<?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=PHNlYXJjaD48cGFyYW1zPjxwYXJhbSBuYW1lPSJzcGVjaWZpY3Rlcm1zIiB2YWx1ZT0iOTI2wqE1MTA1NcKhNTEwODTCoTI5NznCoTExMDUwN8KhOTQ0NzXCoTUwNzDCoTU0MjQiIC8%2BPHBhcmFtIG5hbWU9ImxvY2F0aW9uIiB2YWx1ZT0iMiIgLz48cGFyYW0gbmFtZT0ic3ViamVjdGxvZ2ljIiB2YWx1ZT0ib3IiIC8%2BPHBhcmFtIG5hbWU9InRlcm1zbG9naWMiIHZhbHVlPSJvciIgLz48L3BhcmFtcz48ZmlsdGVycyAvPjxyYW5nZXMgLz48c29ydHM%2BPHNvcnQgZmllbGQ9InB1Ymxpc2hlZCIgb3JkZXI9ImRlc2MiIC8%2BPC9zb3J0cz48cGVyc2lzdHM%2BPHBlcnNpc3QgbmFtZT0icmFuZ2V0eXBlIiB2YWx1ZT0icHVibGlzaGVkZGF0ZSIgLz48L3BlcnNpc3RzPjwvc2VhcmNoPg%3D%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>Virtual Inspection Systems for Highway Construction Projects: A Guide</title><link>http://pubsindex.trb.org/view/2786112</link><description><![CDATA[This report presents a guide for assessing the readiness of digital technologies to support remote virtual inspection (RVI) activities for highway construction projects. This guide was developed around an analytical methodology that incorporates stakeholder interviews, case studies, and a peer review workshop. This guide will be of immediate interest to highway construction engineers, project managers, inspectors, and surveyors seeking to integrate these technologies effectively to support inspection requirements.]]></description><pubDate>Sat, 03 Oct 2026 18:00:47 GMT</pubDate><guid>http://pubsindex.trb.org/view/2786112</guid></item><item><title>Virtual Inspection Systems for Highway Construction Projects: Developing a Guide</title><link>http://pubsindex.trb.org/view/2786113</link><description><![CDATA[This report, developed under NCHRP Project 10-112, offers practical guidelines for implementing digital technologies to support remote virtual inspection (RVI) in highway infrastructure projects. It evaluates the effectiveness, opportunities, and challenges associated with a range of RVI tools, including mobile devices, artificial intelligence (AI), machine learning (ML), lidar, small uncrewed aerial systems (sUAS), remote imaging, augmented reality and virtual reality (AR/VR), and closed-circuit television (CCTV). Drawing on an extensive literature review, a nationwide survey of state departments of transportation (DOTs), and a series of case studies, the report presents a structured methodology for assessing technologies and integrating them into existing inspection workflows. The findings highlight the potential of RVI technologies to improve inspection accuracy, efficiency, and safety. Survey responses indicate that mobile devices are currently the most widely used tool, while more advanced technologies like lidar and sUAS are emerging but less commonly applied. The report also identifies key barriers—such as cost, training, and infrastructure needs—and outlines future directions for broader adoption. A flexible technology assessment framework is introduced to help agencies evaluate the readiness and suitability of RVI technologies based on factors such as technical maturity, operational fit, cost-effectiveness, and implementation challenges. The framework can be tailored to meet the specific needs of individual agencies and project goals. Five case studies from DOT-led projects were used to validate the proposed approach, demonstrating general alignment with agency decision-making and revealing insights for further refinement. A collaborative workshop with DOT and industry stakeholders also informed the development of the Guide, emphasizing the importance of technology champions, return on investment (ROI) tracking, and data management planning. The report concludes with a set of actionable recommendations covering standardization of RVI procedures, workforce development, strategic planning, and addressing implementation hurdles. It also explores future trends, including the integration of AI, ML, and Internet of Things (IoT) sensors to enable more automated, real-time inspection workflows. Overall, the report underscores the transformative potential of RVI technologies in modernizing highway construction inspection. It provides a practical foundation to help transportation agencies navigate the shift toward more digital, efficient, and data-driven inspection practices.]]></description><pubDate>Sat, 03 Oct 2026 18:00:47 GMT</pubDate><guid>http://pubsindex.trb.org/view/2786113</guid></item><item><title>Pointer Amplifier for Vehicle Routing Problem: Solving Time-Constrained Vehicle Routing Problems with Finite Fleet Using End-to-End Neural Combinatorial Optimization</title><link>http://pubsindex.trb.org/view/2782953</link><description><![CDATA[Finding prompt and optimal solutions to the vehicle routing problem (VRP) is a prerequisite for efficient freight transport, seamless logistics, and sustainable mobility. This requires algorithms that can quickly produce accurate solutions in minimal computational time. Traditional optimization methods struggle with the real-world complexity of VRPs that involve various constraints and different objectives. Recently, advances