Search Results (368)

This study focused on developing and validating machine learning models to predict pavement marking retroreflectivity using Light Detection and Ranging (LiDAR) intensity data. The retroreflectivity data was collected using a Mobile Retroreflectometer Unit (MRU) due to its increasing acceptance among states as a compliant measurement device. A comprehensive dataset was assembled spanning more than 1000 miles of roadways, capturing diverse marking materials, colors, installation methods, pavement types, and vehicle speeds. The final dataset used for model development focused on dry condition measurements and roadway segments most relevant to state transportation agencies. A detailed synchronization process was implemented to ensure the accurate pairing of retroreflectivity and LiDAR intensity values. Using these data, several machine learning techniques were evaluated, and an ensemble of gradient boosting-based models emerged as the top performer, predicting pavement retroreflectivity with an R² of 0.94 on previously unseen data. The repeatability of the predicted retroreflectivity was tested and showed similar consistency as the MRU. The model’s accuracy was confirmed against independent field segments demonstrating the potential for LiDAR to serve as a practical, low-cost alternative for MRU measurements in routine roadway inspection and maintenance. The approach presented in this study enhances roadway safety by enabling more frequent, network-level assessments of pavement marking performance at lower cost, allowing agencies to detect and correct visibility problems sooner and helping to prevent nighttime and adverse weather crashes. Full article

The rapid advancement of autonomous vehicle systems (AVS) has introduced complex challenges to road safety. While some studies have investigated the contribution of factors influencing AV-involved crashes, few have focused on the impact of vehicle-specific factors within AVS on crash outcomes, a focus that gains importance due to the absence of a human driver. To address this gap, the advanced machine learning algorithm, LightGBM (v4.4.0), is employed to quantify the potential effects of vehicle factors on crash severity and collision types based on the Autonomous Vehicle Operation Incident Dataset (AVOID). The joint effects of different vehicle factors and the interactive effects of vehicle factors and environmental factors are studied. Compared with other frequently utilized machine learning techniques, LightGBM demonstrates superior performance. Furthermore, the SHapley Additive exPlanation (SHAP) approach is employed to interpret the results of LightGBM. The analysis of crash severity revealed the importance of investigating the vehicle characteristics of AVs. Operator type is the most predictive factor. For road types, highways and streets show a positive association with the model’s prediction of serious crashes. Crashes involving vulnerable road users can be attributed to different factors. The road type is the most significant factor, followed by precrash speed and mileage. This study identifies key predictive associations for the development of safer AV systems and provides data-driven insights to support regulatory strategies for autonomous driving technologies. Full article

During the safety planning stage, accurate crash prediction tools are critical for prioritizing countermeasures and allocating resources effectively. Traditional statistical approaches, while long applied in this field, often depend on distributional assumptions that may introduce bias and limit model accuracy. To address these issues, studies have started exploring Machine Learning (ML)-based techniques for crash prediction, particularly for higher functional class roads. However, the application of ML models on two-lane highways remains relatively limited. This study aims to develop an approach to integrate traffic, geometric, and critically, speed-based factors in crash prediction using Random Forest (RF) and SHapley Additive exPlanations (SHAP) techniques. Comparative analysis shows that the RF model improves crash prediction accuracy by up to 25% over the traditional Zero-Inflated Negative Binomial model. SHAP analysis identified AADT, segment length, and average speed as the three most influential predictors of crash frequency, with speed emerging as a key operational factor alongside traditional exposure measures. The strong influence of speed in the RF–SHAP results depicts its critical role in the safety performance of two-lane highways and highlights the value of incorporating detailed operating characteristics into crash prediction models. Overall, the proposed RF–SHAP framework advances roadway safety assessment by offering both predictive accuracy and interpretability, allowing agencies to identify high-impact factors, prioritize countermeasures, and direct resources more efficiently. In doing so, the approach supports sustainable safety management by enabling evidence-based investments, promoting optimal use of limited transportation funds, and contributing to safer, more resilient mobility systems. Full article

