Abstract-This paper presents a novel peer-to-peer energy trading system between two sets of electric vehicles, which significantly reduces the impact of the charging process on the power system during business hours. This trading system is also economically beneficial for all the users involved in the trading process. An activity-based model is used to predict the daily agenda and trips of a synthetic population for Flanders (Belgium). These drivers can be initially classified into three sets; after discarding the set of drivers who will be short of energy without charging chances due to their tight schedule, we focus on the two remaining relevant sets: those who complete all their daily trips with an excess of energy in their batteries and those who need to (and can) charge their vehicle during some daily stops within their scheduled trips. These last drivers have the chance to individually optimize their energy cost in the time-space dimensions, taking into account the grid electricity price and their mobility constraints. Then, collecting all the available offer/demand information among vehicles parked in the same area at the same time, an aggregator determines an optimal peer-topeer price per area and per time slot, allowing customers with excess of energy in their batteries to share with benefits this good with other users who need to charge their vehicles during their daily trips. Results show that, when applying the proposed trading system, the energy cost paid by these drivers at a specific time slot and in a specific area can be reduced up to 71%.
In order to facilitate the development of dynamic activity-based models for transport 3 demand, the FEATHERS framework was developed. This framework suggests a four 4 stage development trajectory for a smooth transition from the four-step models towards 5 static activity based models in the short term and dynamic activity based models in the 6 longer term. The development stages discussed in this paper range from an initial static 7 activity-based model without traffic assignment to ultimately a dynamic activity-based 8 model incorporating rescheduling, learning effects and traffic routing. 9To illustrate the FEATHERS framework, work that has been done on the development of 10 both static and dynamic activity-based models for Flanders (Belgium) and the 11 Netherlands is discussed. First, the data collection is presented. Next, the four stage 12 activity-based model development trajectory is discussed in detail. 13The paper concludes with the presentation of the modular FEATHERS framework, which 14 discusses the functionalities of the modules and how they accommodate the requirements 15 imposed on the framework by each of the four stages. 16 17
Unmanned aerial vehicles (UAVs), commonly referred to as drones, are one of the most dynamic and multidimensional emerging technologies of the modern era. This technology has recently found multiple potential applications within the transportation field, ranging from traffic surveillance applications to traffic network analysis. To conduct a UAV-based traffic study, extremely diligent planning and execution are required followed by an optimal data analysis and interpretation procedure. In this study, however, the main focus was on the processing and analysis of UAV-acquired traffic footage. A detailed methodological framework for automated UAV video processing is proposed to extract the trajectories of multiple vehicles at a particular road segment. Such trajectories can be used either to extract various traffic parameters or to analyze traffic safety situations. The proposed framework, which provides comprehensive guidelines for an efficient processing and analysis of a UAV-based traffic study, comprises five components: preprocessing, stabilization, georegistration, vehicle detection and tracking, and trajectory management. Until recently, most traffic-focused UAV studies have employed either manual or semiautomatic processing techniques. In contrast, this paper presents an in-depth description of the proposed automated framework followed by a description of a field experiment conducted in the city of Sint-Truiden, Belgium. Future research will mainly focus on the extension of the applications of the proposed framework in the context of UAV-based traffic monitoring and analysis.
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