in neural combinatorial optimization (NCO), which employs generative artificial intelligence to solve combinatorial problems, have demonstrated mature results with promising perspectives. We present an NCO solution method for a time-constrained capacitated VRP with finite vehicle fleet. Our method incorporates customer constraints dynamically, making it flexible and applicable across various transport logistics scenarios. The pointer amplifier, a mechanism introduced here, prioritizes urgent services during tour generation by directly rescaling attention scores in the pointer network. For capacity constraints, we apply reward function penalties with masking to exclude infeasible actions. The architecture uses an encoder-decoder following the Policy Optimization with Multiple Optima protocol and trained via the Proximal Policy Optimization algorithm. The proposed framework was successfully trained and validated for medium and large instances, providing competitive cost-efficient solutions while exhibiting robustness and generalization, compared with state-of-the-art heuristics. Finally, we provide a critical analysis of the solutions generated by NCO and discuss the challenges and opportunities of this new branch of deep learning algorithms.]]></description><pubDate>Tue, 29 Sep 2026 09:52:28 GMT</pubDate><guid>http://pubsindex.trb.org/view/2782953</guid></item><item><title>Spatial Analysis of Driving Under the Influence Origins in Seoul Using Geographically Weighted Machine Learning</title><link>http://pubsindex.trb.org/view/2781299</link><description><![CDATA[Driving under the influence (DUI) of alcohol remains a critical public safety concern, accounting for a significant proportion of road traffic injuries and fatalities. Although numerous predictive models have been developed to estimate DUI risk, many conventional approaches such as ordinary least squares (OLS), random forest (RF), and geographically weighted regression (GWR) are constrained by assumptions of spatial homogeneity, linearity, and multicollinearity. To overcome these constraints, this study applies a geographically weighted random forest (GW-RF) model to investigate the spatially varying determinants of DUI incidents in Seoul, South Korea. The analysis targets DUI occurrence density based on trip origin locations within each administrative neighborhood. Explanatory variables include the density of DUI offenses with or without crashes, alcohol-serving land use, transit accessibility, and demographic composition, reflecting environmental exposure and population characteristics. The results indicate that GW-RF achieves competitive predictive accuracy while more effectively accounting for spatial heterogeneity and reducing residual spatial autocorrelation compared with conventional models. Spatial cross-validation further confirms stable out-of-sample generalization across geographically separated partitions. The relative importance and marginal effects of key predictors, including the density of non-crash DUI offenses, alcohol-related crashes, and general food establishments, vary considerably across space. Partial dependence analyses reveal heterogeneous nonlinear effects, including threshold and saturation patterns. A spatiotemporal comparison reveals stronger nighttime clustering and increased influence of alcohol accessibility, indicating time-dependent variation in DUI risk mechanisms. These findings indicate that spatially adaptive machine learning offers an approach for capturing localized risk structures and supports the design of geographically targeted strategies for DUI prevention.]]></description><pubDate>Sat, 26 Sep 2026 12:29:33 GMT</pubDate><guid>http://pubsindex.trb.org/view/2781299</guid></item><item><title>A Final Approach Precision Management Model Based on Machine Learning</title><link>http://pubsindex.trb.org/view/2780465</link><description><![CDATA[The International Civil Aviation Organization published the Aviation System Block Upgrades in 2016, and mentioned that air traffic management should have a dynamic and time-based separation (TBS) management in the final approach. In this paper, a final approach precision management model is proposed to realize trajectory-level traffic management in the final approach and approach segment. To determine dynamic and TBS minima, a transformer-based model was introduced to realize the TBS concept. It is a new method and model, which is different from the traditional TBS system built by National Air Traffic Service (NATS) and EUROCONTROL in 2012. The experimental results show that TBS values generated by the model suggested, in aerodromes without TBS system, a potential 1-min gap on average from the current separations.]]