Future Transportation

The logistics sector plays a critical role in global trade but faces significant risks due to geopolitical instability, economic downturns, and environmental disruptions. This study investigates risk and crisis management strategies within the logistics industry by integrating qualitative expert interviews with quantitative analysis using the Analytic Hierarchy Process (AHP). It identifies key risks, such as supply chain disruptions, fluctuating market conditions, and infrastructure challenges, and assesses the most effective mitigation strategies. Findings indicate that diversifying transport routes and implementing business continuity planning are the most critical strategies, while technological advancements, including artificial intelligence and predictive analytics, significantly enhance resilience. Collaboration among logistics companies, suppliers, and policymakers is essential for effective crisis management. The AHP analysis ranks crisis management strategies, providing a practical framework for logistics firms to improve risk preparedness. This study contributes to the field by offering actionable recommendations to enhance crisis response and long-term sustainability. The results underscore the necessity of adaptive and proactive risk management approaches in an increasingly volatile global logistics landscape. Full article

The digital transformation of the railway industry is necessary for addressing growing challenges and advancing its sustainable development. Digital technologies include Automatic Train Operation (ATO) and Remote Train Control (RTC), which offer opportunities to potentially optimize operations and enhance safety. Both technologies, however, could pose significant challenges that need to be addressed in order to capture the anticipated benefits in an urban public street environment. This study thus bridges the gap between theory and practice by exploring the projected benefits and challenges of implementing RTC and ATO through a case study of a European public transport operator deploying these technologies in tramway operations. Employing a case study methodology, the research draws on 44 semi-structured interviews with stakeholders from the operator and its supplier. The findings highlight significant anticipated benefits, including increased productivity, improved safety, and enhanced sustainability. Yet, prospective challenges such as regulatory hurdles, technical complexities, and organizational changes pose barriers to implementation. Key obstacles include ensuring robust connectivity, addressing cybersecurity concerns, and managing workforce transitions. This study underscores the importance of collaborative approaches, stakeholder engagement, and incremental deployment to mitigate risks and maximize the impact of automation technologies. By providing actionable insights into the practical adoption of RTC and ATO, this research supports the development of advanced urban transport systems. Full article

Metropolitan Planning Organizations (MPOs) are required to prepare a Transportation Improvement Plan (TIP) that outlines a fiscal strategy over a four-year period in order to qualify for federal funding. However, the growing population and limited financial resources available often pose significant challenges for transportation agencies in aligning their needs with available budgets. This article examines the project selection criteria used by 20 MPOs in the Southeastern United States to identify the best practices for prioritizing projects in TIPs. Using document analysis, this study categorizes the most commonly used criteria into nine broad groups: safety and security; environmental impacts; mobility, accessibility, and connectivity; preservation; environmental justice; equity; economic factors; alignment with other plans; and local support. Many of these categories are further divided into subcategories and metrics. Despite variations in criteria, weighting, scoring, and methodologies across these MPOs, the study identifies several shared factors that support effective decision-making in regional transportation planning. These findings can help transportation planners and policymakers refine their project prioritization strategies, promote consistency, and lead to improved decision-making frameworks for future TIP development. Full article

Urban traffic congestion poses significant challenges to sustainable development in rapidly growing cities. This study examines the spatiotemporal dynamics of traffic congestion in Enugu, Nigeria, a representative mid-sized sub-Saharan city, through a comprehensive analysis of volumetric traffic flows along three major distributors: Abakpa, Nike, and Trans-Ekulu Road. The research employed direct observation and vehicle counts, conducting a week-long traffic census during peak morning (7:30–9:30 AM) and evening (4:00–8:00 PM) periods. Data was analyzed using peak hour factor (PHF), mean plots, and chi-square tests. The results reveal a daily mean of 2334 vehicles/h. Abakpa/Nike Road demonstrated the highest traffic volumes (mean = 809.2 vehicles/h) and most concentrated peak flows (PHF = 0.79), while Trans-Ekulu Road exhibited lower, more uniformly distributed volumes (mean = 719.4 vehicles/h, PHF = 0.93). Evening peaks (6:00–8:00 PM) consistently surpassed morning volumes, with Abakpa/Nike Road reaching 974 vehicles/hour during the evening rush compared to 620 vehicles/hour in the mornings. Chi-square analysis (χ² = 55.5, df = 8) confirmed statistically significant differences in flow distribution among the routes. The complete absence of Monday traffic due to regional “sit-at-home” orders created a distinctive weekly pattern, with Tuesdays experiencing disproportionate congestion as the de facto first workday. Non-linear relationships between volume increases and congestion severity were observed, where modest volume changes produced amplified system-wide effects. Spatial analysis revealed that evening congestion disparities between distributors (14.9%) significantly exceeded morning differences (8.9%), indicating uneven network utilization. These findings illuminate how socio-political factors, activity patterns, and complex network dynamics shape urban mobility in rapidly developing contexts. This study offers empirical evidence supporting targeted interventions, including Tuesday-specific traffic management, evening-focused congestion mitigation strategies, and corridor-specific infrastructure improvements to enhance mobility in this representative mid-sized sub-Saharan city. Full article

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