Future Transp., Volume 5, Issue 4 (December 2025) – 23 articles

Pedestrian crashes remain a leading cause of road traffic fatalities in developing countries (DCs); yet reliable crash data are scarce, constraining the ability to model pedestrian safety risks and evaluate countermeasure effectiveness. This study developed a methodological process for estimating the influence of contextual factors on pedestrian crashes using artificial data. The process integrated literature-derived trend analysis, artificial data generation, external face validity checks, correlation analysis, stepwise negative binomial regression, sensitivity testing, and mapping of results against the International Road Assessment Programme (iRAP) framework. Of the 26 contextual factors considered, 20 were retained in the negative binomial (NB) models, while six were excluded due to weak or inconsistent trend data. Results showed that behavioural and institutional factors, including ad hoc countermeasure implementation, gender composition of pedestrian flows, and vehicle age or technology, exerted stronger influence on crash outcomes than several geometric variables typically emphasised in global models. External validity testing confirmed broad alignment of the artificial dataset with published values, while sensitivity analysis demonstrated the robustness of factor influence values (Fi) across bootstrap resampling and scenario perturbations. The Fi values derived are illustrative rather than decision-ready, reflecting the artificial-data basis of this study. Nonetheless, the findings highlight methodological proof of concept that artificial-data modelling can provide credible and context-sensitive insights in data-scarce environments. Mapping results to the iRAP framework revealed complementarity, with opportunities to extend global models by incorporating behavioural and institutional variables more systematically. The approach provides a replicable pathway for improving pedestrian safety assessment in DCs and informs the development of an enhanced iRAP effectiveness model in subsequent research. Future applications should prioritise empirical calibration with real-world crash datasets and support policymakers in integrating behavioural and institutional factors into countermeasure prioritisation and safety planning. Full article

This paper proposes a method for assessing societal risk along a traffic link by integrating a stochastic formulation of the fundamental diagram. The approach accounts for uncertainty in vehicle speed due to user heterogeneity, vehicle characteristics, and environmental conditions. The risk index is decomposed into occurrence, vulnerability, and exposure components, with the occurrence probability modeled as a function of stochastic speed. The inverse gamma distribution is adopted to represent speed variability, enabling analytical tractability and control over dispersion. Numerical results show that urban and suburban environments exhibit distinct sensitivity to model parameters, particularly the gamma shape parameter η and the composite parameter c = β · v₀ obtained by the product of the occurrence parameter β and the free speed flow v₀. Graphical representations illustrate the impact of uncertainty on risk estimation. The proposed framework enhances existing deterministic methods by incorporating probabilistic elements, offering a foundation for future applications in traffic safety management and infrastructure design. Full article

Engine knock remains a critical limitation in spark-ignition engine design. Future hybrid powertrains employ downsized engines operating on Atkinson cycles, creating different knock conditions compared to modern naturally aspirated or turbocharged engines. At the same time, petroleum-based gasoline is increasingly being replaced by biofuels and electrofuels. This study evaluates knock behavior in projected hybrid engine architectures and examines the chemical composition of emerging fuel blends. The analysis shows that hybrid engines benefit from fuels with lower sensitivity, defined as the difference between the Research and Motor Octane Numbers. This is because the higher end-gas temperatures associated with the Atkinson cycle shift the value of K, which is an interpolation factor used to capture the relationship between fuel sensitivity and anti-knock performance. In conventional engines, K is negative, favoring fuels with higher sensitivity. In hybrid engines, the increased engine temperatures result in K becoming positive, favoring low-sensitivity fuels. Using low-sensitivity fuels allows hybrid engines to operate with higher geometric compression ratios and advanced thermodynamic cycles while reducing knock constraints. Biofuels and electrofuels can meet these requirements by producing paraffinic and naphthenic hydrocarbons with high octane quality and low sensitivity. These findings emphasize the need to align renewable fuel development with hybrid engine requirements to improve thermal efficiency, reduce emissions, and reduce reliance on energy-intensive refinery processes for octane enhancement. Full article

