MARIO: A spatio-temporal data mining framework on Google Cloud to explore mobility dynamics from taxi trajectories

Day‐to‐day variation of travel demand has been rarely studied, due to the limitation of traditional transport data collection methods and the difficulty in high‐dimensional data processing. In this study, singular value decomposition (SVD) is used to study the day‐to‐day regularity of metro travel demand, based on four one‐month datasets from the metro networks of Shanghai and Shenzhen, China. The results show that SVD is a tool to understand the intrinsic structure of daily metro travel demand. It is found that daily metro travel demand can be decomposed into three constituents: periodic part, burst part and other part. The periodic part varies weekly and accounts for a majority of the travel demand of origin–destination matrix. The burst part exhibits short‐lived spikes, which are caused by special events or holidays. Also, other part varies randomly and only contributes a fraction of travel demand. Moreover, the periodic part corresponding to the two largest singular values is very stable in 2 months, and accounts for most of the travel demand. Finally, the burst part is used to analyse the impact of a collision accident. This work is helpful for short‐term travel demand prediction, metro operation schedule and emergency management.

Travel demand forecasting is a vital problem in the development of smart cities, infrastructure planning, and transportation management. The advent of contactless smart card systems has enabled the collection of data regarding daily transit and purchasing activities, providing a rich source of insights into citizen behavior. In this paper, we introduce a new problem of predicting changes in travel demand resulting from the installation of a new facility while preserving privacy. To address this problem, we propose a simple but effective supervised learning method that can capture the relationships between residential areas and existing facility locations, and exploit spatial features to forecast future demand in response to a new facility location. As a workable example, we employ real-world data to predict the future travel demand triggered by the installation of a new station in a railway system. Through extensive experiments, we demonstrate that our method improves the prediction accuracy.

Origin-Destination (O-D) travel demand prediction is a fundamental challenge in transportation. Recently, spatial-temporal deep learning models demonstrate the tremendous potential to enhance prediction accuracy. However, few studies tackled the uncertainty and sparsity issues in fine-grained O-D matrices. This presents a serious problem, because a vast number of zeros deviate from the Gaussian assumption underlying the deterministic deep learning models. To address this issue, we design a Spatial-Temporal Zero-Inflated Negative Binomial Graph Neural Network (STZINB-GNN) to quantify the uncertainty of the sparse travel demand. It analyzes spatial and temporal correlations using diffusion and temporal convolution networks, which are then fused to parameterize the probabilistic distributions of travel demand. The STZINB-GNN is examined using two real-world datasets with various spatial and temporal resolutions. The results demonstrate the superiority of STZINB-GNN over benchmark models, especially under high spatial-temporal resolutions, because of its high accuracy, tight confidence intervals, and interpretable parameters. The sparsity parameter of the STZINB-GNN has physical interpretation for various transportation applications.

A comparison of using proprietary cloud platform Google Cloud Functions and open source platform Apache OpenWhisk for writing serverless applications based on the Google Cloud Platform was made. The following criteria were chosen for comparison: supported programming languages, convenience and speed of development and deployment of functions, possible ways to pass parameters to functions, options for monitoring the status of functions, speed of the cold start. As a result, main advantages and disadvantages of each platform were identified. Recommendations for use cases are given based on the features of the platforms. The results of the study can be used for further studies in the field of FaaS technologies, as there are a lot of solutions in this area that also need to be compared.

Transparent use of commercial cloud resources for scientific experiments is a hard problem. In this article, we describe the first steps of the Data Ocean R&D collaboration between the high-energy physics experiment ATLAS together with Google Cloud Platform, to allow seamless use of Google Compute Engine and Google Cloud Storage for physics analysis. We start by describing the three preliminary use cases that were identified at the beginning of the project. The following sections then detail the work done in the data management system Rucio and the workflow management systems PanDA and Harvester to interface Google Cloud Platform with the ATLAS distributed computing environment, and show the results of the integration tests. Afterwards, we describe the setup and results from a full ATLAS user analysis that was executed natively on Google Cloud Platform, and give estimates on projected costs. We close with a summary and and outlook on future work.

A customized bus (CB) system is a new emerging public transportation that provides flexible demand-oriented transit services for city commuters. Existing CB systems encounter two challenges of 1) collecting travel demands and discovering travel patterns effectively and efficiently and 2) planning profitable bus lines based on travel patterns. In this paper, we propose a bus line planning framework, called T2CBS, by taking full advantage of taxi trajectory data. In T2CBS, similar travel demands are discovered from passenger trajectories with a clustering algorithm, and CB stops are deployed at pick-up and drop-off points of trajectory clusters with integer linear programming. To plan profitable CB lines, we propose a profit estimation model, by considering the number of taxi passengers who can be attracted to CB buses. A routing algorithm (CBRouting) and a timetabling algorithm (CBTimetabling) are proposed to generate a CB line that can achieve the maximum profit for each trajectory cluster. We conduct experiments on one-month taxi trajectory data in Nanjing, China. Experimental results demonstrate that our T2CBS can generate CB lines with higher profit compared with baseline methods, and the moderate increase in travel time along the CB lines is significantly dominated by the savings in bus fare.

