Future Transp., Volume 5, Issue 2 (June 2025) – 37 articles

Transit deserts, defined by insufficient public transit provision relative to demand, aggravate socio-economic inequalities by restricting access to employment, education, and healthcare. With increasing urbanization and growing disparities in public transport accessibility, identifying transit deserts is critical for equitable mobility planning. As urban populations expand, addressing transit accessibility requires advanced data-driven approaches. This study applies machine learning (ML) models, decision trees (DTs), logistic regression (LR), and random forest (RF), within an Intelligent Transport System (ITS) framework to detect transit deserts in Lucknow, India. Employing a 100 × 100 m spatial grid data, the models classify transit accessibility based on economic status, trip frequency, population density, and service access. The results indicate that RF achieves superior classification accuracy, while DT offers interpretability with slightly lower recall. LR underperforms due to its linear assumptions. The findings reveal the spatial clustering of transit deserts in socio-economically disadvantaged areas, highlighting the need for targeted interventions. This study advances ML-driven ITS analytics, offering a novel approach for classifying transit accessibility patterns at a granular level, thereby aiding policy interventions for improved urban mobility. Full article

Maritime trade in Bangladesh is growing significantly, as observed by UNCTAD, which reported 3.20 mTEUs throughput in 2022. Additionally, the principal seaport, Chattogram Port, reported a port throughput of 3.27 mTEUs in 2024, the historical record for any port in Bangladesh. More than 50% of imports and exports, including empty containers, were handled in 2024 through 19 nos close dry ports in Chattogram City by applying small-scale intermodal systems, where the performance of pure intermodal from/to mid-range dry ports (3 Nos) to Chattogram Port is 2.53%. By 2030, the government wants all import and export operations to be conducted through dry ports. Furthermore, the current volume of international goods freight cannot be handled by the dry ports that are currently in place. This research applied mixed methods to explore the opportunities to set more dry ports and the application of intermodal systems for increasing the seaport’s efficiency, productivity, and competitiveness. The Focus Group Discussion (FGD) method was used to know the dry port location, investment, and policy in creating the opportunity to set up more dry ports in Bangladesh. In the findings, 82.50% of participants agreed that existing facilities are not enough and need to establish more dry ports to handle current and future volumes of containers. Moreover, the responses reveal a division of opinion on establishing a dry port outside of Chattogram, with a notable inclination towards opposition. According to 62% of respondents, dry ports outside Chattogram are necessary. To enhance intermodal connectivity and facilitate easier cargo transfers between ports and hinterland regions, integrated infrastructure development would be in line with national economic objectives. The research aims to investigate the possibilities for establishing additional dry ports across the country to boost seaport productivity, efficiency, and competitiveness by utilizing intermodal freight transportation systems to cut costs and time while also considering environmental factors like CO₂ emissions. Full article

The extension of high-speed rail (HSR) lines around the world is increasing. The largest network today is in China, followed by Spain, Japan, France, and Italy; currently, new lines are being built in Morocco and Saudi Arabia. The goal of the new lines built is to drastically reduce the time distances between the extreme railway terminals by intervening on the two main components of time: space and speed. The two components have been investigated in various fields of engineering for design conditions (ex ante/a priori). In the literature, there is no analysis of what happened in the realization of the projects (ex post/retrospective). The research problem that arises is to analyze the high-speed lines built in order to verify, given a pair of extreme terminals, how much the length is reduced by passing from a conventional line to a high-speed line, and to verify how this length is getting closer and closer to the distance as the crow flies. The reduction of spatial distance produces direct connections between two territories, making the railway system (HSR) more competitive compared to other transport alternatives (e.g., air travel). To address the problem posed, information and data are collected on European HSR lines, which constitute a sufficiently homogeneous set in terms of railway and structural standards. The planimetric characteristics of specially built lines such as HSR are examined. A test method is proposed, consisting of a model that is useful to compare the length along the HSR line, with direct lengths, and existing conventional lines. The results obtained from the elaborations offer a first answer to the problem posed, demonstrating that in the HSR lines realized the spatial distances approach the distance as the crow flies between the cities located at the extremes, and are always shorter than the lengths of conventional lines. The final indications that can be drawn concern the possibility of using the results obtained as a reference for decision-makers and planners involved in the transport planning process at national and international level. Future research directions should study the values of the indicators in other large HSR networks, such as those built in Asia, and more generally study all the elements of the lines specially built to allow better sustainable planning, reducing the negative elements found and increasing the positive ones. Full article