Traditional multi-modal dynamic transit assignment (DTA) models predominantly focus on bus and rail systems, overlooking the role of bike-sharing in passenger flow distribution. To bridge this gap, a multi-modal dynamic transit assignment model incorporating bike-sharing (MMDTA-BS) is proposed. This model integrates bike-sharing, buses, rail services, and walking into a unified framework. Represented by the variational inequality (VI), the MMDTA-BS model is proven to satisfy the multi-modal dynamic transit user equilibrium conditions. To solve the VI formulation, a projection-based approach with dynamic path costing (PA-DPC) is developed. This approach dynamically updates path costs to accelerate convergence. Experiments conducted on real-world networks demonstrate that the PA-DPC approach achieves rapid convergence and outperforms all compared algorithms. The results also reveal that bike-sharing can serve as an effective means for transferring passengers to rail modes and attracting short-haul passengers. Moreover, the model can quantify bike-sharing demand imbalances and offer actionable insights for optimizing bike deployment and urban transit planning. Full article

A sustainability appraisal framework helps to ensure the smooth sailing of various activities in transportation departments. A well-developed and flexible framework can serve as a primary tool for the evaluation of tasks in transportation departments. In this study, a framework for the sustainability appraisal of urban transportation is developed and its implementation is presented as a case study. As the transportation sector is placed within a wider context of sustainable development, the framework is based on a holistic approach considering transportation from a sustainable development perspective. The approach adopted for the implementation of the framework involves all stakeholders, including transportation departments and the community, in planning, decision-making, and bringing opportunities to guide the community and shape collective behaviors. By defining context-specific goals, objectives, inputs, and outcome variables, which inclusively represent sustainable development, the framework will be effectively utilized. The framework will also be useful to guide transportation departments to polish their vision, in addition to making policies, designing methodologies, and implementing measurement and monitoring systems for attaining the desired state of sustainability. Full article

Rainy weather conditions can have significant impact on the severity and frequency of traffic crashes. This study investigated factors that influence the severity of vehicle crashes during rainy weather in California. Data from 23,242 rain-related crashes in California were taken from the Highway Safety Information System (HSIS) database. The data was divided into 12 groups based on driver gender (male and female) and crash type (six categories: rear-end, hit object, sideswipe, overturned, head-on, and broadside). Each group was assigned a logistic regression model for crash severity (Property Damage Only (PDO) vs. injuries or fatalities (NotPDO)) yielding 12 models for various combinations of driver gender and crash types. Results indicate that factors such as the number of vehicles involved, vehicle manufacturing year, annual average daily traffic (AADT), road topography, season of crash, number of lanes, and driver age group all significantly influenced crash severity across various scenarios. These findings provide detailed insights into how various factors contribute to crash severity in different scenarios, allowing policymakers to develop targeted interventions. Policymakers can utilize the findings of this study to implement targeted measures in areas with high frequencies of specific crash types, particularly during adverse environmental conditions. Full article

The rapid growth of e-commerce and the pandemic-driven surge in deliveries have intensified the challenges last-mile logistics poses to urban areas. Road transport, the predominant delivery mode, is a major contributor to greenhouse gas emissions. Despite a downward trend since 2008, emissions rose in 2022, reflecting an increased mobility demand. Light commercial vehicles and trucks impact air and noise pollution due to their high emissions and noise levels. Innovative solutions, such as cargo bikes (CBs), have emerged as sustainable alternatives to mitigate these effects. This paper reports a brief literature review on CBs and evaluates their environmental, economic, and social benefits by comparing real-life data from a shipping company operating with CBs in central Rome to simulated data for motorized delivery vehicles. By analyzing their potential to reduce emissions, improve urban livability, and lower operational costs, this study seeks to raise awareness on CBs’ sustainability as a viable alternative for last-mile logistics. Highlighting these advantages can support policymakers, businesses, and urban planners in fostering a transition to more sustainable urban mobility solutions. Full article

Past studies on low-carbon public transport implementation challenges primarily focused on a specific transport mode to find its viability within broader sustainable urban mobility frameworks. A notable gap still exists in analyzing implementation challenges of low-emission urban public transport policies in the Global South. In particular, research about cities in developing countries, including Mongolia, remains limited. Thus, this study attempts to fill this gap by identifying implementation challenges for low-emission public transport policies in Ulaanbaatar, Mongolia’s capital. We collected and systematically examined the most relevant legal and policy documents from 2008 to 2023, including those from transport agencies and research institutions. The low-emission public transport policies were identified using the principles of the Avoid-Shift-Improve approach. The implementation challenges of identified policies were analyzed using the policy implementation analysis framework developed by Sabatier and Mazmanian. We found that low-emission public transport initiatives that were approved by international organizations and the national government were canceled or significantly delayed due to political instability, financial limitations, and poor inter-agency coordination. This paper also shows that, contrary to some past studies that mainly emphasized financial and administrative capacity limitations, Ulaanbaatar’s low-emission public transport policy implementation met more varied challenges due partly to its unique political, social, and institutional factors as well as unpredictable incidents like the COVID-19 pandemic. Full article

