<?xml version="1.0" encoding="utf-8"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>TRB Publications Index</title><link>http://pubsindex.trb.org/</link><atom:link href="http://pubsindex.trb.org/common/TRIS Suite/feeds/rss.aspx?cdatein=1year" rel="self" type="application/rss+xml" /><description></description><language>en-us</language><copyright>Copyright © 2015. National Academy of Sciences. All rights reserved.</copyright><docs>http://blogs.law.harvard.edu/tech/rss</docs><managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor><webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster><image><title>TRB Publications Index</title><url>http://pubsindex.trb.org/Images/PageHeader-wTitle.png</url><link>http://pubsindex.trb.org/</link></image><item><title>Enhancing Rail Obstacle Detection Systems: Optimizing Accuracy in Adverse Weather Conditions</title><link>http://pubsindex.trb.org/view/2711639</link><description><![CDATA[Railway safety is paramount, especially with the increasing reliance on rail transport and the potential for catastrophic consequences from train colliding with obstacles. This paper introduces a novel obstacle detection methodology using Convolutional Neural Networks (CNNs) to enhance detection accuracy, particularly for diverse and unforeseen obstacles, including wildlife intrusion, under challenging environmental conditions. We employ the state-of-the-art (You Only Look Once) YOLOv11-Seg algorithm for simultaneous rail segmentation and obstacle detection, defining a critical safety margin around the tracks. A key contribution of this work is a novel synthetic image generation algorithm designed to address the critical scarcity of real-world obstacle data, particularly for rare and unpredictable hazards such as animals and uncharacterized debris. This algorithm strategically places various obstacles, extracted from diverse sources, at random locations on the rail or within the safety margin. Crucially, it incorporates diverse and realistic environmental conditions, such as train vibrations, rain, snow, dust, fog, and varying light intensities to augment the training data and improve the model’s robustness against these highly transient events. Experimental results demonstrate the effectiveness of the YOLOv11-Seg network, trained on our synthetically augmented data set, in accurately performing both segmentation and obstacle detection in a single step, paving the way for improved railway safety systems.]]></description><pubDate>Fri, 05 Jun 2026 11:27:53 GMT</pubDate><guid>http://pubsindex.trb.org/view/2711639</guid></item><item><title>A Multicriteria Analytical Framework for Site Selection of Mobility Hubs</title><link>http://pubsindex.trb.org/view/2711633</link><description><![CDATA[Mobility hubs (MHs) have emerged as a novel concept to enhance multimodal travel. A MH provides supporting infrastructure, amenities, and services for multimodal travelers at strategic locations, facilitating seamless integration of various modes. Despite growing interest in MHs, cities and transit agencies lack an established analytical framework for selecting optimal MH locations. To address this gap, we propose a multicriteria approach to identify locations for MH development within an existing or planned transit network. Our approach consists of four steps: 1) determine key criteria and collect data, 2) compute MH index for multiple scenarios, 3) identify MHs and assign typology, and 4) determine MH sites with community engagement. Our approach differs from existing methods by using transit stop clusters (obtained using a density-based clustering algorithm) as the unit of analysis, explicitly incorporating first-/last-mile connectivity as a primary consideration, and distinguishing the typology of each hub. We demonstrate and validate the approach by conducting case studies in Gainesville, Florida, and in West Palm Beach, Florida. In both cities, we have engaged the local transportation agencies and residents, whose inputs verified the desirability of the identified MH locations and confirmed the usefulness of the proposed approach. By combing data-driven analysis with community participation, our approach offers a valuable tool for transportation planners and policymakers in MH planning and development across diverse contexts. We acknowledge several limitations of the proposed approach and emphasize the role of this method as one step in a broader, stakeholder-driven planning process.]]></description><pubDate>Fri, 05 Jun 2026 11:27:53 GMT</pubDate><guid>http://pubsindex.trb.org/view/2711633</guid></item><item><title>Health and Safety Impacts of Aircraft Cabin Temperatures</title><link>http://pubsindex.trb.org/view/2709677</link><description><![CDATA[Commercial aircraft cabins expose passengers and flight attendants to a range of environmental conditions, including at times excessively hot or cold temperatures that may affect health, safety, and comfort. While aircraft systems are generally effective at maintaining acceptable cabin conditions, challenges are more likely to arise during ground operations, particularly in extreme outdoor temperatures or when equipment used for thermal control in aircraft cabins is unavailable or not functioning properly. Because passengers and