Nowadays, recycling in pavement engineering is not a novelty. Utilization of recycled aggregates and other waste materials for the asphalt layers appeared as a well-established approach during the last decades, at least at a research level, in favor of preservation of natural resources, economical balance in road construction and reconstruction, and overall pavement sustainability. The focus on the asphalt layers does make sense based on the fact that these layers are to be more frequently replaced in the framework of periodical pavement maintenance or rehabilitation. Taking as a fact that mainly laboratory-scale studies and limited field trials have already proven the performance-based viability of using alternative materials in the asphalt layers, including waste plastic, waste glass, steel slag, waste tires in the form of rubber, reclaimed asphalt pavement (RAP), etc., this study tries to identify additional critical aspects and reasons why recycled materials are not consistently selected and uniformly applied during construction and reconstruction activities in real practice. A comprehensive discussion for interdisciplinary issues is provided with respect to (i) the challenge of comparing the performance of asphalt mixtures containing recycling materials with a reference condition status, related to mechanical testing, (ii) the aspect of recycled material availability versus peculiar conditions applied to some countries, related to socioeconomical issues, (iii) the unawareness of the actual lifecycle assessment of pavement structures with recycled mixtures, related to environmental assessment, and (iv) some legislative and health issues that could make pavement engineers reluctant to extensively use non-conventional materials. After a multi-parametric discussion, some useful remarks for fostering further research are given together with the ambition to bridge the gap between research and practice toward a greener future in pavement engineering. Full article

This paper analyses the evolution of the prices of intercity high-speed rail (HSR) services operated by the Spanish public company, Renfe, on the Madrid–Barcelona route between April 2019 and June 2022. This period marks one of the most important events in the recent history of railways in Spain: the end of Renfe’s monopoly and the opening of this and other corridors to private competitors. Our main objective is to study the impact of the entry of the first competitor, Ouigo, on the incumbent’s pricing strategy. Our analysis confirmed that Renfe perfectly anticipated Ouigo’s entry and adjusted its prices about four months before the market opened. Interestingly, the incumbent also modified its tariff structure in advance according to the target customer’s willingness to pay. These results may be of interest for the forthcoming liberalisation of other intercity corridors. Full article

Electrifying urban last-mile logistics is an important step towards reducing carbon emissions which requires replacing conventional vehicles with low-carbon alternatives that offer comparable operational and cost characteristics. This study presents a methodology for evaluating the feasibility of electrifying an urban delivery fleet, using data from a major beverage company in Seville as a case study. Applying a fleet and route optimization algorithm for various vehicle combinations, we demonstrate that emerging electric vehicle options, combined with a redesigned fleet mix and an optimized routing, can already enable cost-efficient electrification of distribution activities in the city centre. Furthermore, our analysis suggests that full electrification of the company’s local distribution network may be possible by 2030, depending on the availability of larger electric trucks. Our results show that currently available electric vehicles can fully substitute conventional options in the case study context, with higher capital costs offset by lower energy costs in most cases. The electrification of urban logistics can yield significant environmental benefits, particularly if powered by a clean energy mix. Full article

“Bikeability” is a measure of a city’s suitability for a bicycle-based lifestyle. Cities are striving to increase the number of cyclists in their traffic to decrease congestion and increase sustainability. Bikeability is therefore a relevant metric to measure a city’s progress towards this goal. This study is an application of a previously developed programmatic bikeability model. It is used to calculate bikeability for eight different cities in order to compare their bikeability indices. It was found that the bikeability between different cities is more similar than their modal share would suggest. This correlates more strongly with different metrics for measuring city infrastructure quality than with existing studies regarding bikeability. For this reason, this bikeability model is not suited as a replacement for existing indices but has to be evaluated separately. This revealed a disparity between the availability of urban infrastructure, the level of satisfaction with said infrastructure and its statistical use. Possible solutions and options to further develop the model were discussed. Full article

