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

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.

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.

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 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.

A review of 115 studies on Australian local area traffic management (LATM) schemes covers network planning, computer modelling, overall design considerations, the deployment of various traffic control devices, project evaluation and numerous before and after case studies. However, no research has been published about the formulation of LATM policies and the processes involved that were formulated during the 1970s and 1980s and aimed at discouraging non-local through traffic in residential areas, improving road safety, and improving environmental amenity through physical devices. This paper develops a conceptual model of the interactions amongst institutions of government (state and local), organisations (national research institutes and universities), and civil society (the consulting industry, lobby groups and community action groups). The model is implemented through a series of unstructured interviews with key players involved with research and advocacy, capacity building, and state government policy makers that determined: who was responsible for the governance of LATM schemes? What were the respective roles of institutions and organisations in relation to the early formulation of policies and plans, especially issues of authority? Who were the key players in these institutions and organisations? To what extent did external influences of ideas by overseas agents (policy transfer) occur in decision making? A recently implemented LATM scheme (Seven Ways) by Waverley Council describes the latest approaches, including community participation. The conclusions note the importance of a society investing in road research, having universities capable of delivering high-quality professional development programs, and having a consulting industry that is willing to deliver innovative, practical advice to local governments. Suggestions are made about areas for further research.

As an open-source cloud computing platform, Hadoop is extensively employed in a variety of sectors because of its high dependability, high scalability, and considerable benefits in processing and analyzing massive amounts of data. Consequently, to derive valuable insights from transportation big data, it is essential to leverage the Hadoop big data platform for analysis and mining. To summarize the latest research progress on the application of Hadoop to transportation big data, we conducted a comprehensive review of 98 relevant articles published from 2012 to the present. Firstly, a bibliometric analysis was performed using VOSviewer software to identify the evolution trend of keywords. Secondly, we introduced the core components of Hadoop. Subsequently, we systematically reviewed the 98 articles, identified the latest research progress, and classified the main application scenarios of Hadoop and its optimization framework. Based on our analysis, we identified the research gaps and future work in this area. Our review of the available research highlights that Hadoop has played a significant role in transportation big data research over the past decade. Specifically, the focus has been on transportation infrastructure monitoring, taxi operation management, travel feature analysis, traffic flow prediction, transportation big data analysis platform, traffic event monitoring and status discrimination, license plate recognition, and the shortest path. Additionally, the optimization framework of Hadoop has been studied in two main areas: the optimization of the computational model of Hadoop and the optimization of Hadoop combined with Spark. Several research results have been achieved in the field of transportation big data. However, there is less systematic research on the core technology of Hadoop, and the breadth and depth of the integration development of Hadoop and transportation big data are not sufficient. In the future, it is suggested that Hadoop may be combined with other big data frameworks such as Storm and Flink that process real-time data sources to improve the real-time processing and analysis of transportation big data. Simultaneously, the research on multi-source heterogeneous transportation big data is still a key focus. Improving existing big data technology to enable the analysis and even data compression of transportation big data can lead to new breakthroughs for intelligent transportation.

Brain imaging methods have effectively revealed drivers' underlying psychological and neural processes when they perform driving tasks and promote driving behavior research in a more scientific direction. With research no longer limited to indirect inferences about external behavior, some researchers combine behavior and driver brain activity to understand the human factors in driving essentially. However, most researchers in the field of driving behavior still have little understanding of how brain imaging methods are used. This paper aims to review and analyze the application of brain imaging methods in driving behavior research, including bibliometric analysis and an individual critical literature review. Regarding bibliometric analysis, this field's knowledge structure and development trend are described macroscopically, using data such as annual distribution of publications, country/region statistics and partnerships, publication sources, literature co-citation analysis, and keyword co-occurrence analysis. In a review of the individual critical literature, eight research themes were identified that examined driving behavior using brain imaging methods: substance consumption, fatigue or sleep deprivation, workload, distraction, aging brains, brain impairment and other diseases, automated/semi-automated environments, emotions influence and risk-taking, and general driving process. In addition, the study reports on six brain imaging methods and their advantages and disadvantages, involving electroencephalography (EEG), functional magnetic resonance imaging (fMRI), functional near-infrared spectroscopy (fNIRS), magnetoencephalography (MEG), positron emission tomography (PET), and transcranial magnetic stimulation (TMS). The contribution of this study is twofold. The first part relates to providing the researchers with a comprehensive understanding of the field's knowledge structure and development trends. The second part goes beyond reviewing and analyzing previous studies, and the discussion section points out the directions and challenges for future research.