This paper investigates the application of recently proposed practical subarray (SA)-based hybrid beamforming (HBF) architectures—implemented entirely with passive beamforming networks and switches—for millimeter wave (mmWave) multi-user (MU)-MIMO base stations. Building on this practical hardware platform, we propose a joint SA configuration and signal processing framework that exploits the natural non-uniformity of user locations in 3-D space via elevation domain subsectorization. Specifically, we adapt established channel estimation and HBF techniques to the constraints of switch-based SAs, and introduce a novel 2-stage channel estimator that leverages the unique properties of mmWave channels. System-level simulations in realistic line-of-sight (LoS) and non-line-of-sight (NLoS) propagation scenarios demonstrate that the proposed solution delivers strong performance with low complexity, providing a viable path toward practical, scalable mmWave MU-MIMO deployments. In LoS scenarios, using directions-of-arrival-based channel estimation, the proposed framework achieves up to 92.6% of the average spectral efficiency (SE) of a full-digital array antenna with the same number of elements but 4 times more radio frequency chains. In NLoS environments, using the novel 2-stage estimator, this increases up to 99.7%.
Transportation engineering, Transportation and communications
Signal coordination is an effective measure to improve the traffic efficiency of urban road networks, and network partition is an important part of it. Existing studies have proposed indicators based on the characteristics of arterial geometry and traffic flow to determine adjacent intersections that are suitable for signal coordination. However, it is difficult to explicitly identify the benefits and thus the necessity of signal coordination with these indirect indicators. This study defines Intersection Coordination Index (ICI) to evaluate the potential effectiveness of arterial signal coordination. ICI explicitly considers signal timing plans at each intersection and implicitly considers the impacts of the characteristics of arterial geometry and traffic flow. An offset optimization model is formulated to calculate ICI based on sampled trajectories of connected vehicles (CVs). It is a MILP model and can be efficiently solved by existing solvers. To cope with the low penetration rate of CVs, sampled trajectories are aggregated during the same period across multiple cycles. Numerical studies show: the proposed model is adapted to the low penetration rate trajectory environment; the dispersion of arriving vehicles at the downstream intersection reduces the benefits of signal coordination; and ICI outperforms the benchmark indicators in terms of the average cost of delay.
Transportation engineering, Transportation and communications
Bartosz Bursa, Felix Mölk, Gottfried Tappeiner
et al.
Abstract Tourism is an important driver of economic activity in many countries, yet it is also associated with a number of negative externalities. In a quickly warming climate, special attention is being given to tourism-related CO2 emissions, which largely result from travel to and within destinations. Rail travel shows significantly lower emissions than other modes of transportation, but the factors that can increase rail's share in vacation travel vary among different segments of the population, as does the ability of different stakeholders to influence these factors. In this study, we use stated preference data of visitors to the Austrian Alps to identify clusters of travelers with different preferences and different propensities to switch modes. Further, by scrutinizing the attributes that can increase the utility of rail in clusters, we provide a foundation for tailored marketing and effective policy design that can initiate a shift from road to rail. Our findings demonstrate that travel costs are important across clusters, but manipulating them hinges on government interventions. Improved mobility services at the destination, on the other hand, are easier to implement and show great potential in increasing the share of sustainable transportation modes both to and within the destination. We find three clusters: Service-Oriented, Car-Committed, and Budget Travelers, that differ substantially in terms of their preferences concerning long-distance travel mode choice. However, difficulties in predicting cluster affiliation ex-ante prevent us from addressing clusters independently, which would be required to implement targeted actions.
Transportation engineering, Transportation and communications
Road traffic accidents (RTAs) are a significant cause of morbidity and mortality worldwide, with increasing trends in Sub-Saharan Africa (SSA), highlighting the need for better preventive measures. These underscore the vulnerability of motorcyclists and the critical need to address motorcycle safety to reduce the burden of RTAs. Therefore, this study investigates helmet use among commercial motorcyclists in rural Ghana, analysing the prevalence and predictors of this road safety and health behavior. The study’s theoretical rationalisation came from Icek Ajzen’s Theory of Planned Behavior (TPB), which posits that attitudes, subjective norms, and perceived behavioral control shape behavior. Leveraging a TPB-tailored questionnaire, a cross-sectional survey was conducted with 205 commercial motorcyclists in the Afadzato South District, Volta Region; logistic regression analysis helped establish the determinants of helmet-wearing behavior. Findings revealed that only 39.0 % of motorcyclists consistently wore helmets. Key predictors included gender, age, marital status, education, and perceptual factors. The regression analysis indicated that female motorcyclists and those aged 45+ were significantly more likely to use helmets. Notably, the absence of an additional helmet for passengers significantly decreased usage, whereas discomfort associated with helmet use constitutes a major predictor. Additionally, TPB constructs of beliefs, perceptions, and attitudes towards safety were linked to lower helmet adoption. These results highlight critical areas for intervention, suggesting that targeted safety campaigns should address discomfort through context-appropriate helmets, promote positive beliefs, and ensure helmet provision for passengers. The study also emphasises collaborative efforts among stakeholders to enhance helmet use and improve safety among commercial motorcyclists.
