Transportation Research Record: Journal of the Transportation Research Board最新文献

{"title":"Examining Factors Contributing to Motorcycle Collisions with Left-Turning Vehicles at Urban Intersection Locations","authors":"Henrick Haule, Eric Dumbaugh","doi":"10.1177/03611981241245989","DOIUrl":"https://doi.org/10.1177/03611981241245989","url":null,"abstract":"Motorcycle crashes account for a significant proportion of traffic-related fatalities on U.S. roadways. Compared with motor vehicles, motorcycles traveling straight ahead are more susceptible to collisions with left-turning vehicles at intersections (note – in a system where traffic travels on the right-hand side of the road). The limited knowledge of the causes and influences of this specific type of crash deters efforts to improve motorcycle safety and is partly influenced by two issues. First, significant variables are unknown; second, motorcycles comprise a small proportion of vehicles in the traffic stream. This study sought to understand the factors that may contribute to the disproportionate crash risk left-turning vehicles pose for motorcyclists while accounting for the imbalance of vehicle proportions. Data containing motorcycle-motor vehicle and motor vehicle-motor vehicle crashes involving left-turning motor vehicles at intersections in South Florida were collected from 2015 to 2017. The study applied logistic regression on a balanced dataset generated using the random oversampling technique. The proposed model improved the predictive accuracy and enabled the identification of factors contributing to motorcycle crashes with left-turning vehicles. A Bayesian network analysis was also applied to the balanced data to analyze the interrelationship of factors associated with motorcycle crashes with left-turning vehicles. Results indicated that the type of intersection and traffic control, time of day, age of drivers, sex of the motorcyclist, roadway type, and weather were significantly associated with motorcyclists’ susceptibility to collisions with left-turning vehicles. Recognizing these attributes could help devise engineering measures and policies for promoting motorcycle safety.","PeriodicalId":509035,"journal":{"name":"Transportation Research Record: Journal of the Transportation Research Board","volume":"24 24","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140968693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Preliminary Investigation into Using Resistance Techniques to Assess Concrete Curing","authors":"Etienne Beya Nkongolo, Dan King, Peter Taylor, J. Kevern","doi":"10.1177/03611981241245678","DOIUrl":"https://doi.org/10.1177/03611981241245678","url":null,"abstract":"Concrete curing is a critical stage during construction for volume stability, long-term strength development, and ultimate durability. Poor curing can lead to shrinkage, scaling, and other durability issues. Proper concrete curing maintains sufficient moisture in the concrete and allows continuous hydration. Curing for concrete pavements often involves the application of a membrane-forming curing compound to help minimize moisture evaporation and promote desirable concrete property development. However, assessing the application rate of curing compounds and effectiveness on freshly paved concrete is difficult, as most evaluation methods are performed on hardened concrete and are not applicable or difficult to assess for fresh concrete in the field. This study proposes electrical resistance as a measure to assess the drying behavior of fresh concrete to quantify the effectiveness of curing. The findings of this study demonstrate that resistance is able to distinguish between samples with and without curing compounds and significant differences in drying observed between the surface and relatively shallow depths. In addition, the testing techniques were able to differentiate between the quality and rate of curing compound application and evaluate performance across a variety of environmental conditions. These findings indicate that a resistance-based approach could be a low-cost and non-destructive technique to evaluate the effectiveness of curing compound applications in real-time.","PeriodicalId":509035,"journal":{"name":"Transportation Research Record: Journal of the Transportation Research Board","volume":"76 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140967977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Status, Challenges, and Trends of International Research on Roadside Safety","authors":"Lei Han, Zhigang Du","doi":"10.1177/03611981241242363","DOIUrl":"https://doi.org/10.1177/03611981241242363","url":null,"abstract":"Roadside safety refers to the assessment and improvement of safety measures related to roadside environment, design, management, and objects. It encompasses factors such as road design, signage, markings, traffic control devices, and roadside features, and its goal is to reduce accident risk, minimize injuries, and enhance overall safety and comfort for road users. To comprehensively summarize roadside safety research progress, this review retrieved 1637 English papers published between 2000 and 2022, using the Web of Science Core Collection database. VOSviewer software was utilized to visualize and analyze the literature, conduct a situational analysis of publication, create knowledge maps of the main research hotspots and