TRB Publications Index

Uncovering Probabilistic Crash Risk Among Coach Drivers Using a Bayesian Network Based on Violation and Crash Records

Fri, 17 Jul 2026 08:40:49 GMT

Passenger transport is frequently associated with high-fatality crashes, highlighting the need for enhanced risk management. However, most risk-assessment approaches rely on cross-sectional data and fail to capture the cumulative nature of unsafe driving patterns. This study developed a static Bayesian network and a dynamic Bayesian network using violation and crash records from 9,894 coach drivers in Shanghai (2021–2024) to examine causal factors and cumulative crash risk. Overall, 14.75% of drivers were involved in one crash and 6.64% were involved in multiple crashes, while 24.83% committed one violation and 53.95% committed repeated violations. Violations were classified into five categories covering unsafe conditions and behavioral faults. A static Bayesian network identified a key causal pathway: License type/Company size → Passenger-transport violations → Roadway maneuver violations → At-fault crash. Sensitivity analysis showed large variability in crash probability (0.063–0.152), with no violations and consistently compliant operations as the strongest protective factors. Granger causality analysis identified habitual violation patterns (p<0.05), including improper lighting use and poor speed control, which were associated with increased subsequent crash risk. These findings informed an empirical dynamic Bayesian network, which achieved strong performance (Mean Absolute Error = 1.56%, Mean Relative Error = 19.02%) and over 80% predictive consistency. Overall, the study provides a behaviorally interpretable and temporally grounded approach for identifying high-risk drivers, enabling dynamic risk classification and supporting proactive safety-management strategies in coach-transport operations.

Using Cantabro Test Results to Predict Asphalt Mixtures’ Cracking Resistance: A Practical Decision-Support Tool During Production

Thu, 16 Jul 2026 09:36:23 GMT

The Virginia Department of Transportation (VDOT) has adopted the balanced mix design (BMD) framework for selected surface mixtures. The BMD framework provides a performance-oriented approach and offers the potential to overcome limitations commonly associated with traditional mix design methods that rely solely on volumetric requirements. Nonetheless, challenges remain with production-level BMD tests, often limiting producer’s ability to take timely corrective actions to address mixture composition issues that could affect cracking performance. To address this challenge, this study aimed to identify and quantify the impact of volumetric and gradation parameters on asphalt mixtures’ cracking performance. Over 140 mixture compositions were used to establish and verify cracking tolerance index (CT[subscript Index]) predictive equations. In addition to volumetrics and gradation, Cantabro mass loss (CML) data were used to help improve CT[subscript Index] predictions. Pairwise correlation and variance inflation factor analyses were used to remove collinear factors. A forward stepwise multiple linear regression method was used to identify significant factors and develop predictive equations under a tiered approach (Tier 1 through Tier 3) to reflect the different testing frequencies at which volumetrics and gradation data are obtained during production. Results showed that asphalt content, percent passing sieve #200, absorbed asphalt content, and voids in the mineral aggregate are key CT[subscript Index] predictors. The CML results further improved model accuracy. A risk assessment demonstrated that the predictive equation incorporating CML can serve as a practical decision-support tool, enabling practitioners to identify potential mix design issues and determine when additional testing is needed during production.

Evaluating Pedestrian Safety and Service at Alternative Intersections with Three-Critical-Phase and Two-Critical-Phase Traffic Signals

Thu, 16 Jul 2026 09:36:23 GMT

Intersections constitute an important part of transportation infrastructure, with conventional intersection designs being the most dominant. However, these conventional designs might face operational and safety issues, emphasizing the need for innovative and newer intersection designs to address these challenges. A key concern is the inadequate pedestrian accommodation in conventional intersections, primarily as a result of limited attention to pedestrian needs in older designs. On the other hand, alternative intersection designs may have the potential to improve pedestrian performance by reducing vehicle–pedestrian conflicts, shortening signal cycle lengths, and possibly decreasing pedestrian crossing distances. This paper focuses on the pedestrian safety and service of 10 types of alternative intersections with three-critical-phase traffic signals and two types with two-critical-phase traffic signals in a comparison with a signalized conventional intersection design (with four signal phases). The evaluation was conducted using a wide range of hypothetical scenarios in simulation modeling, the 20-design flag method from the National Cooperative Highway Research Program (NCHRP) Research Report 948, and the conflict point analysis to assess pedestrian performance. The findings suggest that three-phase intersection designs such as the partial median U-turn designs may offer better pedestrian operational and safety performance compared with conventional four-phase designs.

