Future Transportation

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

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

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

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

Connected and automated vehicles (CAV) are growing in popularity and could have potential implications on the transportation system. The effects of CAVs have yet to be fully realized because of the newness of the technology. Anticipated effects include increased capacity, faster travel time, improved level of service, increased safety, and overall effectiveness of the transportation system. The Highway Capacity Manual (HCM) published by the Transportation Research Board of the National Academies has incorporated some of these impacts by developing capacity adjustment factors (CAFs) for various scenarios for freeway segments, signalized intersections, and roundabouts. This study builds upon the HCM study of signalized intersections by analyzing the effect CAVs have on a coordinated signalized corridor. Using PTV VISTRO and PTV VISSIM software a seven-intersection corridor along Eagle Road in Boise/Meridian, Idaho was modeled and analyzed with increasing penetration rates of CAVs. Approach delay, queue length, level of service, and travel time along the corridor were studied as CAV penetration rates increased. It was found that approach delay, queue length, and level of service (LOS) improved as the number of CAVs increased. As CAVs increased from 0% to 100%, the LOS increased from an E to an A at small intersections and from a D or F to C at large intersections. The travel time from one end of the corridor to the other decreased. Full article

Motorcycles are a prominent contributor to most fatalities arising from traffic incidents, primarily due to drivers’ failure to adhere to traffic laws. Notably, differences in traffic violation frequency between urban and rural motorcyclists can be ascribed to variations in law enforcement practices and security budget allocations between these areas. This study aims to identify the key determinants influencing the frequency of traffic violations across these distinct geographical regions. The investigation incorporates independent variables such as personal demographics (including gender and age), driving experience, and attitudes toward traffic regulations. The analysis involved the formulation and examination of two separate logit models, each corresponding to urban and non-urban characteristics. The outcomes of a transferability test highlighted distinct disparities between the two models, with the rural model demonstrating a higher number of significant variables. In both models, certain variables consistently influenced the frequency of traffic violations. Lower violation frequencies were associated with factors such as specific age ranges, frequency of driving, and possession of a driver’s license. The insights derived from this study were leveraged to formulate policy recommendations to curb traffic violations among motorcyclists, contributing to enhancing overall traffic safety. Full article

Traffic accidents have become a pressing global public health concern, contributing to millions of deaths and injuries each year. Similar to many countries, the Kingdom of Saudi Arabia is facing significant challenges to overcome the burden of traffic-related injuries and fatalities, prompting the need for effective intervention measures. With the latest advances in sensor fusions, detection, and communication technologies, Automated Traffic Enforcement Systems (ATES) have gained widespread popularity as a solution to improve road safety by ensuring compliance with traffic laws. The objective of this study is to review the effectiveness of ATES in reducing traffic accidents and improving road safety and to identify the challenges and prospects it faced during its implementation. This review uses a detailed overview of different types of ATES deployment, including speed cameras, red-light cameras, and mobile enforcement units, and a comparison between global case studies and local research findings, with special emphasis on the context of Saudi Arabia. This study uses a systematic literature review methodology, using the PRISMA 2020 Protocol, and conducts a scientific literature database search using specific keywords. This study finds that ATES has emerged as an effective tool to ensure traffic compliance and improve overall traffic safety and that various ATES devices have been profoundly effective in reducing traffic crashes. This review concludes that ATES can be an effective solution to improve road safety, but ongoing evaluations and adjustments are necessary to address public perceptions and ensure equitable enforcement. Full article

The European Union (EU) is paving the way toward “Vision Zero”, a future goal of eliminating road fatalities and severe injuries. Railway level crossings are critical safety hotspots where road and rail traffic intersect and present a unique challenge in balancing the safety of both rail and road users while ensuring efficient traffic flow. Collisions at these crossings account for a significant proportion of railway-related fatalities in the EU, underscoring the need for targeted safety interventions. This article explores the impact of signal preemption strategies on the safety and operational performance of railway level crossings through a microsimulation analysis. Using VISSIM, a railway level crossing and its adjacent road intersection were modeled under existing and alternative scenarios. The preemption strategy was designed to clear vehicles from the crossing area before train arrivals, reducing conflict risks and optimizing traffic flow. Key findings reveal that the proposed preemption strategy significantly reduces queue lengths within critical safety zones, mitigating vehicle spillback and enhancing operational efficiency. The analysis highlights the importance of integrating railway operations with traffic signal systems, particularly in urban areas with limited queue storage capacity. Full article

