<?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=PHNlYXJjaD48cGFyYW1zPjxwYXJhbSBuYW1lPSJzdWJqZWN0aWQiIHZhbHVlPSIxNzc2IiAvPjxwYXJhbSBuYW1lPSJsb2NhdGlvbiIgdmFsdWU9IjIiIC8%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>Investigating Nonmotorist Crash Exposure at Highway–Rail Grade Crossings Using Artificial Intelligence-Based Object Detection and Generalized Linear Count Models</title><link>http://pubsindex.trb.org/view/2703807</link><description><![CDATA[A critical aspect of crash prediction models for highway–rail grade crossings (HRGCs) is crash exposure, which is a measure of train and highway traffic. Although data on motor vehicle traffic (e.g., annual average daily traffic) and train traffic at HRGCs are invariably available, nonmotorist traffic data at HRGCs are not readily available. Current Federal Railroad Administration and other HRGC crash models focus on train and motor vehicle traffic, overlooking nonmotorized traffic. Therefore, there is a need to gather nonmotorized traffic data to improve HRGC crash prediction models. To address this gap, nonmotorist traffic video data were recorded in this study at various urban and suburban HRGCs in Nebraska, followed by the application of an artificial intelligence-based You Only Look Once (version 8) algorithm for automated nonmotorist traffic volume detection. Data on HRGC characteristics, including surrounding area population density and land use, were collected to create a comprehensive HRGC safety database for nonmotorists. Three negative binomial models were estimated to analyze pedestrian, bicyclist, and combined nonmotorist exposure in relation to daily volumes, utilizing physical, dynamic, and temporal characteristics of HRGCs. Results indicated that sidewalks, greater visibility, and cloudy weather conditions were associated with increased nonmotorist traffic volume. Conversely, higher vehicular traffic levels, wet road conditions, low population density, and more traffic lanes correlated with lower nonmotorist traffic. This study established an initial framework for nonmotorist traffic monitoring and identified key environmental and technical challenges in automated detection at HRGCs; based on these findings, recommendations for addressing technical limitations were provided for future research.]]></description><pubDate>Tue, 19 May 2026 09:02:16 GMT</pubDate><guid>http://pubsindex.trb.org/view/2703807</guid></item><item><title>A Comprehensive Review of Vehicle–Pedestrian Interactions: Crash Analysis and Conflict Assessment Approaches</title><link>http://pubsindex.trb.org/view/2701383</link><description><![CDATA[Pedestrian–vehicle collisions remain a critical issue in transportation safety, contributing disproportionately to global traffic-related fatalities and injuries. Unlike vehicle occupants, pedestrians lack physical protection and are therefore more susceptible to severe or fatal outcomes when involved in crashes. Understanding the mechanisms of vehicle–pedestrian interactions and contributing factors to pedestrian crashes is essential for uncovering the crash nature and informing the development of effective safety countermeasures and technologies. This review synthesizes recent advancements in the study of pedestrian safety research at intersections, covering historical crash data–based modeling and analysis, as well as conflict-based studies using field observations and simulation. Key topics include the application of statistical and machine-learning models in crash likelihood and severity analysis, the use of surrogate safety measures, and the integration of conflict analysis frameworks such as extreme value theory. Critical challenges related to pedestrian safety modeling methodologies and evaluation metrics, the evolving safety implications in connected and automated vehicle environments, and the practical applications of these insights for policy and infrastructure design are discussed in depth. By reviewing methodological innovations and highlighting emerging research directions, this paper offers a comprehensive foundation for advancing pedestrian safety research and guiding the development of data-driven, context-sensitive policy, operation, and infrastructure solutions.]]