Modeling Expressway Crash Severity with Heterogeneity: Behavior-Specific Analysis of Speeding and Drowsy/Inattentive Driving with Text-Derived Features

Thu, 16 Jul 2026 09:36:23 GMT

This study investigates the heterogeneity in crash severity outcomes for three high-risk driving behaviors—speeding, drowsy driving, and inattentive driving—on South Korean expressways, using 17,876 crash records collected between 2019 and 2024. Leveraging the random threshold random parameter ordered probit model within a Bayesian estimation framework, the analysis accounts for both observed and unobserved heterogeneity by incorporating random parameters and flexible threshold structures. The dataset integrates structured crash data with unstructured narrative text from police reports, analyzed using natural language processing techniques such as term frequency-inverse document frequency and latent Dirichlet allocation to identify behavioral factors contributing to crash severity. Results indicate that drowsy-driving crashes were particularly associated with severe outcomes, especially when occurring on mainline expressway segments and when the offending driver was operating a truck. Inattentive-driving crashes were associated with greater severity outcomes when involving mixed vehicle types and when occurring on mainline facilities, whereas younger and female drivers tended to be linked with lower severity. Within the subset of speeding crashes, injury severity tended to be higher when the offending vehicle was a heavy vehicle such as a truck, whereas rainy conditions were associated with somewhat lower injury severity. These findings underscore the importance of behavior-specific modeling in safety analysis and demonstrate the utility of integrating behavioral text-mining approaches with heterogeneity modeling to improve interpretability and policy relevance.

Transit Agency Customer Experience Programs

Thu, 16 Jul 2026 09:08:41 GMT

In recent years, the public transit industry has seen a significant trend toward establishing formal Customer Experience (CX) programs, with over 60 U.S. transit agencies creating programs to better understand rider needs, instill a customer-first approach to their services, and build ridership and community support. However, the rapid adoption of these programs has occurred without industry-wide standards, leading to a wide variety of organizational models and approaches. This report, TCRP Synthesis 187, produced by the Transportation Research Board's (TRB’s) Transit Cooperative Research Program (TCRP), identifies emerging practices within transit agency CX programs across key themes, including program design, organizational structure, and performance measurement. In addition to results from an industry scan and survey, the report includes five case examples and highlights how CX programs are evolving within the transit industry, becoming a distinct professional specialization that increasingly relies on integrated data, performance measurement, and multidisciplinary collaboration. The report addresses the importance of strategic planning, investment, and the use of an “Influence Model” to guide CX initiatives. It also finds that CX programs are delivering measurable improvements for riders, and it identifies gaps and opportunities for future research to strengthen program effectiveness and advance the field.

Multi-Sensor Drone-Based Imaging Synthetic Aperture Radar System for Railway Bridge Timber Inspection

Thu, 16 Jul 2026 09:08:41 GMT

Regular inspection of wooden railroad bridges is critical to public safety and infrastructure reliability. Traditional inspection methods often require workers to access hard-to-reach areas, making inspections dangerous, time-consuming, and costly. Many bridges are located in remote areas, making them especially difficult to inspect thoroughly using manual techniques. This report, the Transportation Research Board's (TRB’s) Rail Safety Innovations Deserving Exploratory Analysis (IDEA) Final Report for Project RS-57, evaluates the feasibility of and challenges associated with deploying a drone-based imaging synthetic aperture radar (iSAR) system to improve the inspection of wooden railway bridges and pilings. The concept integrates radar, optical, stereoscopic imaging, infrared, and positioning sensors on a heavy-lift drone platform to perform non-destructive, remote assessments of timber structures that are traditionally challenging, hazardous, or costly to inspect manually.

