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

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.

This paper discusses low-cost approaches capable of ranking traffic intersections for the purpose of signal re-timing. We extracted intersections that are comprised of multiple roads, defined by alphanumeric traffic message channel segment codes per international classification standards. Each of these road segments includes a variety of metrics, including congestion, planning time index, and bottleneck ranking information provided by the Regional Integrated Transportation Information System. Our first approach was to use a ranking formula to calculate intersection rankings using a score between 0 and 10 by considering data for different times of the day and different days of the week, weighting weekdays more heavily than weekends and morning and evening commute times more heavily than other times of day. The second method was to utilize unsupervised machine learning algorithms, primarily k-means clustering, to accomplish the intersection ranking task. We first approach this by checking the performance of basic k-means clustering on our data set. We then explore the ranking problem further by utilizing data provided by traffic professionals in the state of Tennessee. This exploration involves using MATLAB to minimize the mean-squared error of intersection rankings to determine the optimum weights in the ranking formula based on a city's professional data. We then attempted an optimization of our weights via a brute-force search approach to minimize the distance from ranking formula results to the clustering results. All the ranking information was aggregated into an online SQL database hosted by Amazon web services that utilized the PHP scripting language.

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.

Every year, a substantial number of children sustain injuries and fatalities in motor vehicle crashes in Wyoming. Understanding the factors contributing to child injury is crucial for the development of appropriate mitigation measures that aid in alleviating the severity of such injuries. In this study, a hierarchical Bayesian binary logit regression model was developed to investigate the factors that contribute to children's injuries resulting from crashes while accounting for possible intra-class correlation effects (those of unobserved factors common to children involved in the same crash). A strong correlation among crashes justified the use of the hierarchical Bayesian logit model. As per the modeling results, the children's ages, safety restraint types, vehicle types, drivers' ages, alcohol/drug involvement, drivers' seat belt use habits, drivers' actions, manners of collision and environmental conditions contributed to child injury risk. The child's age was found to be inversely related to the risk of injury. Similarly, among safety restraint types, rear-facing car seats and forward-facing car seats were found to reduce injury likelihoods in crashes. When it comes to the drivers' characteristics, the probability of incurring injuries among the child population increased in the presence of young, unbuckled and impaired drivers. Furthermore, improper driving actions, such as running off the road, raised the risk of incurring injuries to children. The findings of this study may be beneficial to authorities regarding developing and implementing road safety programs aimed at ameliorating child injury concerns.

The frequency of natural and man-made disasters has increased over the past few decades, which has doubled the significance of evacuation planning because it directly affects people's lives and properties. How evacuees behave during a disaster and the methodologies to assess their behaviour are vital factors in managing any emergency scenario. For example, during the evacuation of a transportation network, users' amount of information and how they react are imperative to achieve a resilient response to a disaster. Even though there are numerous approaches for assessing evacuee behaviour, further research is needed to determine how and when each methodology should be employed. This paper intends to evaluate and classify the methodologies that have been presented up to date to create a more consistent approach to interpreting human behaviour during an evacuation process. The majority of studies focus on how people behave during an evacuation of a building, with less attention paid to how they behave during a transport evacuation. Among the proposed methodologies in this paper, the virtual reality approach, in individual behaviour, and agent-based models, in crowd behaviour, have more advantages than other approaches. Overall, a comparison between the proposed approaches is made in the discussion part. The output of this study provides the classifications and suggestions for researchers to pick an appropriate approach based on the types of problems, and some direction for future studies are introduced. To reach the research goal, 177 papers have been reviewed between 1954 and 2022.