Search Results (20)

Cooperative Intelligent Transport Systems (C-ITS) have emerged as a key enabler of more efficient, safer, and environmentally sustainable road traffic by allowing vehicles and infrastructure to exchange information and coordinate behavior. To evaluate their benefits, impact assessment studies are essential. However, most existing studies focus on individual C-ITS services in isolation, overlooking how combined deployments influence outcomes. This study addresses this gap by presenting the first systematic evaluation of individual and joint deployments of Cooperative Adaptive Cruise Control (CACC) and Green Light Optimal Speed Advisory (GLOSA) under diverse conditions. A dual-model simulation framework is applied, combining controlled artificial networks with calibrated real-world corridors in Upper Austria. This allows both statistical testing and validation of plausibility in real-world contexts. Key performance indicators include travel time and CO₂ emissions, evaluated across varying lane configurations, numbers of traffic lights, demand levels, and equipment rates. The results demonstrate that C-ITS effectiveness is strongly context-dependent: while CACC generally provides larger efficiency gains, GLOSA yields consistent emission reductions, and the combined deployment offers conditional synergies but may also diminish benefits at high demand. The study contributes a guideline for selecting service configurations based on site conditions, thereby providing practical recommendations for future C-ITS rollouts. Full article

Travel policies during crises strongly reshape mobility patterns, raising the challenge of protecting public health while minimizing socio-economic disruption—an essential concern for sustainable development. Most evaluations quantify changes in travel volume, which hampers cross-city comparison and policy monitoring. This study proposes a distance-based sustainability metric—distance equivalence (DE)—that translates policy-induced mobility frictions into interpretable “added distance” within a gravity framework, enabling consistent measurement and monitoring of policy impacts. Using inter-city flows for 358 Chinese cities during the Stay-in-Place for Lunar New Year (SIP) guidance, we map DE, test spatial dependence (Moran’s I; LISA), and apply fuzzy-set Qualitative Comparative Analysis (fsQCA) to identify city-level configurations associated with high DE. DE exhibits significant spatial clustering, concentrating east of the Hu line, where dense urban networks amplify advisory checks. Three recurrent configurations—combining case counts, health-care capacity (hospital beds), and average inter-city distance—are linked to high DE. As a sustainability assessment tool, DE supports adaptive management, region-differentiated strategies, and ex-ante risk assessment for governments, public-health authorities, and transport agencies. The framework generalizes to short-term mobility interventions under crisis conditions, advancing the quantification of policy impacts on sustainable mobility and urban resilience. Full article

This paper deals with minimisation of fuel consumption under real driving conditions using a vehicle speed advisor. The aim is to explore the potential of speed profile optimisation in real driving conditions while assessing the suitability of an application which recommends the driver the optimal vehicle speed sequence that minimises the fuel consumption on a particular route. The speed advisor is based on solving the Optimal Control problem of covering a particular route with minimum fuel consumption with a defined time constraint. The approach presented was applied to and implemented on a real passenger vehicle to obtain a trade-off between fuel consumption and travel time for several trips on the route. Experimental results are presented with and without advisory. With speed advisor, the results approach the pareto front with lesser dispersion. On the other hand, without advisory, the dispersion is higher and largely above the pareto front. Full article

The problem of insecurity is a global phenomenon that has several forms like terrorism, banditry, kidnappings, etc. Insecurity has taken hold in the Sub-Saharan Region of West Africa, especially in Nigeria, for over two decades. Nigeria’s security crisis is more pronounced in the Northern Region, with a new wave in the North-Central Region of Nigeria. It is herculean to assess insecurity in the North-Central Region of Nigeria because of the region’s fuzzy or imprecise nature of insecurity. This constitutes the rationale for deploying the Fermatean fuzzy technique to assess insecurity due to the capacity of the Fermatean fuzzy scheme to handle imprecision. To this end, a new Fermatean fuzzy distance metric is presented to evaluate insecurity in the North-Central Region of Nigeria using a multi-criteria decision-making technique. To express the logic for creating the new Fermatean fuzzy distance metric, some existing Fermatean fuzzy distance metrics are discussed, along with their drawbacks. The mathematical properties of the new technique are discussed, and the new method is applied computationally to assess insecurity in the North-Central Region of Nigeria. The data for the security assessment are collected via Fermatean fuzzy linguistic variables using the opinions of security experts and analyzed using the technique for order of preference by similarity to ideal solution, which is a commonly used multi-criteria decision-making method. Finally, the numerical validity of the new technique is expressed with comparative results, and the finding shows the benefit of the new distance approach over the existing methodologies. The outcome of the work will provide reliable traveling advisories for safe voyages within the region. Full article

