Search Results (22)

Intelligent transport systems are a major component of smart cities because their deployment should result in reduced journey times, less traffic congestion and a significant reduction in road deaths, which will greatly improve the quality of life of their citizens. New technologies such as vehicular networks allow more information be available in realtime, and this information can be used with new analytical models to obtain more accurate estimates of journey times. This would be extremely useful to drivers and will also enable transport authorities to optimise the transport network. This paper addresses these issues using a model-based approach to provide a new way of estimating the delay along specified routes. A journey is defined as the traversal of several road links and junctions from source to destination. The delay at the junctions is analysed using the zero-server Markov chain technique. This is then combined with the Jackson network to analyse the delay across multiple junctions. The delay at road links is analysed using an M/M/K/K model. The results were validated using two simulators: SUMO and VISSIM. A real scenario is also examined to determine the best route. The preliminary results of this model-based analysis look promising but more work is needed to make it useful for wide-scale deployment. Full article

Introduction: Humanitarian Corridors are part of Protected Entry Procedures (PEPs), which allow for the safe and legal arrival in Europe of refugees in need of protection (art. 25 of Regulation (CE) n.810/2009) and were implemented for the first time in Italy in 2015. They represent an alternative to dangerous journeys, and they also bring benefits to local communities. The National Institute for Health, Migration and Poverty (NIHMP) represents the first filter at entry with regard to health needs, as it guarantees an overall individual health assessment to verify the possible presence of ongoing infectious pathologies and of issues that may require management and medical investigation. The aim of this study is to expose the organizational activity and analyze the sociodemographic and health data relating to the Humanitarian Corridors implemented in Italy and in which the NIHMP has participated from 2018 to 2024. Materials and Methods: The organizational lists and health data of each corridor were collected. The analysis was carried out in relation to trend, sociodemographic characteristics of the sample, identification, reception and health. The p-value was considered statistically significant if less than 0.01. In all cases in which the p-value was found to be statistically significant, Cramer’s V was calculated to evaluate the strength of the individual correlation. Results: The NIHMP has participated in 14 Humanitarian Corridors, allowing 1250 refugees to enter Italy; a total of 174 unaccompanied foreign minors (UFMs) arrived, and there were 451 refugees reported as vulnerable (36.1%). Contagious infectious conditions were identified in 223 refugees (17.8%), and other health findings were identified in 414 refugees (33.1%). In the analysis by sex, there are statistically significant differences in the reports of vulnerability and in nationality and education. The inferential analysis carried out by age groups presents statistically significant differences in the reports of vulnerability and in sex and nationality. The analysis relating to the differences by nationality shows statistically significant results in the individual corridors in the reports of vulnerability and in schooling, skin manifestations and infectious diseases and the presence of other health findings. Conclusions: The data reported and analyzed in this work can be considered the first attempt at a complete and detailed analysis regarding the actual implementation and effectiveness of Humanitarian Corridors as programs for legal and safe entry into Italy. The significant gap in research is the reason why there are characteristics of our sample that have no counterpart in the literature. Despite this, for other characteristics, it is possible to find statistical significance and scientific value in line with the data reported both on the websites dedicated to the topic and on the limited scientific literature on the subject. Full article

Reducing losses during pig transport is essential for breeders and transporters, particularly in semi-arid regions, where high temperatures exacerbate transport-related stress and risk of losses. This study aimed to evaluate the effect of transport duration (short vs. long trips) on animal welfare and production losses during the commercial transport of weaner pigs in a semi-arid region. A total of 20 commercial journeys were monitored, with transport times of 30 min (15 km) and 150 min (170 km). Upon arrival, physiological and behavioral stress indicators were assessed in 960 weaner pigs (26.4 ± 2.8 kg body weight, 48 per journey). Production losses were determined by calculating the percentage of injured pigs (NAI), fatigued pigs (NANI), and those that were dead on arrival (DOA), whereas the total loss was expressed by the sum of NAI + NANI + DOA. Weaner pigs transported for 30 min exhibited significantly higher (p < 0.05) rectal temperature, respiratory rate, and stress biomarkers (cortisol and creatine kinase levels). Additionally, this group showed a higher percentage (p < 0.05) of “sitting” pigs and a lower percentage of “lying” pigs in transit, as well as a higher frequency of agonistic behavior after transport compared to those transported for 150 min. Furthermore, higher production losses were recorded in the 30 min transport group, primarily due to the increased percentage of fatigued pigs and DOA pigs. Therefore, shorter transport operations in the Brazilian semi-arid region increased the risk to animal welfare and productive losses, likely due to handling-induced stress during loading. Full article