Visibility on roads can be poor during winters owing to snowstorms and other factors. Optical devices, including Light Detection and Ranging devices, are ineffective under whiteout conditions. Moreover, buildings, trees, and other obstacles reduce the accuracy of the Global Positioning System. Therefore, we investigate vehicle navigation using an Ultrahigh Frequency Radio Frequency Identification (RFID) system. This study extends a previously developed RFID-based navigation system for paved roads to unpaved roads. Unpaved roads, particularly those in mountainous or forested areas, can become unstable because of weather conditions and present unique challenges regarding the stability of RFID tags. We use geocells to provide road stability and maintain the RFID tags at the ideal position and attitude. We insert RFID tags into polyvinyl chloride pipe holders and attach them to geocells. We also use the vehicle heading angle from the inertial navigation system (INS). In some areas, the INS is disturbed and shows incorrect direction. We utilize the RFID tag reading history to improve vehicle positioning accuracy by compensating for errors in the INS. Applying this correction reduces the average deviation from the lane center. Driving experiments are conducted on a straight unpaved road, and good results are obtained. These results validate the robustness of the proposed vehicle navigation system, which combines an RFID system with a geocell, providing insights into its successful implementation on unpaved roads. Full article

Under the Belt and Road Initiative, China and Kazakhstan have developed a strategic partnership in the automotive industry that has progressed through three distinct phases. This study provides a comprehensive analysis of the evolution and future of this cooperation, structured into the Export and Assembly phase (2014 to 2017), the Technology Partnership phase (2018 to 2021), and the Localization and Joint Ventures phase (2022 to 2024). Based on qualitative content analysis of policy documents, industry reports, and media coverage, the paper examines how China’s drive for industrial upgrading aligns with Kazakhstan’s goals of economic diversification and industrial growth. The findings indicate that Chinese automotive companies, such as JAC Motors, have transitioned from exporting vehicles to assembling them locally, transferring technology, and investing in joint ventures, thereby strengthening Kazakhstan’s automotive production and market potential. However, challenges remain, including overcapacity, market saturation, and the need for skilled local labor. The study concludes with recommendations to enhance cooperation through joint research and development, the creation of localized parts manufacturing clusters, and the harmonization of technical standards, offering a replicable model for bilateral partnerships within the Belt and Road framework. Full article

Nowadays, the problems of traffic accidents, inefficiency, and congestion still affect transportation systems. Conventional solutions often do not resolve and can even exacerbate the problems. Intelligent transportation system (ITS) technology, including intelligent vehicles, could provide a solution for these problems. However, such technologies should be thoroughly verified and validated before their large-scale adoption. Computer simulation can be used for this task to avoid the expenses of real-world testing. Modern consumer hardware computers are not powerful enough to handle large-scale scenes with high detail. Therefore, a parallel simulation approach employing multiple computers, each processing a separate scene of limited size, is proposed. To define the requirements for a suitable simulation tool, the needs of ITS simulation and Digital Twin technology are discussed, and existing simulation environments suitable for ITS technology verification and validation are evaluated. Further, an architecture for a parallel and multi-level simulation environment for large-scale detailed ITS modeling is proposed. The proposed integrated simulation environment uses the nanoscopic CARLA and microscopic SUMO simulators to implement multi-level and parallel nanoscopic simulation by creating a large scene on the microscopic simulation level and combining the information from multiple parallelly executed nanoscopic scenes. Special handling for nanoscopic scene logic is proposed using a concept of Buffer Zones that allows traffic participants to perceive environmental information beyond the logical boundary of the scene they belong to. The proposed approaches are demonstrated in a series of experiments as a proof of concept and are integrated into the CAVISE simulation environment. Full article