Background: The forestry industry plays an important role in the economy and environmental sustainability, facing significant logistical challenges such as the geographical dispersion of plantations, the variability of raw materials, and high transportation costs. Artificial Intelligence (AI) emerges as a promising tool to optimize logistics processes, contributing to the reduction in costs, waste, and environmental impacts. Methods: This study combines a literature review and case analysis to assess the impact of AI on forestry logistics. Machine Learning algorithms, optimization systems, and monitoring tools based on the Internet of Things (IoT) and computer vision were analyzed to assess impacts in areas such as transportation planning, inventory management, and forest monitoring. Results: The results demonstrated that optimization algorithms reduced transportation costs and carbon emissions. Predictive tools proved to be effective in inventory management, while real-time monitoring with drones and sensors allowed for the identification and mitigation of environmental risks, such as pests and fires, promoting greater operational efficiency. Conclusions: AI has great potential to transform forestry logistics, improving efficiency and sustainability. However, its implementation faces barriers such as high upfront costs and limitations in data collection, and strategic collaborations are needed to maximize its impact. Full article

Growing populations and environmental issues are a burden for urban transport systems. Current research fails to offer multimodal integrated solutions maximizing time, cost, emissions, and satisfaction. We introduce the first optimization model integrating carpooling with micro-mobility for multi-leg routing in dynamic urban conditions (peak, weather, accidents). In synthetically generated data calibrated with real-world trends, our framework performs up to 25% shorter travel times, 30% reduced peak-hour emissions, and sub-second computation for 40-node networks over single-mode baselines. The model’s scenario-aware flexibility and policy-controllable weights (${\lambda }_1$ to ${\lambda }_4$) offer planners a scalable solution for sustainable mobility. The paper’s primary contribution is its integrated optimization framework integrating carpooling, micro-mobility, and multi-leg routing in dynamic urban conditions, an absent component in prior single-mode or static models. Our scenario-based analysis demonstrates up to 30% travel time and emissions reduction over stand-alone mobility solutions. Full article

This paper shows that using renewable energy sources in the power supply of transportation infrastructure is gradually becoming a new trend. Renewable energy systems are already valuable for railway and automotive infrastructure in various countries; however, this use is limited. This paper examines the improvement of control in a grid-connected, hybrid renewable energy system to meet the needs of a railway transportation infrastructure object by utilizing an additional diesel generator in autonomous mode. The aim is to reduce the depth of battery discharge and limit energy consumption from the grid during peak demand hours, considering the wide fluctuations in power consumption of the object and deviations in renewable energy generation relative to the forecast. Additionally, the task of ensuring long-term autonomous operation of the system is addressed. A control system is proposed based on the deviation of the battery’s state of charge relative to a set schedule, which is determined according to the forecast using an additional variable that sets the power consumption limit. This ensures the minimum possible depth of discharge and peak consumption, taking into account the generation of renewable energy sources, with a power-increase factor ranging from 1 to 1.5 relative to the calculated value. In autonomous mode, the task of minimizing energy consumption by the diesel generator is addressed. Solutions have been developed to implement control in grid and autonomous modes with the corresponding calculation algorithm. The system is not sensitive to the load schedule, and the battery’s depth of discharge limitations are maintained even when renewable energy generation is below the forecast by up to 20%. When generating renewable energy sources below the average monthly value in summer, it is possible to maintain a DoD of no less than 60%. Full article

Road transport systems (RTS) play an essential role in global supply chains, facilitating the efficient transport of goods and services over long distances and thus supporting economic activity on a worldwide scale. However, these systems face numerous challenges, particularly regarding safety, cost, and route optimization, requiring innovative and practical solutions to improve their overall performance. This paper proposes an in-depth analysis of RTS features forming a detailed dataset collected on the route between Dakhla (Morocco) and Paris (France). The study relies on applying advanced mathematical modeling techniques and analyzing several datasets to train various machine learning algorithms. The main objective is to identify optimized routes, combining high safety standards, reduced costs, and shorter transport times. The results show that the adopted approach results in safer and more efficient routes and complies with operational and regulatory constraints. Furthermore, this analysis highlights the importance of data quality and the integration of advanced technologies to deliver an intelligent route optimization system with significant reductions in cost and time. Finally, our results reveal that neural networks outperform other algorithms in this field, proving their superior effectiveness for this specific application. Full article

