Future Transp., Volume 5, Issue 2 (June 2025) – 14 articles

Autonomous ride-sharing services (ARSS) offer promise in enhancing transportation, improving access for underserved populations, and addressing road safety by mitigating human error. However, their development and adoption are influenced by complex interplay of policies, implementation strategies, technological performance, and market penetration. This scoping review examined the evolving ARSS landscape in the US through literature published between 2018 and 2023. The review included 22 studies, capturing some national policies while no federal regulations related to ARSS were identified. The review predominantly covered market penetration, with few studies addressing performance and one study on implementation strategies. Findings were framed using the socio-ecological model. At the individual level, factors such as safety, affordability, and accessibility influence market penetration of ARSS. At the relational level, trust-building interactions, including the role of safety operators, emerged as key to addressing mobility concerns. At the community level, the findings indicate the need for technological improvements, public infrastructure investment, and education initiatives to enhance ARSS performance and implementation. At the societal level, the review did not include all existing policies in the US, requiring further investigation. These findings provide insights for researchers, transportation planners, and policymakers, guiding the development of evidence-based strategies to foster a sustainable transportation future. Full article

This study evaluates the impact of light-emitting diode (LED) illuminated signs, known as active road signs, on road safety at urban intersections. Transportation safety specialists emphasize the importance of visibility and placement of signage. LED signs are increasingly deployed at accident-prone locations to improve safety and regulate traffic. This study focuses on stop-controlled intersections (SCIs) in Montréal, Québec, to propose a new backlit sign for evaluation. An unbiased experiment utilizing multinomial logistic regression (MNL) was designed to compare drivers’ reactions to different signage. Microscopic models based on observed turning movement counters (TMCs) were calibrated for conflict estimation using a genetic algorithm (GA). Generalized linear models (GLMs) estimated accident and conflict frequencies under different treatment scenarios. The results showed significant conflict reductions at intersections with LED-backlit signs (BLSs), including 65.5% at night and 46.8% in daylight. Pedestrian crossing conflicts decreased by 55.6% and 27.8%. This study introduces an evaluation framework that integrates driver compliance behavior into simulation and crash modeling to assess a newly designed BLS treatment. It provides a framework for assessing safety treatments in contexts where crash data are limited. Findings offer insights for improving SCIs and enhancing transportation safety using LED stop signs. Full article

Accurate and transparent vehicle speed data are crucial for enforcing speed limits and other important applications. However, attaining the required levels of accuracy and transparency remains a challenge that needs to be addressed. The potential for further improvement is brought by technological advancements. To address this, it is necessary to understand the current developments in speed detection methods, technologies used in speed detection systems, and challenges of existing systems. This work reviews vehicle speed detection methods and provides a guideline for selecting an appropriate method. This work also reviews technologies for implementing smart systems and proposes an integrated approach for enhancing intelligence, interconnection, and transparency. Not only this, but this work also evaluates existing vehicle speed detection systems and highlights the need for further research. Furthermore, this work proposes a conceptual framework that integrates the Internet of Things, Artificial Intelligence, cloud computing, and blockchain technologies to enhance vehicle speed detection systems, particularly for developing countries. The Internet of Things facilitates data collection and transmission, ensuring system interconnectivity, while Artificial Intelligence is used for data pre-processing in cloud computing to improve system intelligence and scalability. Meanwhile, blockchain guarantees data security and transparency. A proof-of-concept demonstrator was implemented to validate the proposed conceptual framework. Evaluation results demonstrate an auspicious performance regarding end-to-end data delivery and transmission latency. This work provides both theoretical and practical insights regarding smart and transparent vehicle speed detection systems. Full article

As urban environments become increasingly congested, understanding pedestrian behaviors at intersections is essential for ensuring safety and efficiency. This study explores the complexities of pedestrian behavior in urban traffic networks, focusing on sensitivity analysis using a microscopic simulation tool and a pedestrian module based on the social force model (SFM). By examining nine key pedestrian behavior parameters in isolation, this research identifies their impact on gap acceptance behavior. This exploratory approach highlights how individual parameters, such as Lambda—indicative of a pedestrian’s responsiveness to stimuli from behind—affect the variability in and distribution of gap acceptance times. The findings provide valuable insights into the interplay between pedestrian behavior parameters and their influence on decision-making processes. These results serve as a foundation for refining pedestrian behavior models, offering practical guidance for urban planners, traffic engineers, and policymakers. By emphasizing sensitivity analysis, this study demonstrates the utility of microscopic simulation models in achieving a more profound, nuanced understanding of pedestrian dynamics, contributing to the development of safer and more efficient urban environments. Full article

Winter road maintenance levels are currently determined based on criteria with weights that have not yet been scientifically validated in Lithuania. This study aims to address this gap by analyzing global practices related to winter road maintenance levels and their determination methods. A survey of experts was conducted to expand and refine the list of criteria that significantly influence road winter maintenance decisions. Based on expert consensus, the weights for these criteria were calculated. Using this indicator system, an expert assessment of the road winter maintenance levels was performed for selected road sections of the road network. This study proposes a scientifically grounded methodology to determine winter road maintenance levels, which can be applied to all national road networks. Furthermore, the methodology incorporates elements of road safety, emphasizing the need for winter road maintenance not only to improve infrastructure but also to reduce accident risks. This study revealed that the proposed methodology, which was validated through a Lithuanian case study in the Raseiniai Northern District, effectively assesses winter road maintenance levels by combining road safety factors with a multi-criteria evaluation. This thorough approach not only increases road safety but also improves traffic flow, showcasing its potential for wider application in national road networks. Full article

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