This study explores older passengers’ attitudes, behavior, and evaluations of digital air travel, as well as the impact of digital technologies on this demographic, using China as a case study. The findings of this study offer valuable insights for air transport companies to develop sustainable operational strategies, increase passenger satisfaction, and potentially achieve long-term viability. A structured questionnaire survey was conducted targeting this subgroup, applying the Technology Acceptance Model (TAM) and the Theory of Planned Behavior (TPB) as the primary analytical frameworks. While the study’s sample is skewed towards digitally literate individuals, this subgroup remains highly relevant for analyzing digital impact trends, as they are the most likely to interact with and be influenced by digital air travel tools. This study suggests that older passengers, particularly young-old passengers, in China have a generally positive attitude towards the use of digital air travel tools, with time saving, convenience, and cost saving identified as the top three perceived benefits. Over 80% of participants indicated that digital technology influenced their decision to continue choosing air travel, highlighting a link between digital engagement and sustainable passenger behavior. However, as this study is limited to digitally literate “young-old” passengers in China, the findings should be interpreted as exploratory and context-specific rather than globally generalizable. Future studies are needed with broader age groups and mixed methods to verify these results. Full article

Traditional shipping in Indonesia, known as Pelra (Pelayaran Rakyat), plays an important role in connecting the archipelago and supporting inter-island economic activities. However, this sector faces significant challenges due to the low competency levels of human resources, particularly among ship crews. The examination system, including mechanisms and competency standards for traditional shipping crews, has not been updated for the past three decades, while shipping technology has advanced considerably. This study analyzes the competency levels of Pelra ship crews in South Sulawesi, focusing on certification compliance, technical proficiency, and navigational skills. Both quantitative and qualitative approaches were applied, utilizing Spearman correlation and gap analysis to assess crew competency levels. The findings indicate that engine crews face difficulties in meeting certification requirements for Chief Engineer and Motorman positions, while deck crews struggle to fulfill crewing demands as the vessel size increases. Engine crew competencies remain weak in engine maintenance, repair, and installation, whereas deck crews show limitations in compass use, seamanship, and understanding currents and tides. These gaps negatively affect technical performance, safety, and operational efficiency. The study highlights the urgent need for a revised training system, an updated technical curriculum aligned with industry demands, and adaptive policies harmonized with national competency standards to strengthen professionalism and competitiveness in the traditional shipping industry. Full article

This study presents a machine learning framework for predicting traffic accident occurrence and congestion intensity using artificial neural networks (ANNs) trained on real-world traffic data collected from a central urban corridor in Egypt. The research aims to enhance proactive traffic management by providing reliable, data-driven forecasts derived from temporal and environmental road features. Sixty-seven traffic observations were recorded over three months, capturing variations across vehicle flow, speed, weather, holidays, and road conditions. Two predictive models were developed: a binary accident detection classifier and a multi-class congestion level estimation classifier. Both models employed Bayesian optimization for hyperparameter tuning and were evaluated under three validation strategies—5-fold cross-validation, 10-fold cross-validation, and resubstitution—combined with different train/test splits. The results demonstrated that the model using 10-fold cross-validation and a 75/25 split achieved the highest accuracy in accident prediction (93.8% on test data), with minimal variance between validation and testing phases. In contrast, resubstitution validation yielded artificially high training accuracy (up to 100%) but lower generalization performance, confirming overfitting risks. Congestion prediction showed similarly strong classification trends, with the optimized model effectively distinguishing between congestion levels under dynamic traffic conditions. These findings validate the use of ANN-based prediction in real-world traffic scenarios and highlight the critical role of validation design in developing robust forecasting models. The proposed approach holds promise for integrating intelligent transportation systems, enabling anticipatory interventions, and enhancing road safety. Full article

This study investigates the use of deep reinforcement learning techniques to improve traffic signal control systems through the integration of deep learning and reinforcement learning approaches. The purpose of a deep reinforcement learning architecture is to provide adaptive control via a reinforcement learning interface and deep learning for the representation of traffic queues with regards to signal timings. This has driven recent research, which has reported success in the use of such dynamic approaches. To further explore this success, we apply a deep reinforcement learning algorithm over a grid of 21 interconnected traffic signalized intersections and monitor its effectiveness. Unlike previous research, which often examined isolated or idealized scenarios, our model is applied to the real-world traffic network of Via “Prenestina” in eastern Rome. We utilize the Simulation of Urban MObility (SUMO) platform to simulate and test the model. This study has two main objectives: ensure the algorithm’s correct implementation in a real traffic network and assess its impact on public transportation, incorporating an additional priority reward for public transport. The simulation results confirm the model’s effectiveness in optimizing traffic signals and reducing delays for public transport. Full article

