Airport management is frequently faced with a problem of assigning flights to available stands and parking positions in the most economical way that would comply with airline policies and suffer minimum changes due to any operational disruptions. This work presents a novel approach to the most common airport problem – efficient stand assignment. The described algorithm combines benefits of data-mining and metaheuristic approaches and generates qualitative solutions, aware of delay trends and airport performance perturbations. The presented work provides promising solutions from the starting moments of computation, in addition, it delivers to the airport stakeholders delay-aware stand assignment, and facilitates the estimation of risk and consequences of any operational disruptions on the slot adherence.
MULTIFILE
Paris Charles de Gaulle Airport was the second European airport in terms of traffic in 2019, having transported 76.2 million passengers. Its large infrastructures include four runways, a large taxiway network, and 298 aircraft parking stands (131 contact) among three terminals. With the current pandemic in place, the European air traffic network has declined by −65% flights when compared with 2019 traffic (pre-COVID-19), having a severe negative impact on the aviation industry. More and more often taxiways and runways are used as parking spaces for aircraft as consequence of the drastic decrease in air traffic. Furthermore, due to safety reasons, passenger terminals at many airports have been partially closed. In this work we want to study the effect of the reduction in the physical facilities at airports on airspace and airport capacity, especially in the Terminal Manoeuvring Area (TMA) airspace, and in the airport ground side. We have developed a methodology that considers rare events such as the current pandemic, and evaluates reduced access to airport facilities, considers air traffic management restrictions and evaluates the capacity of airport ground side and airspace. We built scenarios based on real public information on the current use of the airport facilities of Paris Charles de Gaulle Airport and conducted different experiments based on current and hypothetical traffic recovery scenarios. An already known optimization metaheuristic was implemented for optimizing the traffic with the aim of avoiding airspace conflicts and avoiding capacity overloads on the ground side. The results show that the main bottleneck of the system is the terminal capacity, as it starts to become congested even at low traffic (35% of 2019 traffic). When the traffic starts to increase, a ground delay strategy is effective for mitigating airspace conflicts; however, it reveals the need for additional runways
This study is the first to systematically and quantitatively explore the factors that determine the length of charging sessions at public charging stations for electric vehicles in urban areas, with particular emphasis placed on the combined parking- and charging-related determinants of connection times. We use a unique and large data set – containing information concerning 3.7 million charging sessions of 84,000 (i.e., 70% of) Dutch EV-users – in which both private users and taxi and car sharing vehicles are included; thus representing a large variation in charging duration behavior. Using multinomial logistic regression techniques, we identify key factors explaining heterogeneity in charging duration behavior across charging stations. We show how these explanatory variables can be used to predict EV-charging behavior in urban areas and we derive preliminary implications for policy-makers and planners who aim to optimize types and size of charging infrastructure.
Receiving the first “Rijbewijs” is always an exciting moment for any teenager, but, this also comes with considerable risks. In the Netherlands, the fatality rate of young novice drivers is five times higher than that of drivers between the ages of 30 and 59 years. These risks are mainly because of age-related factors and lack of experience which manifests in inadequate higher-order skills required for hazard perception and successful interventions to react to risks on the road. Although risk assessment and driving attitude is included in the drivers’ training and examination process, the accident statistics show that it only has limited influence on the development factors such as attitudes, motivations, lifestyles, self-assessment and risk acceptance that play a significant role in post-licensing driving. This negatively impacts traffic safety. “How could novice drivers receive critical feedback on their driving behaviour and traffic safety? ” is, therefore, an important question. Due to major advancements in domains such as ICT, sensors, big data, and Artificial Intelligence (AI), in-vehicle data is being extensively used for monitoring driver behaviour, driving style identification and driver modelling. However, use of such techniques in pre-license driver training and assessment has not been extensively explored. EIDETIC aims at developing a novel approach by fusing multiple data sources such as in-vehicle sensors/data (to trace the vehicle trajectory), eye-tracking glasses (to monitor viewing behaviour) and cameras (to monitor the surroundings) for providing quantifiable and understandable feedback to novice drivers. Furthermore, this new knowledge could also support driving instructors and examiners in ensuring safe drivers. This project will also generate necessary knowledge that would serve as a foundation for facilitating the transition to the training and assessment for drivers of automated vehicles.
