Traffic Junctions Research Articles

Fuzzy Petri Nets for Traffic Node Reliability.

Self-driving cars are one of the main areas of research today, but it has to be acknowledged that the information from the sensors (the perceptron) is a huge amount of data, which is now unmanageable even when projected onto a single traffic junction. In the case of self-driving, the nodes have to be sequenced and organized according to the planned route. A self-driving car in Hungary would have to be able to interpret more than 70,000 traffic junctions to be able to drive all over the country. Besides the huge amount of data, another problem is the issue of validation and verification. For self-driving cars, this implies a level of complexity using traditional methods that calls into question the economics of the already existing system. Fuzzy Petri nets provide an alternative solution to both problems. They allow us to obtain a model that accurately describes the reliability of a node through its dynamics, which is essential in perception since the more reliable a node is, the smaller the deep learning mesh required. In this paper, we outline the analysis of a traffic node's safety using Petri nets and fuzzy analysis to gain information on the reliability of the node, which is essential for the modeling of self-driving cars, due to the deep learning model of perception. The reliability of the dynamics of the node is determined by using the modified fuzzy Petri net procedure. The need for a fuzzy extension of the Petri net was developed by knowledge of real traffic databases.

Optimizing traffic signal control for continuous‐flow intersections: Benchmarking against a state‐of‐practice model

AbstractContinuous‐flow intersections (CFI), also known as displaced left‐turn (DLT) intersections, aim to improve the efficiency and safety of traffic junctions. A CFI introduces additional cross‐over intersections upstream of the main intersection to split the left‐turn flow from the through movement before it arrives at the main intersection which decreases the number of conflict points between left‐turn and through movements. This study develops and examine a two‐step optimization model for CFI traffic signal control design and demonstrates its performance across more than 300 different travel demand scenarios. The proposed model is compared against a state‐of‐practice CFI signal control model as a benchmark. Microsimulation results suggest that the proposed model reduces average delay by 17% and average queue length by 32% for a full CFI compared with the benchmark signal control model.

Spatial and Temporal Dynamics of Homestays on Chongming Island: A Systemic Analysis in the Context of World-Class Eco-Island Development

As the development of world-class eco-islands progresses, the homestay sector on Chongming Island has burgeoned, emerging as a pivotal catalyst for rural tourism and economic prosperity. This study, leveraging Chongming Island as a case study, systematically analyzes the spatiotemporal distribution of homestays across island-wide, township, and village scales, employing methodologies such as the spatiotemporal evolutionary tree model. It delves into the intricate systemic framework underpinning homestay development, elucidating the dynamics of interaction mechanisms. Furthermore, it examines the evolving roles of diverse stakeholders throughout various developmental phases, grounded in stakeholder theory. The study reveals that (1) Chongming’s homestays on the island, townships, and village scales show very obvious agglomeration characteristics, mainly concentrated in the A-level scenic spots and in and out of the island traffic junction. (2) The growth of homestays in Chongming shows an increasing trend year by year, with the growth rate slowing down after 2020, and can be divided into four main phases, which are closely related to the construction phase of the eco-island. (3) The evolution of Chongming Island’s homestay is a multifaceted natural–human system intricately shaped by a confluence of five interdependent subsystems: natural ecology, social culture, multiple stakeholders, geographical location, and transport infrastructure. At different stages of development, under the synergistic effect of multiple stakeholders, the homestays on Chongming Island eventually formed a governance structure led by the government and co-managed by the development company, external entrepreneurs, and local villagers. These insights not only augment the scholarly discourse on stakeholder dynamics within the homestay industry but also offer pragmatic enlightenment for strategic planning and the advancement of holistic urban–rural integration in Chongming and the broader Shanghai region.

