Search Results (296)

The advent of Artificial Intelligence in the cockpit and the air traffic control centre in the coming decade could mark a step-change improvement in aviation safety, or else could usher in a flush of ‘AI-induced’ accidents. Given that contemporary AI has well-known weaknesses, from data biases and edge or corner effects, to outright ‘hallucinations’, in the mid-term AI will almost certainly be partnered with human expertise, its outputs monitored and tempered by human judgement. This is already enshrined in the EU Act on AI, with adherence to principles of human agency and oversight required in safety-critical domains such as aviation. However, such sound policies and principles are unlikely to be enough. Human interactions with current automation in the cockpit or air traffic control tower require extensive requirements, methods, and validations to ensure a robust (accident-free) partnership. Since AI will inevitably push the boundaries of traditional human-automation interaction, there is a need to revisit Human Factors to meet the challenges of future human-AI interaction design. This paper briefly reviews the types of AI and ‘Intelligent Agents’ along with their associated levels of AI autonomy being considered for future aviation applications. It then reviews the evolution of Human Factors to identify the critical areas where Human Factors can aid future human-AI teaming performance and safety, to generate a detailed requirements set organised for Human AI Teaming design. The resultant requirements set comprises eight Human Factors areas, from Human-Centred Design to Organisational Readiness, and 165 detailed requirements, and has been applied to three AI-based Intelligent Agent prototypes (two cockpit, one air traffic control tower). These early applications suggest that the new requirements set is scalable to different design maturity levels and different levels of AI autonomy, and acceptable as an approach to Human-AI Teaming design teams. Full article

The differential toll policy has emerged as an effective method for regulating expressway traffic flow and has positively impacted the efficiency of vehicular movement, as well as balanced the spatial and temporal distribution of the road network. However, the acceptance of differentiated charging policies and the range of rates associated with these policies warrant further investigation. This study employs both revealed preference (RP) and stated preference (SP) survey methods to assess users’ willingness to accept the current differentiated toll scheme and to analyze the proportion of users opting for alternative travel routes and their behavioral characteristics in simulated scenarios. Additionally, we construct a Structural Equation Model-Latent Class Logistics (SEM-LCL) to explore the mechanisms influencing differentiated toll road alternative travel choices while considering user heterogeneity. The findings indicate that different tolling strategies and discount rates attract users variably. The existing differentiated tolling scheme—based on road sections, time periods, and payment methods—significantly affects users’ choices of alternative routes, with the impact of tolling based on vehicle type being especially pronounced for large trucks. The user population is heterogeneous and can be categorized into three distinct groups: rate-sensitive, information-promoting, and conservative-rejecting. Furthermore, the willingness to consider alternative travel routes is significantly influenced by factors such as gender, age, driving experience, vehicle type, travel time, travel distance, payment method, and past differential toll experiences. The results of this study provide valuable insights for highway managers to establish optimal toll rates and implement dynamic flow regulation strategies while also guiding users in selecting appropriate driving routes. Full article

Accurately characterizing the structural features of the spatial correlation network of comprehensive transportation green efficiency (CTGE) is essential for achieving balanced regional transportation development and eliminating regional disparities. This study employs the slacks-based measure-data envelopment analysis (SBM-DEA) model to assess the CTGE of China. Furthermore, the standard deviational ellipse (SDE) model and social network analysis (SNA) method are adopted to delineate the spatiotemporal evolution patterns and spatial correlation network characteristics of CTGE, based on input–output data from the transportation industry across 30 provinces (municipalities and autonomous regions) between 2003 and 2020. The findings reveal that China’s CTGE exhibits a fluctuating trend of an initial decline followed by subsequent increase, with a national average of 0.555 and an average of 0.722 in eastern regions, 0.434 in central regions, and 0.478 in western regions. This demonstrates that China’s CTGE maintains an overall low level while showing significant regional disparities. The spatial center of gravity of China’s CTGE has shifted from a southwestern to a northeastern trajectory, with a generally concentrated spatial distribution pattern. Furthermore, China’s CTGE demonstrates a distinct “core-edge” hierarchical structure, with regions occupying varied roles and statuses within the network. The central and western regions are positioned at the network periphery, predominantly receiving spillover effects from other regions, while the eastern region, driven by its strong spillover effect, serves as the network’s “engine”. The most significant contribution of this study lies in developing a more comprehensive CTGE evaluation framework and precisely identifying the structural positions and functional roles of different regions within the network, which holds substantial theoretical and practical value for advancing sustainable development in China’s transportation sector. Full article