Bridges to the Future: Training Inspectors with Simulation-Based Interactive Storytelling

Thu, 16 Jul 2026 09:08:41 GMT

State departments of transportation (DOTs) face a critical and growing gap in their bridge inspection workforce. Traditional on-the-job (OTJ) and instructor-led training models, while essential, are becoming increasingly difficult to scale because the experts who deliver them are leaving the workforce. In addition, classroom training alone cannot capture the tacit, context-dependent judgment that experienced inspectors rely on every day. This report, the Transportation Research Board's (TRB’s) National Cooperative Highway Research Program (NCHRP) Innovations Deserving Exploratory Analysis (IDEA) Final Report for Project 260, describes a simulation-based training platform that uses interactive storytelling to accelerate the development of entry-level bridge construction inspectors by immersing them in realistic, branching scenarios built from the tacit knowledge of senior inspectors. The platform addresses reinforcing steel placement, bearing pile driving, and post-construction inspection. The platform supports dual-platform deployment, delivering training as both a desktop video game for standard PCs and an augmented reality experience using the Microsoft HoloLens 2 head-mounted display. It also includes a replicable methodology and open-source toolkit, including the interview protocol, narrative construction template, assessment instruments, and complete Unity codebase, that state DOTs can use to develop their own training scenarios tailored to their inspection priorities, enabling them to reuse and expand the platform without rebuilding it from scratch.

Local Resonance-Based NDE Technique for Rail Flaw Detection

Thu, 16 Jul 2026 09:08:41 GMT

Rail defects, such as transverse defects and rail foot fatigue cracking, remain a major safety concern for the railway industry. Conventional nondestructive evaluation (NDE) methods often face limitations in sensitivity, accessibility, inspection speed, and dependence on operator experience. These limitations underscore the need for a new inspection paradigm capable of delivering reliable, repeatable, and scalable rail NDE. This report, the Transportation Research Board's (TRB’s) Rail Safety Innovations Deserving Exploratory Analysis (IDEA) Program Final Report for Project RS-53, describes the development and validation of an innovative rail inspection technology based on the intrinsic local resonances of rails and defect-induced variations in those resonances. The approach leverages zero-group-velocity (ZGV) guided wave modes and cutoff-frequency resonances to enable accurate, efficient, and scalable rail defect detection. By exploiting anomalous local resonance signatures caused by internal rail defects that alter the rail's mass–stiffness distribution, the technology offers a promising new method for identifying flaws with greater reliability and efficiency.

Real-Time Warnings and Variable Speed Limits: Safety and Travel Reliability During Weather Events

Tue, 14 Jul 2026 13:34:59 GMT

Adverse weather conditions such as rain, snow, fog, and high winds pose significant challenges to roadway safety and mobility. These conditions not only increase the risk of crashes but also contribute to travel delays, congestion, and unpredictable driving behavior. To mitigate these risks, transportation agencies are increasingly deploying variable speed limit (VSL) and real-time warning (RTW) systems, which use real-time data to dynamically adjust speed limits and provide timely hazard alerts. This report, produced by Transportation Research Board's (TRB’s) National Cooperative Highway Research Program, is a guide designed to support transportation professionals in understanding, evaluating, and implementing these systems in response to adverse weather conditions. The strategies and case studies included in this guide are intended to help transportation agencies enhance roadway safety, improve traffic flow, and ensure drivers receive accurate, timely information to navigate hazardous conditions effectively. The same project that produced NCHRP Research Report 1176 also documented its methodology and findings in NCHRP Web-Only Document 447: Evaluating the Impacts of Real-Time Warnings and Variable Speed Limits on Safety and Travel Reliability During Weather Events.

