<?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?s=PHNlYXJjaD48cGFyYW1zPjxwYXJhbSBuYW1lPSJzdWJqZWN0aWQiIHZhbHVlPSIxNzc4IiAvPjxwYXJhbSBuYW1lPSJsb2NhdGlvbiIgdmFsdWU9IjIiIC8%2BPHBhcmFtIG5hbWU9InN1YmplY3Rsb2dpYyIgdmFsdWU9Im9yIiAvPjxwYXJhbSBuYW1lPSJ0ZXJtc2xvZ2ljIiB2YWx1ZT0ib3IiIC8%2BPC9wYXJhbXM%2BPGZpbHRlcnMgLz48cmFuZ2VzIC8%2BPHNvcnRzPjxzb3J0IGZpZWxkPSJwdWJsaXNoZWQiIG9yZGVyPSJkZXNjIiAvPjwvc29ydHM%2BPHBlcnNpc3RzPjxwZXJzaXN0IG5hbWU9InJhbmdldHlwZSIgdmFsdWU9InB1Ymxpc2hlZGRhdGUiIC8%2BPC9wZXJzaXN0cz48L3NlYXJjaD4%3D" 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>Air Quality Inside Buses</title><link>http://pubsindex.trb.org/view/2676946</link><description><![CDATA[In 2024, transit buses accounted for approximately 50.4% of total public transportation ridership in the United States, serving more than 3.86 billion riders. In-bus environmental quality plays a critical role in the health and safety of operators and passengers, as well as in overall system performance. Even on short trips, poor indoor air quality can cause discomfort and symptoms such as drowsiness, dizziness, nausea, and fatigue. TCRP Research Report 261: Air Quality Inside Buses presents research findings and practical approaches to air management in transit buses to help maintain a comfortable and safe environment for operators and passengers under normal and emergency conditions, including those involving airborne infectious diseases. The report also provides a comprehensive review of existing studies and current air management practices, with a focus on system configurations and their performance.]]></description><pubDate>Wed, 04 Mar 2026 08:56:10 GMT</pubDate><guid>http://pubsindex.trb.org/view/2676946</guid></item><item><title>Design of Revenue Service Adjustments for Urban Rail System Maintenance</title><link>http://pubsindex.trb.org/view/2672021</link><description><![CDATA[Urban railway systems require regular maintenance to uphold safe and efficient operations. System operators are sometimes forced to perform this maintenance during revenue service hours. The resulting changes in revenue service are called a “revenue service adjustment,” or RSA. Because RSAs typically feature planning horizons of months or even years, operators have an opportunity to design them in ways that minimize the level-of-service (LOS) impacts for passengers. This paper presents a framework for operators to consider LOS impacts early in the RSA planning process. First, a taxonomy of service delivery strategies is developed, where various strategies can be characterized by typical LOS impacts and operational considerations to efficiently identify options to consider. Second, a method is presented to develop optimal service plans to deliver the various strategies. Third, the distribution of LOS impacts on various groups of passengers is found for each service plan. Finally, considerations related to work planning and productivity are quantified and balanced against LOS impacts. The framework is demonstrated on a real-world case study from the Washington Metropolitan Area Transit Authority (WMATA) in which a single track needed to be removed from service. The results show that under the same resource constraints, different RSA design decisions can result in a large range of potential wait time impacts, with ratios ranging from 1.15 to 1.34 compared with normal revenue service, as well as a promising daily time period to perform work that balances a large increase in productivity with a smaller increase in LOS impacts.]]></description><pubDate>Fri, 20 Feb 2026 15:28:56 GMT</pubDate><guid>http://pubsindex.trb.org/view/2672021</guid></item><item><title>Spatial Accessibility for Women: A Systematic Literature Review</title><link>http://pubsindex.trb.org/view/2669694</link><description><![CDATA[“Accessibility” refers to people’s ability to reach a location, influenced by factors such as geography, available destinations, and the quality of the transport system. While socio-demographic factors are important determinants of accessibility, often resulting in inequalities, limited research has examined accessibility specifically for women. This is particularly true for mothers, who can have unique travel patterns and needs because of responsibilities in caring for and transporting children, often requiring adjustments to their travel to accommodate others. This study aims to understand the extent to which there is a focus on accessibility for women, and to what extent women experience lower accessibility than men. A systematic literature review revealed that, of the 98 studies that examined accessibility by socio-demographic group, only 18 studies specifically considered gender. Very few studies have examined women’s accessibility to destinations with regard to their roles in the household and caring responsibilities. Most studies used catchment-based methods to analyze women’s accessibility. Of the studies that focused on women, we found that women tended to have lower levels of accessibility to destinations than men. Women are more likely than men to have a significant role in caring for households and children, resulting in complex travel patterns. Several studies have shown that public transportation systems in some cities still do not meet these travel needs. Several research gaps need to be addressed, such as understanding the differences in women’s accessibility based on their roles, modes of transportation used, and travel purposes.]]