Luxembourg City Research Articles

Estimating coverage and capacity of high frequency mobile networks in ultradense urban areas

High frequency communications (mmWave and TeraHz) in urban areas require a higher density of base stations compared to pre-5G mobile networks, but open the way to a quantum leap in increased throughput and reduced latency. However, we currently have no indication of how much we need to densify the deployment, and on the trade-off between the density of base stations and the performance improvement. This paper studies the problem of base stations placement to guarantee coverage to vehicles and pedestrians in urban areas when using high frequency communications. Our novel methodology takes advantage of vehicular traffic simulations and precise urban maps to generate a realistic demand model for vehicles and pedestrians in urban areas. We use a bounded error heuristic to find the maximal coverage that can be achieved with a given density of base stations, primarily using line-of-sight communications. We implemented the heuristic using CUDA libraries on Nvidia GPUs and evaluated the coverage in an urban area in the city of Luxembourg, for which vehicular traffic patterns are available. We focus on coverage and capacity analysis for the mmWave frequency, but the results are easily extended to TeraHz communications. Our results are the first to show that a reasonably low density (15 base stations per km2) is sufficient to provide coverage for vehicles in urban environments. However, optimizing on vehicles or on pedestrians are competing objectives: the operator needs to choose which one to target based on its business model when designing the network infrastructure. Our algorithms, code and open data can be used to perform this task and reproduce our results in different settings.

Revving up VNDN: Efficient caching and forwarding by expanding content popularity perspective and mobility

Network caching in Vehicular Named Data Networks (VNDN) has the potential to support latency-sensitive services, though given the massive amount of content generated by vehicles in a VNDN, it is challenging to cache sufficiently diverse content across the network for an acceptable hit ratio. Redundant interest transmissions can negatively impact network overhead, wasting network resources when retrieving duplicate content. In the literature, a caching decision is typically based on ongoing popularity, computed from a vehicle’s experience with requests received for individual content. This leads to each vehicle caching the most popular content. Instead, we group content into different categories. All content in a popular category of vehicular service (e.g., tourist information category) is cached equally, not just the most popular content within a category (e.g., information relating to a particular museum). This increases content diversity in the cache. Further, we posit that a service requester from a particular location at a specific time would be interested in the same category of service/application as other users from the same location/time. Leveraging this observation, we consider both forthcoming spatial/temporal and ongoing popularity of vehicular services when ranking their popularity. We also address network overhead with a mobility model that captures the communication duration between vehicles. This parameter, which has been overlooked in existing literature, enables the selection of an appropriate forwarder for content requests. We use ndnSIM VNDN with a mobility trace of Luxembourg city for simulation. Compared to an approach where individual content-wise popularity is computed, our scheme shows more than 100% improvement in cache hit ratio where the number of contents in the network varied between 104 and 105. Simultaneously, we also enhanced the diversity of content caching, while considering the trade-off between cache hit ratio and network overhead.

Translanguaging, Word and Image, and the Danse Macabre

Abstract The late medieval Danse Macabre, a bilingual word-and-image tradition, may be understood through the theoretical lens of translanguaging. This article begins with an analysis of the visual reception of this tradition in a public art installation in Luxembourg City during the coronavirus pandemic, and is followed by the challenges of applying paratranslation to a page of the early printed Danse Macabre (1486). Returning to the earliest translations of the Danse, the article also examines evidence of untranslatability and tensions between three literary languages in the Catalan Dança de la Mort (c. 1490).

Translanguaging, Word and Image, and the Danse Macabre

Abstract The late medieval Danse Macabre, a bilingual word-and-image tradition, may be understood through the theoretical lens of translanguaging. This article begins with an analysis of the visual reception of this tradition in a public art installation in Luxembourg City during the coronavirus pandemic, and is followed by the challenges of applying paratranslation to a page of the early printed Danse Macabre (1486). Returning to the earliest translations of the Danse, the article also examines evidence of untranslatability and tensions between three literary languages in the Catalan Dança de la Mort (c. 1490).

