Journal of Traffic and Transportation Engineering-English Edition最新文献

Damage to semi-rigid base asphalt pavement is related to improper matching of the pavement structure moduli. This study mainly focused on the modulus matching of structural layers and the development of a pavement structure optimization method. First, the modulus loss of existing pavement structures was analysed, and a three-dimensional finite element model was established based on the existing pavement. Second, the influence of the modulus of each structural layer on the mechanical response indicators and fatigue life was analysed. Based on the results, a pavement structure design method using the smoothness of the stress-strain curve as the modulus matching criterion of the structural layers was proposed. And it was found that a strain convex point was present and that the stress mutation between the structural layers was significant when the modulus matching of the pavement structure was reasonable. Further, the evaluation indicators were divided into two groups, namely, mechanical indicators and fatigue life indicators. And it was proposed an optimized pavement structure design method based on modulus matching and multi-indicator range analysis. Finally, the optimal modulus combination of pavement structure was determined by this method. The research systematically studied the influence of the modulus of each structural layer on the mechanical response and fatigue life of the pavement, and proposed the concept and specific executive criteria of modulus matching for the first time. Meanwhile, it also provided an effective optimization method for pavement structure design.

The morphology of graded crushed stone (GCS) particles has an essential influence on the performance of aggregate mixtures. The impact of particle shape is a comprehensive effect that cannot be considered separately, leading to difficulties in establishing the relationship between the mixture properties and the aggregate morphology by using laboratory methods. The discrete element method (DEM) is an effective way widely adopted to reconstruct the morphology of particles and simulate performance tests of granular materials. However, selecting limited particles characterizing a real particle-assembly for simulation is still a challenge in current research due to the inherent rich variability of particle shapes. In this study, based on the acquisition of three-dimensional (3D) aggregate shapes by using laser scanning, ellipsoid index (EDI) translating the particle shape as a function of surface area, volume, and contour length is proposed to comprehensively evaluate aggregate morphology. Further, a particle library capable of characterizing aggregate morphology distribution is established based on the statistics of the corresponding morphological characteristics of particle samples. The model reliability is validated by carrying out a series of experimental and numerical penetration tests with nine different gradations. The established particle library can be used to model aggregate mixtures and the proposed simulation framework is promising for optimizing the mixture gradation design numerically.

The primary goal of this study is the design and construction of semi-flexible pavement (SFP) mixture in accordance with the engineering and mechanical criteria. This study involves the use of a range of gradation curves, air void contents, cellulose and synthesized fibers, and neat and modified asphalt binders to prepare the open-graded asphalt (OGA) mixtures. To analyze the characteristics of these mixtures, a variety of test, namely binder drainage, semi-circular bending (SCB), Cantabro, wheel tracking, indirect tensile strength (ITS), and permeability tests were conducted. Additionally, to analyze the prepared grouting material, flexural strength, compressive strength, and fluidity tests were conducted. In the final stage, SFP was compared to HMA in terms of engineering characteristics and performance. According to the results, SFP was more resistant to skid, rutting, fire, and moisture damage, while HMA had a better performance in fracture tests, including SCB test. According to the results of the mechanical performance tests conducted on OGA mixtures, the highest and lowest values for air void content to achieve the highest mechanical performance level were 30%–35% and 25%, respectively. Also, based on the laboratory results, it was determined that the required void ratio for constructing OGA mixtures was 24%–26% based on the bitumen type and fibers amount in the mixture. Finally, SFP mixture can be regarded as a viable alternative to common pavements thanks to its high resistance to rutting and moisture damage, long freezing-thawing fatigue life, and adequate fire and skid resistance.

The paired approach is a kind of efficiency approach to closely spaced parallel runways (CSPRs), and the point merge system has the powerful interval management function, which is effective to realize the converge of traffic flows from different approach directions. In order to improve the operation efficiency of the airport terminal area, a model of paired approach sequencing based on point merge is proposed to investigate the problem of increasing the operation capacity of the closely spaced parallel runways. Taking the minimum average flight delay time as the objective, the flight distance on sequencing legs, wake turbulence separation and paired approach safety separation as constraints, the genetic algorithm is used to optimize the paired approach sequencing of arrival flights. Taking the closely parallel runways of Shanghai Hongqiao International Airport run south as an example, the point merge program is designed and the effect of model was analyzed. The results show that after optimization, the average delay time and average landing time are reduced by 40.6% and 51.8% respectively, the capacity of the closely spaced parallel runways are 1.1 times higher than the actual, the flight uptime rate can reach 100%. It is concluded that the proposed model is feasible, which can effectively reduce delay times and alleviate congestion in terminal areas.

The transportation sector is one of the major sources of global carbon emissions. In this study, a bibliometric analysis was conducted using CiteSpace and VOSviewer for articles published in the field of transportation carbon emissions (TCEs) between 1997 and 2023. From this analysis, our research shows that: (a) the number of articles on TCEs has grown rapidly since 2010; (b) China, the United States, and the United Kingdom are important research forces, with the Helmholtz Association of German having the highest number of publications; (c) Transportation Research Part D: Transport and Environment is the most cited journal in this field; (d) the current research hotspots mainly focus on theory and methodological approaches, low-carbon travel, green supply chain management, and carbon emission drivers; (e) while, scenario analysis, data envelopment analysis, and vehicle routing problem are popular keywords that have been used in the research field of TCEs in recent years. Finally, using current research trends, our study also proposes a series of future research endeavors for the field of TCEs.