></description><pubDate>Wed, 18 Feb 2026 16:27:47 GMT</pubDate><guid>http://pubsindex.trb.org/view/2669694</guid></item><item><title>Long-Term Trends in Activity Participation Observed with Crowdsourced Data from an Experiment with the “OneBusAway” Application</title><link>http://pubsindex.trb.org/view/2669693</link><description><![CDATA[We discuss the possibility of obtaining long-term travel behavior information through an open-source, crowdsourced, low-energy data collection effort. We utilized the “OneBusAway” travel planner that is used in several regions of the United States to obtain data from occasional and frequent transit users. From those willing to participate, locational data were obtained via Google’s Android Activity Transition Application Programming Interface that collects records as to when the user is changing their mode of movement among the possible states (still, walking, running, cycling, in vehicle). At a transition point the time and location were recorded. We discuss data completeness problems and an approach to clean the data to obtain continuous 24-h records of those users with missing elements during days marked as “unknown.” We further infer the home location of respondents based on the locations the volunteers primarily were located at night. Data were collected from 7,563 users, some of whom provided data for the whole period. We show trends in time spent in-vehicle, walking, and remaining still. Our findings suggest that, considering all activities, the COVID trends were less pronounced. Further, after COVID we found a rebound effect, followed by a decline in activity. In 2023 our sample, especially the residents of Seattle, spent more time being mobile again. The data further suggest that to quantify behavioral trends, it is important to distinguish those presumably experiencing a shift in behavior from those who are likely to have “settled into a pattern.”]]></description><pubDate>Wed, 18 Feb 2026 16:27:47 GMT</pubDate><guid>http://pubsindex.trb.org/view/2669693</guid></item><item><title>Estimating Benefits of Closing Gaps in Active Transportation Networks: A Guide</title><link>http://pubsindex.trb.org/view/2666816</link><description><![CDATA[This report presents methods and strategies to quantify the benefits of closing gaps in active transportation networks. The guide provides case studies, gap closure benefit quantification, and practitioner vetting and usability testing to support its findings. It was developed through a scan of applied methods, interviews, practical experience, and original research. The findings will prove useful to practitioners at state departments of transportation (DOTs) as well as regional and local governments. Active transportation users include pedestrians, bicyclists, e-bike users, and those who use personal conveyances. For active transportation users, these modes not only address mobility and accessibility needs but also increase levels of physical exercise, improve access to transit, and reduce out-of-pocket travel costs. For communities, the benefits from active transportation include more mobility opportunities, healthy and active lifestyles, and local and regional development opportunities. The presence of any gaps in the network reduces the accessibility of valued destinations for system users.]]></description><pubDate>Sat, 14 Feb 2026 19:11:44 GMT</pubDate><guid>http://pubsindex.trb.org/view/2666816</guid></item><item><title>Developing a Guide for Estimating Benefits of Closing Gaps in Active Transportation Networks</title><link>http://pubsindex.trb.org/view/2666817</link><description><![CDATA[The objective of this research was to produce a guide on how to estimate the benefits of closing gaps in active transportation networks that provides: A summary of existing methods to identify gaps; A typology of gaps that reflect user groups and contexts; A practitioner-ready methodology that uses an equity lens to estimate the economic, health, and social benefits of closing gaps; Effective approaches to prioritizing gaps for long-range planning, programming, or project development activities; and  Guidelines on how to effectively communicate the value of closing gaps to transportation decision-makers, local governments, communities, and other stakeholders. Walking, bicycling, and rolling are important and desirable modes of travel. Especially for short trips, shifting to active transportation is one step toward creating more active and healthier communities, improving air quality, and enhancing the vibrancy and resilience of local economies. The system of trails, sidewalks, bike lanes, and roads intended for walking, bicycling, or rolling is known as the active transportation network. In the United States, active transportation networks often have gaps—networks may be interrupted by missing facilities, restricted by vehicular traffic, blocked by human-built infrastructure, or they may fail to cross natural features. These gaps in the network can make active travel harder or feel less safe. People are more likely to walk, bicycle, or roll to nearby destinations if the network is complete and comfortable. Therefore, many communities are focused on increasing the connectivity of active transportation networks including the 40,000 miles of multiuse trails across the nation. Communities are increasingly asked to justify and evaluate investments of all types, including quantifying the benefits of building infrastructure for active transportation. The purpose of this research was to provide best practices for practitioners to estimate the benefits of closing gaps in active transportation networks.]]