Road Weather Information Systems (RWISs) are essential components of modern Intelligent Transportation Systems (ITSs) deployed in cold regions to gather real-time data on winter weather and road surface conditions. Despite their benefits, the high cost associated with RWIS installations demands optimized placement strategies to maximize their utility and cost-effectiveness. Geostatistics-based RWIS location-allocation methods, particularly those involving semivariogram modeling to quantify underlying spatial characteristics, have gained international recognition. However, new locations require unique semivariogram models, a process that is time-consuming and constrained by the availability of comprehensive datasets, often rendering location analysis challenging or infeasible. Addressing these limitations, this study introduces an innovative approach using Wasserstein Distance (WD) to link semivariograms across different datasets. This method streamlines optimization by eliminating the need for repetitive semivariogram modeling in new study areas. Our findings demonstrate that WD-matched models replicate the location choices of original models with a high degree of similarity while ensuring that clean-slate locations remain proximate to those of original models, enhancing geographic equity in RWIS deployment. This validates the practicality of reusing developed semivariogram parameters for WD-matched highways, significantly reducing the need for new geostatistical analyses and enhancing the framework’s applicability and accessibility for RWIS deployment across diverse geographic regions. Full article

Among urban public transport systems, light rail, mass transit, and tram systems offer sustainable travel options. However, many of these systems, particularly in developed countries, fail to meet user needs and the expectations of transport authorities. Increasing the demand for urban rail systems as an alternative to private cars is essential for achieving net zero targets and Sustainable Development Goals. This study investigates the factors influencing urban rail demand using qualitative data analysis, with a focus on thematic analysis. A systematic review of 53 studies from the UK, Europe, and worldwide, including journal articles and transport research reports, was conducted and coded using NVivo Version 15 software. Six main categories emerged: land use and accessibility, service quality, user benefits, governance, sustainability aspects, and user-focused elements. These categories, along with their themes and sub-themes, were analysed using cross-tabulations to compare attributes across domains. The key findings indicate that accessibility and intermodal connectivity are crucial for encouraging urban rail use, while ticketing, station facilities, walkability, travel costs, ventilation, and security also moderately influence user preferences. This study provides essential guidelines for policymakers and transport providers to improve urban rail systems and informed the development of a questionnaire to explore the interrelationships of these factors, discussed in a forthcoming paper. Full article

Efficient toll processing is critical for mitigating traffic congestion and enhancing transportation network efficiency at toll stations. This study explores the Neelamangala Toll Plaza on India’s National Highway 48, employing artificial intelligence (AI) to optimize toll operations. The research integrates a Supervised Learning (SL) time series model for traffic prediction and a Reinforcement Learning (RL) framework based on a Markov Decision Process (MDP), coupled with a randomized algorithm for equitable task distribution. These AI-driven models dynamically adapt to real-time traffic conditions, preventing peak-hour system overload. Key performance metrics—Average Processing Time (APT), Queue Length Reduction (QLR), and Throughput (TP) were used to evaluate the system. Research also demonstrates the model’s superior performance in handling high traffic volumes and reducing congestion. The study underscores the potential of integrating AI and randomized algorithms in modern toll management, offering a scalable and adaptive solution for sustainable transportation infrastructure. Full article

