The development of NVH (Noise, Vibration, and Harshness) characteristics in vehicles is facing new challenges with the widespread utilization of electric drivetrains. This shift introduces new requirements in several areas, such as reduced noise and vibration levels, the need for advanced nonlinear characterization methods, and tuning/masking the typically more prominent tonal noise components. More accurate simulation and measurement techniques are essential to meet these demands. This study focuses on the experimental frequency response function (FRF) testing of electric drivetrain components, specifically on potential phase errors caused by inappropriate measurement settings. The influencing parameters and their quantitative effects are analyzed theoretically and demonstrated using real measurement data. A novel numerical approach, termed Maximum Phase Error Analysis (MPEA), is introduced to systematically quantify the largest potential phase errors due to arbitrary alignment between resonance frequencies and discrete spectral lines. MPEA enhances the robustness of phase accuracy assessment, especially critical for lightly damped systems and closely spaced resonance peaks. Based on the findings, optimal testing parameters are proposed to ensure phase errors remain within a predefined limit. The results can be applied in various dynamic testing scenarios, including durability testing and rattling analysis.

Automated, electric shuttles are expected to be key for the future of public transportation, providing a safe, efficient, and robust operation with a minimum carbon footprint. However, in complex, urban environments, their reliable operation is particularly challenging and shows a lack of performance and comfort. This study presents a quantitative benchmark of an automated shuttle compared to a conventional, human-operated bus on the same route. Speed and acceleration across geofenced segments are systematically analyzed based on over 12 million GNSS and IMU data points. The results show that the automated shuttle operates at about half the average speed of the bus. Furthermore, frequent abrupt decelerations are reducing passenger comfort, while the main distributions and mean values of the measured acceleration indicate a smooth operation of the automated shuttle; outliers reveal critical braking events. The presented methodology enables objective performance tracking and supports the iterative improvement of autonomous shuttles through datadriven optimization.

Self-driving vehicle (SDV) safety and reliability are becoming critical design parameters as SDVs increase their market share. This paper examines public preferences for key SDV safety features (system reliability, sensor resilience, failure behavior, and driver alert methods) using a choice-based conjoint survey of 403 U.S. respondents. A novel integration of conjoint analysis with Least Absolute Shrinkage and Selection Operator (LASSO) regression and generalized linear mixed-effects models (GLMMs) was applied to identify the most influential features and their demographic or behavioral predictors. Results show that multimodal driver alerts (i.e., audio + visual) were the most influential factor, accounting for nearly two-thirds of decision weight. System reliability (i.e., low human intervention rates) and sensor resilience (i.e., low tolerance for failures) were secondary, while failure behavior had minimal influence. Subgroup analyses revealed modest variations by willingness to pay for SDVs, income, race/ethnicity, marital status, education, driving frequency, and risk propensity, though the importance of alerts and reliability remained consistent across groups. This combined conjoint-LASSO-GLMM framework enhances the precision of preference estimation and offers actionable guidance for SDV manufacturers seeking to align safety feature design with consumer expectations.

In principle, transit signal priority (TSP) should be able to reduce bus delays to near zero; however, in U.S. practice, bus delay reductions from TSP are often meager. This may be because, in the U.S., active TSP (green extension and early green) is often applied within an underlying traffic signal control framework that is not TSP-friendly. TSP-friendly signal control means control that minimizes the bus phase’s scheduled red period, offers flexibility to shift the bus phase’s green to match the bus arrival time, and includes compensation mechanisms that allow phases interrupted by priority actions to quickly recover, which in turn allows TSP to be more aggressive. Simulation tests at four sites in Boston find that applying active TSP together with TSP-friendly underlying control reduces bus delay 2 to 3 times as much as applying active TSP on top of existing traffic signal control without negatively impacting other vehicles or pedestrians. Aspects of TSP-friendly signal control demonstrated in the case studies include fully actuated control, reservice for minor bus phases, coordination that follows bus trajectories, phase rotation, and coordination following bus trajectories.

A well-performing road network is essential for modern society. But any road is nothing without its users—cyclists, drivers, pedestrians. Road network cannot be managed without knowing who the roads serve. The gaps in this knowledge lead to decisions that hinder efficiency, equality, and sustainability. This is why monitoring traffic is imperative for road management. However, traditional short-term traffic counting methods fail to provide full coverage at a reasonable cost. This study assessed the economic feasibility of drone-enabled traffic monitoring systems across Estonian urban environments through comparative spatial and economic analysis. Hexagonal tessellation was applied to 255 urban locations, identifying 47,530 monitoring points across 4077 grid cells. Economic modeling compared traditional counting costs with drone-based systems utilizing ultralight drones and nomadic 5G infrastructure. Monte Carlo simulation evaluated robustness under varying operational intensities from 30 to 180 days annually. Analysis identified an 8-point density threshold for economic viability, substantially lower than previously reported requirements. Operational intensity emerged as the critical determinant: minimal operations (30 days) proved viable for 9.0% of locations, while semi-continuous deployment (180 days) expanded viability to 81.6%. The findings demonstrate that drone-based monitoring achieves 60–80% cost reductions compared to traditional methods while maintaining equivalent accuracy (95–100% detection rates for vehicles, cyclists, and pedestrians), presenting an economically superior alternative for 67% of Estonian urban areas, with viability extending to lower-density locations through increased operational utilization.