></description><pubDate>Sat, 14 Feb 2026 19:11:43 GMT</pubDate><guid>http://pubsindex.trb.org/view/2666817</guid></item><item><title>Factors Affecting Urban Sustainable Development by Metro Using the GCN–MGWR model</title><link>http://pubsindex.trb.org/view/2663302</link><description><![CDATA[This study investigates the relationship between urban subway passenger flow and land use intensity, proposing an innovative hybrid model that combines graph convolutional networks and multiscale geographically weighted regression (GMGWR). This model addresses the limitations of traditional methods in handling nonlinearity and spatial heterogeneity. Using metro data from Chengdu, Sichuan, China, this study analyzes the effects of various land use types on metro passenger flow during different time periods, revealing the spatial and temporal dynamics of land use on the urban rail transit system. The results indicate that land use characteristics are key determinants of urban rail transit passenger flow and that the effects of land use intensity on metro passenger flow exhibit dynamic characteristics that change with time and space. The innovation of this study lies in integrating machine learning and spatial econometrics methods. The proposed GMGWR model provides a more accurate representation of the complex nonlinear relationship between land use and metro passenger flow, offering urban transportation planners valuable strategies to enhance public transportation systems. By strategically planning land use around metro stations and promoting transit-oriented development policies, it is possible to create livable, pedestrian-friendly communities that foster green, sustainable urban growth.]]></description><pubDate>Wed, 04 Feb 2026 16:29:39 GMT</pubDate><guid>http://pubsindex.trb.org/view/2663302</guid></item><item><title>Analyzing the Impacts of the 9-Euro-Ticket on Mode Choice using GPS Panel Data and Discrete Choice Models</title><link>http://pubsindex.trb.org/view/2663171</link><description><![CDATA[Estimating behavioral parameters for mode choice typically relies on revealed or stated preference data. However, applying GPS-based revealed preference (GPS-RP) panel data in modeling mode choice, particularly in response to shocks or policy interventions, remains relatively rare and methodologically under-explored. This paper discusses the preparation, processing, filtering, and modeling of (semi-)automated travel diaries collected over 3 months, including the 9-Euro-Ticket fare policy intervention in Germany in 2022. By estimating two multinomial logit models, we investigated how this intervention influenced the value of travel time savings (VTTS) across different modes. Our findings revealed a substantial reduction in VTTS for public transportation during the intervention period, with values approximately half those in the months following the intervention, highlighting the profound impact of this nearly fare-free policy. This study debates the difficulties and complexities of estimating VTTS using GPS-RP data for urban travel behavior. It underscores the importance of robust preprocessing and filtering methodologies when handling complex GPS data, and discusses how the intervention’s effects on VTTS and project appraisal could inform future transportation policy and investment strategies.]]></description><pubDate>Mon, 02 Feb 2026 16:31:32 GMT</pubDate><guid>http://pubsindex.trb.org/view/2663171</guid></item><item><title>Headway Management Strategies for High-Frequency Urban Rail Transit: Dynamic Approach to Real-Time Train Holding in Metro Systems</title><link>http://pubsindex.trb.org/view/2663044</link><description><![CDATA[Urban rail transit systems frequently encounter challenges related to service reliability and passenger crowding, particularly during peak operational hours and in networks with complex service patterns. This paper presents an innovative approach to real-time train holding that addresses the unique challenges posed by systems with scheduled short-turning, where passenger loads at short-turning points can vary significantly. We developed a dual-strategy framework that combines (1) a real-time heuristic that calculates holding times using both historical data and real-time information to minimize passenger-experienced crowding, and (2) a predictive modeling approach that anticipates headway situations when full-length service trains from the terminal arrive at short-turning stations. Unlike conventional headway-equalizing strategies that overlook load variations in high-demand scenarios operating near capacity, our approach explicitly accounts for heterogeneous passenger loads across different service types to reduce denied boarding and passenger wait times. The effectiveness of our framework was evaluated using a microscopic simulation model of a high-frequency, high-demand urban rail transit system. The results demonstrate that the proposed approach reduced denied boarding incidents by 30% through improved train load balancing. The combination of predictive control with downstream holding strategies improved service quality through the proactive regulation of train dispatching at terminals, coupled with adjustments at key stations.]]