The role of traditional analysis methods in improving complex socio-technical system safety has reached a ceiling, and thus systems theory has been utilised to support the investigations and ...countermeasures for road traffic accidents. As two widely applied systems accident analysis models, STAMP (systems theoretic accident model and process) and HFACS (human factors analysis and classification system) have their own advantages in accident analysis and safety improvement. Therefore, this study develops a new hybrid systems method integrating STAMP and HFACS for road traffic accident (SH-RTA), which can adopt HFACS to enhance the identification and analysis ability of STAMP for human factors and employ control concepts and elements of STAMP to cement the characteristic of HFACS. To illustrate the applicability of the hybrid method, a case study of '9·22' major road traffic accident in China is thoroughly analysed. Finally, preventive countermeasures and suggestions are presented.
Practitioner Summary: This paper proposes a new hybrid systems method integrating STAMP and HFACS for road traffic accident. The new method reveals dysfunctional interactions within the parallel level and across levels, and identifies additional human and organisational factors. The recommendations for preventing road traffic accident are provided from higher levels of system.
Abstract
Aims
The present study aimed to disentangle the risk of the three major transportation noise sources—road, railway, and aircraft traffic—and the air pollutants NO2 and PM2.5 on myocardial ...infarction (MI) mortality in Switzerland based on high quality/fine resolution exposure modelling.
Methods and results
We modelled long-term exposure to outdoor road traffic, railway, and aircraft noise levels, as well as NO2 and PM2.5 concentration for each address of the 4.40 million adults (>30 years) in the Swiss National Cohort (SNC). We investigated the association between transportation noise/air pollution exposure and death due to MI during the follow-up period 2000–08, by adjusting noise Lden(Road), Lden(Railway), and Lden(Air) estimates for NO2 and/or PM2.5 and vice versa by multipollutant Cox regression models considering potential confounders. Adjusting noise risk estimates of MI for NO2 and/or PM2.5 did not change the hazard ratios (HRs) per 10 dB increase in road traffic (without air pollution: 1.032, 95% CI: 1.014–1.051, adjusted for NO2 and PM2.5: 1.034, 95% CI: 1.014–1.055), railway traffic (1.020, 95% CI: 1.007–1.033 vs. 1.020, 95% CI: 1.007–1.033), and aircraft traffic noise (1.025, 95% CI: 1.006–1.045 vs. 1.025, 95% CI: 1.005–1.046). Conversely, noise adjusted HRs for air pollutants were lower than corresponding estimates without noise adjustment. Hazard ratio per 10 μg/m³ increase with and without noise adjustment were 1.024 (1.005–1.043) vs. 0.990 (0.965–1.016) for NO2 and 1.054 (1.013–1.093) vs. 1.019 (0.971–1.071) for PM2.5.
Conclusion
Our study suggests that transportation noise is associated with MI mortality, independent from air pollution. Air pollution studies not adequately adjusting for transportation noise exposure may overestimate the cardiovascular disease burden of air pollution.
•Reliability of URTNs considering the traffic congestion diffusion is studied.•An improved NLC model under different attack strategies is proposed to simulate cascading failures.•A load ...redistribution method with the impedance function is provided.•Formation time, diffusion speed, and scale of cascading failures are analyzed.
The cascading failures caused by traffic congestion diffusion may deteriorate traffic network reliability. Comprehending urban traffic congestion mechanisms is essential for road network planning and traffic management against cascading failures. To uncover this, the reliability of urban road traffic network (URTN) under cascading failure considering different attack strategies is analyzed. The cascading failure model is established based on the improved nonlinear load-capacity relationship. Five kinds of attack strategies including Strength Attack (SA), Betweenness Centrality Attack (BCA), Eigenvector Centrality Attack (ECA), Closeness Centrality Attack (CCA), and Random Attack (RA) are selected. In particular, the capacity affected by traffic congestion is considered, providing a new perspective for the study of traffic congestion diffusion. A state update equation for networks is proposed to simulate the network congestion diffusion. Finally, a case study is conducted by using the URTN of Shanghai as the background. The results show that the network will experience large-scale congestion when high-importance nodes are attacked. The congestion degree is the highest under CCA strategy, network efficiency is the lowest under ECA strategy, and traffic quality is the poorest under CCA strategy. As the congestion critical failure threshold decreases, the speed and scale of cascading failures caused by traffic congestion diffusion are greater. Maintaining proper traffic management and control capability can largely reduce the cascading effect to a great extent and improve the reliability of the network. The results can provide a research basis for traffic management to improve network reliability.
The use of transportation and traffic is the basic element for the growth and development of human activities for its active role in the development of economic, social and political functions and ...the achievement of development. In this research, transportation problems were studied, most notably the problem of congestion and traffic accidents on the Heat-Grayy road, in order to find the appropriate solutions. The results of the study showed that land uses for transportation, that vital element responsible for the flow of movement, transport and traffic, are related to a number of problems. The most important of these problems was the transportation means used, as well as the path of movement (the road), which results in traffic accidents that lead to loss of life in many cases. Another problem is visual pollution and its impact on social, environmental and economic aspects. In addition to the poor infrastructure, such as the one-lane road. All of these dilemmas and others require identifying the most important solutions and proposals for them, based on the opinions of a sample of the population. The method of data collection consisted of distributing a questionnaire form to conduct a statistical study to identify those problems, which were discussed to reach the
As cities continue to grow and the number of vehicles on the road increases, traffic congestion and pollution have become major issues. Fortunately, significant efforts have been made in recent ...decades to alleviate these problems through research and the development of Intelligent Transportation Systems (ITS). Governments are now utilizing advanced ITS technologies to better understand traffic patterns and make informed decisions on how to manage traffic. In this paper, we will explore the state-of-the-art methods employed in ITS for predicting traffic flow and speed, as well as classifying different traffic situations. We will also examine the preprocessing techniques used in these tasks, along with the metrics used to evaluate the results. By understanding the latest advancements in ITS, we can work towards creating more efficient and sustainable transportation systems that benefit both individuals and society as a whole.