Screening for familial hypercholesterolaemia in primary school children: protocol for a cross-sectional, feasibility study in Luxembourg city (EARLIE)

IntroductionFamilial hypercholesterolaemia (FH) is a frequent (1:300) autosomal dominantly inherited condition which causes premature (women <60 years, men <55 years) cardio–cerebrovascular disease (CVD). Early detection and initiation of treatment can...

Teletraffic analysis of a mobile crowdsensing system: The pedestrian-to-vehicle scenario

Crowdsensing systems are developed in order to use the computational and communication capabilities of registered users to monitor specific variables and phenomena in an opportunistic manner. As such, the Quality of Experience is not easily attained since these systems heavily rely on the user’s behavior and willingness to cooperate whenever an event with certain interest needs to be monitored. In this work, we analyze the data acquisition phase, where pedestrians opportunistically transmit to vehicles to further disseminate it in the city according to their trajectory. This highly dynamic environment (sensors and data sinks are mobile, and the number of users varies according to the region and time) poses many challenges for properly operating a crowdsensing system. We first study the statistical properties of vehicular traffic in different regions of Luxembourg City where pedestrians share their computational resources and send data to passing cars. Then we propose an Erlang distribution to model the vehicles’ dwelling times and develop a Markov chain accordingly. We model the system using two different queues: we use a single server queue to model the vehicle traffic, while we use an infinite server queue system to model the pedestrian traffic.

On using lumped-parameter models in groundwater protection zones to predict aquifer response to reduced agrochemical input

• Waiting times for aquifers contaminated by agrochemicals are useful for management. • Lumped-parameter models can be used to estimate these waiting times. • Waiting times useful for management must not be confused with mean transit times. In groundwater protection zones, water managers and water providers will typically need to know the time scale of recovery for aquifers contaminated by agrochemicals. Despite the large body of work on the topic in the scientific literature, there still seems to be some need to clarify which waiting times are most relevant for water management planning, and how they relate to metrics such as the mean transit time of tracer. Firstly, we propose a simple nomenclature for the different waiting times and how they relate to the evolution of agrochemical concentration (increasing and decreasing concentration trends, trend reversal, etc.). Secondly, we describe how to select and fit a lumped-parameter model to contaminant time series and environmental isotopes in order to characterize aquifer response to agrochemical contamination. Thirdly, we explain how waiting times can be calculated from the fitted lumped-parameter model. Finally, we present a case study focussing on the contamination of several springs of the city of Luxembourg by a fungicide transformation product combining environmental tracers and pesticide measurements. We find that on the study site, the parameters estimated separately from the environmental tracers and from the pesticide data are nearly the same although they correspond to different areas within the groundwatershed. Although this could be incidental, it might also indicate that robust waiting time estimates can be obtained from agrochemical time series and environmental tracer measurements, at least in simple cases.

An Agent-Based Dynamic Framework for Population Evacuation Management

Evacuating the population during crises to safe zones via optimal paths is vital. The evacuation planning process makes two main decisions: which shelter to reach and which path to take towards the chosen shelter. These decisions correspond to shelter allocation and traffic assignment problems, respectively. Many studies tackled these problems with a static formulation in the literature, while only a few considered a dynamic context. We conduct a comprehensive literature review and highlight that most studies independently solve these two problems while both are correlated with traffic conditions. To fill this gap, we propose a new framework to couple the shelter allocation problem (SAP) and the dynamic traffic assignment (DTA) problem and solve them. To capture traffic dynamics, we use a dynamic agentbased simulator. We assume the system determines the evacuees&#x2019; shelters to minimize the total evacuation time. However, each evacuee&#x2019;s concern is reaching a shelter as fast as possible. Therefore, we formulate the DTA problem under stochastic user equilibrium (SUE) principles, i.e., every evacuee aims to minimize his own perceived travel time. We apply the proposed methodology to the network of Luxembourg City and compare its performance with other advanced methods that solve SAP and DTA separately. The comparison shows that solving the dynamic shelter allocation improves the mean evacuation time and significantly decreases the network clearance time compared to other methods with a fixed plan for SAP. The simulation results prove that considering the network state in the SAP can provide a more effective evacuation plan. Moreover, we perform a sensitivity analysis on optimization parameters and evaluate the computation cost of our methodology.