Evaluating the Impacts of Real-Time Warnings and Variable Speed Limits on Safety and Travel Reliability During Weather Events

Tue, 14 Jul 2026 13:34:59 GMT

Adverse weather conditions such as rain, snow, fog, and high winds pose significant challenges to roadway safety and mobility. These conditions not only increase the risk of crashes but also contribute to travel delays, congestion, and unpredictable driving behavior. To mitigate these risks, transportation agencies are increasingly deploying variable speed limit (VSL) and real-time warning (RTW) systems, which use real-time data to dynamically adjust speed limits and provide timely hazard alerts. This report, NCHRP Web-Only Document 447, produced by the Transportation Research Board's (TRB’s) National Cooperative Highway Research Program, documents the methodology and findings associated with development of NCHRP Research Report 1176: Real-Time Warnings and Variable Speed Limits: Safety and Travel Reliability During Weather Events. Contents include: literature review, state of practice, real-word data analysis, driving simulation, web survey, synthesis of findings, and example case study application.

Fault Classification of Expressway Bridge Electromechanical System Using Fused Stack Sparse Long Short-Term Memory Networks

Tue, 14 Jul 2026 09:40:07 GMT

The state of an expressway bridge’s electromechanical system is crucial to the safety and efficiency of the expressway. Efficient identification of faults in these systems facilitates timely operation and maintenance. Accurately and robustly classifying faults in the electromechanical systems of expressway bridges, given the vast data dimensions and limited fault samples, is a challenging task. In this paper, we comparatively study several typical neural network models. Firstly, we construct the electrical information matrix of the expressway bridge electromechanical system as a tree structure. Secondly, recurrent neural network, gated recurrent unit, and long short-term memory (LSTM) are exploited as base models for comparison. Thirdly, we propose a new network architecture called the fused stack sparse long short-term memory (FSS-LSTM) network, which incorporates sparsity constraints into multi-layer LSTM, and apply this model to the fault classification of expressway bridge electromechanical systems. Finally, comparative experiments using supervisory control and data acquisition (SCADA) data from the Taizhou Yangtze River Bridge in China are conducted. Experiment results demonstrate that the proposed FSS-LSTM network outperforms other models in fault classification, achieving macro-recall, macro-precision, and macro-F1 scores of 0.9344, 0.9283, and 0.9313, respectively. Among the three fault classes—strain, overcurrent, and other external failures—the strain fault is the most difficult to classify. The proposed FSS-LSTM network achieved over 92.61% accuracy for strain faults, and 97.88% and 97.12% accuracy for the other two classes, respectively.

Initial Validation of Balanced Mix Design Performance Test Thresholds Through Laboratory, Field, and Accelerated Testing

Tue, 14 Jul 2026 09:40:07 GMT

Validation of balanced mix design (BMD) test thresholds is essential for building confidence in performance-based asphalt mix designs. To support Texas’ BMD implementation, multiple field test sections statewide have been monitored, accumulating data from preconstruction through several years of service. These sections, exposed to traffic levels ranging from 600 to 23,000 average annual daily traffic (AADT) and up to 1.0 million equivalent single-axle loads (MESALs), provide valuable insights into test thresholds across diverse traffic and regional conditions. Complementary data from three national center for asphalt technology test track sections subjected to accelerated loading compare traditional volumetric designs and BMD specifications. Additionally, materials from the original WesTrack test track were evaluated with current cracking and rutting tests to relate historical field performance with present protocols. Despite challenges in collecting long-term field data and variability from traffic and environmental factors, key conclusions emerged. The indirect tensile asphalt cracking test and Texas overlay tester effectively gauge cracking resistance differences among mixtures, correlating well with field outcomes. The Hamburg wheel tracking (HWT) test reliably identifies mixtures prone to rutting, supported by minimal rutting observed in Texas Department of Transportation (TxDOT) sections. The IDEAL-rutting test (IDEAL-RT) consistently differentiates rutting resistance and predicts field rutting performance, underscoring its value in mix design. Early reflection cracking observed within two years exhibited strong correlation with the cracking tolerance index (CT Index), indicating higher CT Index values improve cracking resistance. Gradation also significantly influences CT Index and crack progression rate (CPR) results, emphasizing the need to incorporate gradation-specific thresholds. These findings enhance understanding of BMD test strengths and limitations, informing refinements aimed at improving pavement durability statewide.