The use of traffic simulation to analyze traffic safety and performance has become common in transportation engineering. Microsimulation methods are increasingly used to analyze driving performance for different road geometries and environmental elements. Drivers’ perception has an important impact on driving performance factors contributing to traffic safety on transportation facilities (highways, arterials, intersections, etc.). Impaired vision leads to failure in drivers’ perception and making right decisions. Various studies investigated the impact of environmental elements (fog, rain, snow, etc.) on driving performance. However, there is limited research examining the potentially detrimental effects on driving capabilities due to differing exposure to natural light brightness, in particular sun exposure. Autonomous vehicles (AVs) showed a significant impact enhancing traffic capacity and improving safety margins in car-following models. AVs may also enhance and/or complement human driving under deteriorated driving conditions such as sun glare. This study uses a calibrated traffic simulation and surrogate safety assessment model to improve traffic operations and safety performance under impaired visibility using different types of autonomous vehicles. A combination of visibility reduction, traffic flow characteristics, and autonomy levels of AVs was simulated and assessed in terms of the number of conflicts, severity level, and traffic operations. The simulation analysis results used to reveal the contribution of conflicts to the risk of crashes varied based on the influence of autonomy level on safe driving during sun glare exposure. The outcome of this study indicates the benefits of using different levels of AVs as a solution to driving under vision impairment situations that researchers, traffic engineers, and policy makers can use to enhance traffic operation and road safety in urban areas. Full article

Electric-connected automated vehicle (CAV) shuttles, as a part of the sustainable microtransit system, have the potential to fill public transit service gaps. Following technology and traveler acceptance tests that are underway around the world, mass-produced CAVs will be considered for shared mobility service, including “first/last mile” travel between public transit hub stations and medical campuses or other activity centres. Thus, there is a need for increased knowledge on treating risk in such applications. This paper covers the planning and economic feasibility of an advanced technology level 4 automated vehicle-based microtransit system, considering uncertain service and economic feasibility factors. The methods used are advanced for addressing uncertainties in travel demand, service factors, and the economic feasibility of investments by public and private sector entities. Specifically, a probability-based macro simulation approach is used to treat demand and supply-side service factors as stochastic, and it is adapted for risk analysis in financial decision-making. The effects of uncertain life-cycle costs on fares and the rate-of-return are described. Results are favourable regarding the technical and economic feasibility of advanced technology-based microtransit first/last mile service. The findings reported here are a contribution to knowledge on the feasibility of implementing CAV-based first/last mile, and other microtransit services, under uncertainty. Full article

Autonomous vehicles (AVs) and shared mobility constitute two of the “Three Revolutions” that portend major changes to surface transportation. AVs promise to reduce accidents, expand accessibility, and decrease congestion, while shared mobility provides the benefits of automotive transportation without requiring the purchase of a vehicle or the ability to drive it. Despite great promise to alleviate the negative externalities imposed by transportation, there remains much to be understood about the combined diffusion and impact of AVs and shared mobility. There is little demonstrated experience and application of AVs to the public, and how and where people would use automated shared mobility relative to their current travel is largely unknown. This study advances our understanding by utilizing an intercept survey of 232 respondents in Ann Arbor, Michigan who had made a discretionary trip to one of two central and two suburban locations. The novel approach of using intercept surveys allows us to gather more valid data about the willingness of respondents to replace the mode they just used for either a privately owned or a shared AV and do so for the trip purpose most conducive to using such a vehicle. We incorporate descriptive and spatial analyses and then utilize multinomial logit models to predict the factors influencing the encouragement or discouragement of substituting a private and a shared AV for their previous trip. We found that active mobility and transit trips work in competition with private AVs, while youth encourages interest. Meanwhile, active mobility, increasing age, and one of our measures of density discourage interest, while female respondents and the same measure of density increase interest. The results suggest that future efforts to facilitate the adoption of shared AVs target areas of the city that are relatively dense and residents in these areas where a shared AV would enhance individuals’ mobility. Full article

This study investigates the application of machine learning (ML) to understand and mitigate winter road risks while addressing model interpretability. Using 26,970 winter crash records collected over four years in Edmonton, Canada, we developed and compared three ML-based winter crash frequency models: XGBoost, Random Forest, and LightGBM. To enhance interpretability, we applied SHapley Additive exPlanations (SHAP), providing insights into feature contributions. Our analysis incorporated micro-level variables such as collision records, weather conditions, and road characteristics, as well as macro-level variables such as land use patterns, spatial characteristics (via Hot Spot Analysis), and traffic exposure (estimated using Ordinary Kriging). Among the models tested, XGBoost outperformed others, achieving a testing R² of 92.67%, MAE of 3.64, and RMSE of 5.77. SHAP analyses on XGBoost provided both global and local explanations. At a global level, road type, speed limit, and traffic enforcement cameras were identified as key factors influencing crash frequency while locally, distinct features of high- and low-crash locations were highlighted, supporting targeted risk mitigation strategies. By bridging the gap between model accuracy and interpretability, this study demonstrates the value of interpretable ML models in improving winter road safety, offering actionable insights for informed decision-making and resource allocation in winter road maintenance. Full article