Travel-time delays due to recurring congestion cause productivity loss, increase the likelihood of accidents, and lead to environmental pollution due to greenhouse gas emissions. The National Highway Traffic Safety Administration in the United States has listed several driver assistance technologies that are now common in most newer vehicles. While these technologies can help reduce the likelihood of traffic-related accidents, they do little to reduce recurring congestion in urban areas. Recurring congestion during rush hours is prevalent, for example, along Interstate 95 and Capital Beltway 495 in the Baltimore-Washington area. Such congestion also enhances the likelihood of crashes. Previous approaches to hotspot identification are primarily theoretical, which limits their practical applicability. In this paper, we develop a Machine Learning (ML) approach that integrates geospatial data with artificial neural networks to predict traffic congestion hotspots during rush hour. The approach uses live traffic sensor data. A case study from Maryland is presented. The result shows top hotspot segments across Maryland. Using a snapshot of hotspots at eight different time periods, the likelihood of hotspot locations is predicted using an artificial neural network. The framework is validated using live loop detector data (speed and volume) from Maryland freeways, particularly I-495 and I-95. The research can serve as a valuable tool for traffic congestion hotspot identification and travel-time prediction.

This study investigates the potential of Floating Car Data (FCD) collected from Volkswagen Group vehicles since 2022 for monitoring pavement conditions along two Italian road stretches. While such data are primarily gathered to analyze vehicle dynamics and mechanical behaviour, here, they are repurposed to support road network assessment through the estimation of the International Roughness Index (IRI). Daily aggregated datasets provided by NIRA Dynamics were analyzed to evaluate their reliability in detecting spatial and temporal variations in surface conditions. The results show that FCD can effectively identify critical sections requiring maintenance, track IRI variations over time, and assess the performance of surface rehabilitation, with high consistency on single-lane roads. On multi-lane roads, limitations emerged due to data aggregation across lanes, leading to reduced accuracy. Nevertheless, FCD proved to be a cost-efficient and continuously available source of information, particularly valuable for identifying temporal changes and supporting the evaluation of maintenance interventions. Further calibration is needed to enhance alignment with high-performance measurement systems, considering data density at the section level. Overall, the findings highlight the suitability of FCD as a scalable solution for real-time monitoring and long-term maintenance planning, contributing to more sustainable management of road infrastructure.

Generating realistic and diverse driving scenarios is essential for effective scenario-based testing and validation in autonomous driving and the development of driver assistance systems. Traditionally, parametric models are used as standard approaches for scenario generation, but they require detailed domain expertise, suffer from scalability issues, and often introduce biases due to idealizations. Recent research has demonstrated that AI models can generate more realistic driving scenarios with reduced manual effort. However, these models typically focused on single scenario types, such as cut-in maneuvers, which limits their applicability to diverse real-world driving situations. This paper, therefore, proposes a unified generative framework that can simultaneously generate multiple types of driving scenarios, including cut-in, cut-out, and cut-through maneuvers from both directions, thus covering six distinct driving behaviors. The model not only learns to generate realistic trajectories but also reflects the same statistical properties as observed in real-world data, which is essential for risk assessment. Comprehensive evaluations, including quantitative metrics and visualizations from detailed latent and physical space analyses, demonstrate that the unified model achieves comparable performance to individually trained models. The shown approach reduces modeling complexity and offers a scalable solution for generating diverse, safety-relevant driving scenarios, supporting robust testing and validation.

Despite increasing interest in AI, IoT, and blockchain for sustainable transportation, existing reviews remain fragmented—focusing on single technologies, descriptive benefits, or narrow applications—without providing an integrated synthesis across domains. This study conducts a systematic literature review (SLR) following the PRISMA 2020 guidelines and a bibliometric analysis of 102 peer-reviewed papers to provide the concurrent integrative synthesis of AI, IoT, and blockchain in enabling sustainable transport. Data were drawn from Scopus, Web of Science, PubMed, Semantic Scholar, and Google Scholar, and analyzed using VOSviewer to identify research clusters, emerging themes, and knowledge gaps. The results reveal three thematic clusters: smart traffic systems for congestion management, sustainable logistics and supply chains, and data-driven urban governance. Across these clusters, AI is more mature in predictive modeling, IoT remains fragmented in interoperability, and blockchain is still at a pilot stage with governance and scalability issues. The analysis highlights synergies (e.g., AI–IoT integration for real-time optimization) and persistent challenges (e.g., standardization, data security). This review contributes a strategic research roadmap linking bibliometric hotspots with policy and practice implications. By explicitly identifying gaps in governance, interoperability, and cross-domain integration, the study offers actionable directions for both researchers and policymakers to accelerate digital transitions in transport.

The paper studies and analyzes electric vehicle engines powered by hydrogen under the WLTP standard driving protocol. The driving range extension is estimated using a specific protocol developed for FCEV compared with the standard value for battery electric vehicles. The driving range is extended by 10 km, averaging over the four protocols, with a maximum of 11.6 km for the FTP-75 and a minimum of 7.7 km for the WLTP. This driving range extension represents a 1.8% driving range improvement, on average. Applying the FCEV current weight, the driving range is extended to 18.9 km and 20.4 km, on average, when using power source energy capacity standards for BEVs and FCEVs.