Smart and sustainable solid waste management systems (SWMS) are of major interest in the development of smart sustainable cities (SSC). Selective waste collection and transportation are known to be major expenditures of city waste management systems. In this paper, we investigate a waste management system for domestic waste in a Norwegian municipality as a case study. Different scenarios for route planning are considered to improve cost and time usage. The study provides an auxiliary management system for multi-objective TSP using Google Maps and operation research (OR) tools for optimal domestic waste collection. Additionally, a prediction model for scheduling future waste collection trips is provided, whereby challenges such as road conditions, road traffic, CO2 and other gases emissions, and fuel consumption are considered. The proposed prediction model considers the hazards associated with food waste bins that need to be emptied more frequently than bins containing other waste types such as plastic and paper. Both proposed models signify consistency and correctness.
Whenever natural and human-made disasters strike, the proper response of the concerned authorities often relies on search and rescue services. Search and rescue services are complex multidisciplinary processes that involve several degrees of interdependent assignments. To handle such complexity, decision support systems are used for decision-making and execution of plans within search and rescue operations. Advances in data management solutions and artificial intelligence technologies have provided better opportunities to make more efficient and effective decisions that can lead to improved search and rescue operations. This paper provides findings from a bibliometric mapping and a systematic literature review performed to: (1) identify existing search and rescue processes that use decision support systems, data management solutions, and artificial intelligence technologies; (2) do a comprehensive analysis of existing solutions in terms of their research contributions to the investigated domain; and (3) investigate the potential for knowledge transfer between application areas. The main findings of this review are that non-conventional data management solutions are commonly used in land rescue operations and that geographical information systems have been integrated with various machine learning approaches for land rescue. However, there is a gap in the existing research on search and rescue decision support at sea, which can motivate future studies within this specific application area.
Smart and sustainable solid waste management in metropolises with systemic methods and other environmental issues are important factors in the development of urban management and circular economy. Extensive progress has been made towards reducing the environmental and human health impact of the generated solid waste in households. Under such a context, the major challenges are to reduce waste generation and optimize waste collection process in a way that lies within the circular economy. A Norwegian municipality has been investigated as a case study for this research. In this regard, a sustainable social enterprise model for solid waste management has been proposed. It has two key points; one is optimal waste collection and other is to observe effects of optimal route planning for achieving sustainable development goals (SDGs). Furthermore, the data analysis has been done to observe the waste generation patterns in different areas of the investigated municipality and how can this be used for future placement and sizes of waste bins. The proposed solution is profitable for a circular economy as the optimal route planning will help to reduce fuel consumption, cost, and time used for waste collection. The social enterprise model (SEM) accomplishes the revenue and achieves the key performance indicators (KPIs) for sustainable development goals.
The analysis of individuals’ movement behaviors is an important area of research in geographic information sciences, with broad applications in smart mobility and transportation systems. Recent advances in information and communication technologies have enabled the collection of vast amounts of mobility data for investigating movement behaviors using trajectory data mining techniques. Trajectory clustering is one commonly used method, but most existing methods require a complete similarity matrix to quantify the similarities among users’ trajectories in the dataset. This creates a significant computational overhead for large datasets with many user trajectories. To address this complexity, an efficient clustering-based method for network constraint trajectories is proposed, which can help with transportation planning and reduce traffic congestion on roads. The proposed algorithm is based on spatiotemporal buffering and overlapping operations and involves the following steps: (i) Trajectory preprocessing, which uses an efficient map-matching algorithm to match trajectory points to the road network. (ii) Trajectory segmentation, where a Compressed Linear Reference (CLR) technique is used to convert the discrete 3D trajectories to 2D CLR space. (iii) Spatiotemporal proximity analysis, which calculates a partial similarity matrix using the Longest Common Subsequence similarity indicator in CLR space. (iv) Trajectory clustering, which uses density-based and hierarchical clustering approaches to cluster the trajectories. To verify the proposed clustering-based method, a case study is carried out using real trajectories from the GeoLife project of Microsoft Research Asia. The case study results demonstrate the effectiveness and efficiency of the proposed method compared with other state-of-the-art clustering-based methods.
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