trends, and summarize research status, methods, systems, challenges, and trends in this field. Results showed an overall increasing trend in relevant research. The countries, institutions, and journals contributing most are the United States, the University of Nebraska, and the Transportation Research Record, respectively. Current research hotspots include evaluation of roadside safety and risk levels, factors influencing roadside safety and driving risks, drunk and drug-impaired driving in relation to roadside traffic accidents, frequency and severity of roadside accidents, and roadside safety assurance techniques and improvement strategies. Current modeling methods mainly consist of mathematical statistical analyses and data-driven modeling based on machine learning. Future research should focus on comprehensive quantitative mapping of influencing factors and evaluation criteria, establishing an active-guidance-based evaluation system and optimization strategy, improving the accuracy of computational problems and model construction, and exploring theories and technologies of intelligent transportation for roadside safety management and improvement.","PeriodicalId":509035,"journal":{"name":"Transportation Research Record: Journal of the Transportation Research Board","volume":"101 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140968098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating Classical Airplane Boarding Methods Focusing on Higher-Risk Passengers during Post-Pandemics","authors":"Xiaojian Hu, Yang Hong, Zhiwei Cui, Tiancheng Xie, Wenjun Fu","doi":"10.1177/03611981241247179","DOIUrl":"https://doi.org/10.1177/03611981241247179","url":null,"abstract":"As the global civil aviation industry recovers and the restrictions imposed because of COVID-19 on the process of aircraft boarding gradually diminish, the issue of how to reduce health risks in special populations who are at higher risk of severe illness from COVID-19 during Post-pandemics has become urgent. In this paper, we propose a health metric for the health risks of boarding groups based on the seat risk metrics used during the COVID-19 pandemic, enabling the comparison of health risks among boarding groups. Secondly, based on the agent-based model using NetLogo, we evaluate the health risk of boarding groups from the boarding methods currently used in airline practice, using the health and efficiency metrics used during the COVID-19 pandemic. As a result, it was confirmed that health risk was associated with the boarding group sequences. As a result, specific boarding groups for high-risk groups are proposed when using the classical boarding methods for passengers at higher risk of severe illness from COVID-19. Our results show that considering the placement of high-risk groups in the reverse pyramid fourth boarding group will contribute to a faster boarding for all methods (20.5% reduction in time) and a lower risk of transmission within this group (73.6% reduction) compared with the standard random boarding procedure.","PeriodicalId":509035,"journal":{"name":"Transportation Research Record: Journal of the Transportation Research Board","volume":"32 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140969907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Field Validation of Deep-Learning-Based Ground Penetrating Radar Image Analysis for Advancing Subsurface Distress Detection","authors":"Ahmad Abdelmawla, Jidong J. Yang, S. Sonny Kim","doi":"10.1177/03611981241242072","DOIUrl":"https://doi.org/10.1177/03611981241242072","url":null,"abstract":"This research introduces an innovative method for detecting subsurface cracks within pavements by leveraging ground penetrating radar (GPR) technology in conjunction with advanced deep learning techniques. Its primary aim is to significantly improve the accuracy and efficiency of pavement assessment, particularly for operational and maintenance purposes. The proposed model, GPR-YOLOR (You Only Learn One Representation), extends the YOLOR framework and incorporates a region of interest within the top pavement layer to detect subsurface cracks. While the model can be trained with annotated data, the main challenge lies in validating results in the field because of the inability to visually inspect subsurface conditions and the high cost associated with direct coring. To overcome this challenge, we propose an alternative approach that utilizes the co-occurrence of surface cracks as pseudo labels, allowing for easy verification. To ensure that surface cracks correspond to subsurface cracks, the focus is exclusively on transverse cracks that develop in a bottom-up manner, such as fatigue and reflective cracks. Through this methodology, our GPR-YOLOR model achieves an F1 score of 0.72, with a precision of 0.76 and a recall of 0.68. The results from field validation underscore the effectiveness of the GPR-YOLOR model in accurately identifying subsurface cracks, highlighting its practical significance in conducting field condition assessments.","PeriodicalId":509035,"journal":{"name":"Transportation Research Record: Journal of the Transportation Research Board","volume":"114 23","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140967994","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Balanced Mix Design Plus for Mixtures that Contain Recycled Asphalt