In the coming decades, a substantial number of electric vehicle (EV) chargers need to be installed. The Dutch Climate Accord, accordingly, urges for preparation of regional-scale spatial programs with focus on transport infrastructure for three major metropolitan regions among them Amsterdam Metropolitan Area (AMA). Spatial allocation of EV chargers could be approached at two different spatial scales. At the metropolitan scale, given the inter-regional flow of cars, the EV chargers of one neighbourhood could serve visitors from other neighbourhoods during days. At the neighbourhood scale, EV chargers need to be allocated as close as possible to electricity substations, and within a walkable distance from the final destination of EV drivers during days and nights, i.e. amenities, jobs, and dwellings. This study aims to bridge the gap in the previous studies, that is dealing with only of the two scales, by conducting a two-phase study on EV infrastructure. At the first phase of the study, the necessary number of new EV chargers in 353 4-digit postcodes of AMA will be calculated. On the basis of the findings of the Phase 1, as a case study, EV chargers will be allocated at the candidate street parking locations in the Amsterdam West borough. The methods of the study are Mixed-integer nonlinear programming, accessibility and street pattern analysis. The study will be conducted on the basis of data of regional scale travel behaviour survey and the location of dwellings, existing chargers, jobs, amenities, and electricity substations.
Stedelijke regio’s streven naar een duurzame mobiliteitstransitie. Deze ambitie staat echter op gespannen voet met het hoge autobezit- en autogebruik. De stormachtige introductie van lichte elektrische voertuigen, oftewel LEVs (denk aan e-scooters, e-steps, e-(cargo)bikes en micro-cars) leek een belangrijke ‘gamechanger’ te zijn. Deze LEVs zijn namelijk klein en efficiënt, zijn nagenoeg emissievrij, bieden mogelijkheden voor het verbeteren van het voor- en natransport van het openbaar vervoer (OV) en worden bovendien door hun gebruikers als prettig ervaren tijdens het reizen.Tot op heden maken LEVs deze beloften echter onvoldoende waar. Bij de introductie, thans met name in de vorm van deelsystemen, komen diverse uitdagingen aan het licht zoals: 1) verrommeling en overlast door verkeerd gepareerde LEVs, 2) ongewenste substitutie van loop-, fiets- en OV-verplaatsingen en beperkte impact op autogebruik en 3) en zorgen over de verkeersveiligheid en beleving, met name op de (al steeds drukker wordende) fietsinfrastructuur in Nederland. Deze problemen komen mede voort uit de snelle introductie waardoor gemeenten achter de feiten aanliepen en geen gericht beleid konden voeren. Langzaam komen we nu in een periode van stabilisatie en regulering maar een doorontwikkeling naar pro-actief LEV beleid is nodig om de potentie van LEVs voor de mobiliteitstransitie te ondersteunen. Het LEVERAGE-consortium, bestaande uit sterke partners uit de triple helix, gaat daarom aan de slag met deze vraagstukken. De centrale onderzoeksvraag is:Wat is de potentie van LEVs voor de mobiliteitstransitie naar bereikbare, duurzame, verkeersveilige, inclusieve en leefbare stedelijke regio’s en hoe kan deze optimaal worden benut door een betere integratie van LEVs in het mobiliteitssysteem en het mobiliteitsbeleid en door een effectieve governance van de samenwerking tussen publieke en private stakeholders?Om deze vraag te beantwoorden heeft het consortium een ambitieus en innovatieve onderzoeksopzet gedefinieerd waarbij veel nadruk wordt gelegd op de disseminatie en exploitatie van kennis in de beleidspraktijk.Collaborative partnersProvincie Noord-Brabant, Metropoolregio Arnhem-Nijmegen, Gemeente Eindhoven, Gemeente Breda, Gemeente Arnhem, Ministerie I&W, Rijkswaterstaat, Arriva, PON, Check, Citysteps, Cenex, TIER, We-all-Wheel, Fleet investment, Goudappel, Kennisinstellingen en netwerkorganisaties, HAN, TU/e, CROW, Connekt, POLIS, SWOV.