QUANTIFYING THE IMPACT OF TRAFFIC-RELATED EMISSIONS ON AIR QUALITY IN KANO CITY, NIGERIA

The aim of this study is to assess the concentration of traffic related air pollutants at some major traffic junctions in Kano metropolis, it comprised of carrying traffic count in the vicinities so as ascertain the sources of the pollution and identify the level of carbon monoxide (CO), nitrogen dioxide (NO 2), Sulphur dioxide (SO 2) for a period of six weeks. Traffic density was estimated via manual counting using tally sheet system. Crowcon gasman to air samplers ware used to measure the concentrations of gases emitted into the atmosphere at the selected locations. A total of 126 samples of air quality measurements were taken out for three hours, with each hour devoted to morning, afternoon and evening peak periods. Results obtained were compared with National Environmental Standard and Regulatory Enforcement Agency (NESREA) guideline limits. Data were analysed using statistical package IBM SPSS version 16.0 to compute descriptive statistics, Pearson correlation, regression analysis and one sample t-test. There was a correlation between CO, NO 2, SO 2 and traffic volume in the entire junctions, and most of the correlation obtained may be attributed to the high vehicular volume in the traffic jam. The result of this indicated that the mean concentration of CO, NO 2 and SO 2 emission at Kabuga, Kofar ruwa, Hotoro NNPC and Aminu Kano Teaching Hospital traffic junctions, gaseous emissions were within NESREA guideline limit for ambient conditions, except for CO concentration of 14.43 and 13.60 ppm at Bata and Kantin Kwari intersections were found to be much higher when compared with the NESREA guideline limits, respectively. This implies that transport-related pollution around Bata and Kantin Kwari is apparently unsafe. Similarly, it is likely that air quality will continue to deteriorate as the city continues to grow. Therefore, there is need for government to institute and enforce traffic emission control and air quality management programs as a priority. They should also work to prevent further environmental degradation by adopting effective policy in Nigeria.

Impact of cultivating different Ocimum species on bioaerosol bacterial communities and functional genome at an agricultural site

The effects of the surrounding environment on the bacterial composition of bioaerosol were well documented for polluted and contaminated sites. However, there is limited data on the impact of plant species, especially those that produce aromas, on bioaerosol composition at agricultural sites. Hence, the aim of this study is to evaluate the variability in bacterial communities present in bioaerosol samples collected from agricultural sites with aroma-producing crops. For this, PM2.5, PM10, and bioaerosol samples were collected from agricultural fields growing Ocimum [two varieties of O. sanctum (CIM-Aayu and CIM-Angana)] and O. kilimandscharicum (Kapoor), nearby traffic junctions and suburban areas. PM2.5 and PM10 concentrations at the agricultural site were in between the other two polluted sites. However, bioaerosol concentration was lower at agricultural sites than at other sites. The culturable bacteria Bacillus subtilis, Bacillus tequilensis, and Staphylococcus saprophyticus were more prevalent in agricultural sites than in other areas. However, the composition of non-culturable bacteria varied between sites and differed in three fields where Ocimum was cultivated. The CIM-Aayu cultivated area showed a high bacterial richness, lower Simpson and Shannon indices, and a distinctive metabolic profile. The sites CIM-Angana and CIM-Kapoor had a higher abundance of Aeromonas, while Pantoea and Pseudomonas were present at CIM-Aayu. Acinetobacter, Staphylococcus, and Bacillus were the dominant genera at the other two sites. Metabolic profiling showed that the CIM-Aayu site had a higher prevalence of pathways related to amino acid and carbohydrate metabolism and environmental information processing compared to other sites. The composition of bioaerosol among the three different Ocimum sites could be due to variations in the plant volatile and cross-feeding nature of bacterial isolates, which further needs to be explored.

Validation of the physical activity neighborhood environment scale using geographic information systems among urban men and women in regional China

AimsTo examine the concurrent validity of the Chinese version of Physical Activity Neighborhood Environment Scale (PANES-CHN) among urban adults in regional China. MethodsWith multistage sampling approaches, 801 eligible urban adults aged 35–74 years were recruited from Nanjing municipality of China between July and September of 2019. The neighbourhood built environment features were measured subjectively with PANES-CHN and objectively with geographic information system. The concurrent validity of PANES was assessed using Spearman’s correlations (rs). ResultsAmong the total 801 participants, the mean age was 54.4 (standard deviation = 11.5), while 48.7 % were men. Overall, of all the five objectively-measurable built environment characteristics, the Spearman correlations were examined significant between subjective and objective measurements for commercial facilities (item 2) (rs = 0.19, 95CI%=0.12, 0.25), recreational facilities (item 6) (rs = 0.10, 95CI%=0.02, 0.16), traffic junctions (item 12) (rs = 0.15, 95CI%=0.07, 0.22), medical/education facilities (item 17) (rs = 0.22, 95CI%=0.15, 0.29), but not for public transport stops. Similar scenarios were observed for participants aged 35–60 years, with sufficient physical activity, men or women. The rs value for medical/education facilities (item 17) was significantly higher in participants aged 35–59 years (0.28 vs. 0.13; p = 0.04) than those aged 60 + years. ConclusionsPANES-CHN generally has an acceptable validity for assessing built environment characteristics among urban adults in China, which implies that PANES-CHN can be used to measure built environment attributes in health-related population studies.