flight attendants have limited ability to leave or substantially modify the cabin environment, understanding and managing temperature-related risks is an important component of safe air travel. This report examines the available evidence on how cabin temperatures and humidity conditions may influence physiological, cognitive, and behavioral outcomes for passengers and flight attendants. The report finds that serious health impacts are uncommon, but conditions causing thermal discomfort may occur more frequently and can affect factors critical to the conduct of flight attendant duties (e.g., concentration, decision-making) and passenger behavior in ways that create safety concerns. The report highlights differences in how passengers and flight attendants experience cabin temperatures because of variations in activity levels, clothing constraints, exposure frequency, age, and underlying health conditions. It also concludes that available data on cabin temperature and humidity conditions are fragmented and insufficient to reliably estimate the frequency of related health and safety events. The report provides recommendations to strengthen management of cabin temperature risks, including integrating temperature and humidity hazards into airline safety management systems, improving collection and use of aviation safety and health event data, strengthening operational practices for thermal control in aircraft cabins, supporting flight attendants in responding to unsafe conditions, and providing passengers with information about temperature-related risks. The report also recommends that the Federal Aviation Administration establish a research program to systematically collect representative data on cabin temperature and humidity conditions to better inform future mitigation strategies and safety oversight.]]></description><pubDate>Thu, 04 Jun 2026 10:58:21 GMT</pubDate><guid>http://pubsindex.trb.org/view/2709677</guid></item><item><title>Responsibility Attribution for Autonomous Vehicle Crashes Based on Causal Inference: Case Study in California, USA</title><link>http://pubsindex.trb.org/view/2709310</link><description><![CDATA[In the autonomous driving environment, the attribution of responsibility becomes complex when multiple crash parties and factors are involved. This study proposes a method to attribute the responsibility of the primary crash vehicle when human drivers and autonomous driving systems coexist, and apply it to the existing Autonomous Vehicles (AVs) crash data from 2019 to 2023 in California, USA. Firstly, a causal network is constructed by integrating the Decision-making Trial and Evaluation Laboratory (DEMATEL) and Interpretative Structural Modeling (ISM) methods to analyze the mutual impact of factors in the crash data. Secondly, Random Forest (RF) is used to obtain the feature importance in AV crashes. Based on the relationship between factors and the main responsible parties, the responsibility among relative stakeholders can be quantified. Under the research data in California, in autonomous driving mode, human drivers of the primary crash vehicle and software developers both account for 31% of the crash. Following behind are other stakeholders at 21% and vehicle manufacturers at 17%. On this basis, adjustments can be made to the responsible proportion in relation to a specific crash. By identifying the impact factors of AV crashes and responsibility attribution, this study offers important insights into safe autonomous driving tests, AV production regulation, and the development of crash responsibility policies. The methodology framework developed in this paper is universal and can be applicable to AV crash analysis in diverse regions and AV penetration rates.]]></description><pubDate>Wed, 03 Jun 2026 09:07:22 GMT</pubDate><guid>http://pubsindex.trb.org/view/2709310</guid></item><item><title>Elevating Urban Public Transit: Consensus-Based Expert Study on Urban Air Mobility and Aerial Cable Car Integration</title><link>http://pubsindex.trb.org/view/2709303</link><description><![CDATA[Airspace is increasingly emerging as a relevant dimension for urban mobility. Urban cable cars, a prime example of airborne modes, have already succeeded in emerging and developing countries, supplementing conventional public transit. However, aerial cable cars are less prevalent in industrialized nations, and integration with high-quality transit in such countries requires careful consideration. Therefore, this study identifies impacts, challenges, and stakeholders associated with cable cars, determines common challenges, and suggests appropriate use cases. An online consensus-based two-wave expert survey involving 63 high-caliber participants from engineering and consulting companies, public authorities, transit agencies, research institutions and cable car manufacturers yielded more than 4,700 answers. Key findings from consolidated expert knowledge indicated that cable cars could effectively supplement transit in industrialized countries. Positive impacts include connectivity to transit, reliability owing to minimal road-level competition, direct connections over obstacles, and being an attractive transit option. Negative impacts include privacy concerns, property ownership