This study applies the Log-Periodic Power-Law (LPPL) framework to three major equity markets—Mexico (IPC), Brazil (IBOVESPA), and the United States (NYSE Composite)—using daily closes from 8 November 1991–30 January 2025 for IPC and NYSE, and 3 May 1993–30 January 2025 for IBOVESPA. Multi-window calibrations (L$ϵ$ 180, 240, 300, 360, 420) are estimated in raw and log space to evaluate bubble signatures and the stability of the critical time $t_c$. Across all indices, log-space fits consistently outperform raw fits in terms of RMSE and $R^2$, and longer windows reduce parameter variability, yielding coherent clusters of $t_c$. Under full-sample conditions, the LPPL structure points to March–April 2025 for NYSE, mid-October 2025 for IBOVESPA, and October–December 2025 for IPC, while shorter windows pull $t_c$ forward. A rolling early-warning ensemble translates these estimates into lead-based risk bands, with numerical reporting used when median leads fall just outside the 60-trading-day decision horizon. The early-2025 weakening in the U.S. market is consistent with the NYSE cluster, whereas Brazil and Mexico remain within their projected windows as of September 2025. The analysis highlights the strengths of LPPL—behavioral interpretability and hazard-based framing—while noting limitations such as window sensitivity and parameter sloppiness, reinforcing the need for conservative communication and the use of longer-window weighting in practical applications. Full article

The automatic emergency braking (AEB) system plays a crucial role in reducing rear-end collisions and is mandatory on certain heavy-duty vehicles, with future regulations extending to passenger cars. However, most current AEB systems are designed based on onboard sensors such as cameras and radar, which may fail to prevent collisions in scenarios where the lead vehicle is already in a collision. To address this issue, this study proposes an enhanced AEB control method based on Vehicle-to-Vehicle (V2V) communication and onboard sensors. The method utilizes V2V communication and onboard sensors to predict obstacles ahead, applying effective braking when necessary. Simulation results in Matlab/Simulink R2022a show that the proposed V2V-based AEB control method reduces the risk of chain collisions, ensuring that the ego vehicle can avoid rear-end collisions even when the lead vehicle is involved in a crash. Three simulation scenarios were designed, where both the subject vehicle and the lead vehicle travel at 120 km/h. The following three distances between the subject vehicle and the lead vehicle were considered: 45 m, 70 m, and 30 m. When the lead vehicle detects an obstacle 30 m ahead and suddenly applies emergency braking, the lead vehicle fails to avoid a collision. In this case, the subject vehicle, equipped only with onboard sensors, is also unable to successfully avoid the crash. However, when the subject vehicle is equipped with both onboard sensors and vehicle-to-vehicle communication, it can prevent a rear-end collision with the lead vehicle, maintaining a vehicle-to-vehicle distance of 1 m, 6.8 m, and 3.1 m, respectively, during the stopping process. This control method contributes to advancing the active safety technologies of autonomous vehicles. Full article

This study investigates the influence of roadway geometry and environmental conditions on traffic crash frequency along a 57 km mountainous segment of the BR-116/SP (Régis Bittencourt Highway), one of Brazil’s most critical freight and passenger corridors. A Generalized Linear Mixed Model (GLMM) with a Negative Binomial distribution was developed using monthly data aggregated by highway segment. Explanatory variables included traffic exposure, geometric design characteristics, and meteorological factors. The results revealed that horizontal curvature and longitudinal grade are key determinants of crash occurrence and that the interaction between these factors substantially amplifies crash risk. Specifically, segments with combined tight curvature (radius < 500 m) and moderate-to-steep grades showed up to a 4.3-fold increase in expected crash frequency compared with straight or flat sections. The model achieved satisfactory fit (RMSE = 1.273) and provided a robust framework for identifying high-risk locations. The findings highlight the importance of geometric consistency and integrated safety management strategies, contributing to sustainable transport management and offering methodological and practical contributions to data-driven road safety policies in Brazil. Full article