Intelligent Traffic Light Control System

Abstract: The "Intelligent traffic light control system " project introduces an innovative approach to traffic management, aiming to expedite emergency vehicle passage. Utilizing Zigbee and Radio-Frequency Identification (RFID) technology, this system assigns a unique RFID tag to each vehicle, enabling precise tracking and control at traffic junctions. The scenarios involving emergency vehicle clearance, the system's responsiveness shines. When an emergency vehicle clearance approaches, the system rapidly receives the signal from zigbee transmitter associated with it and promptly triggers a green signal at the traffic junction. This prioritization ensures that emergency vehicles can navigate the traffic with minimal delays, potentially saving lives. Another remarkable feature of the system is stolen vehicle detection capability. In the event that a vehicle with a specific RFID tag is reported as stolen, the system is programmed to take action. It sends immediate SMS notifications to relevant authorities, such as law enforcement or the vehicle owner. This proactive response enhances the chances of recovering stolen vehicles and contributes to overall security. The hardware components of this system include Arduino Uno. Arduino Uno serves as the central control unit, processing RFID data and executing signal adjustments, while the GSM module facilitates real-time communication, enabling SMS alerts and notifications. The " Intelligent traffic light control system " project exemplifies how RFID technology and zigbee technology can revolutionize traffic management. By incorporating data analysis and automation, this system offers a multifaceted solution to common traffic-related challenges.

Signal Master using YOLO

As urban populations and automobile numbers continue to swell, traffic congestion emerges as a pressing concern, inflicting delays, stress, heightened fuel consumption, and increased air pollution. Megacities, in particular, bear the brunt of this escalating issue. Real-time assessment of road traffic density becomes imperative for efficient signal control and traffic management. Among the pivotal factors influencing traffic flow, the traffic controller stands paramount, necessitating optimization to meet the surging demand. Our proposed system capitalizes on live camera feeds from traffic junctions to conduct traffic density calculations through image processing techniques and artificial intelligence (AI). By harnessing these technologies, the system aims to offer a dynamic solution to the persistent challenge of congestion. The core focus lies in devising an algorithm that dynamically adjusts traffic light signals based on the detected vehicle density, thereby mitigating congestion and fostering smoother transit experiences for commuters. This innovative approach not only promises expedited travel times but also holds the potential to curtail pollution levels, contributing to a more sustainable urban environment. By seamlessly integrating image processing and AI-driven traffic control mechanisms, our system endeavors to pave the way towards a more efficient and eco-friendly transportation infrastructure

Enhancing traffic control systems with live video analytics: Issues, challenges, opportunities, and recent problems

With the rapid urbanization and increasing number of vehicles on the roads, it has become imperative to develop innovative solutions that can monitor and manage traffic congestion automatically. Traffic congestion harms the economy, environment, and overall quality of life. To address these challenges, smart traffic management systems employ cutting-edge technologies such as live video analytics and sensor-based adaptive traffic control systems. These systems can predict traffic patterns, locate congestion hotspots, and uncover abnormalities contributing to road accidents in real time. However, adopting these technologies for traffic control systems raises important concerns such as robustness and sustainability across different traffic junctions, data integration from multiple sources, and computational feasibility for real time computation. Therefore, this paper aims to present an overview of the potential benefits and challenges in adapting the latest technologies including the Internet of things and machine learning for sustainable traffic management. Additionally, a case study of a smart city is presented to evaluate an adaptive traffic control system based on live camera feed analytics by leveraging computer vision techniques. The adaptive traffic control system is accurate in vehicle detection and counting. This system is very useful for smart cities where traffic signals need to be automated according to the density of vehicles.