interference, limited access to adjacent sites along routes owing to aerial routing, lower capacity compared with conventional transit, and knowledge gaps. Handling passenger transfers between cable cars and other transit modes owing to height differences and passenger volumes poses service challenges. Transparent communication with stakeholders is crucial for project acceptance. Municipalities, operators/planners, politicians, and manufacturers are key stakeholders. Cable cars are considered a suitable aerial transit mode, with accessibility and safety levels similar to those of traditional transit modes. High demand is anticipated, especially when bridging barriers. In conclusion, cable cars can complement transit and this study provides valuable consensus-proven planning guidance for policy makers and practitioners.]]></description><pubDate>Wed, 03 Jun 2026 09:07:22 GMT</pubDate><guid>http://pubsindex.trb.org/view/2709303</guid></item><item><title>Traffic Breakdown Prediction Beyond Stochastic Capacity Models: Machine Learning Approach</title><link>http://pubsindex.trb.org/view/2709302</link><description><![CDATA[This study focuses on the application of machine learning models for a short-term prediction of traffic breakdowns on freeways. Traffic breakdowns, which occur when demand exceeds the momentary capacity, are typically predicted using probabilistic methods, but these approaches do not fully capture the short-term variability inherent in traffic flow. In this work, the methodology is advanced by employing machine learning techniques to predict traffic flow conditions, relying exclusively on lane-by-lane analysis of current detector data without utilizing any upstream or downstream information. Traffic conditions are classified into distinct categories, including breakdowns, and a neural network is employed to predict them, providing a robust method for identifying intervals in which the momentary capacity of a freeway is reached. Capacity estimates from the neural network are then compared with those from widely accepted statistical methods, revealing minimal differences, and thereby validating the effectiveness of the neural network approach in capacity analysis. Moreover, comparing the short-term flow conditions predicted based on the two approaches revealed superiority of neural network in providing significantly more accurate classifications. These findings highlight the significant potential of machine learning methods as powerful tools for momentary capacity estimation, with applications across various transportation systems management and operations strategies.]]></description><pubDate>Wed, 03 Jun 2026 09:07:22 GMT</pubDate><guid>http://pubsindex.trb.org/view/2709302</guid></item><item><title>Pulse of the Pedal: Electrocardiogram-Based Assessment of Stress in Urban Bicycling</title><link>http://pubsindex.trb.org/view/2709301</link><description><![CDATA[As awareness of cycling’s multifaceted benefits—spanning health, environmental, economic, and social domains—continues to grow, adoption rates are steadily increasing. Despite these benefits, urban cycling environments pose significant challenges, as cyclists share road space with motor vehicles and pedestrians, thereby increasing the risks of crashes and conflicts. Most existing research has focused on infrastructure aspects; however, few studies have explored physiological dimensions by measuring cycling stress across a ride. This research addresses this gap by using electrocardiogram (ECG) sensors to measure cyclists’ physiological stress levels while accounting for fatigue during cycling an urban route, with heart rate (HR) serving as the primary indicator. Twenty-two participants completed the same urban route twice in different directions while wearing an ECG device. Results from statistical analyses reveal that specific intersections and directional sequences through which cyclists move have a significant influence on their stress levels. Stress tends to increase with the accumulation of physical fatigue. Moreover, stress levels indicated by HR were also elevated at unsignalized intersections with unclear right-of-way, and at major signalized intersections with high traffic volume. These findings demonstrate that considering physiological data provides valuable insights into cycling experiences and can inform transportation planning and intersection design for safer, more comfortable urban cycling experiences.]]></description><pubDate>Wed, 03 Jun 2026 09:07:22 GMT</pubDate><guid>http://pubsindex.trb.org/view/2709301</guid></item><item><title>Optimized Laboratory Fabrication of Small-Specimen Geometry for Streamlining Dynamic Modulus and Cyclic Fatigue Testing of Asphalt Mixtures</title><link>http://pubsindex.trb.org/view/2709230</link><description><![CDATA[The asphalt community is focused on the paradigm shift in mixture design from the volumetrics to an optimization procedure based on performance testing called balanced mixture design. Streamlining performance testing to obtain index properties quickly and using a smaller quantity of materials is critical for the successful implementation. This paper aims to streamline dynamic modulus (|E*|) and cyclic fatigue testing by optimizing the number of 38 mm diameter specimens