></description><pubDate>Wed, 23 Sep 2026 15:57:02 GMT</pubDate><guid>http://pubsindex.trb.org/view/2780465</guid></item><item><title>Application of Machine Learning in Municipal Pavement Condition Predictions</title><link>http://pubsindex.trb.org/view/2780414</link><description><![CDATA[Machine learning (ML) has increasingly become a vital tool for accurate pavement condition prediction, enabling more effective maintenance planning and resource allocation. This study applies the random forest (RF) algorithm to predict the pavement condition index (PCI) for 2022 over a short forecast horizon of 4 years across residential and nonresidential (main, collector, and industrial) streets in Skellefteå Municipality, Sweden. The models are based on manually assessed pavement data from 2014 and 2018 and validated against the 2022 pavement condition data. Several performance metrics were applied to evaluate the models’ flexibility and generalization across a broad set of input variables, including pavement age (A), several distress mechanisms (D), previous PCI ratings (S) and average weekday traffic volumes (T). Models for nonresidential streets were further stratified by maintenance surface treatments to assess predictive performance across treatment types, integrating maintenance effects through pavement age and treatment characteristics. Among the residential street models, the RF(A+D+S) achieved the highest accuracy when using the combined dataset (2014 and 2018). Similarly, for nonresidential streets, the RF(A+D+S+T) model performed the best. Furthermore, model performance varied slightly across maintenance treatment categories. The most significant predictors were Status2018 (the PCI rating assessed in 2018) and the weighted distress value (WDV). Use of the combined dataset did not result in a notable improvement in prediction accuracy compared with use of the 2018 dataset alone. Overall, these findings from a geographically constrained dataset suggest that RF models may be useful for PCI prediction and may support data-driven pavement management at the municipal level.]]></description><pubDate>Wed, 23 Sep 2026 15:57:02 GMT</pubDate><guid>http://pubsindex.trb.org/view/2780414</guid></item><item><title>Comparison of Road Segment-Level Bicycle Volumes Estimation Models: Traditional Regression, Machine Learning, Large Language Models, and Novel Spatial Regression Approaches</title><link>http://pubsindex.trb.org/view/2777812</link><description><![CDATA[Estimating road segment-level bicycle volumes is essential for exposure-based safety analysis and infrastructure planning for vulnerable road users. Existing approaches often require extensive data collection, including short-term counts expanded using continuous counts or direct-demand models based on roadway, socioeconomic, and land-use characteristics. Recent methods incorporating machine learning, deep learning, and crowdsourced data have shown promise, but are often limited by data bias and inconsistent availability across jurisdictions. This study evaluates several benchmark methods, in addition to newly proposed approaches based on spatial proximity effects, including linear interpolation, Bayesian spatial negative binomial, random forest, large language models, graph convolutional networks (GCNs), and spatial lag regression (SLR). A dataset consisting of 1,127 road segments with a full year of bicycle count data (2025), along with site characteristics from the cities of Waterloo and Kitchener, Canada, was used to compute seasonal average daily bicycle volumes. Model performance was assessed using root mean squared error and mean absolute error across varying levels of data availability. The results indicated that the proposed SLR model consistently outperformed all other models across all data availability levels, including the GCN model. These findings demonstrate that incorporating spatial proximity and roadway characteristics can improve estimation accuracy without requiring large training datasets or complex model structures, providing a practical and interpretable solution for bicycle volume estimation in data-constrained environments.]]></description><pubDate>Tue, 15 Sep 2026 09:48:14 GMT</pubDate><guid>http://pubsindex.trb.org/view/2777812</guid></item><item><title>Toward Automatic Pavement-Condition Index Estimation: An Enhanced Multi-Sensor Deep Learning Framework for Comprehensive Pavement Distress Detection and Severity Assessment</title><link>http://pubsindex.trb.org/view/2777782</link><description><![CDATA[Timely and accurate pavement distress detection is essential for sustainable road maintenance. Traditional manual methods are costly, time-consuming, and prone to subjectivity. This study proposes a multi-sensor framework that integrates (Red-Green-Blue (RGB) imagery with low-cost depth sensing for automated detection, classification, and severity quantification of pavement distresses. The system identifies and categorizes 11 common distress types using convolutional neural networks and a sensor fusion strategy. Detected regions are projected onto three-dimensional point clouds to enable class-specific severity assessment. Pothole and rutting severity classification are examined as case studies. Using a low-cost Red Green Blue plus Depth (RGB-D) multi-sensor fusion framework, we report, for the first time, automated rutting severity classification integrated into Pavement-Condition Index (PCI) estimation as a proof-of-concept, alongside enhanced pothole severity quantification and classification. Results show high accuracy, with pothole severity correctly estimated in 96.2% of cases and rutting severity classified with 100% accuracy. The system also reliably detects shallow potholes as small as 15 mm in depth. By automating key measurements required for PCI estimation, this work lays a practical foundation for data-driven pavement evaluation. The proposed approach offers a scalable and cost-effective solution for infrastructure monitoring, reducing reliance on manual inspection and enhancing roadway management, while also demonstrating extensibility to other distress types.]]></description><pubDate>Tue, 15 Sep 2026 09:48:14 GMT</pubDate><guid>http://pubsindex.trb.org/view/2777782</guid></item><item><title>Evaluating Transfer-Learning Strategies for Multi-Label Classification of Concrete Bridge Defects with Limited Data</title><link>http://pubsindex.trb.org/view/2777781</link><description><![CDATA[Bridge inspection images often contain multiple co-occurring surface defects within the same scene, yet much of the existing literature has emphasized single-label classification tasks. This study examines how transfer learning strategy affects multi-label classification of concrete bridge defects using the Concrete Defect Bridge Image (CODEBRIM) dataset and three ImageNet-pretrained convolutional neural network backbones: VGG16, ResNet50, and EfficientNetB0. Each backbone was evaluated under an add-on configuration with a frozen backbone and a selective fine-tuning configuration, both using a common multi-label prediction head with six sigmoid outputs corresponding to the defect classes and the Background class. To further examine the effect of backbone adaptation, performance sensitivity to fine-tuning depth was analyzed by progressively varying the number of trainable final backbone layers. The results showed that selective fine-tuning consistently outperformed the frozen-backbone add-on configuration across all three backbones, and that performance did not improve simply by unfreezing more layers. Instead, moderate selective fine-tuning provided the strongest overall results. Among the evaluated configurations, VGG16 with the last three backbone layers unfrozen achieved the best holdout test-set performance, with a subset accuracy of 0.840 and an F1-score of 0.874. These findings indicate that transfer-learning design choices, particularly the extent of backbone adaptation, play a central role in multi-label bridge-defect classification on limited engineering datasets.]]></description><pubDate>Tue, 15 Sep 2026 09:48:14 GMT</pubDate><guid>http://pubsindex.trb.org/view/2777781</guid></item><item><title>Predicting the Frequency and Duration of Turn-Bay Spillovers and Mutual Lane Blockages over Congested Arterials for Proactive Coordinated Signal Controls</title><link>http://pubsindex.trb.org/view/2777777</link><description><![CDATA[Accurate prediction of queue sizes, spillovers, and lane blockages is critical for urban signal control and traffic management but remains challenging due to the time-varying and spatially interdependent nature of traffic dynamics. These challenges are especially pronounced in congested commuting arterials, where upstream disruptions rapidly propagate and affect traffic states and queue formation at multiple downstream intersections. To contend with such challenges in design of proactive real-time traffic control, this study presents a multibranch, multihead long-short-term memory (LSTM) system for queue dynamic prediction, including queue distance, onset time, and duration of bay spillovers or lane blockages. By assigning each upstream intersection its own dedicated LSTM branch, the proposed prediction model can learn and preserve location-specific temporal patterns at the target location and then exert its multihead fusion layer to receive and integrate these features to capture the information of cumulative traffic states and their impacts from upstream intersections. The proposed LSTM system is structured to reflect the spatial relations between a target intersection and its upstream intersections, and to maintain a balance between spatial resolution and computational efficiency. Performance evaluations using extensive simulations over MD 355 in Bethesda, Maryland, show that the proposed system achieves a 100% detection rate for all queue blockages, with exact predictions of onset times and durations and no false alarms. Compared with benchmark models such as the extended Kalman filter and standard recurrent neural networks, the proposed system demonstrates strong robustness in capturing spatial dependencies and persistent congestion patterns, key attributes for proactive real-time traffic signal control.]]