Pavement","authors":"D. Mocelin, Y. R. Kim","doi":"10.1177/03611981241245687","DOIUrl":"https://doi.org/10.1177/03611981241245687","url":null,"abstract":"Recent efforts have been made to include mixture performance tests at the mix design stage using so-called “balanced” mix design (BMD). This paper evaluates the use of two mixtures that contain intermediate and high recycled asphalt pavement contents in the context of a BMD approach referred to as balanced mix design plus (BMD+). BMD+ utilizes the dynamic modulus, cyclic fatigue, and stress sweep rutting tests and mechanistic models and has three tiers of design based on the amount of performance testing required. As part of the BMD+ approach, index–volumetrics relationships (IVRs) and performance–volumetrics relationships (PVRs) based on the four-corner concept were developed for the two mixtures and verified using the performance data from seven other volumetric conditions. The developed PVRs were used to perform BMD+ Tier 3 analysis of the two mixtures, whereas the IVRs based on two points at the target air void content of 4% were used to conduct BMD+ Tier 2 analysis. The results indicate that the use of BMD+ Tier 2, which requires performance tests at only two conditions and without pavement simulations, yields longer lasting mixtures compared with Tier 1, which follows the Superpave volumetric mix design method. It was also found that Tier 3 requires more testing and pavement simulations but leads to even further gains in predicted life duration compared with Tier 2.","PeriodicalId":509035,"journal":{"name":"Transportation Research Record: Journal of the Transportation Research Board","volume":"27 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140967651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimization of Network Pavement Life-Cycle Cost: A Piecewise Linearized Approach","authors":"Watheq Sayeh, Imad L. Al-Qadi","doi":"10.1177/03611981241242370","DOIUrl":"https://doi.org/10.1177/03611981241242370","url":null,"abstract":"Optimal management of pavement assets becomes important because of the escalating challenges in this field. Managing the network of paved roadways in the United States necessitates the introduction of optimization tools, such as mathematical optimization. Although several efficient optimization techniques are available, an effective approach requires a special structure of the problem. The optimization of a pavement maintenance and rehabilitation schedule poses a complex challenge, primarily because of two factors: nonlinearity and the presence of integer decision variables. Nonlinearity exists as a result of multiple sources. One source is pavement condition, commonly measured by pavement roughness. This study introduces a method that uses piecewise linearization of the pavement roughness progression function. Circular shift was used to linearize the resulting optimization model. A hypothetical city, the size of Cook County in Chicago, was used as a case study. Both agency cost and user cost were considered in the model. Agency cost was determined from consultations with professionals and online data, whereas user data on existing models. The study demonstrated that increasing the agency cost by investing one dollar per lane mile per year has a high return on investment until a certain threshold, beyond which allocating more budget does not lead to a reduction in life-cycle cost.","PeriodicalId":509035,"journal":{"name":"Transportation Research Record: Journal of the Transportation Research Board","volume":"57 24","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140970811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of Environmental Conditions on Predicting Condition Rating of Concrete Bridge Decks","authors":"Chan Yang, Xin Wang, Hani Nassif","doi":"10.1177/03611981241248647","DOIUrl":"https://doi.org/10.1177/03611981241248647","url":null,"abstract":"Highway agencies prioritize maintaining bridge infrastructure through bridge management systems amid budget constraints. The premature deterioration of reinforced concrete (RC) bridge decks caused by more frequent and increasingly heavy truck load spectra coupled with aggressive environmental conditions has become a critical concern. Despite the prevalence of conventional models and the emerging popularity of machine learning (ML) models in bridge deterioration predictions, they fall short in feature selection and handling of climate conditions, leading to suboptimal accuracy. To address these gaps, this study presents a data-driven framework utilizing ML-based techniques to predict the condition rating of RC bridge decks with a focus on identifying the influencing factors that affect the deck condition. The framework employs the XGBoost algorithm for model development, encompassing comprehensive datasets that include structural, geographical, and climate variables from across the U.S. Furthermore, the Shapley additive explanations approach is applied to identify the explanatory variables with the most impact. Age emerged as the most crucial factor, followed by freeze-thaw cycles and truck traffic, as indicated by the average daily truck traffic. Rainfall also plays a substantial role in deck deterioration. Based on feature importance and monotonicity, this study recommends a series of bridge classifications for transportation agencies to incorporate into their deterioration