Experimental Study and Finite Element Analysis of Seismic Performance of Light Steel Stud Walls Clad with Different Composite Panels

Tue, 14 Jul 2026 09:40:07 GMT

Assembled cold-formed steel modular walls are the main lateral force-resisting elements of cold-formed steel homes. In this paper, low-cycle reciprocating loading tests were carried out on cold-formed thin-walled steel shear walls without sheathing panels and clad with three different types of sheathing panels (fire-resistant straw, gypsum, and oriented strand boards [OSB]). This paper analyzes the damage modes, shear strengths, and load–displacement curves of these shear walls, the influence of the sheathing panels’ strengths and elasticities, and the effect of the panels’ nail withdrawal resistance on the skin. The influence of different sheathing panels on the shear capacity of shear walls and the use of screws significantly improves the ratio of shear capacity to ultimate displacement, but reduces the energy dissipation coefficient of the combination of walls. By comparing the three different sheathing panels, fire-resistant strawboard provided the greatest increase in shear capacity for the modular wall, and gypsum board provided the least increase in shear capacity for the modular wall.

Exploring the Potential of Remote Sensing and Machine Learning for Scalable Sidewalk Condition Assessment

Mon, 13 Jul 2026 17:05:41 GMT

Sidewalk condition plays a critical role in ensuring pedestrian safety, accessibility, and compliance with regulatory standards. Conventional assessment methods typically involve manual inspections using categorical ratings, which are labor-intensive, subjective, and limited in spatial coverage. This study evaluates the use of satellite imagery and machine learning to support sidewalk condition assessments. A classification model was developed using synthetic aperture radar (SAR) imagery combined with sidewalk physical attributes, including width, slope, and material type. A random forest classifier was trained to predict four condition categories: good, fair, poor, and severe. To address the substantial imbalance in the distribution of classes, a binary formulation was also tested by grouping segments into defective and nondefective classes. Data resampling techniques combining under- and oversampling were applied to improve model performance. The results indicated that the binary model with combined sampling achieved the best performance, with a recall of 0.85 and G-mean of 0.81. Models trained on the original four classes showed lower performance owing to underrepresentation of the poor and severe categories. Feature-importance analysis highlighted SAR amplitude as the most influential predictor across all scenarios. The findings demonstrated the potential of SAR imagery to support scalable and data-driven evaluation of sidewalk conditions. This approach offers a viable complement to traditional inspection methods by enabling targeted resource allocation and broader spatial coverage in pedestrian infrastructure management.

Optimization of Hot-Mix Asphalt Rolling Pattern Using Ground-Penetrating Radar and Markov Decision Processes

Mon, 13 Jul 2026 08:47:46 GMT

Ground-penetrating radar (GPR) has been used for nondestructive evaluation of hot-mix asphalt (HMA) pavements including density prediction. HMA density is an acceptance quality characteristic (AQC) used across the U.S. AQCs are the basis for quality control and acceptance, and are used by agencies to determine contractors’ pay. Recently, roller-mounted GPR was introduced to monitor HMA density in real-time, enabling roller operators to make more informed decisions to avoid under- and over-compaction of HMA layers. In this study, a Markov decision process (MDP) is formulated to represent the rolling pattern optimization problem. This formulation accounts for GPR prediction error, density spatial variability, and uncertainty in density progression. The introduced MDP provides contractors and roller operators with a tool to minimize operational costs while achieving target density, thereby enhancing pavement service life and reducing maintenance activities. Data collected from several field projects were used in the MDP formulation. The benefits of using the developed MDP for compaction decisions were demonstrated using project data from Illinois, U.S. The analysis was conducted for an actual project scenario under Illinois’ quality control for performance (QCP) program and was extended to a hypothetical pay for performance (PFP) scenario to evaluate the generalizability of the approach under different risk levels. Compared with an experienced roller operator, MDP decisions reduced the construction time by 40.3% and 18.1% and increased the revenue by 9.7% and 50.2% for the QCP and PFP scenarios, respectively. Additional benefits in energy savings, reduced construction-related delays, and improved worker safety are expected.