehicles seeking charging services. The modular design of the model allowed replacing key parameters with different traffic projections, supporting a robust scenario analysis and adaptive infrastructure planning. Replacing the parameters with real-time data opens the path for integration into a digital twin framework of individual EV charging hubs, providing the basis for development of an EV charging hub network digital twin.
Keywords: electric vehicles, EV charging, traffic flow, EV charging hub
Published in DKUM: 11.04.2025; Views: 0; Downloads: 2
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Abstract: The outbreak of COVID-19 caused many changes in people's life. One of the most significant is the travel behaviour and transport mode choice. This study focus on the changes that the inhabitants of Vienna made in their travel choices because of the virus. The same research about spatial modelling the transport mode choice of commuters in Vienna was completed in 2019 and is a topic addressed in our previous work. Based on our developed methodology, this article indicates that public transport is not a dominant transport mode choice as it was before the virus outbreak.The main result of this paper is geographically defined areas of application of individual alternatives shown on the final map of modal shift in Vienna, which could provide theoretical support for policy-makers and transportation planners. For the city of Vienna, we found that the area of the city where cars are now used has increased, which certainly has a negative impact on air quality and life in the city. The advantage of the methodology is that it can also be applied to other cities in the world.
Keywords: coronavirus, city traffic, urban mobility, transport mode choice, passenger transport, geographical methods, spatial modelling, city logistics, Vienna
Published in DKUM: 14.10.2024; Views: 0; Downloads: 21
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Abstract: Natural conditions play an important role as determinants and cocreators of the spatiotemporal road traffic accident Hot Spot footprint; however, none of the modern commercial, or open-source, navigation systems currently provides it for the driver. Our findings, based on a spatiotemporal database recording 11 years of traffic accidents in Slovenia, proved that different weather conditions yield distinct spatial patterns of dangerous road segments. All potentially dangerous road segments were identified and incorporated into a mobile spatial decision support system (SLOCrashInfo), which raises awareness among drivers who are entering or leaving the predefined danger zones on the street network. It is expected that such systems could potentially increase road traffic safety in the future.
Keywords: GIS, mobile application, spatial databases, spatial patterns, traffic safety
Published in DKUM: 20.08.2024; Views: 63; Downloads: 12
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Abstract: The manuscript discusses the investigation of vehicle flow in a predesignated junction byan appropriate traffic flow management with an effort to minimize fuel consumption, the productionof CO2, an essential greenhouse gas (hereinafter referred to as GHG), and related transport costs.The particular research study was undertaken in a frequented junction in the city of Celje, located in the eastern part of Slovenia. The results obtained summarize data on consumed fuel and produced CO2 amounts depending on the type of vehicle, traffic flow mixture, traffic light signal plan, andactual vehicle velocity. These values were calculated separately for three different conditions of traffic flow management. Amounts of fuel consumed were experimentally investigated in real traffic situations, whereas CO2 production was calculated by applying the actual European standardentitled EN 16258:2012 associated with a guideline for measuring emission values, as well as by examining specific traffic flow parameters. The key objective of the manuscript is to present multiple scenarios towards striving to minimize environmental impacts and improve transport operation's economic consequences when implementing proper traffic flow management. As for crucial findings, we quantified fuel consumption and CO2 emissions based on real data on the number and type of vehicles crossing the examined intersection and traffic light switching intervals. The results show that most of the CO2 was produced while waiting and in the accelerating phase in front of traffic lights, whereby in the running phase through the intersection, significantly less fuel was used. This study represents a mosaic fragment of research addressing endeavors to reduce CO2 production in urban transport. Following the experiments conducted, we can see a notable contribution towards reducing CO2 production with known and tested interventions in the existing transport infrastructure. A procedure embracing individual research steps may be deemed as an approach methodology dealing with traffic flow management with an aim to decrease the environmental and economic impacts oftraffic and transport operation; this is where the novelty of the research lies.
Keywords: traffic flow management, urban transport, CO2 production, greenhouse gas, fuel consumption, methodology, logistics, crossroads
Published in DKUM: 19.08.2024; Views: 82; Downloads: 9
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Abstract: How to extract a collection of trajectories for different vessels from the raw AIS data to discover vessel meeting knowledge is a heavily studied focus. Here, the AIS database is created based on the raw AIS data after parsing, noise reduction and dynamic Ramer-Douglas-Peucker compression. Potential encountering trajectory pairs will be recorded based on the candidate meeting vessel searching algorithm. To ensure consistent features extracted from the trajectories in the same time period, time alignment is also adopted. With statistical analysis of vessel trajectories, sailing segment labels will be added to the input feature. All motion features and sailing segment labels are combined as input to one trajectory similarity matching method based on convolutional neural network to recognize crossing, overtaking or head-on situations for each potential encountering vessel pair, which may lead to collision if false actions are adopted. Experiments on AIS data show that our method is effective in classifying vessel encounter situations to provide decision support for collision avoidance.
Keywords: AIS Data, CNN, Dynamic Rammer-Douglas-Peucker, knowledge discovery, maneuvering pattern, traffic pattern, trajectory
Published in DKUM: 19.03.2024; Views: 538; Downloads: 417
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