></description><pubDate>Fri, 30 Jan 2026 09:03:53 GMT</pubDate><guid>http://pubsindex.trb.org/view/2663044</guid></item><item><title>Bayesian Network Investigation of Passengers with Disabilities’ Satisfaction with Public Bus Transportation</title><link>http://pubsindex.trb.org/view/2659297</link><description><![CDATA[Public transportation plays a vital role in promoting environmental sustainability and providing convenient transportation for the public. However, individuals with disabilities encounter significant challenges while using public transportation, necessitating an evaluation of their satisfaction levels to address the barriers they face and ensure social equality. Therefore, in this study, a questionnaire survey was conducted on people with disabilities using public bus transportation in Istanbul, Türkiye, and Bayesian network (BN) analysis was used to identify the most relevant factors affecting passenger satisfaction. Based on the findings, increasing bus frequency, improving accessibility, and adequacy of spaces designated for passengers with disabilities, and enhancing security measures are recommended. This study’s contribution lies in introducing a BN model to visualize probabilistic reasoning among factors, offering scenarios to reduce dissatisfaction levels, and identifying key factors affecting passenger satisfaction, demonstrating practical implications. In particular, the results provide a framework that can guide policy makers and transportation providers in translating statistical findings into concrete improvements in accessibility, service reliability, and passenger safety, enhancing the real-world travel experiences of people with disabilities.]]></description><pubDate>Tue, 27 Jan 2026 09:19:18 GMT</pubDate><guid>http://pubsindex.trb.org/view/2659297</guid></item><item><title>Assessing the Impact of Transit Right-of-Way on Service Reliability via Segment-Level Data Integration and Ensemble Learning</title><link>http://pubsindex.trb.org/view/2658636</link><description><![CDATA[Transit service reliability is a critical determinant of passenger satisfaction and system efficiency. While dedicated transit rights-of-way (ROW)—such as bus lanes and busways—are known to improve travel-time reliability, most existing studies are corridor-specific and do not generalize to system-wide planning. The analysis is often at the route level, ignoring potential reliability variation along routes. Moreover, the effects on travel-time variability of alternative ROW treatments, such as bus-on-shoulder, high-occupancy vehicle (HOV) lanes, and high-occupancy toll (HOT) lanes, remain underexplored. This study addresses these gaps by deriving segment-level reliability metrics using high-resolution automatic vehicle location data and automatic passenger count data for the entire transit network in the Minneapolis-St. Paul Twin Cities metropolitan area. A gradient boosting regression tree model is employed to evaluate how various ROW types, service characteristics, traffic conditions, and land use features affect travel-time variability. Results show that substantial reliability improvements occur only when over half of a route segment is dedicated to bus running, highlighting the limited impact of partial ROW implementations. The study also finds that bus-on-shoulder and HOV/HOT lanes offer limited reliability benefits under non-peak conditions. In addition to ROW, other factors, such as signal density and operating environments along route segments, also significantly affect their travel-time variability. The trained model can support scenario analysis and guide ROW planning by estimating the impacts of specific implementations and helping prioritize investments based on projected user benefits and reliability gains.]]></description><pubDate>Tue, 27 Jan 2026 09:19:18 GMT</pubDate><guid>http://pubsindex.trb.org/view/2658636</guid></item><item><title>2025 Cooperative Research Programs Annual Report</title><link>http://pubsindex.trb.org/view/2656285</link><description><![CDATA[The 2025 Cooperative Research Programs Annual Report highlights progress and provides an overview of the National Cooperative Highway Research Program (NCHRP), Transit Cooperative Research Program (TCRP), Airport Cooperative Research Program (ACRP), and the Behavioral Traffic Safety Cooperative Research Program (BTSCRP). In addition, the report outlines the Cooperative Research Program's history and structure, mission and vision, ongoing process improvement initiatives, and research themes or focus areas. For each research program information includes: oversight committee members; program history and mission; program financial report; role of sponsors/funding agencies; accomplishments and updates; current and pending projects with contract amount, status, start and end dates; and program publications.]]