Understanding household vehicle ownership dynamics in the post-COVID-19 era is critical for designing equitable, resilient, and sustainable transportation policies. This study employs an interpretable machine learning framework to model household vehicle ownership using data from the 2022 National Household Travel Survey (NHTS)—the first nationally representative U.S. dataset collected after the onset of the pandemic. A binary classification task distinguishes between single- and multi-vehicle households, applying an ensemble of algorithms, including Random Forest, XGBoost, Support Vector Machines (SVM), and Naïve Bayes. The Random Forest model achieved the highest predictive accuracy (86.9%). To address the interpretability limitations of conventional machine learning approaches, SHapley Additive exPlanations (SHAP) were applied to extract global feature importance and directionality. Results indicate that the number of drivers, household income, and vehicle age are the most influential predictors of multi-vehicle ownership, while contextual factors such as housing tenure, urbanicity, and household lifecycle stage also exert substantial influence highlighting the spatial and demographic heterogeneity in ownership behavior. Policy implications include the design of equity-sensitive strategies such as targeted mobility subsidies, vehicle scrappage incentives, and rural transit innovations. By integrating explainable artificial intelligence into national-scale transportation modeling, this research bridges the gap between predictive accuracy and interpretability, contributing to adaptive mobility strategies aligned with the United Nations Sustainable Development Goals (SDGs), particularly SDG 11 (Sustainable Cities), SDG 10 (Reduced Inequalities), and SDG 13 (Climate Action). Full article

The increasing concern for road safety has driven the development of advanced driver behavior analysis systems. This study presents a comprehensive review of various techniques to detect unsafe driving behaviors, with a particular emphasis on using smartphone sensors. By leveraging data from accelerometers, gyroscopes, and GPS, these methods allow for the detection of aggressive driving patterns, which may result from factors such as driver distraction or drowsiness. Modern sensor technology plays a crucial role in real-time monitoring and has significant potential to enhance vehicle safety systems. A Long Short-Term Memory (LSTM) network combined with a Conv1D layer was trained to analyze driving patterns using a sliding window technique. As technology continues evolving, its application in driver behavior analysis holds great promise for reducing traffic accidents and improving driving habits. Furthermore, the ability to gather and analyze large amounts of data from drivers in various conditions opens new opportunities for more personalized and adaptive safety solutions. This research offers insights into the future direction of driver monitoring systems and the growing impact of mobile and sensor-based solutions in transportation safety. Full article

Urban mobility plays a vital role in shaping sustainable cities, yet the effectiveness of public transportation is often undermined by poor last-mile connectivity (LMC). In the National Capital Region (NCR) of Delhi, despite the Delhi Metro Rail serving as a key transit system, limited integration with surrounding areas hinders accessibility, which particularly affects women, elderly adults, and socioeconomically disadvantaged groups. This study evaluates LMC performance at two key metro stations, Nehru Place and Botanical Garden, using a mixed-methods approach that includes user surveys, spatial survey, thematic analysis, and infrastructure scoring across five critical pillars: accessibility, safety and comfort, intermodality, service availability, and inclusivity. The findings communicate notable contrasts. Botanical Garden exhibits strong intermodal linkages, pedestrian-friendly design, and supportive signage, while Nehru Place indicates a need for infrastructural improvements, safety advancement and upgrades, and strengthened universal design features. These disparities limit effective metro usage and discourage a shift from private to public transport. The study highlights the importance of user-centered, multimodal solutions and the need for cohesive urban governance to address LMC gaps. By identifying barriers and opportunities for improvement, this research paper contributes to the formulation of more inclusive and sustainable urban transport strategies in Indian metropolitan regions. Full article

The accelerating pace of urbanization has significantly complicated traffic management systems, leading to mounting challenges, such as persistent congestion, increased travel delays, and heightened environmental impacts. In response to these challenges, this study presents a novel deep learning framework designed to enhance short-term traffic flow prediction and support intelligent transportation systems within the context of smart cities. The proposed model integrates Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTM) networks, augmented by an attention mechanism that dynamically emphasizes relevant temporal patterns. The model was rigorously evaluated using the publicly available datasets and demonstrated substantial improvements over current state-of-the-art methods. Specifically, the proposed framework achieves a 3.75% reduction in the Mean Absolute Error (MAE), a 2.00% reduction in the Root Mean Squared Error (RMSE), and a 4.17% reduction in the Mean Absolute Percentage Error (MAPE) compared to the baseline models. The enhanced predictive accuracy and computational efficiency offer significant benefits for intelligent traffic control, dynamic route planning, and proactive congestion management, thereby contributing to the development of more sustainable and efficient urban mobility systems. Full article

