The rapid electrification of the transportation sector offers a promising avenue for ancillary services through Vehicle-to-Grid (V2G) applications. This is particularly critical for low-inertia systems, such as the U.K. grid, where the transition toward converter-based renewable generation necessitates very fast frequency response. Therefore, the viability of V2G for high-value frequency markets is constrained by strict latency requirements (e.g. one second)). Existing literature has predominantly focused on high-level economic aggregation models or communication network delays, largely neglecting the stochastic physical response dynamics of the EV On-Board Charger (OBC). This paper addresses this gap by developing a discrete-time Markov chain model that specifically characterizes the internal dynamics and response latency of OBC hardware. We integrate this model into a discrete-event simulation framework to evaluate end-to-end system latency, coupling stochastic OBC constraints with Over-the-Air (OTA) communication delays. We analyze the performance of fleets comprised of three common OBC ratings: 10 kW, 22 kW, and 43 kW. Contrary to the intuition that higher power ratings yield superior agility, our results demonstrate that high-capacity chargers may exhibit lower success rates in fast frequency markets due to insufficient ramp-rate-to-capacity ratios. Furthermore, we demonstrate that the frequency of mode switching events (switching between charging and discharging) is a dominant factor in performance degradation due to hardware hysteresis. These findings underscore that the efficacy of V2G applications requires precise EV-level control logic rather than relying solely on fleet-level optimization. Finally, the proposed models are evaluated against the PJM interconnection’s composite score methodology. The results demonstrate high accuracy, suggesting the proposed framework can serve as a preliminary, EV-specific V2G assessment tool for market operators.
Transportation engineering, Transportation and communications
Wireless indoor positioning was deeply integrated into numerous scenarios including transportation navigation, industrial manufacturing, and public safety, serving as a crucial pillar for ubiquitous sensing in the 6G era. However, positioning accuracy was severely degraded by non-line-of-sight propagation and multipath characteristics in indoor environments, while its robustness was further undermined by environmental noise and interference. As artificial intelligence was deeply applied in wireless systems and 6G’s integrated sensing and communication capabilities continue to advance, new opportunities were identified to mitigate the aforementioned challenges. A task-oriented technical framework for AI indoor positioning was established, by which a profound progression "single-point location estimation" to "holistic spatial cognition" and further to "data-driven reverse optimization" was revealed across three task categories, namely improving positioning accuracy, enhancing environmental perception and generating positioning data. Subsequently, a comprehensive set of evaluation metrics tailored specifically for AI positioning systems was proposed, which highlighted the distinctive characteristics and multidimensional variations of AI-driven wireless positioning. Finally, critical challenges and future trends in AI-driven wireless indoor positioning technology were discussed, offering fresh insights for next-generation positioning technology advancement.
Panagiotis Pantiris, Petros L. Pallis, Panos T. Chountalas
et al.
<i>Background:</i> The adoption of artificial intelligence (AI) in humanitarian logistics is essential for improving coordination and decision making, especially in the challenging landscape of disaster-relief settings. However, the current literature offers limited empirical evidence with respect to the specific impact of AI on coordination and decision making for real-life humanitarian problems. Based on evidence from the humanitarian sector, this paper focuses on how AI could help humanitarian organizations collaborate better, streamline relief supply-chain operations and use resources more effectively. <i>Methods:</i> Twelve key themes influencing AI integration are identified by the study using a Grounded Theory (GT) approach based on interviews with experts from the humanitarian sector. These themes include data reliability, operational limitations, ethical considerations and cultural sensitivities, among others. <i>Results:</i> The findings suggest that AI improves forecasting, planning and inter-organizational coordination and is especially useful during the preparedness and mitigation stages of relief operations. Successful adoption, however, depends on adjusting tools to actual field conditions, building trust and training and striking a balance between algorithmic support and human expertise. <i>Conclusions:</i> The paper offers useful and practical advice for humanitarian organizations looking to use AI technologies in an ethical way while taking into account workforce capabilities, cross-agency cooperation and field-level realities.