extracted from a single 150 mm diameter Superpave gyratory-compacted (SGC) specimen. The current provisional standard methods require vertical coring of four small specimens from a single SGC specimen. In this study, two sets of testing specimens were fabricated by coring four and five small specimens from each SGC specimen. The success rate in meeting target air voids, the |E*| analysis, and the cyclic fatigue results including cyclic fatigue index parameter (Sₐₚₚ) values were compared between the two sets of specimens. No significant or consistent differences were observed in performance testing results. Furthermore, innovative image analysis and microscopy techniques were used to study air voids distribution and aggregate structure within each specimen and to further validate the proposed coring pattern. Based on these findings, coring five 38 mm diameter testing specimens from one SGC sample is suggested to run |E*| and cyclic fatigue tests. This proposed modification to AASHTO TP 132 and TP 133 may save technicians’ time and allows for the optimal use of materials. The latter may become a significant saving when integrating these methods with laboratory long-term aging protocols and forensic studies.]]></description><pubDate>Tue, 02 Jun 2026 11:01:49 GMT</pubDate><guid>http://pubsindex.trb.org/view/2709230</guid></item><item><title>Mechanistic Analysis and Design Framework for Geosynthetic Stabilized Unpaved Roads</title><link>http://pubsindex.trb.org/view/2709229</link><description><![CDATA[Geosynthetics provide mechanical stabilization benefits to paved or unpaved roads through lateral restraint of unbound aggregate particles and bearing capacity improvement over weak subgrades. The current state of the art incorporating geosynthetics into paved or unpaved road design involves conducting proper elastic layered system mechanistic analysis to determine the improvement of aggregate layer stiffness for increased traffic capacity or reduction in aggregate layer thickness. This paper presents a mechanistic analysis and design pipeline for determining the required aggregate thickness via the finite element (FE) modeling approach. An advanced FE analysis tool, C-FLEX, was employed to analyze axisymmetric multilayered unpaved road structures, accounting for the nonlinear stress-dependent behavior of unbound aggregates. The modulus enhancements were quantified for 10 different geosynthetics using the latest Bender Element sensor technology in both triaxial and large-scale tests conducted on typical dense-graded base aggregates. They were then incorporated into base course stiffness characterization via a sublayering approach for the unpaved road comprising aggregate base placed over soft subgrade. Both the measured enhanced moduli and the the extent of geosynthetic influence zones were adequately established in the sublayering approach. Further, sensitivity analysis was conducted for different aggregate modulus models and different sublayer structures, which verified the proposed design pipeline to provide satisfactory results. The method was also compared with the Giroud and Han method, which revealed the inherent difference in the two methods, given that the design here is based on the critical pavement responses and subgrade strength, while the Giroud and Han method also incorporated the field data with performance evaluation.]]></description><pubDate>Tue, 02 Jun 2026 11:01:49 GMT</pubDate><guid>http://pubsindex.trb.org/view/2709229</guid></item><item><title>Enhancing Data Accessibility through Automated Personally Identifiable Information De-Identification in Crash Narratives</title><link>http://pubsindex.trb.org/view/2709228</link><description><![CDATA[Unstructured crash narratives in police reports contain rich textual information that can uncover key insights into crash circumstances, such as contributing factors and driver behavior, that are often missing from the structured fields of crash data. However, the presence of personally identifiable information (PII) within these narratives, and the lack of scalable, domain-specific redaction tools, limit their broader use because of privacy concerns and legal restrictions. To address this challenge, a scalable, privacy-preserving pipeline for automated PII de-identification from crash narratives was developed and evaluated. The proposed method utilizes a generalist model for named entity recognition using bidirectional transformer (GLiNER), which is known for its strong zero-shot, few-shot, and fine-tuned performance across diverse entity types. The model was fine-tuned on a manually annotated training set to adapt it to the crash narrative domain. It was found that combining this fine-tuned named entity recognition model with a rule-based post-processing module improved PII detection performance by resolving span misalignments and recovering entities that were initially missed. Evaluation on a test set achieved an F1 score above 80%, particularly for frequent PII categories such as names and addresses. Post-processing further reduced false negatives by 32%. The pipeline was developed and tested on local machines to ensure data confidentiality. Additionally, the workflow supports accessibility and future use through GLiNER-Studio, a user-friendly tool that enables non-programmers to fine-tune models on new datasets. This study contributes a practical solution to the need for automated PII de-identification in transportation safety data, enabling secure data sharing and ethical analytics for research and policymaking.]]