CoTEV: Trustworthy and Cooperative Task Execution in Internet of Vehicles

Due to the increasing number of service requests from the vehicles, the load at the road side units (RSUs) increases, which affects the delay-sensitive vehicle services. In Internet of Vehicles (IoV), the vehicles can communicate directly with other vehicles and take help from the vehicles to cooperatively accomplish a task. However, it is very challenging to cooperatively execute a task in an IoV environment with high traffic and dynamic vehicle movements. Furthermore, it is difficult for a task vehicle to choose trustworthy and cooperative vehicles. In this paper, we propose algorithms for cooperative task execution by taking the help of trusted vehicles, when it is not possible to complete a deadline-specified task through the RSUs. We propose a hedonic coalition formation game-based approach to form distributed coalitions of cooperative vehicles. We consider the trust score of the vehicles along with their computational capabilities and journey routes. After each task execution, the service feedback is reflected in the trust score of each cooperative vehicle in the coalition. Our proposed algorithms allow the cooperative vehicles to autonomously choose the coalitions and select a vehicle task to maximize their payoffs. To satisfy the task deadlines in multiple coalitions, we design the merging of vehicle coalitions. We consider the simulation of urban mobility (SUMO) tool to generate the mobility traces of the vehicles in a real road network of Berlin city, which considers the traffic junctions and vehicle density on the roads. Through extensive simulations, we show that the proposed algorithms significantly increase the service rate of delay-sensitive task requests by at least <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$30.5 \%$</tex-math></inline-formula> and the trust score by at least <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$20.61 \%$</tex-math></inline-formula> , compared to the benchmark schemes.

A simulation based comparative analysis of interconnected signalized intersections and roundabouts from the aspect of permeability

The utilization of the tools provided by digitalization became increasingly important for the micro-level modelling of road traffic, since it typically takes the form of a highly dynamic system. In the publication, the PTV Vissim traffic simulation software is used for the modelling of such a system, a traffic junction formed by two interconnected signalized intersections. The first goal of the analysis was to reveal how the optimization of the signal program in a specific way affects the operation of the entire system in terms of throughput during peak periods. After that, the possibility of modelling the same traffic junction with roundabouts was also explored. Finally, the results for the total traffic throughput of the system during the peak periods were compared in case of the different model variants. The resulting comparison present the permeability variation in a well-structured way and could also provide a useful example for the field.

E-Challan Based Intelligent Assistance System for Smart City

Traffic management and enforcement of road safety regulations have become increasingly crucial in modern urban environments, as the number of vehicles on the roads continues to surge. With the rise in traffic violations, particularly signal disobedience, there is a pressing need for innovative solutions that can efficiently detect and penalize offenders while reducing the burden on law enforcement personnel. The E-Challan System harnesses the power of cutting-edge computer vision techniques, most notably Automatic Number Plate Recognition (ANPR), to accurately capture and decipher vehicle number plates in real- time. High-resolution cameras strategically positioned at traffic junctions and signal intersections actively monitor passing vehicles, automatically detecting those that violate traffic signals. Upon successful identification of a signal violation, the E-Challan System promptly generates an electronic challan, or e-challan, containing detailed information about the violation, such as the date, time, and location of the incident. This comprehensive documentation serves as indisputable evidence of the offense and provides an essential legal foundation for imposing fines on violators. with an Auto Debit Fine mechanism.

Traffic Surveillance Using Computer Vision and Deep Learning Methods

From the early 21st century to the present date, Traffic surveillance has been one of the most challenging problems and over the years many solutions were proposed for tackling this problem. However, flaws are can still be noticed in the currently existing systems for performing this task and some of them will be further addressed in this work. In our project Traffic Surveillance using Computer Vision and Deep Learning, we propose a smart vision-based system which accurately provides live traffic statistics like the speed of the traffic and the count of Vehicles passed on a road in between a time interval. The system takes a video feed as an input, which could be live footage provided by a CCTV camera at a traffic junction, and on querying starting and ending timestamps, it outputs the count of the vehicles and speed of the traffic in between the queried timestamps. We divide this problem into two major sub-problems, firstly detecting a vehicle, followed by tracking the detected vehicle. Although there exist many algorithms for detecting objects as well as for tracking objects, one of the major problems is integrating both. As ambiguities like detecting an already detected vehicle, many occur while tracking the vehicles. In our work, we emphasize handling such ambiguities using Deep Learning and we also provide a comparative performance analysis of our system with the already existing ones.