The General Transit Feed Specification (GTFS) is an open standard format for recording transit information, utilized by thousands of transit agencies worldwide. In this study, a new tool named GTFS2STN for converting GTFS data into the spatiotemporal networks is introduced. To analyze the travel time variability, it is important to transform a transit network to a spatiotemporal network to enable a comprehensive analysis of transit system accessibility. GTFS2STN is a new tool that converts General Transit Feed Specification (GTFS) data into spatiotemporal networks, addressing the lack of open-source solutions for transit analysis. The tool includes a web application that generates isochrone maps and calculates travel time variability between locations. Validation against Google Maps APIs shows that journey time (i.e., the summation of the transit time, walking time, and waiting time) differences in the Mean Absolute Percentage Error are typically within 12%. A before–after analysis shows that for the transit journey time in 2024 in Nashville, Tennessee, 8 out of 10 pivotal bus stops showed a significantly decreased journey time compared with the case of 2019. A further set of before–after analyses shows that although journey time between transit sites significantly dropped on May 2020 during COVID-19 emergencies, the journey time almost totally recovered to the before-COVID-19 level by November 2020. By supporting any valid GTFS schedule, GTFS2STN enables the analysis of historical and planned transit systems, making it valuable for long-term accessibility assessment and travel time variability studies. Full article

To reduce the carbon footprint of transport, policymakers are simultaneously stimulating cleaner vehicles and more sustainable mobility choices, such as a shift to rail for short-haul flights within Europe. The purpose of this study is to determine the climate impact of a journey within Europe by aircraft, train or passenger car, and to better understand what factors drive this impact in order to make smarter and more sustainable fact-based mobility choices. The study consists of a life cycle inventory (LCI) and life cycle impact assessment (LCA) of greenhouse gas emissions of specific vehicles in five case study travel scenarios in Europe. The energy and resulting direct emissions (including non-CO₂) of the aircraft scenarios were calculated for the purpose of this study using the Mission Aircraft and Systems Simulation tool developed by the Royal Netherlands Aerospace Centre NLR. For other LCA phases and other modes of transport, the study relies on emission factors from public literature. A trip by train results in three to five times less emissions than a comparable trip by aircraft. In most scenarios, the passenger car with two people onboard emits significantly more than a train but slightly less than an aircraft. The study also shows what drives the climate impact of such a trip and how this is very different for different modes of transport. The study further highlights a lack of high-quality data, especially in the areas of indirect emissions and infrastructure, poor consistency among studies and a general under-documentation and lack of transparency regarding assumptions. Full article

In 2022, the European Commission introduced, for the first time in its history, a windfall profit tax to be applied on “excessive” profits realized by qualified businesses operating in the “Oil and Gas” sector. Immediately after its implementation, questions arose as to its sustainability and its consistency with constitutional principles of the different member states regulating the domestic power to tax. To assess the consistency with the aforesaid rules, the article samples two countries, inside and outside the EU (Italy and Australia, respectively), and the historical precedents of the matter. Italy has been chosen due to the particularly stringent set of principles regulating the power of the legislature to tax, and Australia has been chosen because of the long-standing experience with superprofit taxes. In most of the scenarios analyzed, one common feature emerged: the complexity in defining the “Extra” nature of the profits and, consequently, the uncertainties in the calculation of the taxable base. In the case of Italy, for instance, the legislator had to intervene in several different moments to fine-tune the taxable base and restore certainty to the tax system. As a conclusion, while the taxation of extra profits should not per se be disregarded, its implementation demands a more robust and precise legal framework together with the understanding that the introduction of such a levy would be a one-way journey for the tax systems: windfall profits taxes would be here to stay. Full article