></description><pubDate>Tue, 15 Sep 2026 09:48:14 GMT</pubDate><guid>http://pubsindex.trb.org/view/2777777</guid></item><item><title>Computation of the Dynamic Deflection Basin of Asphalt Pavement Based on Convolutional Neural Network, Long Short-Term Memory, and Attention Mechanism</title><link>http://pubsindex.trb.org/view/2775500</link><description><![CDATA[Accurately obtaining the dynamic deflection basin (DB) under falling-weight-deflectometer (FWD) loading is a critical indicator for pavement construction quality acceptance and pavement health condition assessment. To provide substantial and reliable data support for pavement construction and maintenance, this study proposes a dynamic DB database construction method based on convolutional neural network, long short-term memory, and attention mechanism (CNN-LSTM-AM). The model uses pavement structural layer parameters and static DB data derived from layered elastic system theory as inputs. Through CNN, local features are efficiently extracted, and the resulting feature vectors are transformed into time-series inputs for the LSTM network model to uncover temporal dependencies. Finally, AM is introduced to dynamically allocate weights to key information, enhancing the model’s ability to capture core features, ultimately constructing a dynamic DB generation model for asphalt pavements. The model demonstrates a relative mean absolute percentage error (MAPE) below 0.09% when predicting dynamic DBs for three-layer and four-layer pavement structures compared with theoretical calculations. Both FWD-measured dynamic DBs and three-dimensional finite element simulation results show high consistency with the computational outcomes based on this method, indicating the model’s excellent accuracy and generalization capability. This provides a new approach for batch acquisition of reliable dynamic DB data, supporting pavement modulus inversion and health assessment, and holds significant practical implications for optimizing maintenance decisions and reducing engineering costs.]]></description><pubDate>Tue, 15 Sep 2026 09:48:14 GMT</pubDate><guid>http://pubsindex.trb.org/view/2775500</guid></item><item><title>Domain Adaptation Framework for Turning Movement Count Estimation with Limited Data</title><link>http://pubsindex.trb.org/view/2772603</link><description><![CDATA[Accurate turning movement counts (TMCs) at intersections are crucial for traffic signal control, congestion mitigation, and road safety. Recent advancements in machine learning and data-driven approaches have offered promising alternatives for estimating TMCs. However, traffic patterns can vary significantly across different intersections because of factors such as road geometry, traffic signal settings, and local driver behaviors. This domain discrepancy limits the generalizability and accuracy of machine learning models when applied to unseen intersections. To address these limitations, this research proposes a novel framework leveraging domain adaptation (DA) to estimate TMCs at intersections by using traffic-controller-event-based data, road infrastructure data, and point-of-interest data. Evaluated on 30 intersections in Tucson, Arizona, U.S., the proposed DA framework consistently outperformed state-of-the-art models, achieving the lowest error values across all movements. The framework obtained mean absolute error values of 12.29 for left turns, 34.39 for through movements, and 16.17 for right turns. A similar pattern was observed for root mean squared error, with the model achieving 15.73 for left turns, 43.90 for through movements, and 20.08 for right turns, outperforming all comparison methods.]]></description><pubDate>Thu, 03 Sep 2026 09:08:04 GMT</pubDate><guid>http://pubsindex.trb.org/view/2772603</guid></item><item><title>Dual-Layer Machine Learning Approach for Predicting Switching Times of Vehicle-Actuated Signal Phases</title><link>http://pubsindex.trb.org/view/2768459</link><description><![CDATA[To enable green light optimal speed advisory systems and similar cooperative traffic applications, accurate predictions of traffic signal switching times are mandatory. However, in the case of traffic-actuated traffic signals, which adjust their switching behavior based on the current traffic situation, estimations cannot be derived based on bare statistics. Those types of traffic signals are most common in urban areas. While recent studies have applied machine learning models to this task, vehicle-actuated signal phases remain difficult to forecast because of their irregular and request-driven nature. To address this challenge, this study proposes a dual-layer machine learning (DLML) prediction procedure that separates event detection and timing estimation into two consecutive stages. The first layer classifies whether a signal phase will be released within the next 30 s; only in those cases does the second layer predict the exact switching time. As a benchmark, we apply a conventional time-to-signal-change prediction procedure. Both methods are implemented using the XGBoost algorithm and evaluated on real-world data from two traffic-actuated signalized intersections. The DLML approach consistently outperforms the conventional approach across all classification and regression metrics and reduces unnecessary computation by focusing prediction efforts on essential cases.]]