Recently, the development of autonomous vehicles (AVs) and intelligent driver assistance systems has drawn significant attention from the public. Despite these advancements, AVs may encounter critical situations in real-world scenarios that can lead to severe traffic accidents. This review paper investigated these critical scenarios, categorizing them under weather conditions, environmental factors, and infrastructure challenges. Factors such as attenuation and scattering severely influence the performance of sensors and AVs, which can be affected by rain, snow, fog, and sandstorms. GPS and sensor signals can be disturbed in urban canyons and forested regions, which pose vehicle localization and navigation problems. Both roadway infrastructure issues, like inadequate signage and poor road conditions, are major challenges to AV sensors and navigation systems. This paper presented a survey of existing technologies and methods that can be used to overcome these challenges, evaluating their effectiveness, and reviewing current research to improve AVs’ robustness and dependability under such critical situations. This systematic review compares the current state of sensor technologies, fusion techniques, and adaptive algorithms to highlight advances and identify continuing challenges for the field. The method involved categorizing sensor robustness, infrastructure adaptation, and algorithmic improvement progress. The results show promise for advancements in dynamic infrastructure and V2I systems but pose challenges to overcoming sensor failures in extreme weather and on non-maintained roads. Such results highlight the need for interdisciplinary collaboration and real-world validation. Moreover, the review presents future research lines to improve how AVs overcome environmental and infrastructural adversities. This review concludes with actionable recommendations for upgrading physical and digital infrastructures, adaptive sensors, and algorithmic upgrades. Such research is important for AV technology to remain in the zone of advancement and stability. Full article

This study explores the trends and causes of disengagement events in Autonomous Vehicles (AVs) using data from the California Department of Motor Vehicles (CA DMV) from 2019 to 2022. Disengagements, defined as instances where control transitions from the AV to a human driver, are crucial indicators of the reliability and trustworthiness of Autonomous Driving Systems (ADS). The analysis identifies a significant correlation between cumulative mileage and disengagement frequency, revealing that 77% of disengagements were initiated by safety drivers. The research categorizes disengagements into system-initiated, driver-initiated, or planned for testing purposes, highlighting that environmental factors and interactions with other road users are the primary causes attributed to the AV system. The findings indicate a downward trend in the ratio of disengagements to mileage, suggesting improvements in AV technology and increasing operator trust. However, the persistent rate of manual disengagements underscores ongoing challenges regarding driver confidence. This research enhances the understanding of ADS performance and driver interactions, offering valuable insights for improving AV safety and fostering technology acceptance in mixed-traffic environments. Future studies should prioritize enhancing system reliability and addressing the psychological factors that influence driver trust in ADS. Full article

The problem addressed in this paper is the identification and management of risks associated with the implementation and operation of green hydrogen in the Spanish port system. The growing demand for clean energy and environmental regulations are driving the adoption of green hydrogen as a viable solution to decarbonize shipping. However, this transition comes with significant challenges, including safety, infrastructure, and hydrogen handling risks. In the existing literature, several authors have used methodologies such as qualitative and quantitative risk analysis, techniques such as FMEA (Failure Modes and Effects Analysis), and the evaluation of impacts and probabilities of occurrence to identify and manage risks in similar projects. These approaches have made it possible to identify potential threats and propose effective mitigation measures. In this work, a combined methodology is proposed that includes the identification of threats, risk assessment through risk matrices, and classification of these risks for their proper management. The SWIFT method (Structured What-If Technique) and the use of impact-probability matrices are applied. The main conclusion of the work is that, although green hydrogen has great potential for the decarbonization of the port sector, its implementation requires careful management of the risks identified. The proposed mitigation measures are essential to ensure the safety and viability of green hydrogen projects in Spanish ports. Full article

India is committed to becoming a net-zero emitter by 2070 to fight climate change; however, road transportation causes a major challenge for decarbonising transport in India. This paper investigates the low-carbon strategy and implementation of fiscal and financial policies in India. The research delves into the innovative strategies to address unique regional hurdles and transportation demands. These strategies include customised policies to incentivise EVs, creating charging infrastructure networks, the integration of renewable energy sources in public transport systems, and the formulation of specific regulations to curb emissions from high-traffic areas. Findings from the review of low-carbon strategies and financial policies in road transportation advocate for penalising high-emitters, subsidising clean technology, and reorienting government expenditure towards sustainable infrastructure for combating climate change and adhering to India’s commitment announced at COP26. This paper suggests the efficacy and replicability of these new strategies, thus, providing valuable insights to policymakers and stakeholders for creating a more sustainable and efficient road transportation network in India. Full article