></description><pubDate>Thu, 14 May 2026 17:01:53 GMT</pubDate><guid>http://pubsindex.trb.org/view/2701383</guid></item><item><title>Multi-Pedestrian Tracking Based on Improved YOLOv8 and OC-SORT</title><link>http://pubsindex.trb.org/view/2701297</link><description><![CDATA[Multi-pedestrian tracking is an important task for the environment perception systems of autonomous vehicles. In the multi-pedestrian tracking task, mutual occlusion, posture changes, small size, and poor lighting conditions usually pose challenges. To overcome these problems, we propose a detection-based multi-pedestrian tracking method, that is, combining the improved You Only Look Once (YOLO) v8 object detection algorithm with the improved observation-centric simple online and real-time tracking (OC-SORT) algorithm. Specifically, first, we improve the YOLOv8 pedestrian detector by constructing a C2f-Clo block, introducing an explicit visual center block, and designing a lightweight shared convolutional detection head. Second, we improve the OC-SORT tracker using a height-modified intersection over union. Results of experiments on the MOT17 and MOT20 pedestrian tracking datasets show that our method achieves 7.1% and 6.5% HOTA boosts, 8.5% and 8.8% MOTA improvements, 5.5% and 6.1% MOTP increases, 5.2% and 6.7% IDF1 boosts, and 648 and 692 IDSW decreases, respectively, compared with the baseline.]]></description><pubDate>Wed, 13 May 2026 17:00:16 GMT</pubDate><guid>http://pubsindex.trb.org/view/2701297</guid></item><item><title>Signal Control Optimization Method Considering the Effect of Pedestrian Crossing Violation</title><link>http://pubsindex.trb.org/view/2701228</link><description><![CDATA[The continuous increase in traffic organization refinement requirements is prompting signal control for pedestrian friendliness to receive more attention. Under signal control, pedestrian waiting tolerance time significantly affects signal performance. This study examines the effect of pedestrian crossing on vehicle operations during passing phases and proposes a signal control optimization method considering pedestrian crossing violations. This study establishes a crossing intention model based on pedestrian waiting tolerance time, calculates the number of crossing violations, proposes a method to calculate vehicle delays caused by crossing violations, and optimizes pedestrian crossing time allocation in the passing phase. A signal timing decision model considering pedestrian violations was established. The model includes vehicle delay signal optimization (VDSO), pedestrian violation-based signal optimization (PVSO), and coordinated pedestrian and vehicle signal optimization (CPVSO), with three submodels addressing different optimization objectives. These models were solved using the enhanced Fox optimization algorithm, with simulation experiments using real data. The results show that the CPVSO strategy reduces vehicle delays by 35% and pedestrian violations by 64% compared with traditional fixed signal timing. Sensitivity analysis for parameters, including pedestrian and vehicle flow, determined the main application ranges of the CPVSO, VDSO, and PVSO. This study reduces the probability of pedestrian violations during the red light period by optimizing combined pedestrian and vehicle phases while balancing vehicle passage efficiency, thereby decreasing pedestrian–vehicle collisions caused by violations.]]></description><pubDate>Mon, 11 May 2026 12:24:46 GMT</pubDate><guid>http://pubsindex.trb.org/view/2701228</guid></item><item><title>Examining Risk Factors Influencing Fatal Pedestrian Crashes on Rural Highways: Matched Case-Control Study Design</title><link>http://pubsindex.trb.org/view/2701225</link><description><![CDATA[Pedestrian fatalities on high-speed rural roads are a growing concern in low- and middle-income countries (LMICs), where infrastructure often prioritizes vehicular mobility over pedestrian safety. This study investigates the effects of factors contributing to fatal pedestrian crashes on high-speed rural roads in India using a matched case-control (C-C) study design. Six high-speed road stretches were selected, and data related to crashes, road geometry, built environment, pedestrian exposure, and traffic characteristics were collected. Four models using conditional logistic regression on four datasets with varying matching ratios (1:1 to 1:4) and one model using binary logistic regression on unmatched data were developed to estimate odds ratios for potential risk factors. Key predictors of fatal pedestrian crashes included the presence of junctions, authorized median gaps, service roads, bus stops, schools, roadside eateries, canals/bridges/culverts, petrol pumps, and segments with high pedestrian population exposure. Model 4 (1:4 matched) was the best fit, demonstrating that matching improved estimate precision and controls for confounding. Segments with medians showed protective effects, while the unauthorized median gap produced a counterintuitive result. This study highlights the impact of geometric and environmental factors on pedestrian safety in the rural areas of LMICs. It also demonstrates the feasibility and effectiveness of the matched C-C design in data-constrained settings. The findings provide valuable insights to guide targeted interventions, including infrastructural enhancements, speed management, and strategic planning of bus stop locations and pedestrian crossing facilities within rural settlements along highways.]]