With the continuous development of information technology, the information environment while driving is constantly being enriched, and driver information processing and application are also dynamically evolving. Analysing information processing and application can better provide information services and is particularly important for traffic safety. Based on VOSviewer bibliometric software, this paper explores the research hotspots and future development trends of the driver information processing and application fields using the Web of Science (WoS) core collection as the data source. The results show that the field has a long history and has grown steadily in recent years. The United States, China and Germany are the top three countries in terms of the number of published articles. “Situational awareness and visual load”, “route selection under variable information signs”, “en-route information and behaviour” and “new information technology attitudes” are important knowledge bases for driver information processing and application. En-route information sources, human-computer interaction, and autonomous vehicle information are the research trends of the driver information processing and application field. The results of this research can help people comprehensively and systematically understand the current situation of driver information processing and application research, provide directions for future driver information processing and application research, and promote the engineering application of such research.

In order to fully understand the research progress of human factors and traffic safety in curve driving, from the perspective of driver-vehicle-road-environment dynamic traffic system, this paper explored the current research status and development trend of human factors of curve driving, and displayed the development process and structural relationship of human factors research of curve driving by using scientific knowledge map. Through the core collection database of Web of Science, 1408 English literatures related to human factors research of curve driving published from 2012 to 2022 (as of October 1, 2022) were obtained, and the literatures in this field were sorted and analyzed based on the VOSviewer visualization software. The results show that China, Tongji University and Accident Analysis and Prevention are the country, institution and journal with the largest contribution rate in the field of human factors research on curve driving. Co-citation analysis shows that the research contents in this field are divided into 5 clusters: driver's visual characteristics, risk of collision, vehicle dynamics characteristics, the influence of traffic engineering facilities on driving behavior, selection of driving speed. The co-occurrence analysis of keywords shows that the topics of curve geometry design and vehicle dynamics, driving behavior and risk, driving speed and safety, behavior prediction and intervention measures are the current research hotspots in the research field. It is found that the development trend of traffic safety improvement in curves is to construct a continuous, consistent, multi-level visual reference frame conforming to driving expectation through visual guiding technology, and summarizes the technical concept of linear visual guidance. This study can provide a reference for the study of human factors of curve driving.

To better understand the research focus and development direction in the field of driving behavior active intervention, thereby laying a scientific foundation for further research, we used the combination of topic words and keywords to retrieve relevant articles from the Core Collection Database of Web of Science (WOS). A total of 578 articles published from 1992 to 2022 were finally obtained. Firstly, the time distribution characteristics, country distribution, institution distribution and main source journal distribution of published articles were explored. Then, by using the CiteSpace and VOSviewer software, cited reference co-citation analysis, keyword co-occurrence analysis and burst detection analysis were carried out respectively to visually explore the knowledge base, research topic, research frontier and development trend of this field. The results indicate that the USA, Australia and China are the three most active countries in the studies of driving behavior active intervention. Accidental Analysis & Prevention, Transportation Research Part F: Traffic Psychology and Behavior, and Journal of Safety Research are widely selected journals for publications related to this field. The research frontiers in the field of driving behavior active intervention focus on: “traffic safety and crashes analysis, as well as enforcement intervention”, “driving risk and education for young drivers”, “information provision and driving behavior”, “workload and situation awareness for automated driving”. It is worth noting that in recent years, “warning system”, “time”, “work load” have become research hotspots in this field. To sum up, by a bibliometric overview of research on driving behavior active intervention over the past thirty years, this paper clarifies the development skeleton of this research field, determines its hot topics and research progress, and provides a reference for the follow-up exploratory scientific research in this field.

Road traffic safety should be evaluated throughout the entire life-cycle of road design, operation, maintenance, and expansion construction. However, traditional methods for evaluating road traffic safety based on traffic accidents and conflict technology are limited by their inability to account for the complex environmental factors involved. To address this issue, a new road safety evaluation method has emerged that is based on driving behavior. Because drivers' behaviors may vary depending on the driving environment and their personal characteristics, evaluating road safety from the perspective of driver behavior has become a popular research topic. This paper analyzes current research trends and mainstream journals in the field of road safety evaluation of driving behavior. Additionally, it reviews the three most commonly used driving behavior data collection methods, and compares the advantages and disadvantages of each. The paper proposes the main application scenarios of road safety evaluation methods based on driving behavior, such as road design, evaluation of the effects of road appurtenances, and intelligent highways. Furthermore, the paper summarizes a driving behavior index system based on vehicle data, driver's physiological and psychological data, and driver's subjective questionnaire data. A comprehensive evaluation method based on the fusion of each index system is presented in detail. Finally, the paper points out current research problems and the future development direction of the road safety evaluation method based on driving behavior.

Road traffic injuries and crashes are one of the major public concerns contributing to mortality and morbidity figures across the globe. Researchers estimated that around 90% of all causative factors for crashes are attributed to road users of which drivers are the principal controlling elements. Therefore, understanding complex human driver behavior and their possible violations or errors are necessary to control and prevent accident occurrence to a considerable extent. Studies on driver behavior of commercial vehicles such as trucks are scattered widely and scarcely explored hindering the possibility of road safety outcomes. This underscores the need to excavate and synthesize the past studies for an effective understanding of human factors causing truck crashes. In this paper, an attempt has been made to systematically review the pieces of literature and to identify the causative factors affecting truck driver behavior. The trend of studies shows a promising framework for improving truck driver safety on taking care of human factors influencing crashes. Most kinds of literature have cited unsafe driving behaviors as a predominant source of truck crashes. The outcomes of this research can be utilized by transportation firms and stakeholders for identifying the possible lags to develop pragmatic and possible effective preventive measures featuring truck driver safety.