Evaluation of Vegetation Configuration Models for Managing Particulate Matter along the Urban Street Environment

As a green infrastructure component, urban street vegetation is increasingly being utilized to mitigate air pollution, control microclimates, and provide aesthetic and ecological benefits. This study investigated the effect of vegetation configurations on particulate matter (PM) flows for pedestrians in road traffic environments via a computation fluid dynamics analysis based on the road width (four and eight-lane) and vegetation configuration (single-, multi-layer planting, and vegetation barrier). Airflow changes due to vegetation influenced PM inflow into the sidewalk. Vegetation between roadways and sidewalks were effective at reducing PM concentrations. Compared to single-layer planting (trees only), planting structures capable of separating sidewalk and roadway airflows, such as a multi-layer planting vegetation barrier (trees and shrubs), were more effective at minimizing PM on the sidewalk; for wider roads, a multi-layer structure was the most effective. Furthermore, along a four-lane road, the appropriate vegetation volume and width for reducing PM based on the breathing height (1.5 m) were 0.6 m3 and 0.4 m, respectively. The appropriate vegetation volume and width around eight-lane roads, were 1.2–1.4 m3 and 0.8–0.93 m, respectively. The results of this study can provide appropriate standards for street vegetation design to reduce PM concentrations along sidewalks.

Urban street scene analysis using lightweight multi-level multi-path feature aggregation network

Urban street scene analysis is an important problem in computer vision with many off-line models achieving outstanding semantic segmentation results. However, it is an ongoing challenge for the research community to develop and optimize the deep neural architecture with real-time low computing requirements whilst maintaining good performance. Balancing between model complexity and performance has been a major hurdle with many models dropping too much accuracy for a slight reduction in model size and unable to handle high-resolution input images. The study aims to address this issue with a novel model, named M2FANet, that provides a much better balance between model’s efficiency and accuracy for scene segmentation than other alternatives. The proposed optimised backbone helps to increase model’s efficiency whereas, suggested Multi-level Multi-path (M2) feature aggregation approach enhances model’s performance in the real-time environment. By exploiting multi-feature scaling technique, M2FANet produces state-of-the-art results in resource-constrained situations by handling full input resolution. On the Cityscapes benchmark data set, the proposed model produces 68.5% and 68.3% class accuracy on validation and test sets respectively, whilst having only 1.3 million parameters. Compared with all real-time models of less than 5 million parameters, the proposed model is the most competitive in both performance and real-time capability.

Construction of Smart City Street Landscape Big Data-Driven Intelligent System Based on Industry 4.0

With the application of engineering management in smart city construction under Industry 4.0, the intelligent design of urban street landscape has attracted extensive attention. Affected by the low intelligent level of traditional landscape design, the existing urban landscape composite system has difficulty in meeting the needs of smart city construction. Therefore, this paper proposes the construction of street landscape big data-driven intelligent decision support system based on Industry 4.0. Based on the complex network theory, this paper analyzes the structure, links, nodes, driving forces, and functional requirements of urban street landscape and then puts forward the construction content and implementation method of urban street landscape intelligent decision support system. The system consists of four aspects: intelligent infrastructure, service, protection and maintenance, and management and evaluation system. Its implementation not only reflects the cooperation and effective application of intelligent technology in each stage of street landscape construction, but also provides reference for the application of engineering management in other fields under Industry 4.0.

Refusing relocation: Urban street vendors and the problem of the neoliberal device

In 2010, the City of Dakar published its new master plan for a clean, competitive, modern city. This plan entailed the relocation of thousands of walking street vendors to free up traffic circulation and reduce the economic costs of congestion. Unlike previous relocations, this program required the political participation of vendor associations in the planning and design of a new commercial center. It also required the vendors to pay user charges: monthly payments for the use of the center and its utilities. Yet most Dakar's street vendors unequivocally refused to relocate, citing the building's poor location, bad design, and high price. Such user charges have become a contentious device with which governments across the world are financing the provision of public services. In this article, I analyze the politics of this device by tracing the linkages from Dakar's relocation program back to the political philosophies of prominent intellectuals commonly associated with “neoliberalism.” In doing so, I reveal how popular refusal is not beyond or opposed to a depoliticizing neoliberalism, but instead forms an integral part of neoliberal reflections on popular politics. I conclude by analyzing the political effects of this neoliberal device in Dakar: it introduced a new style of political engagement—consumption—through which individual vendors could dispute their relocation. And this individualized refusal to consume incited their representative associations to extend a popular mode of valuation—negotiation—into the calculation of the building's price.

IMPROVEMENT OF THE URBAN TRANSPORTATION SYSTEM BY THE INTRODUCTION OF ADAPTIVE TRAFFIC CONTROL SYSTEMS

The article is devoted to the prospects of improving the quality of traffi c in the junction of the urban street and road network through the introduction of intelligent transport systems, especially automatic traffi c control systems (ATCS). The paper analyzes the problems of implementing intelligent transport systems in urban conditions, taking into account the current regulatory framework. The classifi cation of local automated traffi c control systems according to the adaptability of traffi c light regulation to the changing parameters of traffi c fl ows is given. For the decision of a problem of practicability of introduction of ACSDS, the technique including construction of imitation models for more exact forecasting of eff ect of introduction of local ACSDS on the considered site of an urban street-road network is off ered. The application of the methodology is demonstrated on the example of the intersection of Engels Avenue and Suzdal Avenue in St. Petersburg. Two variants of the organization of control of phases of traffi c light objects are analyzed: static and adapted according to the time of day. The infl uence of ADCS implementation on average speed of vehicles and characteristics of traffi c jams in the junction in question was estimated based on simulation modeling in PTV Vissim. In accordance with the analysis, the prospects of introducing adaptive local ACSDS in the considered transport junction are described