></description><pubDate>Mon, 11 May 2026 12:24:46 GMT</pubDate><guid>http://pubsindex.trb.org/view/2701225</guid></item><item><title>Analysis of Elementary School Children’s Street Crossing Safety Behavior Based on Random Parameters Ordered Logit Model</title><link>http://pubsindex.trb.org/view/2701111</link><description><![CDATA[In the context of rapid urbanization and increasingly diverse modes of transportation, urban traffic safety issues are becoming more pronounced. Elementary school children, as a vulnerable group in traffic environments, face significant challenges in ensuring safe street crossings. Therefore, it is crucial to conduct an in-depth exploration of the potential threats and hazards associated with their daily street crossing behaviors to mitigate road safety risks. This study carried out a field investigation at signalized intersections, aiming to thoroughly analyze the intrinsic connection between elementary school children’s street crossing behaviors and the severity of traffic conflicts. The random-parameter ordered logit model is used to analyze the data. The results show that intersection characteristics, unsafe behaviors, and the attributes of the road crossers significantly influence the severity of traffic conflicts. Specifically, shorter green light durations, shorter signal cycle lengths, and smaller crosswalk dimensions exacerbate conflict severity. While restricting right-turn signals can reduce moderate and minor conflicts to some extent, it may lead to more severe conflicts. Furthermore, unsafe behaviors such as running red lights and rushing across streets significantly increase the risk of severe conflicts, especially when children cross alone. Notably, the law-abiding behaviors of adults also affect the severity of conflicts. To enhance street crossing safety for elementary school children, it is recommended to extend green light durations, appropriately adjust crosswalk dimensions, and strengthen traffic safety education. Collaboration between parents and schools is essential to promote traffic safety education for children through example and supervision.]]></description><pubDate>Mon, 11 May 2026 08:51:42 GMT</pubDate><guid>http://pubsindex.trb.org/view/2701111</guid></item><item><title>Integrating Interdependencies of Demand for Public Transportation, Shared Micro-Mobility, and Land Use Within a System of Equations Modeling Framework</title><link>http://pubsindex.trb.org/view/2698382</link><description><![CDATA[Traditional and emerging transportation services in the form of public transportation and shared micro-mobility services, respectively, along with land use are typically hinted at as significant determinants of sustainable urban planning. This study evaluates the dynamic interrelationships between the demand for public transportation, shared micro-mobility services, and land use characteristics in a car-centric urban environment using a seemingly unrelated regression and two-stage least squares modeling approach. Beyond empirical findings, the study aims to develop a system-of-equations-framework enabling the estimation of interdependent urban mobility components. By analyzing data at the ZIP code level, the study assesses the extent to which factors such as distance to urban centers, road length, types of land use, the ridership of public transportation, and use of shared bikes affect population density. The results indicate that distance to urban center is negatively influenced by building density. While public transportation ridership is positively associated with building density and service frequency—highlighting the importance of accessible and frequent public transportation in dense areas—shared micromobility usage is found to be lower in such settings. This suggests that shared micromobility plays a more complementary role to public transportation in lower-density areas, where it can help bridge access gaps and extend the reach of fixed-route services. Moreover, the analysis on elasticities shows that road infrastructure influences the impacts of urban sprawl. The provided insights on the dynamics of urban mobility and land use can inform policymakers, highlighting the need of integrating transportation and land use planning on advocating sustainable urban mobility.]]