The increase in private car usage in cities has led to limited knowledge and uncertainty about traffic flow. This results in difficulties in addressing traffic congestion. This study proposes a novel technique for dynamically calculating the shortest route based on the costs of the most optimal roads and nodes using instances of road graphs at different timeslots to help minimize congestion for actual drivers in urban areas. The first phase of the study involved reducing traffic congestion in one city. The data were collected using a mobile application installed on more than 10 taxi drivers’ phones, capturing data at different timeslots. Based on the results, the shortest path was proposed for each timeslot. The proposed technique was effective in reducing traffic congestion in the city. To test the effectiveness of the proposed technique in other cities, the second phase of the study involved extending the proposed technique to another city using a self-adaptive system based on a similarity approach regarding the structures and sub-regions of the two cities. The results showed that the proposed technique can be successfully applied to different cities with similar urban structures and traffic regulations. The proposed technique offers an innovative approach to reducing traffic congestion in urban areas. It leverages dynamic calculation of the shortest route and utilizes instances of road graphs to optimize traffic flow. By successfully implementing this approach, we can improve journey times and reduce fuel consumption, pollution, and other operating costs, which will contribute to a better quality of urban life. Full article

In this paper, a real-time dynamic programming (RTDP) approach was developed for the first time to jointly carry a slung load using two unmanned aerial vehicles (UAVs) with a trajectory optimized for time and energy consumption. The novel strategy applies RTDP algorithm, where the journey was discretized into horizons consisting of distance intervals, and for every distance interval, an optimal policy was obtained using a dynamic programming sweep. The RTDP-based strategy is applied for dual-UAV collaborative payload transportation using coordinated motion where UAVs act as actuators on the payload. The RTDP algorithm provides the optimal velocity decisions for the slung load transportation to either minimize the journey time or the energy consumption. The RTDP approach involves minimizing a cost function which is derived after simplifying the combined model of the dual-UAV-payload system. The cost function derivation was also accommodated to dynamically distribute the load/energy between two multi-rotor platforms during a transportation mission. The cost function is used to calculate transition costs for all stages and velocity decisions. A terminal cost is used at the last distance interval during the first phase of the journey when the velocity at the end of the current horizon is not known. In the second phase, the last stage or edge of the horizon includes the destination, hence final velocity is known which is used to calculate the transition cost of the final stage. Once all transition costs are calculated, the minimum cost is traced back from the final stage to the current stage to find the optimal velocity decision. The developed approach was validated in MATLAB simulation, software in the loop Gazebo simulation, and real experiments. The numerical and Gazebo simulations showed the successful optimization of journey time or energy consumption based on the selection of the factor $\lambda$. Both simulation and real experiments results show the effectiveness and the applicability of the proposed approach. Full article

This study aims to analyze the variables that affect the accidents experienced by e-scooter users and to estimate the probability of an accident during travel with an e-scooter vehicle. The data of e-scooter drivers, offered for use via rental application in 15 different cities of Turkey, were run in this study. The methodology of this study consists of testing the effects of the input parameters with the statistical analysis of the data, estimating the probability of an e-scooter accident with machine learning, and calculating the optimum values of the input parameters to minimize e-scooter accidents. By running SVM, RF, AB, kNN, and NN algorithms, four statuses (completed, injured, material damage, and nonapplicable) likely to be encountered by shared e-scooter drivers during the journey are estimated in this study. The F1 score values of the SVM, RF, kNN, AB, and NN algorithms were calculated as 0.821, 0.907, 0.839, 0.928, and 0.821, respectively. The AB algorithm showed the best performance with high accuracy. In addition, the highest consistency ratio in the ML algorithms belongs to the AB algorithm, which has a mean value of 0.930 and a standard deviation value of 0.178. As a result, the rental experience, distance, driving time, and driving speed for a female driver were calculated as 100, 10.44 km, 48.33 min, and 13.38 km/h, respectively, so that shared e-scooter drivers can complete their journey without any problems. The optimum values of the independent variables of the rental experience, distance, driving time, and driving speed for male drivers were computed as 120, 11.49 km, 52.20 min, and 17.28 km/h, respectively. Finally, this study generally provides a guide to authorized institutions so that customers who use shared and rentable micro-mobility e-scooter vehicles do not have problems during the travel process. Full article