Optimization of Carsharing Fleet Placement in Round-Trip Carsharing Service

In a round-trip carsharing system, stations must be located in such a way that allow for maximum user coverage with the least walking distance as well as offer certain degrees of flexibility for returning. Therefore, a balance must be stricken between these factors. Providing a satisfactory system can be translated into an optimization problem and belongs to an NP-hard class. In this article, a novel optimization model for the round-trip carsharing fleet placement problem, called Fleet Placement Problem (FPP), is proposed. The optimization in this work is multiobjective and its NP-hard nature is proven. Three different optimization algorithms: PolySCIP (exact method), heuristics, and NSGA-II (metaheuristic) are investigated. This work adopts three real instances for the study, instead of their abstracts where they are most commonly used. They are two instance:, in the city of Luxembourg (smaller and larger) and a much larger instance in the city of Munich. Results from each algorithm are validated and compared with solution from human experts. Superiority of the proposed FPP model over the traditional methods is also demonstrated.

Dynamic stochastic electric vehicle routing with safe reinforcement learning

Dynamic routing of electric commercial vehicles can be a challenging problem since besides the uncertainty of energy consumption there are also random customer requests. This paper introduces the Dynamic Stochastic Electric Vehicle Routing Problem (DS-EVRP). A Safe Reinforcement Learning method is proposed for solving the problem. The objective is to minimize expected energy consumption in a safe way, which means also minimizing the risk of battery depletion while en route by planning charging whenever necessary. The key idea is to learn offline about the stochastic customer requests and energy consumption using Monte Carlo simulations, to be able to plan the route predictively and safely online. The method is evaluated using simulations based on energy consumption data from a realistic traffic model for the city of Luxembourg and a high-fidelity vehicle model. The results indicate that it is possible to save energy at the same time maintaining reliability by planning the routes and charging in an anticipative way. The proposed method has the potential to improve transport operations with electric commercial vehicles capitalizing on their environmental benefits.

Monitoring of urban forests using 3D spatial indices based on LiDAR point clouds and voxel approach

Modern cities face challenges in responding to the needs of diverse groups, therefore urban space must be appropriately shaped to be as resident-friendly as possible. Particular attention needs to be paid to urban vegetation, which is an essential component of a suitable quality of life. Research to date has often relied on two-dimensional (2D) mapping of urban vegetation using remote sensing imagery and vegetation indicators, where greenery is evenly distributed regardless of the cubature. However, in reality, vegetation’s spatial and vertical structure varies, and the layers often overlap. In the current paper concerning Luxembourg City, we propose a novel 3D method exploring such indices as Vegetation 3D Density (V3DI) and Vegetation Volume to Building Volume (VV2BV). The goal of the study is to investigate the spatial relationship between the volume of vegetation and of buildings in the rapidly developing Luxembourg City. The vegetation volume was calculated using airborne laser scanning point clouds (ALS LiDAR) processed into voxels (0.5 m). The volume of the buildings was calculated based on the results of 3D ALS LiDAR point cloud modelling. Proposed spatial indices were estimated for districts, for cadastral parcels, in a cell grid of 100 m and for each building individually, using a 100 m buffer. We found that in 2019, urban forests covered 1689 ha of Luxembourg City, accounting for 33 per cent of the entire administrative area. The 3D GIS analyses show that the total volume of vegetation (> 1.0 m above ground) was about 40 million m3, equating to 328 m3 of greenery per resident. The V3DI produced a value of 0.77 m3/m2. The overall VV2BV(%) index calculated for Luxembourg was 41.6 per cent. Only five districts of Luxembourg were characterized by a high value for the VV2BV index, which indicates areas with a high level of green infrastructure to contribute to health and a better quality of life.