></description><pubDate>Tue, 05 May 2026 10:16:53 GMT</pubDate><guid>http://pubsindex.trb.org/view/2698382</guid></item><item><title>Does Light Influence Route-Choice Behavior? Discrete-Choice Modeling Study</title><link>http://pubsindex.trb.org/view/2694544</link><description><![CDATA[Crowd management attempts to guide pedestrian crowds effectively and efficiently. Static crowd-management measures, such as fences, are often used to guide the crowd. Another way to steer pedestrian walking behavior is through “nudging,” that is, gently coaxing people into the “preferred” direction, for instance, by lighting conditions. This paper examines the impact of light intensity (brightness) and color on pedestrian route-choice behavior using data from a virtual reality (VR) experiment. The study develops two types of discrete-choice models—a panel mixed logit model and a latent class choice model—featuring the route-choice behavior of pedestrians under varying lighting conditions in a virtual maze in a controlled virtual reality experiment. We found that pedestrians avoided red and dark corridors and chose green and blue corridors. On average, the green light most effectively “pulled” people toward a specific route. In addition, this study uncovered three segments in the population: (1) light-sensitive individuals, (2) darkness-avoiding individuals, and (3) individuals with a severe right-handed bias. We found that the impact of color and brightness levels on route-choice behavior differed greatly across segments.]]></description><pubDate>Thu, 23 Apr 2026 09:10:01 GMT</pubDate><guid>http://pubsindex.trb.org/view/2694544</guid></item><item><title>From Perception to Prediction: Modeling Pedestrian Satisfaction Using Multilevel Statistical and Sensitivity Methods</title><link>http://pubsindex.trb.org/view/2693758</link><description><![CDATA[This study presents an integrated modeling approach to evaluate pedestrian satisfaction in new urban cities characterized by rapid growth and limited multimodal connectivity. A structured questionnaire, distributed to stratified participants across residential, administrative, and service zones, captured user perceptions of 13 key urban design features, including safety, accessibility, visual coherence, and economic vibrancy. Descriptive statistics and visual analytics revealed that accessibility, protection from crime and traffic, and urban aesthetics were strong correlates of satisfaction. To model these relationships quantitatively, the study employed both ordinal and multinomial logistic regression, with the latter achieving 92.45% classification accuracy. K-means clustering and principal component analysis further uncovered latent user typologies, highlighting the heterogeneity of pedestrian priorities. Local and global sensitivity analyses, including mutual information metrics, identified easy access, protection from traffic, and crime prevention as the most influential features. Response surface modeling illustrated nonlinear interactions among key variables, emphasizing the multidimensional and synergistic nature of satisfaction outcomes. The findings showed that pedestrian experience is shaped not by isolated design features, but by their interactive effects across spatial, psychological, and infrastructural domains. The study offers actionable insights for human-centered urban design, while the presented analytical framework is scalable and supports evidence-based interventions in emerging urban contexts.]]></description><pubDate>Fri, 17 Apr 2026 08:57:14 GMT</pubDate><guid>http://pubsindex.trb.org/view/2693758</guid></item><item><title>Modeling and Transferability of Pedal Cycle Volumes Using Ground Truth, Sensor, Weather, and Crowdsourced Data</title><link>http://pubsindex.trb.org/view/2692321</link><description><![CDATA[Understanding active transportation is critical for transportation planning, infrastructure development, and safety improvements. Unlike motor vehicles, which have widespread automated counting stations, cycling and walking automated counting has limited coverage. Given the limited data and unique characteristics of active transportation, it is crucial to evaluate the accuracy of counting technologies and account for temporal variations, weather effects, and transferability when estimating volumes. Data from four sites in Wisconsin were analyzed with 5 years of hourly sensor, weather, and Strava data, along with 268 h of manually processed ground truth video data. Ground truth hourly count trends showed that pedal cycles were the main users in the shared paths (78%–87%). There were peak and directional hourly trends by week or weekend days, higher volumes and a shift in the type of user were observed on weekends. Automatic sensor count data accuracy from inductive loop and infrared sensors was evaluated and compared with ground truth data. Inductive loop counting technology showed high levels of pedal cycle count accuracy (91%–92%). Infrared sensors counted passersby with a reduced degree of accuracy (54%–67%). Negative binomial regression modeling was implemented to account for overdispersion in the count data. Key predictors included time of day, day of the week, month, temperature, precipitation, and Strava counts. Site-specific models were developed, transferability across sites was assessed, and models were generalized with data from sites that shared similar characteristics applicable to high-volume, urban commuting and recreational paths. Models were not transferable to isolated sites with low volume and unreliable sensor count data.]]