The aims of this study are to analyze the tourism and air-transport industries in Africa and determine how African governments could improve the safety of tourists and local communities in this region to improve resident quality of life and tourism demand. Indeed, this study tries to improve African people’s lives through the tourism and travel sectors so that they can thrive in terms of their quality of life and happiness. The findings of the current study reveal that Morocco, Egypt, South Africa, and Tunisia are the most visited countries by international tourists; in fact, these four countries are the ones that generated the most income from international tourism in the period analyzed. Moreover, the results suggest that the tourism and air-transport industries in Africa could improve national economies, infrastructure, and resident quality of life thanks to international tourism receipts and increasing security levels around travel and tourism activities. Obviously, the tourism industry cannot be developed when there is constant insecurity, terrorism, and perpetual armed conflicts, as is the case in Nigeria, Somalia, DR Congo, Libya, Mali, and Cameroon, among many others. Security is the most important factor for a tourist destination because the safety and security of residents and tourists are the primary factors affecting tourism growth. The novelty of this research resides in its willingness to improve African people’s quality of life through air-transport and tourism activities, providing security guarantees for tourist and resident safety. This manuscript also contributes to enhancing and bootstrapping the literature on security in the travel and tourism sectors industry, particularly in Africa, where security is a priority more than a necessity. Full article

COVID-19 transmission models have conferred great value in informing public health understanding, planning, and response. However, the pandemic also demonstrated the infeasibility of basing public health decision-making on transmission models with pre-set assumptions. No matter how favourably evidenced when built, a model with fixed assumptions is challenged by numerous factors that are difficult to predict. Ongoing planning associated with rolling back and re-instituting measures, initiating surge planning, and issuing public health advisories can benefit from approaches that allow state estimates for transmission models to be continuously updated in light of unfolding time series. A model being continuously regrounded by empirical data in this way can provide a consistent, integrated depiction of the evolving underlying epidemiology and acute care demand, offer the ability to project forward such a depiction in a fashion suitable for triggering the deployment of acute care surge capacity or public health measures, and support quantitative evaluation of tradeoffs associated with prospective interventions in light of the latest estimates of the underlying epidemiology. We describe here the design, implementation, and multi-year daily use for public health and clinical support decision-making of a particle-filtered COVID-19 compartmental model, which served Canadian federal and provincial governments via regular reporting starting in June 2020. The use of the Bayesian sequential Monte Carlo algorithm of particle filtering allows the model to be regrounded daily and adapt to new trends within daily incoming data—including test volumes and positivity rates, endogenous and travel-related cases, hospital census and admissions flows, daily counts of dose-specific vaccinations administered, measured concentration of SARS-CoV-2 in wastewater, and mortality. Important model outputs include estimates (via sampling) of the count of undiagnosed infectives, the count of individuals at different stages of the natural history of frankly and pauci-symptomatic infection, the current force of infection, effective reproductive number, and current and cumulative infection prevalence. Following a brief description of the model design, we describe how the machine learning algorithm of particle filtering is used to continually reground estimates of the dynamic model state, support a probabilistic model projection of epidemiology and health system capacity utilization and service demand, and probabilistically evaluate tradeoffs between potential intervention scenarios. We further note aspects of model use in practice as an effective reporting tool in a manner that is parameterized by jurisdiction, including the support of a scripting pipeline that permits a fully automated reporting pipeline other than security-restricted new data retrieval, including automated model deployment, data validity checks, and automatic post-scenario scripting and reporting. As demonstrated by this multi-year deployment of the Bayesian machine learning algorithm of particle filtering to provide industrial-strength reporting to inform public health decision-making across Canada, such methods offer strong support for evidence-based public health decision-making informed by ever-current articulated transmission models whose probabilistic state and parameter estimates are continually regrounded by diverse data streams. Full article