Future mobility concepts, such as Shared Autonomous Mobility-on-Demand (SAMOD) services, have the potential to contribute to sustainability goals and enhance connectivity between rural areas and urban public transport networks. The SAMOD concept relies on virtual stops, accessible via a smartphone application, where passengers are individually picked up. This study analyzed the importance of six key attributes of a SAMOD journey: travel time, price, available information, distance to the stop, navigation to the virtual stop, and identification of the virtual stop. Using a choice-based conjoint analysis (N = 461), participants were repeatedly presented with two SAMOD journey options, each varying in attributes, and were asked to indicate their preference. The findings reveal that all six attributes significantly influenced travel decisions. Subgroup analyses further indicated that the importance of these attributes varied by gender, age, travel context, and frequency of public transport use. Notably, SAMOD-specific attributes, such as navigation to and identification of the virtual stop, were rated as nearly as critical as traditional factors like travel time and cost. Based on these findings, actionable recommendations for transport planners and policymakers are proposed to facilitate the successful implementation of SAMOD services. Full article

This study investigates how Industry 4.0 technologies can optimize transportation efficiency and contribute to global sustainability goals by reducing CO₂ emissions. In response to the pressing climate emergency, the research examines the role of the Internet of Things (IoT), Artificial Intelligence (AI), and predictive analytics in enhancing operational performance and aligning transportation systems with Sustainable Development Goals (SDGs), particularly Goal 13 (climate action) and Goal 9 (industry, innovation, and infrastructure). Using a qualitative research approach, semi-structured interviews and focus groups were conducted with industry experts, and the data were analyzed using thematic analysis and qualitative network mapping in NVivo software. The findings reveal that IoT enhances real-time monitoring, AI enables dynamic route optimization, and predictive analytics supports proactive maintenance, collectively achieving an average emission reductions of 30%. However, adoption is hindered by infrastructure gaps, high implementation costs, skill shortages, and fragmented regulatory frameworks. This study integrates the Technology–Organization–Environment (TOE) framework and Sustainable Corporate Theory to provide a structured analysis of digital transformation in transportation. The findings offer strategic insights for policymakers and industry stakeholders, highlighting the need for stronger regulatory support, targeted incentives, and digital infrastructure investments. Full article

Urban cycling has evolved significantly over the last decade, becoming a key component of many cities’ sustainability strategies, including Prague, which is the focus of this study. This research explores the potential impacts of the proposed mandatory helmet law (MHL) on urban cycling in the city, particularly focusing on bike-sharing programs. While helmets are proven to reduce head injuries, mandatory laws may discourage cycling, counteracting efforts to promote sustainable transport. This study utilizes survey data from 448 urban cyclists to examine the relationship between helmet legislation, cycling rates, and sustainable mobility goals. Results indicate diverse attitudes towards helmet use, with many cyclists perceiving MHL as inconvenient, potentially leading to reduced cycling frequency. Bike-sharing users, less likely to wear helmets, may be particularly affected, risking a decline in spontaneous cycling and undermining Prague’s climate commitments. Potential actions, including educational campaigns, helmet availability at bike-share stations, and infrastructure improvements, could enhance safety while encouraging cycling. Full article

The risk of serious crashes is notably higher among young and novice drivers. This increased risk is due to several factors, including a lack of recognition of dangerous situations, an overestimation of driving skills, and vulnerability to peer pressure. Recently, advanced driver assistance systems (ADAS) have been integrated into vehicles to help mitigate crashes linked to these factors. While numerous studies have examined ADAS broadly, few have specifically investigated its effects on young and novice drivers. This study aimed to address that gap by exploring ADAS’s impact on these drivers. Most studies in this review conclude that ADAS is beneficial for young and novice drivers, though some research suggests its impact may be limited or even negligible. Tailoring ADAS to address the unique needs of young drivers could enhance both the system’s acceptance and reliability. The review also found that unimodal warnings (e.g., auditory or visual) are as effective as multimodal warnings. Of the different types of warnings, auditory and visual signals proved the most effective. Additionally, ADAS can influence young drivers’ car-following behavior; for instance, drivers may maintain greater safety buffers or drive closely to avoid alarm triggers, likely due to perceived system unreliability. Aggressive drivers tend to benefit most from active ADAS, which actively intervenes to assist the driver. Future research could explore the combined effects of multiple ADAS functions within a single vehicle on young and novice drivers to better understand how these systems interact and impact driver behavior. Full article