></description><pubDate>Wed, 15 Apr 2026 10:36:10 GMT</pubDate><guid>http://pubsindex.trb.org/view/2692321</guid></item><item><title>Midblock Pedestrian Crossing Volumes and Crash Rates in Milwaukee, WI</title><link>http://pubsindex.trb.org/view/2691798</link><description><![CDATA[Despite the majority of US fatal and severe pedestrian injuries occurring at midblock locations, few communities have collected counts to understand pedestrian midblock exposure and crash rates. This study developed a midblock pedestrian crossing count protocol and applied it to 61 street segments in the City of Milwaukee, WI. We counted midblock and adjacent intersection pedestrian crossings manually from 24-h video recordings. Midblock pedestrian crossings were common: 46 of the segments (75%) averaged more than one per hour. Among 48 segments with complete counts for both the midblock and an adjacent intersection crossing zone, 15 (31%) had more crossings in the midblock zone. We estimate that 17% of all pedestrian crossings along these 48 segments were midblock. Using these counts, we developed a negative binomial direct demand model of midblock pedestrian crossing volumes in Milwaukee. Midblock volumes were positively associated with nearby job density, commercial retail properties, and bus stops and negatively associated with posted speed limit and nearby parks. We demonstrated the value of this model by calculating pedestrian crash rates for all of our study segments and by estimating pedestrian crossing volumes for 133 additional street segments along seven roadway corridors. Expanding these methods beyond Milwaukee could lead to improved understanding of midblock pedestrian volumes and crash rates, ultimately helping communities reduce midblock pedestrian injuries and fatalities.]]></description><pubDate>Tue, 14 Apr 2026 10:08:40 GMT</pubDate><guid>http://pubsindex.trb.org/view/2691798</guid></item><item><title>MASH Evaluation of Minnesota Department of Transportation Bicycle and Pedestrian Bridge Rail and Hybrid Bridge Rail</title><link>http://pubsindex.trb.org/view/2691797</link><description><![CDATA[The Minnesota Department of Transportation (MnDOT) currently uses two bridge rail systems that are configured with a concrete bridge rail and a steel beam and post rail. The first is a concrete bridge rail with an attached bicycle and pedestrian rail and the second is a hybrid concrete bridge rail with a lower brush curb and an upper steel beam and post railing structure. Both bridge railings were previously developed and successfully crash tested under the safety criteria of NCHRP Report 350. MnDOT desired to evaluate these bridge railings to the current safety standards of AASHTO’s 𝘔𝘢𝘯𝘶𝘢𝘭 𝘧𝘰𝘳 𝘈𝘴𝘴𝘦𝘴𝘴𝘪𝘯𝘨 𝘚𝘢𝘧𝘦𝘵𝘺 𝘏𝘢𝘳𝘥𝘸𝘢𝘳𝘦 (MASH). The concrete railing with a bicycle and pedestrian rail was crash tested to MASH TL-3, and it satisfied all safety criteria for MASH test 3-11. The hybrid bridge railing was subjected to all three prescribed crash tests in the MASH TL-4 matrix. The two tests with passenger vehicles were conducted near the downstream end transition to a concrete end buttress to evaluate vehicle snag, while the single-unit truck test was conducted near the middle of the rail to evaluate the railing’s strength. The hybrid railing passed all safety performance criteria.]]></description><pubDate>Tue, 14 Apr 2026 10:08:40 GMT</pubDate><guid>http://pubsindex.trb.org/view/2691797</guid></item><item><title>Beyond Low-Stress Bicycle Lanes: Assessing the Role of Bicycle Network Density in Ridership</title><link>http://pubsindex.trb.org/view/2691032</link><description><![CDATA[While the installation of lower-stress bicycle facilities has been linked with greater increases in bicycle commuting, the extent to which facilities’ effectiveness is influenced by broader bicycle network characteristics remains unclear. To what degree does bicycle network density amplify the effect of bicycle facilities on bicycle commuting? Using multiple linear regression models and elasticity analyses, this study examined the interplay between bicycle facility installation and bicycle network density and their influence on bicycle commuting in 14,011 block groups across 28 U.S. cities. Findings suggest that bicycle network density exhibited stronger associations with ridership growth than the installation of individual facilities, with network effects exceeding facility installation effects by a factor of 4.6. More specifically, the installation of protected and buffered bicycle lanes was consistently and significantly associated with increased bicycle commuting, but the installation of standard bicycle lanes lost