Predicting of atmospheric corrosivity and durability of structural materials. Part II. Impact of urban traffic pollution

The results of the atmospheric corrosivity assessment in the immediate vicinity of streets of different traffic volume in Warsaw, Krakow and Katowice are derived . On the bases of annual exposures in 2014–2018 years an equation describing the impact of environmental parameters and street traffic volume on corrosion losses of zinc and zinc coating on steel was selected.

Fast Detection of Multiple Objects in Traffic Scenes with a Common Detection Framework

Object visual detection (OVD) intends to extract precise ongoing on-street traffic signs, which includes three stages: discovery of objects of interest, acknowledgment of recognized items, and following of items moving. Here OpenCV instruments give the calculation backing to various item identification. Item discovery is a PC innovation that is associated with picture handling and PC vision that manage recognizing occasion objects of certain class in computerized pictures and recordings. This paper describes how object recognition is a difficult work in image processing based PC applications, here CNN and RCNN algorithm is used to recognize objects. It is accustomed to distinguishing whether a scene or picture object has been there or not. In this paper, we will introduce procedures and techniques for distinguishing or perceiving objects with different advantages like effectiveness, precision, power and so forth.

A Balanced Algorithm for In-City Parking Allocation: A Case Study of Al Madinah City

Parking in heavily populated areas has been considered one of the main challenges in the transportation systems for the past two decades given the limited parking resources, especially in city centres. Drivers often waste long periods of time hunting for an empty parking spot, which causes congestion and consumes energy during the process. Thus, finding an optimal parking spot depends on several factors such as street traffic congestion, trip distance/time, the availability of a parking spot, the waiting time on the lot gate, and the parking fees. Designing a parking spot allocation algorithm that takes those factors into account is crucial for an efficient and high-availability parking service. We propose a smart routing and parking algorithm to allocate an optimal parking space given the aforementioned limiting factors. This algorithm supports choosing the appropriate travel route and parking lot while considering the real-time street traffic and candidate parking lots. A multi-objective function is introduced, with varying weights of the five factors to produce the optimal parking spot with the least congested route while achieving a balanced utilization for candidate parking lots and a balanced traffic distribution. A queueing model is also developed to investigate the availability rate in candidate parking lots while considering the arrival rate, departure rate, and the lot capacity. To evaluate the performance of the proposed algorithm, simulation scenarios have been performed for different cases of high and low traffic intensity rates. We have tested the algorithm on in-city parking facility in the city of Al Madinah as a case study. The results show that the proposed algorithm is effective in achieving a balanced utilization of the parking lots, reducing traffic congestion rates on all routes to candidate parking lots, and minimizing the driving time to the assigned parking spot. Additionally, the proposed algorithm outperforms the MADM algorithm in terms of the selected three metrics for the five periods.

What Kerbstones Do: A Century of Street Space from the Perspective of One Material Actor

In this article, kerbstones are analysed as historical actors that participated in the changes of urban space and street traffic during the hundred years between the 1880s and the 1980s. Using the approach of new materialism and a large photographic source material from the Finnish city Turku, the article provides a new perspective into the tremendous changes many cities went through during this period and proposes possibilities of including non-human actors in the historical analysis of such change. Focusing on non-human actors also sheds new light on human agency. Such actions as moving in street space or planning cities and traffic infrastructure appear as co-actions of shifting and affective constellations of soft and hard bodies. In the changing street space, the kerbstone was able to assume both enabling and resisting agency as a rather permanent, hard and persistent presence. In intra-actions with the other bodies of the street space it softened or hardened as a border toward different vehicles, living bodies, materials and artefacts, thus also forming them.

Traffic Monitoring Using Cloud Computing and RFID Technology

The article presents the architecture and implementation of a street traffic monitoring system. It uses RFID identifiers to recognize vehicles, including special meaning, such as ambulances, city buses, vehicles with reduced exhaust gas emissions. Traffic data is sent to the IoT Hub service in the Azure cloud. On their basis, road situations are analyzed and decisions are made regarding traffic control. Control information is fed back to traffic control devices by means of street lights, barriers, information boards. The article describes the method of communication with the computing cloud and the possibilities of implementing traffic monitoring and control algorithms using IoT Hub.