></description><pubDate>Tue, 02 Jun 2026 11:01:49 GMT</pubDate><guid>http://pubsindex.trb.org/view/2709228</guid></item><item><title>Pickup and Delivery Problem with Synchronous and Asynchronous Transshipment for Q-Commerce Delivery</title><link>http://pubsindex.trb.org/view/2709227</link><description><![CDATA[This study explores ways to improve delivery efficiency and reduce costs in the rapidly growing quick commerce (Q-commerce) market, which is expanding because of advancements in Internet of Things technology and the rise of contactless consumption. However, it faces significant challenges, such as high transportation costs and inefficient vehicle utilization. To address these challenges, a novel delivery system is proposed that simultaneously incorporates synchronous and asynchronous transshipment. The proposed system, the Pickup and Delivery Problem with two types of transshipment, is formulated as a mathematical optimization model. Since the problem is Nondeterministic Polynomial-time hard (NP-hard), implying that finding an optimal solution is computationally intensive as the problem scale increases, a two-phase heuristic algorithm is developed that combines adaptive large neighborhood search metaheuristic with transshipment scheduling. In Phase 1, adaptive large neighborhood search is employed to improve the initial Pickup and Delivery Problem solution. In Phase 2, the two transshipments are integrated into the improved solution. Experimental results from various scenarios show that the proposed two-phase algorithm effectively reduces Q-commerce delivery costs by approximately 10.97% compared with initial solutions. Of note, simultaneously considering synchronous and asynchronous transshipment resulted in an additional 0.5 percentage points (pp) improvement in the objective function value compared with using a single transshipment. These findings suggest that transshipment solutions can effectively reduce vehicle operating times and request travel distances, contributing to the future popularization of multi-echelon delivery systems.]]></description><pubDate>Tue, 02 Jun 2026 11:01:49 GMT</pubDate><guid>http://pubsindex.trb.org/view/2709227</guid></item><item><title>A Data-Driven Simulation and Machine Learning Framework for Shopping Trip Forecasting with Spatial Clustering</title><link>http://pubsindex.trb.org/view/2709131</link><description><![CDATA[Retailing plays a pivotal role in the functioning of urban systems. While upstream supply chain activities such as manufacturing and distribution primarily affect freight movement, the retail interface translates consumer demand into individual travel behavior, shaping local traffic conditions and feeding back into upstream logistics. Despite its importance, shopping-related travel remains under-modeled in urban mobility research. To address this gap, this study develops a purpose-specific travel forecasting and simulation framework for predicting shopping trip demand in urban areas. The forecasting model integrates commercial-environment attributes, trip characteristics, and sociodemographic factors. A suite of machine learning (ML) models is evaluated, and the best-performing model is selected for the proposed simulation. Microlevel predictions are then scaled to the full urban region, followed by zonal aggregation and k-means spatial clustering to identify distinct retail-demand patterns and support scenario testing. Numerical results show that the random forest model outperforms alternative ML classifiers and, when implemented in the simulation, generates a citywide estimate indicating that shopping trips represent 14.3% of all weekday travel, in line with external regional benchmarks. The combined ML–simulation framework demonstrates strong predictive performance and reveals meaningful spatial and behavioral insights relevant to policymaking and planning applications. Although applied to Halifax, the modular structure of the framework makes it transferable to other urban regions and adaptable to additional trip purposes, supporting future extensions involving multiactivity modeling, causal impact analysis, and integration with passive mobility datasets.]]></description><pubDate>Tue, 02 Jun 2026 11:01:49 GMT</pubDate><guid>http://pubsindex.trb.org/view/2709131</guid></item><item><title>Exploring Aggregate Morphological Characteristics under Laboratory Polishing for Enhanced Pavement Skid Resistance</title><link>http://pubsindex.trb.org/view/2709130</link><description><![CDATA[As the use of recycled asphalt pavement (RAP) in pavement construction grows for sustainable development, it becomes essential to investigate potential frictional deterioration over time. This study evaluated the friction properties of recovered RAP material aggregates compared with raw aggregates across various polishing cycles. The micro-Deval test was employed to simulate aggregate loss of texture, while morphological and friction properties were measured using an aggregate