significance after the presence of a wider bicycle network was accounted for (the installation of shared-lane markings and off-road trails demonstrated non-significant relationships with bicycle commuter changes). Protected bicycle lane installations also produced meaningful ridership gains even in lower-density bicycle network contexts (elasticity of 0.48) with diminishing returns as bicycle network density increased (elasticity of 0.24). In contrast, higher-stress facilities demonstrated higher elasticities when moving from medium to high network density (elasticity of 0.57), indicating that their effectiveness is more dependent on a well-connected bicycle network. Taken together, these findings highlight the importance of prioritizing not only high-quality, low-stress bicycle facilities but also the development of continuous and connected low-stress networks.]]></description><pubDate>Mon, 13 Apr 2026 16:48:10 GMT</pubDate><guid>http://pubsindex.trb.org/view/2691032</guid></item><item><title>Does Height Matter? Analysis of Contributing Factors to Tall-Vehicle/Pedestrian Crashes</title><link>http://pubsindex.trb.org/view/2691021</link><description><![CDATA[With increasing urbanization, interactions between pedestrians and vehicles have become more frequent, raising safety concerns. Now comprising about 40% of the consumer fleet, tall vehicles such as trucks and sport utility vehicles pose unique risks with their elevated front profiles and large blind zones. This “car bloat” trend, combined with distracted and risky driving behaviors, has contributed to an 80% increase in pedestrian fatalities in the U.S. since its record low in 2009. In this study, an in-depth analysis of 15 years of single-vehicle/single-pedestrian crash data (2008–2022) from Wisconsin uncovers that tall vehicles, defined as 5.5 ft (66 in.) or greater in height, are disproportionately involved in crashes during left turns and backing maneuvers, with higher risks across specific pedestrian locations, pedestrian actions, and area type (urban versus rural). The results of a binary logistic model quantify that tall vehicle involvement was significantly associated with specific driver actions, such as left turns, as well as road types, and pedestrian presence in crosswalks, in addition to risk factors such as speed and driver behavior. The findings are instrumental to identify effective countermeasures to improve pedestrian visibility to tall vehicles and prioritize targeted strategies for roadway design, integrated planning, data-driven safety analysis, and targeted driver education addressing tall-vehicle risks.]]></description><pubDate>Mon, 13 Apr 2026 08:41:21 GMT</pubDate><guid>http://pubsindex.trb.org/view/2691021</guid></item><item><title>Commoning Cycling: Grassroot Initiatives for Inclusive Mobility Transitions Among People Facing Barriers to Cycling</title><link>http://pubsindex.trb.org/view/2686281</link><description><![CDATA[Improving the conditions for sustainable mobilities is the focus of considerable political and public attention. This paper identifies various ways that civil society initiatives promote cycling inclusion for marginalized groups facing barriers to cycling. An emphasis on commoning of cycling and cycling inclusion highlights how grassroot initiatives and their “commoners” take action to achieve an ambition to make cycling more inclusive in a bottom-up approach. In total, 12 initiatives have been studied, broadly categorized as bike kitchens, bike schools, bike to school, and bike promotion initiatives, located in Belgium, Greece, Italy, Portugal, Romania, and Sweden, respectively. These initiatives address groups that experience marginalization in the current mobility system (i.e., women with immigrant background, children, people with disabilities, seniors), and promote cycling through various forms of learning activities and skills to lower the thresholds to cycling and making cycling more diversified. The paper takes a holistic approach to shed light on the various ways that grassroot initiatives promote cycling inclusion, and, based on the experiences of participants and facilitators, explores what such initiatives may achieve given differences in context and target groups.?While the results show that cycling was mainly available for those participants that managed to adjust to the presumed norm of the confident, able-bodied, and individualized mobile subject, the initiatives had unexpected impacts on social dynamics of relevance to social justice. The results show the importance for cycling policy and planning to support concrete actions to improve the conditions for cycling to realize the full potential of cycling in the green transition.]]></description><pubDate>Fri, 03 Apr 2026 10:06:10 GMT</pubDate><guid>http://pubsindex.trb.org/view/2686281</guid></item></channel></rss>