A measure of the importance of roads based on topography and traffic intensity

Mathematical models of street traffic allowing assessment of the importance of their individual segments for the functionality of the street system is considering. Based on methods of cooperative games and the reliability theory the suitable measure is constructed. The main goal is to analyze methods for assessing the importance (rank) of road fragments, including their functions. A relevance of these elements for effective accessibility for the entire system will be considered.

ClefNet: Recurrent Autoencoders with Dynamic Time Warping for Near-Lossless Music Compression and Minimal-Latency Transmission

The onset of coronavirus disease 2019 (COVID-19), an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has sparked unprecedented change. Due to the public health guidelines imposed during the COVID-19 pandemic, there is no longer sufficient street traffic for remaining buskers to generate sufficient revenue, leading a majority of street musicians to pursue remote music production. However, real-time music production is notoriously difficult due to the excessively high latencies that current video call platforms such as Zoom and Google Meet harbor. In this paper, we propose an architecture for a platform with end-to-end, near-lossless audio transmission tailored specifically to online joint music production, called Latent Space. We discuss the usage of a recurrent autoencoder with sequence-aware encoding (RAES) and a 1D convolutional layer for audio compression, which we dub ClefNet, as well as propose a new evaluation metric for naive autoencoders (AEs), MSE-DTW loss, which combines the traditional mean square error (MSE) loss function with dynamic time warping (DTW) to prevent an increase in loss when the target sequence predicted by the AE is strictly a temporal variation of the source sequence. Moreover, we detail the logistics of a live system implementation which uses the Web Audio API to extract raw audio samples in real-time to feed into our client-side model before relaying the traffic using peer-to-peer WebRTC technology. The Latent Space platform can be accessed at https://latent-space.tech, and the code and data can be found under the MIT License at https://github.com/rvignav/ClefNet.

Detecting anomalous road traffic conditions using VGG19 CNN Model

Detection on the real time road traffic has tremendous application possibilities in metropolitan road safety and traffic management. Due to the effect of numerous factors, for example: climate, viewpoints and road conditions in real-time traffic scene, Anomaly detection actually faces many difficulties. There are many reasons for vehicle accidents, for example: crashes, vehicle on flames and vehicle breakdowns, which exhibits distinctive and obscure behaviours. In this paper, we approached with a model to identify oddity in street traffic by monitoring the vehicle movement designs in two unmistakable yet associated modes which is 1. The vehicle’s dynamic mode and 2. The vehicle’s Static mode. The vehicle’s static mode investigation is gained using the background modelling after the detection of a vehicle, this strategy is useful to locate the unusual vehicle movement which keep still out and about. The dynamic mode vehicle examination is gained from identified and followed vehicle directions to locate the strange direction which is distorted from the predominant movement designs. The outcomes from the double mode investigations are at long last fused together by driven a distinguishing proof model to get the last peculiarity. For this research we are using traffic-net Dataset, VGG19 CNN model along with ImageNet weights and OpenCV.

Development of traffic noise prediction model for major arterial roads of tier-II city of India (Surat) using artificial neural network

In the issue of expanding noise levels the world over, road traffic noise is main contributor. The investigation of street traffic noise in urban communities is a significant issue. Ample opportunity has already passed to understand the significance of noise appraisal through prediction models with the goal that assurance against street traffic noise can be actualized. Noise predictions models are utilized in an increasing range of decision-making applications. This study’s main objective is to assess ambient noise levels at major arterial roads of Surat city, compare these with prescribed standards, and develop a noise prediction model for arterial roads using an Artificial Neural Network. The feed-forward back propagation method has been used to train the model. Models have been developed using the data of three roads separately, and one final model has also been developed using the data of all three roads. Among the prediction in three arterial roads, the predicted output result from the model of Adajan-Rander showed a better correlation with a mean squared error (MSE) of 0.789 and R2 value of 0.707. But with the combined model, there is a slight deterioration in mean squared value (MSE) 1.550, with R2 not getting changed much significantly, i.e., 0.755. However, the combined model’s prediction can be adopted due to the variety of data used in its training.