COVID-19 can be easily transmitted to passengers by inhaling exhaled droplets from the infected person in a bus. Therefore, studying droplet dispersion would provide further insight into the mechanism of virus transmission and predict the risk of infection among passengers on a bus. In this research, a bus equipped with air-conditioning was employed as the research object. To determine the dispersion path, concentration distribution, and escape time of the droplets, computational fluid dynamic (CFD) was applied to simulate the flow field and the droplets’ dispersion. The effect of the air supply rate, the location of vents, and the location of infected persons on the dispersion were discussed. Based on the distribution of droplets in the cabin calculated by CFD, a superposition method was used to determine the number of virus particles inhaled by every individual passenger over a four-hour journey. Then, infection risk was assessed by the Wells-Riley equation for all the passengers in the cabin after the whole journey. The results show that the distribution of droplets in the cabin is greatly influenced by the location of the infected person, and the airflow pattern is highly associated with the air supply rate and the location of vents. The infection risk of passengers located at the droplet dispersion path and the distance from the infected persons less than 2.2 m is over 10%. The increase in the air supply rate could speed up the spread of the droplets but at the same time, it could reduce the infection risk. Full article

Recent transformations from internal combustion engines (ICE) to electric vehicles (EVs) are challenged by limited the driving range per charge, thereby requiring the improvement or substantial deployment of rapid charging infrastructure to stimulate sufficient confidence in EV drivers. This study aims to establish the necessary level of EV motorway service station infrastructure for the United Kingdom (UK) based market. The investigation is founded on increasing the appropriate rapid charger availability and shorter charging times. EV charging patterns are determined, focusing on two Volkswagen iD3 EV models by measuring power curves across field-based rapid chargers at one-minute intervals. Datasets are analysed throughout rapid charging field tests. Additionally, variance synthesis is applied to establish variables within this study’s assessment for rapid charger capacity requirements in the UK. The operational performance for the utilised rapid chargers is correspondingly recorded, whilst the EV range is calculated at 3 miles per kWh, revealing a mean power delivery rate of just 27 kW per hour using a 50 kW rapid charger. Time-of-day charging sessions are used to generate data that is then amalgamated into our previous study data, confirming that rapid charging points on UK motorways are used primarily for EV journey range extension. If fully utilised for an entire 24h period, 434 chargers (with a variance consolidation number of 81) are required to service the UK-based motorway EV user base. Moreover, this study establishes that simply replacing current fuel pumps with individual rapid chargers on a like-for-like basis reduces availability and support for novel and existing users and may impact short-term grid availability. Full article

This article moves beyond analysis methods related to a traditional relational database or network analysis and offers a novel graph network technique to yield insights from a hospital’s emergency department work model. The modeled data were saved in a Neo4j graphing database as a time-varying graph (TVG), and related metrics, including degree centrality and shortest paths, were calculated and used to obtain time-related insights from the overall system. This study demonstrated the value of using a TVG method to model patient flows during emergency department stays. It illustrated dynamic relationships among hospital and consulting units that could not be shown with traditional analyses. The TVG approach augments traditional network analysis with temporal-related outcomes including time-related patient flows, temporal congestion points details, and periodic resource constraints. The TVG approach is crucial in health analytics to understand both general factors and unique influences that define relationships between time-influenced events. The resulting insights are useful to administrators for making decisions related to resource allocation and offer promise for understanding impacts of physicians and nurses engaged in specific patient emergency department experiences. We also analyzed customer ratings and reviews to better understand overall patient satisfaction during their journey through the emergency department. Full article

