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

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.

In recent decades, the increase in terrorist attacks highlights the necessity and importance of understanding of structural performance under accidental and intentionally malicious blast loads. As an important part of transportation infrastructure, bridges would inevitably suffer explosion hazards especially close-in explosion. Reinforced concrete (RC) bridge column is the most critical components of bridge structures, which is more prone to severe local damage under the action of close-in blast loading and can lead to progressive collapse with catastrophic consequences in most cases. Therefore, the blast performance of RC bridge columns under close-in explosions is of particular concern. Towards a better RC bridge column protection against close-in blast loadings, efforts have been mainly devoted to understanding dynamic response predictions of RC columns, numerical simulation techniques of close-in explosion and damage assessment of blast-damaged RC columns. This article presents a state-of-the-art review of the research status of RC columns subjected to close-in blast loads. The blast loading, experimental study and failure mode, uncoupled and coupled simulation method, damage assessment based on residual axial capacity, vulnerability analysis and machine learning are considered and reviewed. The merits and defects of the existing approaches are discussed, and some suggestions for possible improvement are proposed. Further investigation into the future development of this topic has also been identified.

State of health (SOH) estimation is important for a lithium-ion battery (LIB) health state management system, and accurate estimation of SOH is influenced by the degree of degradation of the LIB. However, considering the complex electrochemical reactions within Li electrons and the influence of many external factors on internal reactions, it is difficult to accurately estimate the SOH based on the surface state characteristics of the battery (including current, voltage, and temperature). Thus, in this study, the knowledge graph method is employed to analyze keyword co-occurrences and citations in the literature on LIB degradation and SOH estimation to determine research hotspots. Based on the research trends, findings regarding the internal and external degradation mechanisms and influencing factors of (LIBs) are reorganized, and chemical and physical degradation processes, including solid electrolyte interface (SEI) layer formation, fracture, Li plating, and dendrite formation, are systematically introduced based on the modeling perspective. The interrelationships between these degradation factors and their effects on capacity and power decay as well as their correlation with SOH estimation are evaluated. Additionally, a comparative analysis of existing SOH estimation methods is presented, and the applicable scenarios and technical problems of each method are summarized. The key issues such as model simplification, estimation methods based on random data, and second-life SOH are also analyzed and discussed. The results show that the estimation results of methods mixing multiple models tend to be more accurate. Finally, the development trend of SOH estimation methods under complex degradation conditions and usage scenarios is analytically discussed.

Unsignalized intersections are identified as the critical locations due to higher number of road crashes at these locations. The primary causes of crashes at unsignalized intersections are limited sight distance, incorrect assessment of gaps by drivers on the minor road, and higher speeds of vehicles on the major road. In an effort to improve safety and reduce the severity of crashes at unsignalized intersections, an intelligent transportation system (ITS) called the intersection conflict warning system (ICWS) has been developed. The ICWS consists of an activated warning sign and sensors that detect vehicles approaching the intersection. This paper aims to summarize the performance evaluation of ICWS that has been published in the literature. The review commences with an overview of the purpose of designing and installing ICWS at unsignalized intersections. It then discusses driving performance measures considered to evaluate the effectiveness of ICWS in three different environments, i.e., real world, driving simulator, and simulations, and their results are presented. The paper also discusses the type of sensors used to detect vehicles approaching the intersection and their accuracy in vehicle detection. The results reveal that ICWS had substantially improved driver behaviour. In the presence of ICWS, drivers resulted in shorter reaction time, lower approach speed, increased critical gap acceptance, and reduction in conflicts. An improvement in the performance of ICWS can be achieved by educating drivers about the sign, maintaining system reliability, and further examining the effect of various traffic factors, driver factors, and environmental factors on ICWS. The findings of this study can help researchers and engineers to design a better ICWS that can greatly enhance driving performance and safety at unsignalized intersections.

Modern smartphones differ vastly from classic cellphones with enormous capabilities, changing cellphones from making phone calls and short messages to pocket-sized computers with full internet connectivity. The downside of modern phone evolution is changing drivers' behavior and phone usage addiction. Several researchers have investigated the safety implications of phone usage while driving on accidents risk. Nevertheless, the effect of phone usage on the performance of traffic facilities did not receive equal attention.

Hence, this research aims to study the impact of smartphone usage on traffic signal operations. The parameters considered in the study were drivers' activity while waiting at the signal, startup lost time, and saturation flow rate. The study team covered 25 intersections in the study area, observed the activities of over 6800 drivers while waiting at the signal, and recorded their corresponding discharge headways. The study team then supplemented field data with a behavioral survey questionnaire for drivers living in the study area to interpret field observations. Finally, the collected data were processed and analyzed, resulting in mathematical models for the relationship between the reduction in saturation flow rate and the red time interval.

The team found two primary conclusions. The study's first finding was that the traditional concept for estimating startup lost time adopted by the highway capacity manual (HCM) appears inaccurate. The field results revealed that the lost times caused by other distracted drivers upstream in the queue were comparable to the estimated startup lost time following the HCM method for estimating startup lost time. The second finding was that the reduction in saturation flow rate at the traffic signal is an exponential function of the red time interval, reducing the overall capacity for the intersection and increasing the average delay. Finally, the authors suggest mitigating the problem and making recommendations for future research.