Transportation and communication, Management. Industrial management
Mariana Matias Santos, Beatriz Rosana Gonçalves de Oliveira Toso, Altamira Pereira da Silva Reichert
et al.
ABSTRACT Objectives: to validate and implement a home care flow protocol for children with special healthcare needs. Methods: mixed-method research conducted in home care services in Paraíba, Brazil, with 13 judges for content validity and 11 interviews with healthcare professionals after clinical implementation. Quantitative data were analyzed by calculating the Content Validity Index and Cronbach’s alpha, and qualitative data were analyzed by inductive thematic analysis. Mixed-method analysis was performed by integrating data in a joint display. Results: the instrument was considered substantial and valid, with a Cronbach’s alpha of 0.87. There was a need for adjustments to the protocol flowchart in the “transportation”, “communication with Primary Health Care”, “Singular Therapeutic Project implementation”, “telecare” and “emergency service activation” items. Conclusions: the protocol was considered functional for use by home services after incorporation of the suggested adjustments.
The rapid proliferation of the Internet of Things (IoT) in intelligent transportation systems has revolutionized vehicular communication by enabling real-time data exchange between vehicles, infrastructure, and cloud services. However, high mobility and dynamic network topologies result in increased energy consumption and heightened vulnerability to data breaches. To address these challenges, this research proposes an efficient low-power encryption framework for cluster-based Internet of Vehicles (IoV), designed to ensure end-to-end vehicular data protection while minimizing energy usage within sustainable IoT networks. The proposed model introduces a block cipher–based Scalable Ant Colony Optimized PRESENT (SACO-PRESENT) encryption scheme for secure data communication and energy-efficient path optimization. A Scalable Ant Colony Optimization (SACO) algorithm is employed to optimize clustering and adaptive link selection within an enhanced AODV routing protocol. This optimization enables the selection of energy-efficient communication paths based on residual energy, node mobility, and signal strength, thereby extending network lifetime. To ensure user privacy and identity authentication, the system incorporates lightweight symmetric encryption and anonymous trust authentication protocols, providing strong security without compromising the performance of resource-constrained devices. Simulation results demonstrate that the proposed model significantly improves key performance metrics achieving a higher packet delivery ratio (93.88 %), reduced end-to-end delay, lower energy consumption, and an overall extension of network lifetime. Overall, the proposed framework delivers a scalable, low-power, and secure solution for vehicular communication, contributing to the advancement of resilient and sustainable IoT-based intelligent transportation systems.
In urban Internet of vehicles (IoV) systems, the road side unit (RSU) was commonly deployed at road intersections to provide two-dimensional service coverage. However, due to vehicle dynamics, RSU might suffer from massive power waste. A novel RSU deployment strategy was proposed that dynamically adjusted the operating mode and transmission power of RSU, based on the spatial distribution of vehicles. An adaptive mapping mechanism between RSU power consumption and vehicle distribution patterns was established that overcame the power consumption bottleneck caused by traditional RSU's fixed coverage to minimize the total power consumption of RSU. Furthermore, the concept of candidate communication service area (CCSA) was introduced, utilized in combination with an exploratory RSU deployment algorithm (ERDA) and an enhanced genetic algorithm (eGA) to implement the RSU deployment strategy. Analysis and simulation results indicate that, compared with conventional methods, the proposed RSU deployment stra-tegy significantly reduces redundant road coverage, leading to minimized RSU power consumption. In low vehicle density, the ERDA can achieve optimal solutions with low computational complexity. In high vehicle density, the eGA provides a reasonable balance between complexity and performance. Therefore, the proposed strategy provides practical engineering guidance for urban transportation systems.
