A GIS-based approach to determining optimal location for decentralized inner city smart filters: Toward net zero cities.

Climate change has already begun to take visible effect globally in recent years. Given the climate change paradox and urbanization trends, cities' success would not only depend on smartness and sustainability, but also resilience to all forthcoming economic, environmental, or behavioral changes. Numerous technologies have surfaced and proved effective in CO2 removal from the local environment. However, the optimal placement of these smart filters is a complex task and require logical and strategic decision-making. Determining the optimal location is one of the key factors for establishing a network of smart air filters. This study used a GIS-based suitability analysis for identifying optimal locations for smart filters based on pollution hotspots (population and spatial proximity to industry, commercial centers, roads, high-traffic areas, and intersections). The spatial analysis involves the determination and preparation of input layers, ranking layers, assigning weights to each criterion, and generation of a suitability map. The sites with a higher suitability score (7 or above) are optimum sites for air filters. The sites are spatially distributed over different regions. The findings revealed that GIS-based suitability analysis can be an effective technique for placing smart filters within an urban environment. These findings can help decision-makers to prioritize the location considering environmental constraints. The proposed solution aims to pave the way for fostering resilient, smart, and sustainable cities through a community sensing platform targeting hotspots within spatial variations.

Utilization of Radium-Bead Material for Road Safety: An Application of the Circular Economy Concept.

Road safety has become a serious issue in both developed and developing countries, costing billions of dollars every year. Road accidents at nighttime especially in low illumination situations are common and severe and have gained a lot of attention. To improve visibility and avoid traffic accidents, a series of efforts have been made but the existing mechanism is facing continuous challenges and highlighting a need for smart highways with high efficiency, road safety, and strength. In this study, the use of radium polymer beads (RPB) is proposed to avoid road accidents. The effect of RPB was investigated by comparing the results of the beads' surface and modified asphalt mixtures using the three-stage testing methodology. Utilizing the circular economy, RPB have been introduced as a solution to the problem. Results indicated that in the first phase, the addition of RPB on the mixture surface improved the mechanical performance of the road pavement and helped in avoiding road accidents due to their ability to absorb the light from the source and then reflect in the night. Moreover, the mechanical properties using Marshall stability standard parameters (stability 9 kN and flow 2-4 mm range) were fulfilled as a standard testing requirement. The proposed radium bead layer can reduce road accidents and provide a direction towards future smart highways by using new reflective materials in road construction.

Relationship between road traffic features and accidents: An application of two-stage decision-making approach for transportation engineers.

INTRODUCTION: An efficient decision-making process is one of the major necessities of road safety performance analysis for human safety and budget allocation procedure. METHOD: During the road safety analysis procedure, data envelopment analysis (DEA) supports policymakers in differentiating between risky and safe segments of a homogeneous highway. Cross-risk, an extension of the DEA models, provides more information about risky segments for ranking purpose. After identification of risky segments, the next goal is to identify the factors that are major contributors in making that segment risky. RESULTS: This research proposes a methodology to analyze road safety performance by using a combination of DEA with the decision tree (DT) technique. The proposed methodology not only provides a facility to identify problematic road segments with the help of DEA but also identifies contributing factors with the help of DT. Practical applications: The applicability of the proposed model will help policymakers to identify the major factors contributing to road accidents and analysis of safety performance of road infrastructure to allocate the budget during the decision-making process.