Vehicle remote driving services are increasingly used in urban settings. Yet, vehicle-operator communication time delays may pose a challenge for teleoperators in maintaining safety and efficiency. The purpose of this study was to examine whether Predictive Displays (PDs), which show the vehicle’s predicted real-time position, improve performance, safety, and mental workload under moderate time delays typical of 4G/5G networks. Twenty-nine participants drove a simulated urban route containing pedestrian crossings, overtaking, gap acceptance, and traffic light challenges under three conditions: 50 ms delay (baseline), 150 ms delay without PD, and 150 ms delay with PD. We analyzed the counts of crashes and navigation errors, task completion times, and the probability and intensity of braking and steering events, as well as self-reports of workload and usability. Results indicate that though descriptive trends indicated slightly sharper steering and braking under the 150 ms time delay conditions, the 150 ms time delay did not significantly degrade performance or increase workload compared with the 50 ms baseline. In addition, the PD neither improved performance nor reduced workload. Overall, participants demonstrated tolerance to typical 4G/5G network time delays, leaving little room for improvement rendering the necessitating of PDs. Full article

This study develops comprehensive Safety Performance Functions (SPFs) for various intercity road types in Saudi Arabia, including freeways, multilane highways, and two-lane two-way roads. Data spanning 2017–2019 were analyzed for five regions—Riyadh, Makkah, Eastern, Aseer, and Tabuk—using Negative Binomial (NB) regression models aligned with the Highway Safety Manual (HSM). A total of 26 SPFs were developed to predict total and fatal and injury (FI) crashes, incorporating contextual variables (e.g., tunnel density, U-turn frequency, high-speed vehicle proportion) and developing models separately for each region. It was found that as the median width increases on freeway roads in the Riyadh region, the predicted number of total and fatal and injury crashes decreases. Also, as the percentage of heavy vehicles and U-turn density increases, the number of total and fatal crashes increases on multilane roads in the Makkah region. Moreover, as the degree of curvature increases, the predicted number of total and fatal andinjury crashes increase on multilane and two-lane two-way roads in Tabuk. Lastly, in Aseer, median double marking and tunnel density along curves were significantly affecting crashes on two-lane two-way roads. This study is useful to enhance the methodology used to identify hotspots on the intercity roads in KSA. Full article

Background/Objectives: The Manchester Colour Wheel (MCW) was developed as an alternative way of assessing health status, mood and treatment outcomes. There has been a dearth of research on this alternative assessment approach. The present study examines the sensitivity of the MCW to pain, psychological factors and recovery status in 1098 people with insured injuries treated in an interdisciplinary clinic. Methods: A deidentified data set of clients treated in a multidisciplinary clinic was conveyed to the researchers, containing results of MCW and injury-specific psychometric tests at intake, as well as recovery status at discharge. Systematic machine modelling was applied. Results: There were no significant differences between the four injury types studied: motor crash-related Whiplash Associated Disorder (WAD) and workplace-related Shoulder Injury (SI), Back Injury (BI) and Neck Injury (NI) on the MCW. Augmenting the MCW with Machine Learning (ML) models showed overall classification rates for Classification and Regression Tree (CRT) of 75.6% for Anxiety, 70.3% classified for Depression and 68.5% for Stress, and Quick Unbiased Efficient Statistical Trees could identify 68.5% of Pain Catastrophisation and 62.7% of Kinesiophobia. Combining MCW with psychometric measurements markedly increased the predictive power, with a CRT model predicting WAD recovery status with 80.7% accuracy, SI recovery status 81.7% accuracy and BI recovery status with 78% accuracy. A Naïve Bayes Classifier predicted recovery status in NI with 96.4% accuracy. However, this likely represents overfitting. Conclusions: Overall, MCW augmented with ML offers a promising alternative to questionnaires, and the MCW appears to measure some unique psychological features that contribute to recovery from injury. Full article

