Abstract Environmental sustainability is gaining traction as a shared value between brands and consumers. Beyond adopting sustainable practices, brands must effectively communicate their initiatives to ensure they resonate and drive meaningful consumer engagement. This study examines whether green brand storytelling—embedding a brand’s sustainability initiatives within a compelling narrative—positively impacts consumers’ purchase intentions, mediated by self-brand connection and brand trust. To investigate this, a mixed-method approach was employed, combining a survey based experimental design with thematic analysis. In the quantitative phase, four storytelling-based video advertisements—two representing high-involvement and two low-involvement products—were used. Participants (N = 280) were randomly assigned to view one of the ads via an online survey, with randomization managed through Google Apps Script to mitigate selection bias. In the qualitative phase, thematic analysis of open-ended survey responses (N = 32) revealed six recurring themes: Emotional resonance, Narrative transportation, Identity alignment, Brand credibility, Narrative clarity, and Behavioral influence. Findings demonstrate a significant positive association between green brand storytelling and purchase intentions, with both self-brand connection and brand trust serving as key mediators. These results denote that storytelling can make sustainability communication more compelling, foster trust, deepen emotional connections, and drive purchase behavior, ultimately transforming sustainability messaging into a strategic tool for long-term competitive advantage.
Activity-based travel demand models (ABMs) have the capacity to represent emerging activity dimensions; however, they lack integration of physical and virtual activity spaces. This study introduces a novel ABM framework that captures the dynamic interactions between physical–virtual activity spaces and implements it within an integrated transport, land-use, and emission framework. The study develops information and communication technology (ICT) adoption models, such as internet access and device ownership, into the agent-based structure. Markov chain Monte Carlo (MCMC) and conditional probability algorithms are utilized to schedule activities in physical and virtual environment reflecting factors, such as work arrangements, employment status, mobility and ICT tool ownership. Comprehensive calibration and validation processes are performed to ensure that the model can generate population mimicking real-world conditions. A prototype version of the model is implemented for the Halifax Regional Municipality (HRM), Canada. A scenario simulation is conducted that examines the impacts of ICT tool and virtual work adoption on activity-travel patterns. Results show that increased ICT tool adoption significantly boosts the duration of virtual maintenance and discretionary activities while reducing time spent on mandatory activities. A 10% rise in virtual work reduces vehicle kilometers traveled (VKT) in HRM by 51,800 km/day and lowers carbon dioxide (CO 2 ) emissions by 6.216 metric tons/day. The study confirms the complex, nonlinear impacts of ICT on travel, while showing the potential of virtual-work in reducing peak-hour travel and VKT. The developed tools in this study can aid policymakers in assessing the impacts of virtual activities on transport and land use systems and help achieve regional sustainability goals.
Lukas Ostermann, Asrat Gobachew, Andreas Schwung
et al.
<i>Background</i>: The increasing integration of automated transport drones into logistics networks presents transformative potential for addressing contemporary logistics challenges, particularly in last-mile delivery, healthcare, disaster response, urban mobility, and postal services. However, their effective integration into varied logistics contexts remains hindered by infrastructure, regulatory, and operational limitations. This study aims to explore how drone-based logistics systems can be systematically planned and evaluated across diverse operational environments. <i>Methods</i>: A structured literature review was conducted, employing thematic synthesis to analyze current research on drone logistics. The analysis focused on identifying the key planning dimensions and interrelated components that influence the deployment of drone-based transport systems. <i>Results</i>: The review identified seven central planning dimensions: areas of application, system components, transport configuration, geographic areas, optimization and analysis methods, logistical planning, and performance assessment. These dimensions inform a conceptual framework designed to guide the planning and assessment of drone logistics networks. <i>Conclusions</i>: While existing studies contribute valuable insights into route optimization and drone deployment strategies, they often overlook integrative approaches that account for societal and environmental factors. The study emphasizes the need for interdisciplinary collaboration and context-specific planning frameworks to enhance the sustainable and effective implementation of drone-based logistics systems.
