The Internet of Things (IoT) is a paradigm characterized by a network of embedded sensors and services. These sensors are incorporated to collect various information, track physical conditions, e.g., waste bins’ status, and exchange data with different centralized platforms. The need for such sensors is increasing; however, the proliferation of technologies comes with various challenges. For example, how can IoT and its associated data be used to enhance waste management? In smart cities, an efficient waste management system is crucial. Artificial intelligence (AI) and IoT-enabled approaches can empower cities to manage the waste collection. This work proposes an intelligent approach to route recommendation in an IoT-enabled waste management system given spatial constraints. It performs a thorough analysis based on AI-based methods and compare their corresponding results. Our solution is based on a multiple-level decision-making process in which bins’ status and coordinates are taken into account to address the routing problem. Such AI-based models can help engineers design a sustainable infrastructure system.

The percentage of processed large-scale heterogeneous data is exploding and technology is the most obvious reason for the big data issue. Nowadays, the results of data expansion are showing up in different fields. The users' contextual data are valuable in engineering and business domains, e.g., transportation, location-based services, and advertisement industry. Take mobile phones as an example. There are billions of subscriptions worldwide and sensor devices are digitizing people interactions. The data volume generated by mobile phones and the need to make better, fact-based, and real-time decisions, are the challenges facing researchers. Recently, new technologies based on cloud computing have emerged to process and analyze a large volume of data. We have utilized such technologies for the analysis of call detail records with the collaboration with a telecommunications company. We present an exploratory spatial data analysis algorithm and its analysis results. To prioritize different areas, detecting hotspots in a fast and accurate way is our objective. The findings of this research work can be helpful for urban planning and development as well as telecommunication infrastructure upgrading.

Mobile phones have emerged as an essential part of people's lives. The data produced from them can be utilized to derive the spatio-temporal information of their users' whereabouts. We can obtain a rich data set of human activities, interactions, social relationships, and mobility. Hence, it has been possible to explore these information sources with applications ranging from disaster management to disease epidemiology. In this paper, we have focused on the use of call detail records to explore and interpret patterns embedded in interaction flows of people through their mobile phone calls. To do so, we consider the geographical context of subscribers/celltowers to discover structures of spatio-temporal interactions and communities' patterns in Macau. We have explored the inter and intra-polygon interaction flows. The results suggest that subscribers tend to communicate within a spatial-proximity community. In order to delineate relatively contiguous objects with similar attribute values, we have implemented an efficient hierarchical clustering approach. By identifying key objects and their close associates and exploring their communication patterns, we can detect shared interests and dominant interactions that influence societal patterns. Such insight is useful for resource optimization in network planning, content distribution, and urban planning.

Transport emissions, including road, rail, air, and marine transportation, account for a large part of the overall emissions; hence, there is a need to review strategies for managing associated issues and coping with negative impacts. A simultaneous improvement in economic efficiency can help us achieve our desired objectives in the concerned context. Sharing economy, i.e., a peer-to-peer-based sharing of access to assets, can help reduce the total resources required and consequently reduce carbon footprints. In line with this objective, we propose an intelligent model to study carbon dioxide emissions from road transport using taxi trips in Dublin, Ireland. The proposed method is a hybrid unsupervised learning approach tailored for the particular structure of the problem. We present how an intelligent approach can be implemented to model CO2 emissions from road transport. The model categorizes taxis based on different features related to the emissions they release. Five clusters are detected, which can be attributed to varying levels of emissions. Accordingly, those vehicles labeled as the highest emitters can be targeted for further improvements in reducing CO2, i.e., replacing pollutant cars with electric cars or including them in the taxi fleet as sharing ones only.

Most of the techniques involved in customer clustering and segmentation are based on conventional methods of quantitative analysis or traditional data mining approaches such as the K-Means algorithm. However, clustering approaches based on artificial neural networks (ANNs), evolutionary algorithms, and fuzzy methods can be more efficient since they can reveal nonlinear patterns. They also seem to be more robust in coping with noise-related issues and relevant noise handling operations. They do not make any statistical distributional assumptions regarding the nature of the data. In this article, we develop a hybrid approach based on ANNs and swarm intelligence to reveal the underlying pattern structure of customers of an insurance company in the Republic of Ireland. This model is tailored to the scope of segmenting administrative districts, or “small areas,” given policyholders’ spatial characteristics. To that end, the geospatial features of customers are taken into account. Geodemographically speaking, by implementing such a hybrid model, the relative similarity among spatial objects (small areas in this work) are preserved. In this way, the similarity of each small area to all other small areas is characterized. Consequently, the pattern of customers is analyzed using an optimal and intelligent solution. We can also visualize the results of this study.

Spatial analysis ranges from simple univariate descriptive statistics to complex multivariate analyses and is typically used to investigate spatial patterns or to identify spatially linked consumer behaviours in insurance. This paper investigates if the incorporation of publicly available spatially linked demographic census data at population level is useful in modelling customers’ lapse behaviour (i.e. stopping payment of premiums) in life insurance policies, based on data provided by an insurance company in Ireland. From the insurance company’s perspective, identifying and assessing such lapsing risks in advance permit engagement to prevent such incidents, saving money by re-evaluating customer acquisition channels and improving capital reserve calculation and preparation. Incorporating spatial analysis in lapse modelling is expected to improve lapse prediction. Therefore, a hybrid approach to lapse prediction is proposed – spatial clustering using census data is used to reveal the underlying spatial structure of customers of the Irish life insurer, in conjunction with traditional statistical models for lapse prediction based on the company data. The primary contribution of this work is to consider the spatial characteristics of customers for life insurance lapse behaviour, via the integration of reliable government provided census demographics, which has not been considered previously in actuarial literature. Company decision-makers can use the insights gleaned from this analysis to identify customer subsets to target with personalized promotions to reduce lapse rates, and to reduce overall company risk.