></description><pubDate>Tue, 01 Sep 2026 09:06:46 GMT</pubDate><guid>http://pubsindex.trb.org/view/2768459</guid></item><item><title>Multi-Intersection Traffic Signal Control Based on Multi-agent Reinforcement Learning: A Cooperative Approach</title><link>http://pubsindex.trb.org/view/2767459</link><description><![CDATA[Adaptive traffic signal control has been widely investigated for several decades as an effective solution to mitigate urban traffic congestion. Recently, Multi-agent Reinforcement Learning (MARL) has emerged as a promising approach for optimizing traffic signal control in complex urban networks. However, the decision-making process becomes significantly more challenging in dynamic traffic environments involving multiple interconnected intersections. In such settings, effectively coordinating traffic signals using information collected from the Internet of Things remains a major challenge for improving the overall efficiency of road networks. Despite recent advances, existing MARL-based approaches still exhibit several limitations. First, many methods struggle to effectively integrate heterogeneous traffic information from complex traffic environments. Second, they often neglect the correlations among agents and fail to adequately capture the spatial–temporal dependencies between neighboring intersections. To address these challenges, this paper proposes a novel cooperative MARL-based approach for adaptive traffic signal control in multi-intersection networks. The proposed framework employs double deep Q-network architecture for each agent and introduces a cooperation mechanism that enables agents to share aggregated traffic information from neighboring intersections. Furthermore, a pheromone-based state representation is incorporated to model the collective traffic conditions and enhance coordination among agents. Extensive experiments conducted under different traffic scenarios demonstrate that the proposed approach significantly outperforms existing methods in relation to average pheromone intensity, average noise emission, and average waiting time. These results highlight the effectiveness of the proposed cooperative MARL framework in improving traffic efficiency and reducing congestion in multi-intersection urban traffic networks.]]></description><pubDate>Fri, 28 Aug 2026 08:33:48 GMT</pubDate><guid>http://pubsindex.trb.org/view/2767459</guid></item><item><title>What Really Drives Behavior Change? A Causal Analysis of Impaired Driving Prevention Factors Using Machine Learning and Survey Data</title><link>http://pubsindex.trb.org/view/2767445</link><description><![CDATA[Impaired driving kills over 10,000 people in the US each year. Whether educational campaigns cause behavior change remains unclear because many evaluations rely on correlational rather than causal evidence. This study addresses a fundamental question in transportation safety: Do educational campaigns truly cause changes in impaired driving behavior? We applied advanced causal inference methods, Linear Non-Gaussian Acyclic Model (LiNGAM), and the DoWhy framework, to survey data from 876 respondents to identify genuine causal drivers of behavior change intentions. Our approach combined LiNGAM-based causal discovery to learn the complete directed acyclic graph among variables, followed by DoWhy’s systematic causal effect estimation with backdoor adjustment to estimate average treatment effects (ATE). Multiple refutation tests validated the robustness of our findings. The results showed that perceived campaign effectiveness (ATE = 0.264), concern about neighborhood driving under the influence incidents (0.254), and belief in sobriety measure effectiveness (0.195) emerged as significant positive causal drivers of behavior change likelihood. In contrast, traditional awareness-based interventions, including campaign exposure (−0.125) and enforcement knowledge (−0.101), demonstrated negative causal effects, challenging conventional assumptions about information-based prevention strategies. These findings suggest that successful impaired driving prevention requires shifting from information dissemination to strategies that actively cultivate risk perceptions and intervention efficacy beliefs. This study’s findings show how modern causal discovery and validation tools can provide actionable evidence for policymakers, demonstrating that campaigns perceived as credible and locally relevant drive meaningful behavioral shifts, while mere exposure to educational content may be counterproductive.]]></description><pubDate>Thu, 27 Aug 2026 13:46:11 GMT</pubDate><guid>http://pubsindex.trb.org/view/2767445</guid></item></channel></rss>