SEARCH OF OPTIMAL SPATIAL PARAMETERS FOR GEODETIC DELINEATION OF MOTORWAY INTERCHANGES WITH BRAKING TRANSITION CURVES

The most widespread grade-separated road junction — cloverleaf interchange — is a complex engineering structure, in which all the parameters and limitations of the plan and longitudinal profile must be coordinated with each other. The goals of the analysis: traffic safety, technical and economic indicators of the movement of transport units, the compactness of left-turn and right-turn exits and, as an additional, but extremely important aspect, the minimization of the area occupied by the entire transport structure, which is very important in tight urban conditions. &#x0D; First, let's emphasize that the trajectories of cars at the intersection must correspond to their real variable speeds on left-turn exits, since this is traffic on curves of small radii. In our previous studies, it has been experimentally proven that cars entering a left-turning exit reduce their speed, and conversely, when leaving the exit, their speed increases. Therefore, it is necessary to use braking curves as transition curves of left-turn exits, and not clothoid curves, which are often used, and which are designed for constant speed of movement.&#x0D; Some braking transition curves designed for variable speed are also used in traffic junctions [4]. But, analyzing these curves, we came to the conclusion that they do not meet the classical purpose of the transition curve, namely, they do not provide the necessary radius at the end of the curve, which is equal to the radius of the circular insert for a smooth transition from a transition curve to a circular curve. This leads to dangerous moments of the car's movement at the junction of the transition and circular curves.&#x0D; Therefore, we previously derived the formulas of refined braking transition curves that meet the requirements of the radii of classical transition curves [1, 2]. In addition, it later turned out that these refined transition curves have, as an additional effect, the property of reducing the area occupied by the left-turn exit, as shown in Fig. 1.&#x0D; We offer a method for optimizing the spatial parameters of cloverleaf interchanges with refined braking transition curves.

A Vehicle Density Estimation Traffic Light Control System Using a Two-Dimensional Convolution Neural Network

One of the world’s challenges is the amount of traffic on the roads. Waiting for the green light is a major cause of traffic congestion. Low throughput rates and eventual congestion come from many traffic signals that are hard coded, irrespective of the volume of the amount of traffic. Instead of depending on predefined time intervals, it is essential to build a traffic signal control system that can react to changing vehicle densities. Emergency vehicles, like ambulances, must be given priority at the intersection so as not to spend more time at the traffic light. Computer vision techniques can be used to improve road traffic signal control and reduce real-time traffic delays at intersections without the requirement for substantial infrastructure analysis. Long wait times and significant energy consumption are just two of the problems of the current traffic signal control system. To optimal efficiency, the traffic signal’s duration must be dynamically changed to account for current traffic volume. To lessen congestion, the approach taken in this research focuses on modifying traffic signal time determined by the density of vehicles at the crossroads. The main purpose of this article is to demonstrate heavy traffic and emergency vehicle prioritization from all directions at the traffic intersection for a speedy passage. Using the Pygame tool, the proposed method in this study, which includes a mechanism for estimating traffic density and prioritization by counting vehicles at a traffic junction, is demonstrated. The vehicle throughput for the adaptive traffic light built using Pygame is compared with the vehicle pass rate for the adaptive traffic light built using Simulation of Urban Mobility (SUMO). The simulation results show that the adaptive traffic light built using Pygame achieves 90% throughput compared to the adaptive traffic light built using SUMO. A Two-Dimensional Convolutional Neural Network (2D-CNN) is implemented using Tensorflow for vehicle classification. The 2D-CNN model demonstrated 96% accuracy in classifying vehicles using the test dataset. Additionally, emergency vehicles, such as ambulances, are given priority for quick passing.

Vehicular Volume and Concentration of Gaseous Atmospheric Pollutants and Particulate Matter at Selected Road Junctions in Port Harcourt, Nigeria

The increase in industrial and vehicular activities has led to the proliferation of atmospheric pollutants in cities globally. The Niger Delta region is an industrial hub and serves as the nation's resource base by exploring crude oil. This work postulated that there would be high pollutants and particulate matter at road junctions due to the multiplicity of several vehicles owned by the surging human population. The number of vehicles at each junction were counted. A multi-parameter gas monitor (A multi–RAE PLUS (PGM-50) was used to collect air samples at different junctions (n = 5). The concentration of nitrogen dioxide (NO2), sulfur dioxide (SO2), carbon monoxide (CO), particulate matter (PM2.5, PM10), temperature, relative humidity, and wind were analyzed. The result revealed that the volume of the vehicles at road traffic junctions follows the order Rumuola&gt;Mile 3/UST Roundabout&gt;Choba Junction&gt;Garrison&gt;Lagos Bus Stop. Choba Junction had the highest concentration of pollutants. The highest concentration of vehicular pollutants was observed in the morning. Carbon monoxide (CO) had the highest concentration, followed by SO2 and NO2. While for particulate matter, PM2.5 had a higher concentration than PM10. The result implies that the number of vehicular traffic does not necessarily determine the concentration of pollutants because other anthropogenic activities are causing a spike in atmospheric pollutants within the city.