This study proposes a methodology that integrates real-time data and predictive modeling to identify the passenger flow and occupancy levels within a multimodal transport hub. This tool enables the implementation of control and planning strategies to ensure a high Level of Service (LOS). The tool is based on a Long Short-Term Memory (LSTM) model and heterogeneous data sources, including an Automatic Passenger Counting (APC) system, which are utilized to estimate the real-time passenger flow and area occupancy. The Module A of the Moncloa Interchange in Madrid is the case study, and the results reveal that transport-dedicated zones have higher occupancy levels. Methodologically, time series data were standardized to a uniform frequency to ensure consistency, and the training set consisted of seven months of available data. The model performs better in high-occupancy zones. Despite maintaining a LOS A, some periods experience temporary congestion. These findings indicate that the variations in occupancy levels influence the service quality and highlight the essential role of dynamic interchange management. Tailored operational strategies can optimize the service levels and improve the user experience by anticipating congestion through predictive modeling. This can help enhance public transport’s attractiveness, minimize the perceived transfer penalties, make transfers more efficient, and reinforce transport hubs’ role in sustainable urban mobility. Full article

Recent advancements in aviation and automation technologies have catalyzed the emergence of Urban Air Mobility (UAM), an innovative transportation paradigm involving the use of automated vertical take-off and landing aircraft for intra-city passenger travel. Despite growing global interest, the development and application of integrated geospatial frameworks for UAM infrastructure planning—particularly vertiport siting—remain limited. Thus, this study proposes a three-stage geospatial analysis framework, which consists of (1) Suitability analysis, employing multi-criteria decision-making techniques; (2) Regulation analysis, which screens out parcels restricted by aviation safety standards, land-use policies, and other statutory constraints; and (3) Location-allocation analysis, which spatially optimizes vertiport distribution in accordance with urban master plans and strategic transport priorities. Then, this framework is empirically applied to two South Korean UAM pilot sites—Busan and Jeju. The findings reveal that high-suitability areas are predominantly concentrated in dense urban cores with strong multimodal connectivity and mixed land-use configurations. However, a significant proportion of these zones are rendered infeasible due to regulatory exclusions, such as military flight paths and restricted airspace. Additionally, areas with lower suitability—often home to marginalized populations—raise critical equity concerns. This study contributes to the advancement of urban geospatial analytics by presenting a replicable methodological framework for vertiport site selection, while offering strategic insights to inform early-stage UAM deployment initiatives. Full article

This paper presents a calibration technique for the Intelligent Driver Model (IDM), a car-following model that considers the physical interpretation of each parameter. Using an instrumented vehicle, trajectory data were gathered for a group of Portuguese drivers. The data included various basic scenarios, such as unrestricted acceleration and deceleration maneuvers, as well as following other cars in steady-state conditions. The calibration process involved two steps. In the first step, specific parameters that have clear physical interpretations were manually adjusted to accurately reproduce the speed patterns of basic driving scenarios. In the second step, the obtained results were used to establish the limits of values for a simultaneous calibration procedure. The results demonstrate that the calibration procedure enables precise replication of the actual trajectories. Nevertheless, the model validation results indicate that calibrating without limitations on the parameter search space produces estimates with greater explanatory capability, contradicting previous research and supporting the need for additional analyses. Full article

Vehicle ride-sharing commute in smart cities is a service that has changed the way of citizens’ daily life and transportation schedule. Research in vehicle ride sharing aims to provide passengers with a comfortable living and well-being within the city. Ride sharing has a significant role in vehicle transportation services provided to passengers during their daily schedule from a certain origin to a desired destination within smart cities. Combining ride sharing with spatial, temporal, and social context has an impact on passenger satisfaction. In this paper, a vehicle ride-sharing algorithm is introduced, which incorporates certain spatial, temporal, and social behavior context restrictions that are able to provide a satisfactory routing trajectory that serves the daily needs of passengers in the smart city of Athens, Greece. Real data sources were exploited to evaluate certain spatial, temporal, and social matching distance functions, which define specific spatial, temporal, and social matching similarity thresholds of passengers’ social mobility behavior. The proposed algorithm is evaluated experimentally with real data based on specific evaluation metrics assessing its efficiency with regards to certain spatial, temporal, social, capacity, and satisfaction contexts. The evaluation process has an impact on the adoption of the proposed algorithm in vehicle ride-sharing commute in smart cities. Full article