Cultural representation in Luxembourgish street naming practices

Abstract This paper investigates street naming practices in Luxembourg. Starting from a theoretical sketch of how the curation of a given cityscape by dint of cultural artifacts (e.g., street names) establishes complex orders of cultural representation, it discusses three case studies for street naming campaigns from Luxembourg. These case studies represent different types of action modes, ideological motives and linguistic materials involved in street naming. First, the naming process for a newly established neighborhood in Luxembourg City illustrates the default mode of street naming by administrative action. Second, the Germanization of Luxembourg City under German occupation during World War II demonstrates the forced alignment of a given cityscape through political octroi. And third, the recent first naming of preexisting streets in the rural municipality of Wincrange provides an example of a participatory naming process that establishes an order of cultural representation based on local traditions.

Sustainable Construction Logistics in Urban Areas: A Framework for Assessing the Suitability of the Implementation of Construction Consolidation Centres

Transport in construction is responsible for up to 30% of freight movement in cities, with a subsequent impact in terms of pollutant emissions. Different solutions have been provided to alleviate the negative impact of freight transport related to construction activity, although no guidance has been provided for replicability in cities. One solution whose potential benefits are being studied with the support of policy makers is Construction Consolidation Centres (CCC). This paper proposes a method based on the Analytic Hierarchy Process (AHP) and provides an index based on 45 indicators to evaluate the suitability of the implementation of a CCC in terms of the pillars of sustainability in combination with a technical pillar. Three real construction projects were assessed in Luxembourg City, Paris, and Valencia. Two critical attributes were identified: the estimated operational costs of the construction company and the potential demand level in the area. The results of the analysis allow for: (i) the extraction of knowledge related to the sustainability of the construction project, and (ii) logistics to be integrated into the planning and design stages of the construction activity. Furthermore, a general structure is also proposed for integrating other construction solutions where CCC is not suitable.

Electric vehicle routing problem with machine learning for energy prediction

Routing electric commercial vehicles requires taking into account their limited driving range, which is affected by several uncertain factors such as traffic conditions. This paper presents the time-dependent Electric Vehicle Routing Problem with Chance-Constraints (EVRP-CC) and partial recharging. The routing method is divided into two stages, where the first finds the best paths and the second optimizes the routes. A probabilistic Bayesian machine learning approach is proposed for predicting the expected energy consumption and variance for the road links, paths and routes. Hence it is possible to consider the uncertainty in energy demand by planning charging within a confidence interval. The energy estimation is validated with data from electric buses driving a public transport route in Gothenburg-Sweden as well as with realistic simulations for 24 hours traffic in the city of Luxembourg connected to a high fidelity vehicle model. Routing solutions are compared with a deterministic formulation of the problem similar to the ones found in the literature. The results indicate high accuracy for the energy prediction as well as energy savings and more reliability for the routes.

Spatiotemporal Changes in 3D Building Density with LiDAR and GEOBIA: A City-Level Analysis

Understanding how, where, and when a city is expanding can inform better ways to make our cities more resilient, sustainable, and equitable. This paper explores urban volumetry using the Building 3D Density Index (B3DI) in 2001, 2010, 2019, and quantifies changes in the volume of buildings and urban expansion in Luxembourg City over the last two decades. For this purpose, we use airborne laser scanning (ALS) point cloud (2019) and geographic object-based image analysis (GEOBIA) of aerial orthophotos (2001, 2010) to extract 3D models, footprints of buildings and calculate the volume of individual buildings and B3DI in the frame of a 100 × 100 m grid, at the level of parcels, districts, and city scale. Findings indicate that the B3DI has notably increased in the past 20 years from 0.77 m3/m2 (2001) to 0.9 m3/m2 (2010) to 1.09 m3/m2 (2019). Further, the increase in the volume of buildings between 2001–2019 was +16 million m3. The general trend of changes in the cubic capacity of buildings per resident shows a decrease from 522 m3/resident in 2001, to 460 m3/resident in 2019, which, with the simultaneous appearance of new buildings and fast population growth, represents the dynamic development of the city.