imaging measurement system (AIMS-II), along with a British pendulum tester (BPT) and dynamic friction tester (DFT). Additionally, Fourier transform infrared spectroscopy (FTIR) was employed to assess its potential in determining the origin and composition of RAP material aggregates. A simple method was used to fabricate custom aggregate rings, allowing for accurate testing in the DFT setup. The aggregate testing results revealed notable variations across the measurement techniques. AIMS-II analysis showed that traprock (maroon-colored) exhibited the highest surface texture, while DFT and BPT results indicated that certain limestones outperformed traprock in friction properties. Additionally, the testing results demonstrated that the RAP materials were comparable to, or even outperformed, certain limestone sources. However, because of potential variability within RAP stockpiles, careful quantification is necessary to assess their suitability. FTIR analysis demonstrated its ability to distinguish between carbonate-rich and silica-rich aggregates; however, further research is needed to build a library of aggregate sources. Finally, a machine learning algorithm identified the loss of aggregate DFT₂₀ values as the most significant aggregate property representing friction loss in asphalt mixtures.]]></description><pubDate>Tue, 02 Jun 2026 11:01:49 GMT</pubDate><guid>http://pubsindex.trb.org/view/2709130</guid></item><item><title>Evaluating the Safety Impact of Roadway Rightsizing in Jefferson County, Kentucky</title><link>http://pubsindex.trb.org/view/2709128</link><description><![CDATA[This research provides a safety assessment of rightsizing projects that took place in Jefferson County, Kentucky. Rightsizing has become increasingly popular as a solution for multimodal access improvements and enhancing roadway safety. A cross-sectional before–after analysis was applied to a 15-year panel dataset from 2010 to 2024 to estimate the impact of rightsizing on crash frequency. A matched control group was developed using traffic volume and segment length using nearest-neighbor approach. Negative binomial safety performance functions were estimated with untreated sites and adjusted with annual calibration factors for seasonal changes consideration. Empirical Bayes methods were applied to correct for regression-to-the-mean bias and estimate counterfactual crash frequencies. Crash modification factors (CMFs) were calculated and disaggregated by crash type (all, bicycle, pedestrian, and intersection-related) and severity level (KA, BC, O). The analysis reveals that rightsizing treatments were associated with a 32% reduction in fatal and severe injury crashes, and consistent crash reductions at intersections. However, elevated CMFs across all severity levels for bicycle crashes suggest increased risk, potentially because of higher exposure without corresponding protective infrastructure. Pedestrian findings varied by severity level. The findings highlight crash severity reduction potential for rightsizing while indicating a requirement for including facilitative infrastructure for protection of vulnerable road users. The study includes practical recommendations for transportation agencies considering rightsizing as part of a broader safety and multimodal mobility initiative.]]></description><pubDate>Tue, 02 Jun 2026 11:01:49 GMT</pubDate><guid>http://pubsindex.trb.org/view/2709128</guid></item><item><title>Factors Associated with the Gender Gap in the Mobility of Care within Two-Parent Households in Mexico City</title><link>http://pubsindex.trb.org/view/2708409</link><description><![CDATA[Mobility of care (MOC) is a term that has recently been introduced into the transportation planning literature to refer to travel related to activities needed for the upkeep of the home and the well-being of its members. Gender bias is an issue since such travel is mainly undertaken by women and remains largely hidden in traditional travel surveys. The emergent quantitative work has focused on identifying MOC patterns across individual aspects; however, this approach limits the understanding of the interdependent nature of MOC among household members. There is a lack of household-level studies which would help better understand the MOC gender gap. This study in the Mexico City Metropolitan Area develops robust regression models to understand the determinants of the MOC gender gap, controlling for two-parent household structures. The results consistently demonstrate that travel time to work is strongly and inversely associated with the availability to participate in MOC for the head-of-household (predominantly men) and the spouse (predominantly women). The spouse who is dedicated to home chores is the main actor carrying out MOC, increasing the gap in their larger load as the household grows (e.g., with children in basic education), and when MOC is done solely on weekdays. The built environment showed limited associations with the gender MOC gap. Other significant associations, and results were compared with the literature and discussed for their relevance to reducing the gender gap.]]></description><pubDate>Tue, 02 Jun 2026 11:01:49 GMT</pubDate><guid>http://pubsindex.trb.org/view/2708409</guid></item></channel></rss>