This study analyzes the use of new technologies, such as building information modeling (BIM), to simplify process management in the Architecture, Engineering, and Construction sector. By providing dynamic, realistic system models, BIM enables innovative collaboration within a common data environment. In that context, this study focuses on the operational management of construction sites. Recognizing the limitations of BIM for spatial analyses, this research integrates geographic information systems (GIS) through a three-phase methodology: (a) development of BIM models; (b) definition of geographic information within the GIS; (c) BIM-GIS integration. To demonstrate the effectiveness of the methodology, the research applies it to a case study on managing an airport construction site, focusing on obstacles and hazards for air navigation. However, the methodology identified in this study is generalizable and could be adapted for other applications with proper calibration of the required information parameters. The application highlights the potential of integrating GIS and BIM systems, particularly for safety and operational management. However, inherent structural differences between the two systems often require model simplifications, especially in complex projects. The study also reveals that GIS-BIM interoperability is not always seamless, emphasizing the need for further research to enhance integration processes and improve system compatibility in future applications. Full article

Background: The transition to low-carbon economies has become a global priority, particularly in sectors with high greenhouse gas emissions, such as maritime transport. Renewable fuels, especially methanol, have emerged as promising alternatives to conventional fossil fuels due to their potential to reduce carbon footprints and contribute to sustainable logistics systems. Methods: This study employs a combined qualitative and quantitative approach to assess the impact of renewable fuel production on maritime transport decarbonization. The analysis integrates economic feasibility, energy efficiency, and environmental benefits, providing a comprehensive evaluation of methanol’s role in reducing emissions. Results: Findings indicate that methanol offers significant potential for the decarbonization of maritime transport. Its relatively low production costs and high energy density position it as a viable alternative to traditional fuels. Additionally, the study highlights the substantial reduction in greenhouse gas emissions that methanol adoption could achieve, reinforcing its role in mitigating climate change effects. Conclusions: The study concludes that integrating methanol as a primary fuel in maritime transport can accelerate the sector’s decarbonization. However, successful implementation depends on supportive policy regulations and further research to optimize production and supply chain integration. The findings emphasize the strategic importance of renewable fuels in developing sustainable and resilient logistics systems. Full article

Travel time is one of the most important pieces of information that the Dynamic Message Sign (DMS) provides to drivers. However, several variables, including traffic-related factors and DMS message characteristics, might impact the accuracy of the travel time when the DMS is used to display the travel time for alternate routes. Therefore, this study aims to look at the variables that affect the route choice’s displayed travel time accuracy as it relates to the individual driver. The accuracy of the travel time displayed on a DMS on I-75 in Saginaw, Michigan, was examined using logistic regression analysis. The results suggest that for effective traffic management for high traffic demand times (peak hour and day of the week), avoiding using travel time information displayed with other types of messages at the same time (phasing) and adapting a message update time between 2 to 3 min can improve the DMS travel time information accuracy. Practitioners and planners can use the findings to improve driver compliance with the DMS message that is being displayed. Full article

This work explores the classification of driving behaviors using a hybrid deep learning model that combines Convolutional Neural Networks (CNNs) with Long Short-Term Memory (LSTM) networks (ConvLSTM). Sensor data are collected from a smartphone application and undergo a preprocessing pipeline, including data normalization, labeling, and feature extraction, to enhance the model’s performance. By capturing temporal and spatial dependencies within driving patterns, the proposed ConvLSTM model effectively differentiates between normal and aggressive driving behaviors. The model is trained and evaluated against traditional stacked LSTM and Bidirectional LSTM (BiLSTM) architectures, demonstrating superior accuracy and robustness. Experimental results confirm that the preprocessing techniques improve classification performance, ensuring high reliability in driving behavior recognition. The novelty of this work lies in a simple data preprocessing methodology combined with the specific application scenario. By enhancing data quality before feeding it into the AI model, we improve classification accuracy and robustness. The proposed framework not only optimizes model performance but also demonstrates practical feasibility, making it a strong candidate for real-world deployment. Full article