The static analysis of long-span suspension bridges under live loads has been a concern in serviceability design. However, the formulation of closed-form solutions is very challenging, owing to the highly static indeterminacy of suspension bridges. For all that, in this paper, a surrogate model is presented that consists of several equivalent springs to represent the horizontal stiffnesses of the cables and towers. Based on this model, explicit static solutions for typical live load cases are derived, which are also verified using the finite-element method and field measurements. The solutions can also be extended to suspension bridges with sliding saddles. Subsequently, parametric studies are conducted on the two general layout parameters, that is, the sag-to-span ratio and side-span to main-span ratio. The findings indicate that these two parameters have substantial effects on the cable horizontal stiffness and tower top displacement and are strongly related to the maximum vertical displacement for cases of either full-span or half-span live loads. Overall, the proposed surrogate model offers a practical and rapid method, with acceptable accuracy, for the static analysis of long-span suspension bridges in their conceptual design.
<i>Background:</i> The dynamic vehicle routing problem (DVRP) is a complex optimization problem that is crucial for applications such as last-mile delivery. Our goal is to develop an application that can make real-time decisions to maximize total performance while adapting to the dynamic nature of incoming orders. We formulate the DVRP as a vehicle routing problem where new customer requests arrive dynamically, requiring immediate acceptance or rejection decisions. <i>Methods:</i> This study leverages reinforcement learning (RL), a machine learning paradigm that operates via feedback-driven decisions, to tackle the DVRP. We present a detailed RL formulation and systematically investigate the impacts of various state-space components on algorithm performance. Our approach involves incrementally modifying the state space, including analyzing the impacts of individual components, applying data transformation methods, and incorporating derived features. <i>Results:</i> Our findings demonstrate that a carefully designed state space in the formulation of the DVRP significantly improves RL performance. Notably, incorporating derived features and selectively applying feature transformation enhanced the model’s decision-making capabilities. The combination of all enhancements led to a statistically significant improvement in the results compared with the basic state formulation. <i>Conclusions:</i> This research provides insights into RL modeling for DVRPs, highlighting the importance of state-space design. The proposed approach offers a flexible framework that is applicable to various variants of the DVRP, with potential for validation using real-world data.
Transportation and communication, Management. Industrial management
Vehicle-to-everything (V2X) communication is a pivotal technology for advanced driving, encompassing autonomous driving and Intelligent Transportation Systems (ITS). Beyond direct vehicle-to-vehicle (V2V) communication, vehicle-to-infrastructure (V2I) communication via Road Side Unit (RSU) can play an important role for efficient traffic management and enhancement of advanced driving, providing surrounding vehicles with proper road information. To accommodate diverse V2X scenarios, heterogeneous traffic with varied objectives, formats, and sizes needs to be supported for V2X communication. We tackle the challenge of resource allocation for heterogeneous traffic in the RSU-deployed V2X communications, proposing a decentralized Multi-Agent Reinforcement Learning (MARL) based resource allocation scheme with limited shared resources. To reduce the model complexity, RSU is modeled as a collection of virtual agents with a small action space instead of a single agent selecting multiple resources at the same time. A weighted global reward is introduced to incorporate traffic heterogeneity efficiently. The performance is evaluated and compared with random, 5G NR mode 2, and optimal allocation schemes in terms of Packet Reception Ratio (PRR) and communication range. The proposed scheme nearly matches the performance of the optimal scheme and significantly outperforms the random allocation scheme in both underload and overload situations.
The digitalization of the power grid to smart grid provides value added services to the prosumers and other stakeholders involved in the energy market, and possibly disrupts existing electricity services in smart cities. The use of Electric Vehicles (EVs) do not only challenge the sustainability of the smart grid but also promote and stimulate its upgrading. Undeniably, EVs can actively promote the development of the smart grid via two-way communications by deploying Vehicle-to-Grid (V2G) and Grid-to-Vehicle (G2V). EVs have environmental benefits as compared to hybrids or even internal combustion engine vehicle as they can help minimize noise levels, pollution, and greenhouse gas emissions. The integration of EVs could bring substantial changes for the society not only in providing transportation services but also shifting economies from petroleum and reducing the carbon dioxide (CO2) emission from the transportation sector. Therefore, this study employs secondary data from the literature to explore how EVs can achieve sustainable energy as a service business model in smart cities. Findings from this study suggest that EVs are major assets for a sustainable energy future as EV batteries offers an untapped opportunity to store electricity from renewable energy sources. Implications from this study discusses the issues and recommendations for EVs integration in smart cities.