Transportation and communication, Management. Industrial management
Adwitiya Mukhopadhyay, Aryadevi Remanidevi Devidas, Venkat P. Rangan
et al.
Addressing the inadequacy of medical facilities in rural communities and the high number of patients affected by ailments that need to be treated immediately is of prime importance for all countries. The various recent healthcare emergency situations bring out the importance of telemedicine and demand rapid transportation of patients to nearby hospitals with available resources to provide the required medical care. Many current healthcare facilities and ambulances are not equipped to provide real-time risk assessment for each patient and dynamically provide the required medical interventions. This work proposes an IoT-based mobile medical edge (IM<sup>2</sup>E) node to be integrated with wearable and portable devices for the continuous monitoring of emergency patients transported via ambulances and it delves deeper into the existing challenges, such as (a) a lack of a simplified patient risk scoring system, (b) the need for architecture that enables seamless communication for dynamically varying QoS requirements, and (c)the need for context-aware knowledge regarding the effect of end-to-end delay and the packet loss ratio (PLR) on the real-time monitoring of health risks in emergency patients. The proposed work builds a data path selection model to identify the most effective path through which to route the data packets in an effective manner. The signal-to-noise interference ratio and the fading in the path are chosen to analyze the suitable path for data transmission.
Abstract Space weather events, including solar flares, coronal mass ejections, and geomagnetic storms, have significant effects on various transportation systems. This review provides a comprehensive examination of the current understanding and future outlook of space weather effects on air, maritime, railway, and ground transportation. It explores the mechanisms through which space weather causes communication blackouts, satellite navigation failure, elevated cosmic radiation, and geomagnetically induced currents, leading to disruptions in transportation operations. Historical events are analyzed to underscore the diversity and severity of these impacts. Additionally, this review discusses the anticipated challenges posed by the upcoming solar maximum of Solar Cycle 25 and highlights the need for improved forecasting, mitigation strategies, and resilient infrastructure to safeguard transportation systems against space weather threats. By integrating findings from recent studies and historical data, this review aims to enhance the preparedness and response strategies of the transportation sector in the face of evolving space weather risks.
<i>Background</i>: The growing concern for environmental and social issues has led to a focus on designing sustainable supply chains and increasing industrial responsibility towards society. In this paper, a multi-objective mixed-integer programming model is presented for designing a sustainable closed-loop supply chain. The model is aimed at the minimization of the total cost with the total used facilities, the negative environmental impacts, and the maximization of the positive social impacts. <i>Methods</i>: The epsilon-constraint method is utilized for solving the model and further extracting the Pareto solutions. <i>Results</i>: The result of the research clearly shows an optimal trade-off between the conflicting objectives, where, by paying more attention to the social and environmental aspects of sustainability, the total costs are increased or by optimizing the number of facilities, a better balance between the dynamics associated with the short-term and long-term goals is reached. The results of the sensitivity analysis also show that increasing the demand of the supply chain has the greatest impact on the supply chain costs compared to other objectives. <i>Conclusions</i>: Consequently, investigating such comprehensive sustainable objectives provides better insights into the impact of design variables on the expectations of stakeholders.
Transportation and communication, Management. Industrial management
Groneberg Maik, Poenicke Olaf, Mandal Chirag
et al.
The paper describes a system approach to use LiDAR sensors for capturing dynamic point cloud data in industrial process environments and to interpret the captured scenes with AI based object detection. The object detection is used to distinguish between humans and other mobile objects in safety relevant workspaces. Several AI methods relevant for such application are analysed. One method is applied with annotated test data and evaluated concerning its accuracy.