models. Overall, this research enhances understanding of the primary causes of bridge deck deterioration, enabling more informed decisions about funding allocation and bolstering bridge performance against environmental challenges.","PeriodicalId":509035,"journal":{"name":"Transportation Research Record: Journal of the Transportation Research Board","volume":"7 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140968962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ensemble Machine Learning Classification Models for Predicting Pavement Condition","authors":"Frederick Chung, Andy Doyle, Ernay Robinson, Yejee Paik, Mingshu Li, M. Baek, Brian Moore, B. Ashuri","doi":"10.1177/03611981241240766","DOIUrl":"https://doi.org/10.1177/03611981241240766","url":null,"abstract":"Forecasting pavement performance condition is essential within the pavement management system to optimize decisions with regard to planning maintenance and rehabilitation projects. Accurate forecasts facilitate timely interventions and assist in formulating cost-effective asset management plans. Data-driven machine learning models that utilize historical data to improve forecasting precision have gained attention in the field of asset management. Although numerous studies have employed regression-based models to forecast pavement condition, transportation asset management often operates according to condition index ranges rather than exact values. Therefore, classification models are suitable for predicting pavement condition grades and determining the appropriate maintenance type for pavement assets. This research focuses on developing five machine learning classification models to predict pavement condition: random forest; gradient boost; support vector machine; k-nearest neighbors; and artificial neural network. To enhance prediction performance, these models are integrated using ensemble methods, including voting and stacking. The classification models are developed using a dataset from the Georgia Department of Transportation that documented the condition of asphalt pavements for predefined maintenance sections between 2017 and 2021. A voting ensemble model constructed with the two best-performing individual classification models reached the highest accuracy rate at 83%. Although the performance of individual models fluctuates, ensemble models consistently produce a top-tier performance regardless of the variations in data sampling. Therefore, ensemble methods are recommended for developing pavement condition prediction models to improve accuracy and achieve a more consistent quality of predictions. The findings of this research will provide transportation agencies with information to help them strengthen their forecasting practices in relation to pavement condition, thereby improving their maintenance planning and cost savings.","PeriodicalId":509035,"journal":{"name":"Transportation Research Record: Journal of the Transportation Research Board","volume":"28 14","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140971503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of Dynamic Wheel Loading on Flexible Pavement Responses for Non-Free Rolling Conditions","authors":"Johann J. Cardenas, Imad L. Al-Qadi","doi":"10.1177/03611981241242378","DOIUrl":"https://doi.org/10.1177/03611981241242378","url":null,"abstract":"Since 2000, pavement design methodologies have transitioned from empirical to mechanistic-empirical procedures. However, the complexities of loading patterns, contact stress distribution, material characterization, vehicle maneuvering, and dynamic load amplification are still not fully considered, despite their significant effect on pavement performance. In this study, state-of-the-art numerical models were used to investigate the changes in critical pavement responses derived from the combined effect of roughness-induced dynamic loading and vehicle maneuvering. A decoupled vehicle–tire–pavement interaction approach composed of a random process to generate artificial road roughness profiles, a mechanical full truck model, and three-dimensional finite element tire and flexible pavement models allow the prediction of the impact of road roughness on vehicle dynamics. In addition, the study quantifies the effect of dynamic loading on contact stresses, and consequently, on pavement critical responses. Because of the expected increase in axle weight from truck electrification, overweight scenarios were also considered. Changes in load history and distribution of strain fields were assessed for two typical pavement structures (thin and thick). The combined effect of roughness-induced dynamic loading and vehicle maneuvering greatly altered the critical pavement responses associated with bottom-up fatigue cracking, near-surface cracking, and rutting. Under the most adverse conditions, the critical tensile strains at the bottom of the asphalt concrete increased up to 125%, the shear strain increased up to 100%, and the compressive strain escalated to 120% when compared with the reference cases. Higher temperatures exacerbated the impact of dynamic wheel loading and vehicle maneuvering.","PeriodicalId":509035,"journal":{"name":"Transportation Research Record: Journal of the Transportation Research Board","volume":"7 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140970188","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}