Out of eight deaths caused worldwide, one death is caused due to air pollution exposure, making it one of the top global killers. Personal exposure measurement for real-time monitoring has been used for inhaled dose estimation during various modes of workplace commuting. However, dose-exposure studies during long commutes are scarce and more information on inhaled doses is needed. This study focuses on personal exposures to size-fractionated particulate matter (PM₁, PM_2.5, PM₄, PM₇, PM₁₀, TSP) and black carbon (BC) inside a bus traveling more than 270 kms on a highway between Albany, NY and Boston, MA. Measurements were also made indoors, outdoors, and while walking in each city. Mean PM (PM₁, PM_2.5, PM₄, PM₇, PM₁₀, TSP) and mean BC concentrations were calculated to estimate the inhaled exposure dose. The highest average PM_2.5 and PM₁₀ exposures concentrations were 30 ± 12 and 111 ± 193 µg/m³, respectively, during Boston to Albany. Notably, personal exposure to BC on a bus from Albany to Boston (5483 ± 2099 ng/m³) was the highest measured during any commute. The average inhaled dose for PM_2.5 during commutes ranged from 0.018 µg/km to 0.371 µg/km. Exposure concentrations in indoor settings (average PM_2.5 = 37 ± 55 µg/m³, PM₁₀ = 78 ± 82 µg/m³, BC = 5695 ± 1774 ng/m³) were higher than those in outdoor environments. Carpeted flooring, cooking, and vacuuming all tended to increase the indoor particulate level. A high BC concentration (1583 ± 1004 ng/m³) was measured during walking. Typical concentration profiles in long-haul journeys are presented. Full article

This paper deals with the issues of the impact of vertical vibrations on a child seated in a child seat during a journey. Its purpose was to assess the impact of fastening the child seats and road conditions on the level of vibrations recorded on child seats. The paper describes the tested child seats, the methodology of the tests and the test apparatus included in the measuring track. The tests were carried out in real road conditions where the child seats were located on the rear seat of a passenger vehicle. One was attached with standard seat belts, and the other with the ISOFIX base. When driving on roads with three types of surface, the following vertical accelerations were measured: seat of the child seats, the rear seat of the vehicle and the ISOfix base. The recorded accelerations were first analyzed in the time domain and then in the frequency domain. Three indexes (r.m.s, rmq and VDV) were used to assess the vibration comfort. Research has shown that the classic method of fastening a child seat with standard seat belts is more advantageous in terms of vibration comfort. Calculated indicators confirmed the negative impact of separating the child seat from the rear seat of the vehicle using the IQ ISOFIX base. Full article

The article is aimed at presenting the survey respondents’ involvement in tourist activities, taking into account certain factors at the time of the COVID-19 pandemic. The main objective may be divided into three direct aims, each of which consists of the determination of the factors that can influence the choice of tourist journeys: (1) organizational factors, (2) social-economic ones, and (3) sustainable development. The authors’ own research findings are used to verify the objective. The research was conducted in April and May 2020 with the use of a diagnostic survey method and a questionnaire. Five-hundred sixty-four respondents from Poland (Podlaskie, Masovian, and Lesser Poland Voivodeships) and 133 respondents from the US (New York State, New Jersey, and Illinois) took part in the research. It was divided into organizational, social-economic, and sustainable development related factors. The research confirmed, inter alia, a great impact of the COVID-19 pandemic on the organization of tourist travels by the respondents in 2020. The issue presented in the article is a new one; it has not yet been a subject matter of research. That is the major reason the authors aimed to conduct it. What is a new methodological element in the article is the organization of some concepts concerning tourism and a presentation of the influence of COVID-19 on tourism. In the authors’ opinion, the issues presented are new and have a considerable impact on new trends in the development of tourism at the time of the COVID-19 pandemic. The issue discussed is very broad, and the article does not exhaust it. The research findings are compared to the research findings reported by other authors, and standard deviations are calculated. Full article