The paper proposes a pavement safety index, the estimated available friction at the expected travel speed, FRS(v), to model the composed effect of low-slip speed friction and macrotexture on roadway crashes. This index seems to capture the relative contributions of microtexture and macrotexture across different operating speeds. Speed-dependent available friction at 40, 55, and 70 mph was estimated using the speed-correction procedure in ASTM E1960-07 and integrated into Safety Performance Function (SPF) development. Comparison of the resulting SPF models suggests that FRS values corresponding to typical operating speeds can capture the combined influence of SFN (40) and macrotexture on expected crashes for freeways and rural two-lane, two-way highways. For freeways, the estimated available friction at 70 mph (FRS113) produced the most appropriate SPF, evidenced by the lowest AIC. For rural two-lane, two-way highways, the estimated available friction at 40 mph (FRS65) resulted in the lowest AIC value, consistent with the typical operating speeds on these facilities. In contrast, none of the speed-specific friction estimates produced satisfactory model performance for urban and suburban arterials, likely due to the wide variation in traveling speeds and geometric characteristics on these facilities. The applicability of the proposed metric was demonstrated through the development of illustrative investigatory friction levels based on observed crash data, and the identification of candidate roadway segments for friction improvement interventions, and the estimation of the corresponding return on investment for these interventions. Full article

Railroad-highway grade crossings (RHGCs) are critical points of conflict between roadway and rail systems, contributing to over 2000 crashes and 250 fatalities annually in the United States. This study applied machine learning methods (ML) techniques to model and predict crash frequency at RHGCs, using a comprehensive dataset from the Federal Railroad Administration (FRA) and Tennessee Department of Transportation (TDOT). The dataset included 807 validated crossings, incorporating roadway geometry, traffic volumes, rail characteristics, and control features. Five ML models—Random Forest, XGBoost, PSO-Elastic Net, Transformer-CNN, and Autoencoder-MLP—were developed and compared to a traditional Negative Binomial (NB) regression model. Results showed that ML models significantly outperformed the NB model in predictive accuracy, with the Transformer-CNN achieving the lowest Mean Squared Error (21.4) and Mean Absolute Error (3.2). Feature importance analysis using SHAP values consistently identified Annual Average Daily Traffic (AADT), Truck Traffic Percentage, and Number of Lanes as the most influential predictors, findings that were underrepresented or statistically insignificant in the NB model. Notably, the NB model failed to detect the nonlinear relationships and interaction effects that ML algorithms captured effectively. While only three variables were statistically significant in the NB model, ML models revealed a broader spectrum of critical crash determinants, offering deeper interpretability and higher sensitivity. These findings emphasize the superiority of machine learning approaches in modeling RHGC safety and highlight their potential to support data-driven interventions and policy decisions for reducing crash risks at grade crossings. Full article

Accurate prediction of road traffic crash severity is essential for developing data-driven safety strategies and optimizing resource allocation. This study presents a predictive modeling framework that utilizes Random Forest (RF), Gradient Boosting (GB), and K-Nearest Neighbors (KNN) to estimate segment-level frequencies of fatalities, serious injuries, and slight injuries on Hungarian roadways. The model integrates an extensive array of predictor variables, including roadway geometric design features, traffic volumes, and traffic composition metrics. To address class imbalance, each severity class was modeled using resampled datasets generated via the Synthetic Minority Over-sampling Technique (SMOTE), and model performance was optimized through grid-search cross-validation for hyperparameter optimization. For the prediction of serious- and slight-injury crash counts, the Random Forest (RF) ensemble model demonstrated the most robust performance, consistently attaining test accuracies above 0.91 and coefficient of determination (R²) values exceeding 0.95. In contrast, for fatalities count prediction, the Gradient Boosting (GB) model achieved the highest accuracy (0.95), with an R² value greater than 0.87. Feature importance analysis revealed that heavy vehicle flows consistently dominate crash severity prediction. Horizontal alignment features primarily influenced fatal crashes, while capacity utilization was more relevant for slight and serious injuries, reflecting the roles of geometric design and operational conditions in shaping crash occurrence and severity. The proposed framework demonstrates the effectiveness of machine learning approaches in capturing non-linear relationships within transportation safety data and offers a scalable, interpretable tool to support evidence-based decision-making for targeted safety interventions. Full article