Ascertaining the underlying pattern of road traffic crashes (RTCs) and identifying hotspots is essential for improving safety on the road network. Researchers have employed various statistical modelling and spatial methods to predict crash frequency and identify their hotspots on the road network. In India, the road network length has been increasing, especially the expressway network length. The increase in the network length has also increased RTCs. Hence, it is essential to assess the crash pattern and identify hotspots on the intercity expressways in India. This study aims to identify the fatal crash hotspots on the selected intercity expressway using geospatial methods. First, in this study, hotspot sections were identified using ordinary kriging (OK) and, kernel density estimation (KDE), network kernel density estimation (NKDE) methods. Next, the employed techniques were compared to know their predictive effectiveness in identifying the hotspots. The study used the fatal crash data from August 2012 to October 2018 for the selected 165 km intercity expressway. Outcomes of the geospatial methods revealed some of the common hotspots are identified by both methods. The comparative analysis indicated that the NKDE method is more effective in identifying the hotspots in smaller segments than the other two methods. Consequently, this research's outcomes would facilitate intercity expressway-owning agencies to select a practical and readily applicable hotspot identification methodology in LMICs.
“Los festejos por los 200 años de la Universidad de Buenos Aires (UBA) nos llevaron a reflexionar sobre sus orígenes, sus primeros pasos en el conocimiento de las distintas disciplinas y las personalidades que hicieron posible esta universidad que hoy nos enorgullece”. Con estas palabras, comenzaba la arquitecta María Nélida Galloni en un escrito del año 2021, la presentación de parte de un trabajo realizado por el Centro de Investigaciones en Barreras Arquitectónicas, Urbanísticas y del Transporte (en adelante CIBAUT) que, con motivo de esta celebración se presentaría en el contexto del Seminario anual del Programa Universidad y Discapacidad de la UBA, como homenaje a la trayectoria de este espacio y de su fundadora y pionera en la temática, la arquitecta investigadora Clotilde Amengual, junto a todos los profesionales y alumnos que desde allí vienen investigando y trabajando en favor de la discapacidad y la accesibilidad, para mejorar con ésta, la calidad de vida de las personas con discapacidad y por ende de la comunidad en su conjunto.
Smart cities are being developed to boost the wellness and quality of life of their citizens. To achieve this, we see an increase in the convergence of technologies, scientific knowledge and political will, and it is safe to say that in the next few years, there will be new societal trends and challenges in the fields of health, wellness, security, safety, transportation, energy, mobility, and communications. One promising solution to face these challenges is that of Digital Twins. According to Gartner, the digital twin (DT) is in the top 10 technological trends for 2020. More than 50% of IoT companies' teams have a digital twin in their annual plan as a strategic mandate. According to MarketsandMarkets [1], it is expected that the digital twin market will reach US $35.8 billion by 2025.
Vehicular ad hoc networks (VANETs) leverage information and communications technology to make transportation systems intelligent, safe, and efficient, hence improving people’s driving experience. Unfortunately, due to the openness of wireless channels and vehicular mobility, privacy leakage in VANETs poses serious privacy concerns. Once a user’s identity is leaked, it will cause serious threats to his/her property and personal safety as a malicious attacker, such as a stalker could utilize the targeted identity to track particular driver and/or launch malicious attack. To address such a privacy problem, by observing the nice properties of ring signature like anonymity, spontaneity, flexibility, and membership equality, we design a novel conditional privacy protection scheme based on ring signcryption, which utilizes the salient features of identity-based cryptosystems and ring signature to achieve conditional privacy. Through security analysis and experiments, we have demonstrated the advantage of our scheme over most existing solutions.
Alejandro Fernández Gil, Eduardo Lalla-Ruiz, Mariam Gómez Sánchez
et al.
Road freight transport is one of the sectors with the highest greenhouse gas emissions and fuel consumption in the logistics industry. In recent years, due to the increase in carbon dioxide emissions, several companies have considered reducing them in their daily logistics operations by means of better routing management. Green vehicle routing problems (GVRPs) constitute a growing problem direction within the interplay of vehicle routing problems and environmental sustainability that aims to provide effective routes while considering environmental concerns. These NP-hard problems are one of the most studied ones in green logistics, and due to their difficulty, there are many different heuristic and hybrid techniques to solve them under the need of having high-quality solutions within reasonable computational time. Given the role and importance of these methods, this review aims at providing a comprehensive overview of them while reviewing their defining strategies and components. In addition, we analyze characteristics and problem components related to how emissions are being considered. Lastly, we map and analyze the benchmarks proposed so far for the different GVRP variants considering emissions.
Transportation engineering, Transportation and communications
An intelligent transportation system (ITS) is one of the main systems which have been developed to achieve safe traffic and efficient transportation. It enables the vehicles to establish connections with other road entities and infrastructure units using vehicle-to-everything (V2X) communications. As a consequence, all road entities become exposed to either internal or external attacks. Internal attacks cannot be detected by traditional security schemes. In this article, a recommendation-based trust model for V2X communications is proposed to defend against internal attacks. Four types of malicious attacks are analyzed. In addition, we conduct various experiments with different percentage of malicious nodes to measure the performance of the proposed model. In comparison with the existing model, the proposed model shows an improvement in network throughput and the detection rate for all types of considered malicious behaviors. Our model improves the packet dropping rate (PDR) with 36% when the percentage of malicious nodes is around 87.5%.