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This article presents the validation of a non-linear FE numerical model of a multi-span stone arch railway bridge based on experimental tests and under in-service freight trains. Static loading tests allow evaluating the bridge response in terms of vertical displacements in the arches, opening/closure deformations on specific block joints of the arches, and vertical compressive stress variations in the piers. The bridge FE model is developed by combining the potentialities of a global continuous homogeneous model, based on FEM and Drucker-Prager model, and a local modelling approach based on a dedicated non-linear contact model. The freight vehicle modelling is based on a flexible FE approach, and the validation of the dynamic behaviour of the train-bridge system involved the comparison between numerical and experimental responses. All the numerical responses are in very good agreement with the experimental responses. Finally, a simulation of the dynamic behaviour of the train-bridge system is performed for realistic scenarios of freight traffic considering speeds between 40 and 140 km/h.

This paper reports on a numerical study focused on the characterization of the masonry and infill material behaviour of stone arch railway bridges. The study is based on experimental data from testing campaigns carried out in materials of two granite stone bridges. The campaigns comprise lab tests on stone masonry samples (blocks and joints) collected from the bridge structure, as well as in-situ flat-jack testing and Menard pressuremeter tests on masonry and infill components, respectively. A strategy was developed resorting to Finite Element (FE) methodologies involving local scale numerical simulation of the joint lab tests (shear tests) and both in-situ tests (Flat-jack and Menard pressuremeter tests), aiming at the calibration and validation of the material parameters to be used in global scale numerical simulations of the bridges' structural behaviour. The joint shear behaviour is simulated using a FE discrete formulation based on contact elements whereas the Flat-jack and Menard pressuremeter testing is simulated using FE continuous homogeneous nonlinear materials based on a Drucker-Prager (D-P) model. The calibration of the D-P model input parameters is performed through a sensitivity analysis followed by an optimization strategy based on a genetic algorithm. The methodology showed the potential to calibrate the material constitutive parameters to be adopted in FE numerical models of this type of bridge.

The COVID-19 pandemic has changed the way how the people live, work and move, and naturally the transport sector became one of the most affected by this global crisis. Beyond the sudden fall of mobility at the beginning of the pandemic, it is important to understand how people are regaining trust in travelling, even if it is still unpredictable if and when the transport sector will recover to the pre-pandemic levels. This study focuses on the analysis of commuting trips and the changes of travel mode preferences over the first eight months of the pandemic in Germany. A survey with an orthogonal design based on sets of cards containing different transport mode alternatives and attributes was conducted in three waves (April, June, and October 2020). The individual characteristics and the preferences of around 4800 commuters were collected through the survey and modelled using a conditional logit approach. The results show that commuters have regained some trust on public transport since the April-May 2020 lockdown, but this has occurred at a slow pace. The reduction of public transport ticket fares can be the most effective strategy to recover some of the users lost to other modes.

This study explores attitudes towards urban air mobility (UAM) for e-commerce deliveries. UAM, which utilizes drones, has the potential to revolutionize transport services and logistics, leading to economic benefits and reductions in congestion and pollution. However, public acceptance is crucial for a successful implementation; thus, understanding the people’s perspective is key. Descriptive statistics were employed to evaluate survey results from three different European regions, followed by a cluster analysis to define potential user profiles. This study revealed slightly different perceptions towards UAM between the analyzed regions, but also a generally positive attitude. The most important expected identified benefits from UAM were a decrease in congestion and pollution in city centers. High-acceptance segments are gender-balanced and correspond mostly to active population and frequent online shoppers that perceive value in drone deliveries, especially related to an increased convenience and speed. They support public investment in UAM and are willing to pay more for these services and to have their home flown over. Opposite attitudes were expressed by low-acceptance segments, which are female-dominated and include mostly non-frequent online shoppers. The identified user profiles in this study can support the development of public policy and marketing strategies to increase acceptance and adoption.

The simulation of the dynamic behavior of the train-track system is strongly dependent on the accuracy of the numerical models of the train and track subsystems. The use of calibrated numerical models of the railway vehicles, based on experimental data, enhances their ability to correctly reproduce the dynamic responses of the train under operational conditions. In this scope, studies involving the experimental calibration of freight wagon models are still scarce. This article aims to fill this gap by presenting an efficient methodology for the calibration of a numerical model of a freight railway wagon based on experimental modal parameters. A dynamic test was performed during the unloading operation of the train, adopting a dedicated approach which does not interfere with its tight operational schedule. From data collected during the dynamic test, five natural frequencies and mode shapes associated with rigid-body and flexural movements of the wagon platform were identified through the Enhanced Frequency-Domain Decomposition (EFDD) method. A detailed 3D finite-element (FE) model of the loaded freight wagon was developed, requiring precise knowledge of the vehicle design details which, in most situations, are difficult to obtain due to confidentiality reasons of the manufacturers. The model calibration was performed through an iterative method based on a genetic algorithm and allowed to obtain optimal values for seven numerical parameters related to the suspension’s stiffnesses and mass distribution. The stability of the parameters considering different initial populations demonstrated the robustness of the optimization algorithm. The average error of the natural frequencies decreased from 8.5% before calibration to 3.2% after calibration, and the average MAC values improved from 0.911 to 0.950, revealing a significant improvement of the initial numerical model., The authors would like to acknowledge the support of the Base Funding UIDB/04708/2020 and Programmatic Funding UIDP/04708/2020 of the CONSTRUCT (Instituto de I&D em Estruturas e Construções) funded by national funds through the FCT/MCTES (PIDDAC). The authors also express their gratitude to Dr. Nuno Pinto and Mr. Valdemar Luís, both technicians of LESE laboratory, for their indispensable assistance during the preparation and execution of the experimental test

A transit network design frequency setting model is proposed to cope with the postpandemic passenger demand. The multiobjective transit network design and frequency setting problem (TNDFSP) seeks to find optimal routes and their associated frequencies to operate public transport services in an urban area. The objective is to redesign the public transport network to minimize passenger costs without incurring massive changes to its former composition. The proposed TNDFSP model includes a route generation algorithm (RGA) that generates newlines in addition to the existing lines to serve the most demanding trips, and passenger assignment (PA) and frequency setting (FS) mixed-integer programming models that distribute the demand through the modified bus network and set the optimal number of buses for each line. Computational experiments were conducted on a test network and the network comprising the Royal Borough of Kensington and Chelsea in London.