Zahra Halimi, Mohammad SafariTaherkhani, Qingbin Cui
Ground transportation infrastructure significantly impacts community connectivity, economic growth, and access to essential services such as jobs, education, and healthcare. However, in practice, these infrastructures do not provide equitable services for all, and historical disparities have led to inequitable conditions for many individuals. This paper explores diverse definitions of equity relevant to ground infrastructures, examines the consequences of inequitable transportation systems throughout the history of the U.S. highway system, and explores various approaches for conducting equity analysis. Based on the collected information, a generalized framework is introduced to analyze the impact of these infrastructures on transportation equity. The study also provides a novel equity index that can be used to assess equity considering accessibility and affordability. Finally, the framework is applied to a case study in Baltimore City, Maryland, examining the equitable distribution of electric vehicle (EV) chargers. By demonstrating its practical application, the paper offers managers and policymakers a concise step-by-step approach to analyze transportation equity. This method assesses both the socioeconomic features of affected populations and the distribution of services, contributing to the development of a more sustainable ground transport system.
IntroductionThe COVID-19 pandemic, which began in the last quarter of 2019, has had a significant impact on urban transportation. With increasing demand for urban transport, the internal roads and public spaces of university campuses play an important role in facilitating commuting and communication between various functional zones. While considerable research has been conducted on route planning, pedestrian-vehicle segregation, and safety management in the internal transportation environment of university campuses, empirical investigations exploring barrier-free inclusive campus environment design and the subjective evaluation of road and public space users in the aftermath of the COVID-19 pandemic are lacking. Recent developments in travel behavior models and positive psychology have led to an increased focus on the correlation among subjective perceptions, attitudes, emotions, and commuting satisfaction in urban transportation and planning design.MethodsTo elucidate this relationship, a study was conducted on the new campus of Central South University in Changsha, Hunan Province, China. Using 312 valid samples, a structural equation model was constructed to analyse the relationship between commuting satisfaction and the barrier-free environment perception of university students regarding the internal transportation environment of the campus.ResultsThe results revealed that individuals' instantaneous barrier-free environment perceptions and long-term established positive emotions had a significant positive effect on commuting satisfaction. Furthermore, positive emotions were found to mediate the relationship between commuting attitudes induced by COVID-19, barrier-free environment perceptions, and commuting satisfaction.DiscussionThe results of this study provide a theoretical basis for the necessity of accessibility design in the post-COVID era. In addition, this study considers the perspective of users to provide ideas for the planning and construction of barrier-free campus environments that are based on convenient and inclusive design.
In the narrow Intelligent Transportation System (ITS) band, avoiding wireless channel congestion is essential. Reporting vehicle kinematics in the Cooperative Awareness Message (CAM) only when there are notable changes in vehicle dynamics is a standardized approach to reducing bandwidth usage of periodic CAM messages that are exchanged between vehicles, and is called the CAM generation rule. However, in cellular vehicle-to-everything (V2X) communication, aperiodicity due to frequent omissions of periodic CAM raises problems of resource waste and instability in resource scheduling. The problem can be solved by reserving a resource only for actual CAM transmission times in the future. This article demonstrates that a neural network-based scheme can predict the next CAM generation times at an average accuracy of over 94%, which can be utilized for resource reservation under the CAM generation rule.
Vehicle-to-vehicle (V2V) communication has gained significant attention in the field of intelligent transportation systems. In this paper, we focus on communication scenarios involving vehicles moving in the same and opposite directions. Specifically, we model a V2V network as a dynamic multi-source single-sink network with two-way lanes. To address rapid changes in network topology, we employ random linear network coding (RLNC), which eliminates the need for knowledge of the network topology. We begin by deriving the lower bound for the generation probability. Through simulations, we analyzed the probability distribution and cumulative probability distribution of latency under varying packet loss rates and batch sizes. Our results demonstrated that our RLNC scheme significantly reduced the communication latency, even under challenging channel conditions, when compared to the non-coding case.
Rampure Rahul, Tiruvallur Raghav, Acharya Vybhav
et al.
Air traffic management is becoming highly complex with the rapid increase in the number of commercial and cargo flights, leading to increased traffic congestion and flight delays. To mitigate these issues, we present a flight path generation system that distributes the aeroplanes across the airspace and imparts minimal delays to the flight if required, thus ensuring that the aircraft follows the shortest route wherein it encounters the least amount of traffic. We develop a parallel genetic algorithm in CUDA-C with a novel fitness function allowing the system to reach an optimal solution where the air traffic density is minimised. The proposed algorithm was tested on one day's domestic flight schedule and achieved an 18% reduction in traffic density, with the flight times and delays remaining proportional to the data observed in the existing air traffic management system.