This study quantified the effects of seven proposed traffic management strategies (MS) to leverage the synergy between Active Traffic Management (ATM) and connected and automated vehicles (CAV) to mitigate congestion, reduce queue lengths, and improve travel time after incident occurrence. First, three proposed MS are discussed: (a) controlling speed limit but not restricting lane changes, (b) directing CAV to change lanes earlier, and (c) restricting CAV in open lanes from lane changes near incidents. Then, combinations of these strategies are presented. At 10% CAV MP, MS1 that focuses on longitudinal control reduced travel time by 11.6% compared to 1.9% with no MS. Similarly, MS2, which directs CAV to change lanes earlier, were most effective when applied at 1-mile upstream of the incident site, achieving a notable 6.0% travel time reduction compared to 1.9% with no MS. The beneficial impact of MS3, which restricts CAV in open lanes from making lane changes near incident sites, became more pronounced with increasing CAV MP. Among the combined strategies (MS4 to MS7), some strategies proved more effective than others. Findings from Vissim simulation runs showed the importance of combining CAV and MS. Full article

The performance of urban transport is a critical aspect of a city’s functionality, which needs to be supported by innovative data sources to analyze travel patterns. This study explores the use of Google Location History (GLH) as a participatory geographic information system for mobility surveys, offering a cost-effective and more detailed alternative to traditional approaches. GLH is a novel data source with high potential, but still underutilized and underresearched, especially in developing countries. This study uses a new approach in GLH data collection and data processing. Data were collected from 420 respondents in Medan, Binjai, and Deli Serdang (Mebidang) in Indonesia, to examine urban travel patterns, including trip distances, modes, and purposes, while addressing issues of data accuracy, privacy, and representation. GLH provides granular insights into mobility, reducing biases associated with self-reported surveys and identifying discrepancies between stated and actual transport usage. The findings highlight GLH’s potential for understanding spatial mobility patterns linked to demographic characteristics and travel purpose in more detail. However, technical challenges, such as data anomalies and the reliance on two devices for data collection, underscore the need to improve location readings and develop add-on tools capable of direct data export for large-scale mobility surveys. This study advances the application of GLH in mobility research, demonstrating its potential use and challenges for large-scale mobility surveys. Future research should address privacy concerns and optimize data collection to enable more inclusive and sustainable urban mobility strategies. Full article

In recent years, interest in drone-based logistics has grown due to the increasing demand for efficient and sustainable package transportation, driven by the expansion of e-commerce and rising environmental awareness. In this study, we focus on flight scheduling for the efficient transportation of packages between logistics bases, rather than on last-mile delivery. In scenarios where the number of packages handled at each base varies, efficient transportation can be achieved by having drones visit high-demand bases more frequently. To this end, we consider a system with two types of drones: local drones that visit all bases, and express drones that visit only selected high-demand bases. We formulate this problem as a mixed integer linear programming (MILP) model that minimizes the total transportation time. This model simultaneously determines which bases should be visited frequently and computes flight schedules that enable efficient package delivery. Unlike existing transportation models that assume fixed linear routes, our model allows for flexible routing, including direct flights and loop-based paths between bases. To ensure scalability, we also propose an approximation method that significantly reduces the computational cost. As the number of logistics bases increases, the exact solution of the MILP becomes intractable. Therefore, we pre-select candidate high-demand bases based on package volume and spatial layout, thereby reducing the number of decision variables. This makes it possible to compute high-quality solutions even in large-scale environments. Through numerical experiments, we show the effectiveness of our proposed methods for the transportation of packages between logistics bases. Full article

The transportation sector is encountering escalating issues associated with technical progress, environmental laws, economic upheavals, and deficiencies in personnel capabilities. Although prior research has examined these concerns separately, there is an absence of thorough studies investigating the interaction between these elements and their influence on the long-term sustainability of the transportation industry. This study aims to address this research vacuum by providing a comprehensive examination of the impact of technical breakthroughs, environmental requirements, supply chain obstacles, and worker skills on the advancement and sustainability of the transport sector. This research employs expert assessment and regression analysis to investigate data from important stakeholders in the transport industry, aiming to elucidate the interrelationships among these four parameters. The results demonstrate a robust association between technical progress and environmental restrictions, while also underscoring considerable economic challenges, especially supply chain disruptions that impede the adoption of new technology. The lack of trained labour is seen as a significant element that intensifies these issues. This study enhances the current literature by providing a comprehensive view of the elements influencing the sustainability of the transport industry. The report offers actionable suggestions for industry stakeholders, including measures to improve staff training, streamline supply chain management, and more efficiently incorporate new technology. These results have considerable significance for governments and transport corporations aiming to secure the industry’s long-term sustainability. Full article

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