Army installation commanders need timely weather information to make installation closure decisions before or during adverse weather events (e.g., hail, thunderstorms, snow, and floods). We worked with the military installation in Fort Carson, CO, and used their Weather Warning, Watch, and Advisory (WWA) criteria list to establish the foundation for our algorithm. We divided the Colorado Springs area into 2300 grids (2.5 square kilometers areas) and grouped the grids into ten microclimates, geographically and meteorologically unique regions, per pre-defined microclimate regions provided by the Fort Carson Air Force Staff Weather Officers (SWOs). Our algorithm classifies each weather event in the WWA list using the National Weather Service’s and National Digital Forecast Database’s data. Our algorithm assigns each event a criticality level: none, advisory, watch, or warning. The traffic network data highlight the importance of each road segment for travel to and from Fort Carson. The algorithm also uses traffic network data to assign weight to each grid, which enables the aggregation to the region and installation levels. We developed a weather dashboard in ArcGIS Pro to verify our algorithm and visualize the forecasted warnings for the grids and regions that are or may be affected by weather events. Full article

This study investigates the socio-economic characteristics, behavioral preferences, and consumption of individuals who own crypto-assets. Our empirical analysis utilizes data from a German personal finance management app where users connect their bank accounts and depots. We conducted a survey and elicited behavioral factors for financial decision-making. By combining survey with account and security account data, we identify crypto investors’ preferences for financial decision-making and financial advice. Our results suggest that, in particular, students or self-employed, young, and male individuals who are risk-seeking and impatient are more likely to have invested in crypto-assets. Most crypto owners have less experience with financial advisory. They see it as too time-consuming and qualitatively poor, and instead, they prefer to decide on their own as they have self-reported high financial literacy. Investigating their consumption in more detail we conclude that crypto investors more often spend on travelling, electronics, and food delivery and less on health. Our findings suggest policymakers in identifying high-risk consumers and investors, and help financial institutions develop appropriate products. Full article

In the ongoing series of Nova Interviews, Challenges Advisory Board member Alan C. Logan meets with thought leaders, scientists, scholars, healthcare professionals, artisans and visionaries concerned about health at the scales of persons, places, and the planet. In this interview, Dr. Nalini M. Nadkarni, of the University of Utah, responds to a set of questions posed by Nova for Challenges. For over forty years, Dr. Nadkarni has been studying the fragility and resiliency of rainforest ecosystems. During this time, Dr. Nadkarni has prioritized science communication with an emphasis on highlighting the interdisciplinary relevancy of research findings. Dr. Nadkarni has worked to promote an integrative way of thinking about the various branches of science and medicine, with an eye toward shifting public policy. Her research career has taken her where only a small minority of scientists have traveled—from remote cloud forests to segregated housing within prison facilities. Dr. Nadkarni successfully challenged the Mattel Corporation to update their globally-recognized toy, Barbie, with women in science in mind. Here, Dr. Nadkarni reflects on the early influences that shaped her career, updates Challenges on the latest directions of her work, and discusses the ways in which the canopy ecosystem can help us understand the complex interconnected challenges of our time. Full article

The exponential increase in the number of daily traveling vehicles has exacerbated global warming and environmental pollution issues. These problems directly threaten the continuity and quality of life on the planet. Several techniques and technologies have been used and developed to reduce fuel consumption and gas emissions of traveling vehicles over the road network. Here, we investigate some solutions that assist drivers to follow efficient driving tips during their trips. Advanced technologies of communications or vehicle manufacturing have enhanced traffic efficiency over road networks. In addition, several advisory systems have been proposed to recommend to drivers the most efficient speed, route, or other decisions to follow towards their targeted destinations. These recommendations are selected according to the real-time traffic distribution and the context of the road network. In this paper, different high fuel consumption scenarios are investigated over the road networks. Next, the details of efficient driving techniques that were proposed to tackle each case accordingly are reviewed and categorized for downtown and highway driving. Finally, a set of remarks and existing gaps are reported to researchers in this field. Full article

Despite loose restrictions and a low mortality rate due to COVID-19, Japan faced the challenge of stabilizing its economy during the pandemic. Here, we analyzed how the Japanese government attempted to maintain a balance between the health of the population and the health of the economy. We used a mix of quantitative data, information from policy documents, and news agency publications. Features of the Japanese government’s handling of the pandemic include the lack of constitutional authority to enforce a lockdown, the laxer restrictions compared with other countries in which citizens were advised only to exercise self-restraint and avoid close social contact, and the existence of expert panels that had only an advisory role. Our findings address the slow initial response of the government, which feared that the 2020 Tokyo Olympics would be canceled, and the increased testing when the Olympics were postponed, as well as the expansion of vaccination efforts after the Olympics. In addition, there was a targeted campaign to promote national travel to increase economic revenue in the tourism sector, but this led to an increase in COVID-19 cases. Full article