With the rapid development of information and communication technologies (ICT), businesses have addressed to new applications in their operations. Nowadays, almost all activities in logistics sector are carried out in the digital environment through emerging technologies. Accordingly, the purpose of this study to identify the success factors that critical for sustaining e-logistics activities. At first, a comprehensive literature review was conducted, and then expert opinion was taken to determine the criteria. By the help of feedbacks and literature review, five criteria were investigated in this study. The AHP-VIKOR integrated method which is widely used in multi-criteria decision-making methods (MCDM) was applied to evaluating success factors in e-logistics. The criteria weights were determined by the AHP method, and the relevant criteria were ranked by the VIKOR method. The results indicated that the reliability criterion has the highest weights, while transportation criterion has the lowest weight in identifying the key success factors of e-logistics.
Transportation is the backbone of the economy and urban development. Improving the efficiency, sustainability, resilience, and intelligence of transportation systems is critical and also challenging. The constantly changing traffic conditions, the uncertain influence of external factors (e.g., weather, accidents), and the interactions among multiple travel modes and multi-type flows result in the dynamic and stochastic natures of transportation systems. The planning, operation, and control of transportation systems require flexible and adaptable strategies in order to deal with uncertainty, non-linearity, variability, and high complexity. In this context, Reinforcement Learning (RL) that enables autonomous decision-makers to interact with the complex environment, learn from the experiences, and select optimal actions has been rapidly emerging as one of the most useful approaches for smart transportation. This paper conducts a bibliometric analysis to identify the development of RL-based methods for transportation applications, typical journals/conferences, and leading topics in the field of intelligent transportation in recent ten years. Then, this paper presents a comprehensive literature review on applications of RL in transportation by categorizing different methods with respect to the specific application domains. The potential future research directions of RL applications and developments are also discussed.
The transportation system is an interplay between infrastructure, vehicles, and policy. During the past century, the rapid expansion of the road network, blended with increasing vehicle production and mobility demands, has been stressing the system's capacity and resulting in a shocking amount of annual costs. To alleviate these costs while providing passengers with safe and efficient travel experiences, we need to better design and plan our transportation system. To start with, not only the design of our road network is topologically flawed but also our infrastructure likely facilitates inequality: roads and bridges are found to better connect affluent sectors while excluding the poor. While technological advancements such as connected and autonomous vehicles (CAVs) and novel operation modes such as shared economy have offered new opportunities, questions remain. First, what is the relationship between the road network, community development, demographics, and mobility behaviors? Second, by leveraging the insights from studying the first question, can we better plan, coordinate, and optimize vehicles in different modalities such as human-driven and autonomous to construct safe, efficient, and resilient traffic flows? Third, how can we build an intelligent transportation system to promote equity and fairness in our community development? This proposal is the first step toward answering these questions.
High-speed communication and accurate sensing are of vital importance for future transportation system. Integrated sensing and communication (ISAC) system has the advantages of high spectrum efficiency and low hardware cost, satisfying the requirements of sensing and communication. Therefore, ISAC is considered to be a promising technology in the future transportation system. However, due to the low transmit power of signal and the influence of harsh transmission environment on radar sensing, the signal to noise ratio (SNR) at the radar receiver is low, which affects the sensing performance. This paper introduces the intelligent reflecting surface (IRS) into ISAC system. With IRS composed of M sub-surfaces implemented on the surface of the target. The SNR at the radar receiver is 20lg(M) times larger than the scheme without IRS. Correspondingly, radar detection probability is significantly improved, and Cramer-Rao Lower Bound (CRLB) for ranging and velocity estimation is reduced. This paper proves the efficiency of IRS enabled ISAC system, which motivates the implementation of IRS to enhance the sensing capability in ISAC system.