•In-depth analysis of road traffic classification and prediction methods from Europe.•Identification of the most relevant machine learning practices for prediction and classification of traffic flow.•Emphasis on the importance of proper pre-processing to achieve high-quality results in traffic prediction and classification.
The urban road networks undergo frequent traffic congestions during the peak hours and around the city center. Capturing the spatiotemporal evolution of the congestion scenario in real-time in an ...urban-scale can aid in developing smart traffic management systems, and guiding commuters in making informed decision about route choice. The congestion scenario is often represented by a set of distinguishable network partitions that have a homogeneous level of congestion inside them but are heterogeneous to others. Due to the dynamic nature of traffic, these partitions evolve with time in terms of their structure and location. In this paper, we propose a comprehensive framework to capture the evolution by incrementally updating the partitions in an efficient manner using a two-layer approach. The physical layer maintains a set of small-sized road network building blocks in a fine granularity, and performs low-level computations to incrementally update them, whereas the logical layer performs high-level computations in order to serve as an interface to query the physical layer about the congested partitions in a coarse granularity. We also propose an in-memory index called Bin that compactly stores the historical sets of building blocks in the main memory with no information loss, and facilitates their efficient retrieval. Our experimental results show that the proposed method is much efficient than the existing re-partitioning methods without significant sacrifice in accuracy. The proposed Bin consumes a minimum space with least redundancy at different time stamps.
SDVN is a promising architecture to extend the computation resources which break through the limitations of current vehicular networks. It is possible to learn new networking schemes by observing the ...surrounding environment in SDVN. However, within SDVN, the construction and application of such schemes still lack proper consideration in data collection, prediction, verification, and validation before applying these schemes in the real network, which is due to the limited knowledge of the physical environment. Intelligent Digital Twin (IDT) was initially designed for realizing intelligent manufacturing by virtualizing and learning the data of the physical space in cyberspace. Hence, bringing IDT to networking can provide additional valuable functionalities to meet the above considerations by constructing a virtual intelligent network space, aiming to realize the iterative update of the networking schemes in an adaptive way. In this article, we introduce a new network architecture, IDT-SDVN, by maximizing the advantages of SDVNs. We present the challenges and open issues of IDT-SDVNs. A case study is presented to demonstrate the effectiveness of SDVNs. The experimental results show that significant improvement of performance is achieved for vehicular networking with the proposed IDT-SDVNs.
Road traffic is one of the main sources of particulate matter in the atmosphere. Despite its importance, there are significant challenges in quantitative evaluation of its contribution to airborne ...concentrations. This article first reviews the nature of the particle emissions from road vehicles including both exhaust and non-exhaust (abrasion and re-suspension sources). It then briefly reviews the various methods available for quantification of the road traffic contribution. This includes tunnel/roadway measurements, twin site studies, use of vehicle-specific tracers and other methods. Finally, the application of receptor modelling methods is briefly described. Based on the review, it can be concluded that while traffic emissions continue to contribute substantially to primary PM emissions in urban areas, quantitative knowledge of the contribution, especially of non-exhaust emissions to PM concentrations remain inadequate.
•Road traffic contributes emissions from exhaust, abrasion and re-suspension sources.•Chemical and physical properties of the emitted particles are described.•Available methods for quantification of traffic-derived concentrations are reviewed.
•We analyzed associations between air pollution, noise, greenness, and metabolic syndrome.•PM10, PMcoarse, PM2.5, PM2.5abs were positively associated with prevalent metabolic syndrome.•No significant ...associations were observed for incident metabolic syndrome.
A growing number of epidemiological studies show associations between environmental factors and impaired cardiometabolic health. However, evidence is scarce concerning these risk factors and their impact on metabolic syndrome (MetS). This analysis aims to investigate associations between long-term exposure to air pollution, road traffic noise, residential greenness, and MetS.
We used data of the first (F4, 2006–2008) and second (FF4, 2013–2014) follow-up of the population-based KORA S4 survey in the region of Augsburg, Germany, to investigate associations between exposures and MetS prevalence at F4 (N = 2883) and MetS incidence at FF4 (N = 1192; average follow-up: 6.5 years). Residential long-term exposures to air pollution – including particulate matter (PM) with a diameter < 10 µm (PM10), PM < 2.5 µm (PM2.5), PM between 2.5 and 10 µm (PMcoarse), absorbance of PM2.5 (PM2.5abs), particle number concentration (PNC), nitrogen dioxide (NO2), ozone (O3) – and road traffic noise were modeled by land-use regression models and noise maps. For greenness, the Normalized Difference Vegetation Index (NDVI) was obtained. We estimated Odds Ratios (OR) for single and multi-exposure models using logistic regression and generalized estimating equations adjusted for confounders. Joint Odds Ratios were calculated based on the Cumulative Risk Index. Effect modifiers were examined with interaction terms.
We found positive associations between prevalent MetS and interquartile range (IQR) increases in PM10 (OR: 1.15; 95% confidence interval 95% CI: 1.02, 1.29), PM2.5 (OR: 1.14; 95% CI: 1.02, 1.28), PMcoarse (OR: 1.14; 95% CI: 1.02, 1.27), and PM2.5abs (OR: 1.17; 95% CI: 1.03, 1.32). Results further showed negative, but non-significant associations between exposure to greenness and prevalent and incident MetS. No effects were seen for exposure to road traffic noise. Joint Odds Ratios from multi-exposure models were higher than ORs from models with only one exposure.
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