Conceptual Design of Smart Network Adaptive Traffic Light in Creating Low-Carbon City

The research is led to the development of a smart sensory network adaptive traffic light to optimise traffic flow and reduce congestion on Jalan Persisiran Seri Alam in Pasir Gudang. With variable time management, this system can reduce the time spent at traffic junctions and carbon emitted by vehicles, thus supporting the SDG 11 for low carbon smart city. By integrating the dynamic duration of each traffic signal on each dense pathway, this system can help to reduce traffic congestion, fuel consumption, and CO2 emissions. It can also ensure that traffic is managed in a more efficient manner, thus improving the quality of life for the people of Pasir Gudang. The success of this system will be a major step towards achieving the goal of a low carbon smart city, as it will help to reduce air pollution, noise pollution, and improve road safety. Additionally, it can help to improve the efficiency of traffic flow, leading to better traffic management and reduced congestion.

Curbside Parking Monitoring With Roadside LiDAR

Cities worldwide are striving to find more efficient approaches to address the prevalent parking challenges in urban areas. A key aspect of achieving an optimal parking environment is the collection of curbside parking data, which enables informed decision-making and effective management of on-street parking spaces. This study proposes a solution for curbside parking monitoring and data collection using roadside LiDAR systems. By leveraging laser beam variation detection, this solution can extract essential information about parking usage. Unlike existing solutions, such as imagery or embedded sensor-based monitoring, our solution offers portability and ease of deployment for short-term or long-term curbside parking data collection. Additionally, the LiDAR sensor captures only three-dimensional data and is independent of illumination conditions, ensuring stable operation throughout the day while safeguarding privacy by not capturing imagery. These features align with the requirements of city agencies for parking data collection. The workflow follows a simple trend without the need for complex training, as typically seen in machine learning-based methods, and instead relies on parameter tuning based on real-world environmental factors. To validate the effectiveness of our method, we collected curbside parking data for five days at a midtown traffic junction with eight parking spaces. Manual validation confirmed a 95% match between identified parking events and observed data across different time periods. The study further presents parking statistics based on the identified events, revealing crucial insights about parking usage in the study area.

Seasonal and diurnal measurement of ambient benzene at a high traffic inflation site in Delhi: Health risk assessment and its possible role in ozone formation pathways.

Benzene is the most toxic and hazardous pollutant among volatile organic compounds (VOCs), as it comes under group 1 carcinogens recognized by the International Agency for Research on Cancer (IARC). It also plays a significant role in forming secondary pollutants like ozone. The benzene concentration was measured using a charcoal sorbent tube by active sampling at a traffic junction and analysis was done using GC-FID. The maximum average concentration of benzene in ambient air was found to be 33 μg/m3. A diurnal study of benzene measurement shows higher benzene concentrations in the evening compared to the morning. Seasonal variation of benzene is found to be winter > spring > summer > autumn > monsoon and OFP was found to be 21, 19, 14, 13, and 10 respectively. Cancer (ILCR) and non-cancer (HQ) health risk assessment was done to determine the impact of ambient benzene on the residents of urban areas. The yearly average value of ILCR was found to be 2×10-6 ± 1×10-6 which ranges from acceptable value to three times the WHO acceptable value i.e 1×10-6. The correlation of ozone and its precursor, benzene with meteorological parameters is also evaluated. The correlation of benzene and ozone with solar radiation shows the influence of photochemical reactions on the levels of benzene and ozone at the study site, although it is low.

Umfahrung Weyer – Tunnelbau im Lockergestein unter Bebauung

AbstractThe small town of Weyer is located in Upper Austria on the B121 Weyerer Straße. The B121 leads through the historic market square of Weyer, which is characterised by confusing bottlenecks that can only be passed in one lane. For decades, these bottlenecks have caused congestion, mainly due to heavy traffic. For residents, the burden of air pollution and noise is high. The roads department of the Upper Austrian Government has been planning the construction of a bypass for many years. Construction finally started in the summer of 2022. The Weyer bypass construction lot is 1.2 km long and includes the construction of two NATM tunnels, a vertical escape shaft, a bridge over the river Dürnbach, two roundabouts, and one traffic junction. The tunnel construction works are carried out in urban areas, in loose material with little overburden and partly under existing buildings. These boundary conditions make the project a challenge for all involved at every stage.