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
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.
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.
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.
•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.
Road traffic is an important component of the national economy and social life. Promoting intelligent and Informa ionization construction in the field of road traffic is conducive to the construction ...of smart cities and the formulation of macro strategies and construction plans for urban traffic development. Aiming at the shortcomings of the current road traffic system, this article, on the basis of combining convolution neural network, situational awareness technology, database and other technologies, takes the road traffic situational awareness system as the research object, and analyzes the information collection, processing, and analysis process of road traffic situational awareness system. Convolutional neural networks (CNN), region-CNN (R-CNN), fast R-CNN, and faster R-CNN are used for vehicle class classification and location identification in road image big data. The deep convolutional neural network model based on road traffic image big data was further established, and the system requirements analysis and system framework design and implementation were carried out. Through the analysis and trial of actual cases, the results show the application effect of the realized road traffic situational awareness system, which provides a scientific reference and basis for the establishment of modern intelligent transportation system.
This study conducts a comprehensive spatial analysis of road traffic accidents (RTAs) in Najran, a city emblematic of rapid urbanization in Saudi Arabia, which is facing significant public safety ...challenges due to an increase in vehicular traffic. By means of a dataset from 2022, we explore the spatial distribution of RTAs across the city's districts by employing advanced clustering algorithms, including Density-based Spatial Clustering of Applications with Noise (DBSCAN) and Hierarchical Agglomerative Clustering (HAC), as well as GIS-based density analysis, proximity analysis, and spatial interpolation, to unveil accident hotspots and disparities in emergency service coverage. Our findings reveal that (1) the HAC model, based on the Silhouette and Calinski-Harabasz Scores, performs better in identifying accident hotspots; (2) significant concentrations of accidents are observed along major highways and arterial roads, pinpointing critical hotspots within the city's fabric; (3) proximity analysis indicates gaps in the coverage of ambulance services and public hospitals relative to high-incident areas; (4) through spatial interpolation, detailed visualizations of RTA distributions are provided, revealing diverse accident patterns across Najran. The study highlights the critical role of spatial analysis in identifying high-risk areas and provides valuable insights for transport planners and public safety officials, supporting the development of targeted strategies to improve road safety and enhance emergency service responses.
Traffic-related particulate matter (PM) plays an important role in urban air pollution. However, sources of urban pollution are difficult to distinguish. This study utilises a mobile particle ...concentrator platform and statistical tools to investigate factors affecting roadway ambient coarse particle (PM10–2.5) and fine particle (PM2.5–0.2) concentrations in greater Boston, USA. Positive matrix factorization (PMF) identified six PM10–2.5 sources (exhaust, road salt, brake wear, regional pollution, road dust resuspension and tyre-road abrasion) and seven fine particle sources. The seven PM2.5–0.2 sources include the six PM10–2.5 sources and a source rich in Cr and Ni. Non- exhaust traffic-related sources together accounted for 65.6% and 29.1% of the PM10–2.5 and PM2.5–0.2 mass, respectively. While the respective contributions of exhaust sources were 10.4% and 20.7%. The biggest non-exhaust contributor in the PM10–2.5 was road dust resuspension, accounting for 29.6%, while for the PM2.5–0.2, the biggest non-exhaust source was road-tyre abrasion, accounting for 12.3%. We used stepwise general additive models (sGAMs) and found statistically significant (p < 0.05) effects of temperature, number of vehicles and rush hour periods on exhaust, brake wear, road dust resuspension and road-tyre abrasion with relative importance between 19.1 and 62.2%, 12.5–42.1% and 4.4–42.2% of the sGAM model's explained variability. Speed limit and road type were also important factors for exhaust, road-tyre and brake wear sources. Meteorological variables of wind speed and relative humidity were significantly associated with both coarse and fine road dust resuspension and had a combined relative importance of 38% and 48%. The quantifying results of the factors that influence traffic-related sources can offer key insights to policies aiming to improve near-road air quality.
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•Sources of PM10–2.5 and PM2.5–0.2 were investigated in 90 near-road environments.•Coarse and fine non-exhaust traffic-related sources combined accounted 65.6% and 29.1% respectively.•Stepwise GAM identified ambient and road factors affecting coarse and fine non-exhaust sources.•Temperature, vehicle number and rush-hours influenced all non-exhaust sources.•Road dust resuspension & road-tyre abrasion were influenced by wind speed, relative humidity and vehicle speed.
The Grand Cooperative Driving Challenge (GCDC), with the aim to boost the introduction of cooperative automated vehicles by means of wireless communication, is presented. Experiences from the ...previous edition of GCDC, which was held in Helmond in the Netherlands in 2011, are summarized, and an overview and expectations of the challenges in the 2016 edition are discussed. Two challenge scenarios, cooperative platoon merge and cooperative intersection passing, are specified and presented. One demonstration scenario for emergency vehicles is designed to showcase the benefits of cooperative driving. Communications closely follow the newly published cooperative intelligent transport system standards, while interaction protocols are designed for each of the scenarios. For the purpose of interoperability testing, an interactive testing tool is designed and presented. A general summary of the requirements on teams for participating in the challenge is also presented.
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