Urban freight transportation is facing significant challenges due to increasing demand, driven by globalization, e-commerce growth, and the adoption of just-in-time logistics. These trends have led to rising vehicle flows in urban areas, negatively impacting sustainability, economic efficiency, and road safety. In response, cities are exploring innovative last-mile delivery strategies that emphasize sustainability, flexibility, and cost efficiency. Among these strategies, cargo bikes—particularly electric cargo bikes (e-cargo bikes)—are emerging as promising low-emission solutions for urban freight distribution. However, despite their potential, a generalized methodology for estimating their demand share in urban contexts remains underdeveloped. This study proposes a comprehensive modelling framework to evaluate the freight demand share that can be addressed by e-cargo bikes, integrating quantity, restocking service, modal, and delivery sub-models, calibrated using data from a case study in Italy. The results demonstrate that e-cargo bikes could fulfil up to 20% of urban freight demand, depending on the category of goods transported, and underscore the feasibility of integrating e-cargo bikes into urban logistics systems. However, critical challenges related to scalability and cost-effectiveness persist, highlighting the need for further research and reliable cost data to support broader implementation. Full article

Complete Streets (CS) are defined as streets that accommodate all types of users, regardless of ability, safely and equitably allowing for the presence of pedestrians, bicyclists, transit users, and vehicle drivers to share the roadway. Several agencies have developed CS policies as a vital strategy to create more inclusive and accessible environments for all road users. CS are an efficient way to support the implementation of a multimodal transportation system, providing alternatives to car-oriented roadway designs. The Kentucky Transportation Cabinet recently developed the Complete Streets, Roads, and Highways Manual, aiming to implement a safe and equitable transportation system throughout the state. However, there is a need to evaluate the benefits of CS regarding their safety performance. This study aims to present crash data and summary statistics for CS projects that have been completed in Kentucky. The methodology involves a comparative analysis of safety data collected before and after the implementation of these projects. The results reveal that CS can be an effective approach to improve safety for all road users, including vulnerable and motor vehicle users. The findings also contribute to the existing knowledge on CS, offering insights into their impact on safety performance. Given that transportation agencies continue to prioritize sustainable and inclusive transportation solutions, the outcomes of this study will provide practical guidance for urban planners, policymakers, and transportation engineers seeking evidence-based solutions for creating safer roads. Full article

Road traffic crash valuation is essential for understanding the economic and social impacts of road safety, especially in low- and middle-income countries (LMICs) where data constraints hinder effective policymaking. This study aims to enhance the understanding of data requirements for road safety valuation in LMICs, using Uganda as a case study. Due to the absence of a unified crash database, secondary data were collected through institutional reports, interviews with key personnel, and referrals to access unpublished datasets. This study examines key cost components for effective valuation and explores three main methods: Restitution Cost, Human Capital, and Willingness-to-Pay, highlighting their data requirements and constraints in the LMIC context. It identifies existing data sources, evaluates their accessibility and relevance, and maps stakeholders involved in data collection and management. Despite challenges such as fragmented data and underreporting, this study underscores the importance of accurate crash valuation for evidence-based policymaking and resource allocation. The findings offer actionable recommendations to improve data collection, integration, and accessibility, highlighting the need for unified databases and standardised terminologies. By addressing these gaps, Uganda and other LMICs can reduce road crash impacts, enhance safety outcomes, and foster sustainable socio-economic development, contributing to global road safety efforts. Full article

Traffic congestion in urban areas is a pressing challenge, with roundabouts and signalized intersections offering different operational benefits. This study explores the integration of these two intersection types, focusing on the optimal distance between them to ensure efficient traffic flow. Using traffic microsimulations in VISSIM, the research examines multiple scenarios involving isolated roundabouts and those adjacent to signalized intersections, considering variables such as peak-hour traffic volume, flow distribution, and intersection spacing. Results indicate that shorter distances (<50 m) between roundabouts and signalized intersections lead to increased traffic indicators due to congestion spillback. In contrast, distances exceeding 100 m mitigate these inefficiencies, approaching the performance of isolated roundabouts. Balanced traffic distribution between approaches (50:50) enhance system performance at lower volumes but exacerbate congestion at higher volumes. A novel aspect of this study is the development of a regression model that integrates microsimulation outputs to predict travel time based on peak-hour traffic volume, flow ratios, and intersection distance, demonstrating a 90.9% explanatory power. These findings emphasize the need for strategic planning in integrating roundabouts and signalized intersections to balance operational efficiency. Full article