In this study, a Green Light Optimal Speed Advisory (GLOSA) system for buses (B-GLOSA) was developed. The proposed B-GLOSA system was implemented on diesel buses, and field tested to validate and quantify the potential real-world benefits. The developed system includes a simple and easy-to-calibrate fuel consumption model that computes instantaneous diesel bus fuel consumption rates. The bus fuel consumption model, a vehicle dynamics model, the traffic signal timings, and the relationship between vehicle speed and distance to the intersection are used to construct an optimization problem. A moving-horizon dynamic programming problem solved using the A-star algorithm is used to compute the energy-optimized vehicle trajectory through signalized intersections. The Virginia Smart Road test facility was used to conduct the field test on 30 participants. Each participant drove three scenarios, including a base case uninformed drive, an informed drive with signal timing information communicated to the driver, and an informed drive with the recommended speed computed by the B-GLOSA system. The field test investigated the performance of using the developed B-GLOSA system considering different impact factors, including road grades and red indication offsets, using a split-split-plot experimental design. The test results demonstrated that the proposed B-GLOSA system can produce smoother bus trajectories through signalized intersections, thus producing fuel consumption and travel time savings. Specifically, compared to the uninformed drive, the B-GLOSA system produces fuel and travel time savings of 22.1% and 6.1%, on average, respectively. Full article

Research indicates that rural transgender and gender diverse (TGD) populations have a greater need for health services when compared with their urban counterparts, face unique barriers to accessing services, and have health disparities that are less researched than urban TGD populations. Therefore, the primary aim of this mixed-methods study (n = 24) was to increase research on the health care needs of TGD people in a rural Appalachian American context. This study was guided by a community-engaged model utilizing a community advisory board of TGD people and supportive parents of TGD children. Quantitative results indicate that travel burden is high, affirming provider availability is low, and the impacts on the health and mental health of TGD people in this sample are notable. Qualitative results provide recommendations for providers and health care systems to better serve this population. Integrated mixed-methods results further illustrate ways that rural TGD people and families adapt to the services available to them, sometimes at significant economic and emotional costs. This study contributes to the small but growing body of literature on the unique needs of rural TGD populations, including both adults and minors with supportive parents, by offering insights into strategies to address known disparities. Full article

The Advisory Council for Aeronautics Research in Europe (ACARE) Flight Path 2050 focuses on ambitious and severe targets for the next generation of air travel systems (e.g., 75% reduction in CO₂ emissions per passenger kilometre, a 90% reduction in NOx emissions, and a 65% reduction in the noise emissions of flying aircraft relative to the capabilities of typical new aircraft in 2000). Degradation is an inevitable phenomenon as aero-engines age with significant impacts on the engine performance, emissions level, and fuel consumption. The engine control system is a key element capable of coping with degradation consequences subject to the implementation of an advanced management strategy. This paper demonstrates a methodological approach for aero-engine controller adjustment to deal with degradation implications, such as emission levels and increased fuel consumption. For this purpose, a component level model for an aero-engine was first built and transformed to a block-structured Wiener model using a system identification approach. An industrial Min-Max control strategy was then developed to satisfy the steady state and transient limit protection requirements simultaneously while satisfying the physical limitation control modes, such as over-speed, surge, and over-temperature. Next, the effects of degradation on the engine performance and associated changes to the controller were analysed thoroughly to propose practical degradation management strategies based on a comprehensive scientometric analysis of the topic. The simulation results show that the proposed strategy was effective in restoring the degraded engine performance to the level of the clean engine while protecting the engine from physical limitations. The proposed adjustments in the control strategy reduced the fuel consumption and, as a result, the emission level and carbon footprint of the engine. Full article

The COVID-19 pandemic has entailed an unprecedented health crisis with significant economic impacts in many sectors worldwide. The tourism sector has been one of the most affected, with significant impacts on the number of cancelled reservations, a decrease in international travel and changes in consumption behaviour. This study aims to analyse the main changes in promotion and marketing in the tourism sector in Spain after the pandemic. To this end, a qualitative analysis was carried out via questionnaire-based interviews with 65 experts in the areas of marketing, consumer behaviour and tourism. The main findings show that online information sources gained weight over consulting friends and relatives, and a great advance in digitization is expected, where physical travel agencies will be displaced by online platforms, except for specialized and advisory services. Additionally, technologies such as virtual reality (VR) or artificial intelligence (AI) may play an increasingly important role in the medium term. Full article