This study identified the main factors affecting car selection decisions through discrete choice experiments based on a large dataset collected in four European countries in 2023 using stated choice questionnaires. The choice set includes six current and popular car powertrains with factors related to vehicle features, user characteristics, and specific geographical contexts, which can influence the adoption of vehicles with electrified powertrains. An easily applicable multinomial logit model was first proposed to explore the effects of selected attributes and the model’s ability to reproduce user preferences with different incentive policies, geographical contexts, and energy prices. A mixed logit model and a segmented multinomial logit model were introduced to consider the sample’s heterogeneity. The first captures the preference dispersion among respondents related to incentives and operational costs. The second, which specifically classifies users based on car market segments, showed a greater variation in factors related to the purchase cost and battery range. The models estimate the weight of nine factors, offering support for targeted policy recommendations. Cost-related factors confirm their relevance in choices, and the analysis shows that users who want to enhance their vehicle range by 1 km are willing to pay approximately EUR 80. Full article

The inherent uncertainty in travel forecasting models—arising from potential and unknown errors in input data, parameter estimation, or model formulation—is receiving increasing attention from both the scholarly and practicing communities. In this research, we investigate the variance in forecasted traffic volumes resulting from varying the mode and destination choice parameters in an advanced trip-based travel demand model. Using Latin hypercube sampling to construct several hundred combinations of parameters across the plausible parameter space, we introduce substantial changes to implied travel impedances and modal utilities, on the order of a 10 percent variation. However, the aggregate effects of these changes on forecasted traffic volumes are small, with a variation of approximately 1 percent on high-volume facilities. It is likely that in this example—and perhaps in others—the network assignment places constraints on the possible volume solutions and limits the practical impacts of parameter uncertainty. Nevertheless, parameter uncertainty may not be the largest contributor to error in practical travel forecasts. Further research should examine the robustness of this finding across other less constrained networks and within activity-based travel model frameworks. Full article

This research investigates the Saudi marine sector’s response to climate change. In particular, it assesses industry stakeholder awareness, attitudes, and actions concerning climate-related challenges. A complete survey was distributed to a varied range of industry participants, including executives, managers, seafarers, and academics, to assess their understanding and involvement. The research indicates moderate levels of awareness and engagement, and significant challenges, including financial limitations, a lack of experience and knowledge, and insufficient regulatory support, to implementing more sustainable practices. The study also mentions ongoing attempts to satisfy International Maritime Organization (IMO) requirements, while present mitigating techniques have limited efficacy. Compared to other regions, Saudi Arabia mostly depends on fossil fuels, which poses specific difficulties in the transformation of sustainable maritime practices. The study identifies current strategies and proposes prospects such as raising financial assistance, and the adoption of innovative technologies. These findings are critical to providing the link between the Saudi marine sector and the climate targets, as well as the Saudi Vision 2030. Full article

The wing-in-ground (WIG) effect occurs when air pressure is created beneath a craft moving close to the ground. The pressure created adds upwards lift, resulting in less need for propulsion for moving forward. Over the years, several companies in various countries have developed wing-in-ground crafts—marine vessels, looking like airplane, that operate using the ground effect. However, no commercial routes are currently in operation using such crafts. This article seeks to identify the critical factors that contribute to the successful commercialization of WIG crafts. The study is composed of a literature review, a company comparison and an analysis of one case study close to successful commercialization. The study indicates that the following actions are critical for the commercial success of a company engaged in WIG operations: engaging community, enhancing R&D, establishing a robust technological system and focusing on safety and compliance. It is also noted that technological readiness itself does not guarantee the successful implementation of WIG crafts on commercial routes. Full article