Mahdi Soltani Nejad, Seyed Mohammad Mousavi Gazafroudi
The speed profile of the train will be determined according to criteria such as safety, travel convenience, and the type of electric motor used for traction. Due to the passengers and cargo on the train, the electric train load is constantly changing. This will require reassigning the speed controller’s parameters of the electric train. For this purpose, the Gravitational Search optimization Algorithm (GSA) will be used to minimize the error between the setpoint speed profile and the speed profile obtained from the speed controller by using the appropriate assignment of control parameters. This algorithm has a low computational cost and high accuracy, but tuning the adjustable parameters of this algorithm according to the decision space will increase its accuracy. Therefore, by using fuzzy logic Type-I and Type-II, and considering the diversity of population in decision space and generation of population, adjustable parameters of GSA such as 𝐾𝑏𝑒𝑠𝑡 and 𝛼 will be tuned. Finally, a dynamic model of the electric train between two traction power supply substations (TPS) and a proportional-integral-derivative (PID) controller will be simulated in MATLAB software to control the train speed. Then, the controller parameters will be assigned using the GSA algorithm.
Matteo Ravasio, Gian Paolo Incremona, Patrizio Colaneri
et al.
Recently, the introduction of electric vehicles has given rise to a new paradigm in the transportation field, spurring the public transport service in the direction of using completely electric bus fleets. In this context, one of the main challenges is that of guaranteeing an optimal scheduling of the charging process, while reducing the power supply requested from the main grid, and improving the efficiency of the resource allocation. Therefore, in this paper, a power allocation strategy is proposed in order to optimize the charging of electric bus fleets, while fulfilling the limitation imposed on the maximum available power, as well as ensuring limited charging times. Specifically, relying on real bus charging scenarios, a charging optimization algorithm based on a Nonlinear Additive Increase Multiplicative Decrease (NAIMD) strategy is proposed and discussed. This approach is designed on the basis of real charging power curves related to the batteries of the considered vehicles. Moreover, the adopted NAIMD algorithm allows us to minimize the sum of charging times in the presence of saturation constraints in a distributed way and with a small amount of aggregated data sent over the communication network. Finally, an extensive simulation campaign is illustrated, showing the effectiveness of the proposed approach both in allocating the power resources and in sizing the maximum power capacity of charging plants in progress.
Jorge Collao, Haiying Ma, Jose Antonio Lozano-Galant
et al.
Emissions from transportation have a severe impact on the current climate crisis. Therefore, the estimation of these pollutants requires precise measurements that integrate both traffic and vehicle fleet information within a specific country or area. However, the current estimation tools continue using vehicle fleet standards based on recommendations or local studies. A problem for the current estimation models arises due to the difficulty of centralizing the large number of vehicle statistics. This article has taken advantage of the capabilities of both visual programming tools and building information modeling (BIM) to centralize databases from different sources, generating a model that integrates current traffic data and vehicle fleet statistics. The proposed platform estimates emissions and the carbon footprint using TIER 1 emission factors recommended by the European Environmental Agency (EEA). This platform has been successfully applied to a case study to estimate the carbon footprint of the B-20 road in Barcelona, using current vehicle restriction scenarios. This case study presents a maximum difference of −2.72% compared with the estimations made by another similar report. This proposed platform more completely automates the communication among the equations and databases required to estimate traffic road emissions.
Masoud Zafarzadeh, Magnus Wiktorsson, Jannicke Baalsrud Hauge
A data-driven approach in production logistics is adopted as a response to challenges such as low visibility and system rigidity. One important step for such a transition is to identify the enabling technologies from a value-creating perspective. The existing corpus of literature has discussed the benefits and applications of smart technologies in overall manufacturing or logistics. However, there is limited discussion specifically on a production logistics level, from a systematic perspective. This paper addresses two issues in this respect by conducting a systematic literature review and analyzing 142 articles. First, it covers the gap in literature concerning mapping the application of these smart technologies to specific production logistic activities. Ten groups of technologies were identified and production logistics activities divided into three major categories. A quantitative share assessment of the technologies in production logistics activities was carried out. Second, the ultimate goal of implementing these technologies is to create business value. This is addressed in this research by presenting the “production logistics data lifecycle” and the importance of having a balanced holistic perspective in technology development. The result of this paper is beneficial to build a ground to transit towards a data-driven state by knowing the applications and use cases described in the literature for the identified technologies.
Transportation and communication, Management. Industrial management