Emerging Technologies for Smart Cities’ Transportation: Geo-Information, Data Analytics and Machine Learning Approaches

Ang, Kenneth Li-Minn; Seng, Jasmine Kah Phooi; Ngharamike, Ericmoore; Ijemaru, Gerald K.

doi:10.3390/ijgi11020085

Cited by 41 publications

(28 citation statements)

References 190 publications

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“…In particular, urban mobility is a sub-field of urban computing that refers to the mobility of people and vehicles within cities, including the challenges and opportunities associated with the planning, management and optimization of urban transport systems [10]. The analysis of large amounts of geotagged data generated by IoT devices installed on means of transport and road infrastructures can be used for many purposes, including traffic flow monitoring and transport route planning, decision-making to improve the quality of urban life and the provision of location-based services to citizens [11].…”

Section: Related Workmentioning

confidence: 99%

Edge-Cloud Continuum Solutions for Urban Mobility Prediction and Planning

et al. 2023

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In recent years, there has been an increase in the use of edge-cloud continuum solutions to efficiently collect and analyze data generated by IoT devices. In this paper, we investigate to what extent these solutions can manage tasks related to urban mobility, by combining real-time and low latency analysis offered by the edge with large computing and storage resources provided by the cloud. Our proposal is organized into three parts. The first part focuses on defining three application scenarios in which geotagged data generated by IoT objects, such as taxis, cars, and smartphones, are collected and analyzed through machine learning algorithms (i.e., next location prediction, location-based advertising, and points of interest recommendation). The second part is dedicated to modeling an edge-cloud continuum architecture capable of managing a large number of IoT devices and executing machine learning algorithms to analyze the data they generate. The third part analyzes the experimental results in which different design choices were evaluated, such as the number of devices and orchestration policies, to improve the performance of machine learning algorithms in terms of processing time, network delay, task failure, and computational resource utilization. The results highlight the potential benefits of edge and cloud cooperation in the three application scenarios, demonstrating that it significantly improves resource utilization and reduces the task failure rate compared to other widely adopted architectures, such as edge-or cloud-only architectures.INDEX TERMS Edge-cloud architecture, IoT infrastructure, Edge computing, Urban computing, Smart cities, Urban mobility

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Section: Related Workmentioning

confidence: 99%

Edge-Cloud Continuum Solutions for Urban Mobility Prediction and Planning

et al. 2023

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“…The persistent issues facing urban logistics remain challenging due to conflicting priorities around cost, time, and developing suitable technologies. Scientific literature demonstrates artificial intelligence's (AI) potential through critical reviews and monitoring of road transport infrastructure surveillance utilizing ground penetrating radar [1], as well as recommended energy transport and CO 2 emissions modeling [2], or evidenced in studies of emerging smart city technologies applying geospatial data, advanced analytics and machine learning approaches in internet of things frameworks [3]. Machine learning (ML) and deep learning (DL) methods have enhanced predictive, planning and uncertainty models across diverse aspects of urban development [4].…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study on AI-Based Algorithms for Cost Prediction in Pharmaceutical Transport Logistics

Farchi,

Touzi

et al. 2023

ATAIML

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Pharmaceutical transport logistics, especially in humanitarian and hospital contexts, is becoming increasingly essential with a growing need to monitor associated costs. In Morocco, however, studies focusing on the cost implications of pharmaceutical delivery conditions are conspicuously absent. This creates a high-dimensional classification framework, where the selection of variables becomes challenging in the face of correlated distribution predictors. The integration of Artificial Intelligence (AI) in cost prediction has emerged as a vital necessity amidst escalating complexities and cost considerations. Cost prediction, being inherently correlated with almost all variables and inputs, offers an interpretable value in performance management, financial planning, and contract negotiation. This study undertakes a comparative analysis of a broad spectrum of prediction algorithms applied to the same, albeit reduced, database. A dozen such algorithms are put into practical use, with variable selection implemented through importance measures. The primary objective of this comparative evaluation is to determine the superior performing algorithm -one that delivers optimal adaptation to the context within a fixed environment. The prediction algorithm incorporates a myriad of inputs and constraints derived from data collection systems. AI's application facilitates the inclusion of diverse variables such as transportation routes, congestion, distances, freight weight, and environmental factors, thereby enhancing the accuracy and efficiency of cost estimation. The Orthogonal Matching Pursuit model emerged as the most successful, boasting an R² value nearing unity. Accurate cost prediction in transport can yield valuable insights into budgeting, estimation, customer service, managerial risk, environmental considerations, and strategic deployment for a company. Improved decision-making and resource allocation can thereby be achieved, leading to enhanced profitability and sustainability.

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“…In this study, we analyze numerous data technologies for spatial data administration in urban and tourism contexts and then propose a novel technique for integrative data management to aid decision-making in tourism and behavior [11] [12]. The research examines what is needed to create a new data strategy utilizing the principles of the spatial data warehouse [13] [14].…”

Section: Introductionmentioning

confidence: 99%

Spatial data warehouse: an analysis in tourism sector of west java province

Fauzi

Pribadi

Alifi

2023

ijobas

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In the tourism business, the use of geospatial, sometimes known as geospatial information systems, is essential to the collection of data in various different ways. A collection of historical data and a set of tools to enable decision-making are both components of a geographic data warehouse. In this study, we investigate the requirements for developing a recommendation of spatial data warehouse (SDW) that makes advantage of the implementation the geographical data analysis and data visualization in tourism sector. Methodology of this research using qualitative analysis. The SDW Tourism sector technology model, on which work has been going on for some time, will be a driving factor in this study that aim to create a recommendation for integrated tourism data environments to assist with decision-making. It is possible to bring together in a single location not only non-spatial data but also spatial data, as well as applications that are now running on multiple platforms and databases. The output of this research makes a recommendation to construct a spatial data warehouse based on existing data, and diagram of how the data from the data warehouse will be used in the tourism sector.

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Emerging Technologies for Smart Cities’ Transportation: Geo-Information, Data Analytics and Machine Learning Approaches

Cited by 41 publications

References 190 publications

Edge-Cloud Continuum Solutions for Urban Mobility Prediction and Planning

Edge-Cloud Continuum Solutions for Urban Mobility Prediction and Planning

A Comparative Study on AI-Based Algorithms for Cost Prediction in Pharmaceutical Transport Logistics

Spatial data warehouse: an analysis in tourism sector of west java province

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