Relational Cities Disrupted: Reflections on the Particular Geographies of COVID-19 For Small But Global Urbanisation in Dublin, Ireland, and Luxembourg City, Luxembourg.

This paper looks at the particular geographies associated with the COVID‐19 outbreak through the lens of cities that are products of relational urbanisation. This includes small but highly globalised cities, such as financial centres or hot spots of politics and diplomacy, which are usually situated between different political, economic or cultural systems and their boundaries. These cities experienced strong growth due to internationalisation and a dedicated politics of extraversion. Our argument is that such places are unusually affected by the current lock‐down, illustrated by two empirical cases, the cities of Dublin, Ireland, and Luxembourg City, Luxembourg. Both have experienced striking growth rates recently, but now suffer from disruption. Their development trajectories remain unclear, since a return to the ‘old normal’ seems unlikely, and the emergent ‘new normal’ calls for adaptation towards more state involvement in areas hitherto governed by the market. The paper addresses possible alternative geographies for both cases.

Enhancing Bus Holding Control Using Cooperative ITS

We introduce a novel hybrid controller, which aims to maintain regularity on bus operations while reducing the number of stops at signalized intersections. The controller combines a rule-based holding criterion with a cooperative intelligent transportation systems based strategy, namely green light optimal dwell time adaptation (GLODTA), which exploits signal phase and timing information to modify dwell times at stops. In order to address the bi-objective criterion, holding control is integrated with transit signal priority (TSP) and an additional driving advisory system, which adjusts speed to reduce stopping at signals. The different controllers are evaluated using simulation of major bus line in Luxembourg City. The results show that buses are efficiently operated without necessarily providing additional priority via TSP, hence without negatively affecting the capacity of the private vehicles system. The new controller improves further the performance in terms of regularity and reduces the number of stops at traffic lights.

Towards Dynamic Controller Placement in Software Defined Vehicular Networks.

The emerging SDVN (Software Defined Vehicular Network) paradigm promises to bring flexibility and efficient resource utilization to vehicular networks, enabling the emergence of novel Intelligent Transportation Services. However, as it was initially designed with wired network in mind, applying the SDN paradigm to a vehicular context faces new challenges related to the peculiar characteristics of this network (high node mobility and node density, and the presence of wireless links). In this paper, we focus on one of the critical architectural elements of SDVN, namely, the SDN Controller Placement, and promote the use of dynamic placement methods that take into account the dynamicity of vehicular networks’ topology. We also describe the different approaches towards a dynamic controller placement and also propose an ILP (Integer Linear Programming) based dynamic placement method that adaptively readjusts the number and placement of controllers according to road traffic fluctuations. The proposed method is evaluated using a realistic traffic trace from Luxembourg City. Simulation results show that our approach outperforms the static approach as proposed in the literature.

Melusine's Footprint: Tracing the Legacy of a Medieval Myth

Melusine's Footprint: Tracing the Legacy of a Medieval Myth

Model and Solution Methods for the Mixed-Fleet Multi-Terminal Bus Scheduling Problem

Public transport services are currently executing or planning a fundamental transition from traditional buses to electric buses. During this transition phase, the public transport offering is fulfilled with a mixed fleet across multiple bus terminals, which poses operational challenges for optimal vehicle scheduling, a problem not yet addressed in literature. As researchers in Transport Engineering and Operational Research at the University of Luxembourg, in collaboration with the Roma Tre University, we support the Ministry of Transport of Luxembourg and Volvo buses by modelling and simulating this transition phase, to help them managing and solving such challenges. In this work we develop a mixed-integer linear programming (MILP) formulation of the problem and implement a time-based decomposition framework, through which we can optimize real-life daily instances. This method is tested on the main urban bus lines that connect Central Station, Luxembourg Airport and ten other major terminals within Luxembourg City, providing (near) optimal solutions that explicitly consider the energy constraints arising from electric bus operations, while establishing an advantageous trade-off between delaying trips, to implement quick-charging of electric buses, and performing the same trip with costlier traditional buses. The results show a consistent decrease of operational costs as the percentage of e-buses in the fleet increases.