The passenger car market has experienced a radical shift: the rise of SUV, crossover vehicles, but also Battery Electric Vehicle (BEV) and Plug-In Hybrid Vehicle (PHEV), has blurred the borders between traditional vehicle segments as well as body types, resulting in reduced applicability of conventional taxonomies of vehicle types. This study aims to provide an overview of the vehicle market by proposing a new, machine-learning-based segmentation of the entire German vehicle fleet covering the past years. We merge over 40 million registered vehicles with a technical specifications database and apply data-mining techniques to derive an improved market segmentation. We demonstrate that unsupervised learning techniques, specifically Ward and k-means clustering, yield clusters with enhanced separation, clarity, and practical usability. Clustering was applied to both raw technical features and engineered features designed to capture aspects of economy, ecology, usability, and performance. The silhouette scores can reach 0.19, a significant increase over the +0.05/−0.05 scores of the existing vehicle segments or chassis types. Full article

Bus stops in Riobamba had significant deficiencies in safety, accessibility, and comfort, which limited the effective use of public transport and affected the urban mobility of the population. Improving these conditions was crucial to promote sustainable, inclusive and safe mobility in the city. This study was quantitative and descriptive, based on 420 user surveys and the direct observation of 140 stops, complemented with georeferencing and comparative review of specialized literature. The findings showed that most of the stops lacked adequate lighting, shelter, signage and universal access, with 68% of users perceiving low safety. The most in-demand technologies included real-time information systems (72%) and video surveillance (65%). The proposed model of smart stops will improve accessibility, safety and comfort for users, encouraging greater use of public transport. By addressing the main infrastructure and technology gaps, the intervention contributed to inclusive and safe urban mobility, directly contributing to Sustainable Development Goal 11 and offering a replicable framework for other medium-sized cities seeking to optimize their public transport systems. Full article

Virtual reality driving simulators have been increasingly used for training purposes, but they are still lacking effective driver assistance features, and poor use of user interface (UI) and guidance systems leads to users’ performance being affected. In this paper, we investigate image–arrow aids in a virtual reality driving simulator (VRDS) that enables trainees (new drivers) to interpret instructions according to the correct course of action while performing their driving task. Image–arrow aids consist of arrows, texts, and images that are separately rendered during driving in the VRDS. A total of 45 participants were divided into three groups: G1 (image–arrow aids), G2 (audio and textual aids), and G3 (arrows and textual aids). The results showed that G1 (image–arrow guidance) achieved the best performance, with a mean error rate of 8.1 (SD = 1.23) and a mean completion time of 3.26 min (SD = 0.56). In comparison, G2 (audio and textual aids) had a mean error rate of 10.8 (SD = 1.31) and completion time of 4.49 min (SD = 0.67), while G3 (arrows and textual aids) had the highest error rate (18.4, SD = 1.43) and longest completion time (6.51 min, SD = 0.68). An evaluation revealed that the performance of G1 is significantly better than that of G2 and G3 in terms of performance measures (errors + time) and subjective analysis such as usability, easiness, understanding, and assistance. Full article

This paper presents a novel methodology for predicting the tire–road friction coefficient in real-time under challenging climatic conditions based on a fuzzy logic inference system. The core innovation of the proposed approach lies in the integration and probabilistic weighting of a diverse set of input data, which includes signals from ambient temperature and precipitation intensity sensors, activation events of the anti-lock braking system (ABS) and electronic stability control (ESP), windshield wiper operation modes, and road marking recognition via a front-facing camera. This multi-sensor data fusion strategy significantly enhances prediction accuracy compared to traditional methods that rely on limited data sources (e.g., temperature and precipitation alone), especially in transient or non-uniform road conditions such as compacted snow or shortly after rainfall. The reliability of the fuzzy-logic-based predictor was experimentally validated through extensive road tests on dry asphalt, wet asphalt, and wet basalt (simulating packed snow). The results demonstrate a high degree of convergence between predicted and actual values, with a maximum modeling error of less than 10% across all tested scenarios. The developed methodology provides a robust and adaptive solution for enhancing the performance of Advanced Driver Assistance Systems (ADASs), particularly Automatic Emergency Braking (AEB), by enabling more accurate braking distance calculations. Full article