Although many instruments assess driving behaviour, the validity of the Chinese versions of these tools in assessing the driving behaviours of drivers aged over 60 years remains largely unexamined. Additionally, the number of Chinese drivers over 55 obtaining licenses continues to rise, yet links between risky driving and crashes in this group are underexplored. To address these gaps, the Road Traffic Behaviours Questionnaire (RTBQ) was adapted to 320 drivers aged 55–65 years. Participants completed questionnaires assessing personality traits, social desirability, and driving behaviour. The finalized Chinese version of the RTBQ contains 13 questions and demonstrates excellent reliability. Significant associations among the RTBQ score, personality traits, social desirability and aggressive and prosocial driving behaviours suggest that its convergent and discriminant validity are acceptable. Finally, drivers with previous traffic accidents scored significantly higher on the RTBQ than those without traffic accidents, indicating its known-group validity is satisfactory. The RTBQ score can also predict traffic accidents in the following 6 months. The reliable and validated RTBQ has the potential to be used for subsequent research on Chinese drivers aged 55–65 years and provides empirical evidence for traffic safety policy-making in China. Full article

Texting while driving (TWD) is a leading cause of distracted driving-related crashes, especially among college students. This study applied the Multi-Theory Model (MTM) of health behavior change to predict initiation and sustenance of refraining from TWD among university students. A cross-sectional survey was conducted among 164 students from a Southwestern U.S. public university using a 49-item validated MTM-based questionnaire. Structural equation modeling and hierarchical multiple regression analyses were employed to assess reliability, construct validity, and predictors of behavioral initiation and sustenance. Cronbach’s alpha coefficients ranged from 0.71 to 0.93, indicating strong reliability. The MTM demonstrated good fit (CFI = 0.950, RMSEA = 0.057 for initiation; CFI = 0.992, RMSEA = 0.039 for sustenance). Behavioral confidence (β = 0.30, p < 0.001) significantly predicted initiation, explaining 51.5% of the variance, while emotional transformation (β = 0.41, p < 0.001) and practice for change (β = 0.27, p = 0.0105) predicted sustenance, accounting for 61.5% of the variance. The MTM effectively explained both initiation and sustenance of refraining from TWD among college students. Interventions aimed specifically at reducing texting while driving should prioritize strengthening behavioral confidence for initiating change and supporting emotional transformation and practice-for-change strategies to sustain long-term abstinence from TWD. MTM-based approaches hold strong potential for designing theory-driven, culturally relevant distracted driving prevention programs. Full article

Road traffic crashes are a major global challenge, resulting in significant loss of life, economic burden, and societal impact. This study seeks to enhance the precision of traffic accident prediction using advanced machine learning techniques. This study employs an ensemble learning approach combining the Random Forest, the Bagging Classifier (Bootstrap Aggregating), the Extreme Gradient Boosting (XGBoost) and the Light Gradient Boosting Machine (LightGBM) algorithms. To address class imbalance and feature relevance, we implement feature selection using the Extra Trees Classifier and oversampling using the Synthetic Minority Over-sampling Technique (SMOTE). Rigorous hyperparameter tuning is applied to optimize model performance. Our results show that the ensemble approach, coupled with hyperparameter optimization, significantly improves prediction accuracy. This research contributes to the development of more effective road safety strategies and can help to reduce the number of road accidents. Full article