></description><pubDate>Wed, 21 Jan 2026 10:46:07 GMT</pubDate><guid>http://pubsindex.trb.org/view/2656285</guid></item><item><title>Passenger to Train Assignment Using Only Smart Card Data</title><link>http://pubsindex.trb.org/view/2654563</link><description><![CDATA[With the introduction of smart card systems, sophisticated data collection in urban railways has become possible. One key challenge is identifying the trains taken by passengers, which relies on synchronizing smart card data with train arrival data. This paper proposes a train-level assignment method based solely on smart card data. The proposed approach consists of four steps: tap-out time clustering, train arrival generation, train schedule generation, and trip assignment. First, tap-out times from smart card data were used to cluster trip-alighting patterns using the mean shift algorithm. Second, the train arrival schedule was generated by labeling each group with the earliest tap-out time. Third, these station-specific arrival schedules are connected to generate the overall train routes based on the passengers’ travel times between origins and destinations. Lastly, trips were assigned to the generated train schedules, and they were used to estimate congestion levels. The proposed approach was applied to Seoul metro Line 9 in South Korea. The results showed that the generated schedule was consistent with the actual train arrival data and produced coherent operational patterns across all stations. With the generated train schedules, the trip assignment was conducted, and the results showed that 48.7% of passengers used local trains, while 51.3% used express trains. The congestion levels were also identified with the generated train schedule and assigned trips. As such, the proposed approach contributes to trip assignment using smart card data in a simple manner.]]></description><pubDate>Tue, 20 Jan 2026 10:11:05 GMT</pubDate><guid>http://pubsindex.trb.org/view/2654563</guid></item><item><title>Public Transportation in Rural Areas and Tribal Communities: Guide for Service Improvements</title><link>http://pubsindex.trb.org/view/2646958</link><description><![CDATA[This report provides a guide to help public transit agencies in rural and tribal communities initiate new or enhance existing public transportation services to improve mobility and accessibility, enhance performance, and consider appropriate innovations. The guide will help transit providers determine the service designs and modes that are most appropriate within their community and support implementation of those services. This guide will be of immediate use to public transportation providers and stakeholders who plan and provide public transportation services in rural areas and tribal communities throughout the United States.]]></description><pubDate>Sat, 10 Jan 2026 11:18:43 GMT</pubDate><guid>http://pubsindex.trb.org/view/2646958</guid></item><item><title>Evaluating Accessibility and Equity Impacts of Pandemic Transit Service Adjustments: Case Study of the San Francisco Bay Area</title><link>http://pubsindex.trb.org/view/2647068</link><description><![CDATA[The COVID-19 pandemic forced transit agencies to quickly adapt to new challenges, with service reductions as part of the response to reduced ridership, rising fiscal pressures, and staffing shortages. However, approaches to service adjustment varied significantly across agencies. While pandemic literature often focuses on ridership impacts, less attention has been given to how transit service changes affect accessibility and equity. This study addresses this gap by examining the impacts of pandemic service adjustments on accessibility and equity; it is important to address this, given the absence of formal requirements for equity evaluation of temporary service changes. The analysis explored spatiotemporal patterns in service adjustment and evaluated the equity impacts on job accessibility for three major San Francisco Bay Area transit agencies in the U.S. Using publicly available transit schedule and census data, metrics for transit service levels, job accessibility, and accessibility inequality were used to trace changes from 2020 to 2023. The findings reveal distinct approaches to service reduction and restoration, with agencies prioritizing service differently based on travel needs and racial/ethnic minority populations. While equity briefly improved for some agencies during the pandemic, these changes were temporary, with all agencies returning to their pre-pandemic states of inequity. These insights can guide transit agencies in developing equitable service adjustment strategies and highlight the need for decision-making tools to help transit operators balance competing needs and respond flexibly to disruptions.]]></description><pubDate>Fri, 09 Jan 2026 16:59:37 GMT</pubDate><guid>http://pubsindex.trb.org/view/2647068</guid></item></channel></rss>