Uncovering the spatiotemporal patterns of CO2 emissions by taxis based on Individuals' daily travel

Cloud computing is being embraced more and more by telecommunication operators for on-demand access to computing resources. Knowing that 5G Core reference architecture is envisioned to be cloud-native and service-oriented, we propose, in this paper, offloading to the cloud, some of 5G delay-tolerant Network Functions and in particular the Network Data Analytics Function (NWDAF). The dynamic selection of cloud resources to serve off-loaded 5G-NWDAF, while incurring minimum cost and maximizing utilization of served next generation Node-Bs (gNBs) requires agility and automation. This paper introduces a framework to automate the selection process that satisfies resource demands while meeting two objectives, namely, cost minimization and utilization maximization. We first formulate the mapping of gNBs to 5G-NWDAF problem as an Integer Linear Program (ILP). Then, we propose an algorithm to solve it based on branch-cut-and-price technique combining all of branch-and-price, branch-and-cut and branch-and-bound. Results using pricing data from a public cloud provider (Google Cloud Platform), show that our proposal achieves important savings in cloud computing costs and reduction in execution time compared to other state-of-the-art frameworks.

Serverless computing has fundamentally transformed cloud technology by enabling developers to deploy applications without the burden of managing server infrastructure. This paper delves into the serverless offerings from major cloud providers—AWS (Amazon Web Services), Azure (Microsoft Azure), and GCP (Google Cloud Platform)—and explores how each contributes to this paradigm shift. The discussion highlights the core advantages of serverless computing, including cost efficiency through a pay-as-you-go model, automatic scaling that adjusts to changing demands, and reduced operational complexity, which allows developers to focus more on coding and less on infrastructure concerns. However, serverless computing is not without its challenges. Notable issues include "cold start" delays, where initial function executions may experience latency, impacting overall performance. Additionally, the shared nature of serverless environments raises potential security concerns, and the limited visibility into the execution environment can make debugging and optimization more complex. The paper also considers future trends in serverless computing across AWS, Azure, and GCP. It discusses emerging approaches such as combining serverless with traditional computing models to create hybrid architectures that leverage the strengths of both. Furthermore, it examines ongoing advancements aimed at improving support for complex, stateful applications, seeking to address current limitations and enhance the capabilities of serverless platforms. This exploration provides a detailed look at the current state of serverless computing and anticipates future developments, focusing on how AWS, Azure, and GCP are shaping the evolution of this technology

This chapter focuses on designing and building scalable and resilient architectures on Google Cloud Platform (GCP). The chapter begins by introducing the principles of scalable and resilient design, including fault tolerance, elasticity, and scalability. You will learn about the key components of scalable and resilient architectures on GCP, including load balancing, autoscaling, and distributed databases. The chapter also covers best practices for building scalable and resilient architectures on GCP, including using managed services, designing for failure, and monitoring and alerting. Overall, this chapter provides a comprehensive guide to building scalable and resilient architectures on GCP, with practical examples and real-world use cases.

Cloud Functions for Firebase lets you run code on a cloud-based back end in response to events triggered by some of Firebase features and/or HTTPS requests. You'll code using Node.js, and the code is stored and executed on Google's cloud in a managed environment. The goal is to help you avoid managing middleware servers and just focus on the code. Scaling happens automatically because the code runs on the Google Cloud Platform. It's based on Google Cloud Functions, which you can learn about at . This book will focus on how these functions work on Firebase.

Proceeding for an elucidating draft for the robot’s path planning, there are several aspects which need to be addressed and discuss in detail. Some key points include the definition of the working environment, type of technique used to solve a particular type of problem, their subclassification and interdependencies with each other. It is also required to discuss in detail to understand and solve the problem of path planning in the right perspective. To do so, this paper presents the evolution of path planning algorithms of the last 5 decades, and then a broad classification of all those algorithms. After that, algorithms are categorized into different sections and has been discussed which are very often used in recent times to make the path planning approach more effective and efficient. This paper presents an extensive and elaborative survey of path planning algorithms and associated techniques for robots whose excellent contribution to the filed is invaluable. There are multiple issues related to the working environment of robots, whether it belongs to the two dimensional or three dimensional, static or dynamic, single robot or multi-robot, single objective/multi-objective and many more. This paper addresses all these issues in a detailed way. Another critical issue is related to the scope of algorithms which has been discussed at length in this paper like whether the algorithm is compatible for global/local path planning, it is Exact or heuristic in nature. This issue is systematically and hierarchically described to get a clear understanding of the problem domain. The effort is to bring an insight into the classification and evolution of path planning algorithms with its technical detail and discussions. The paper presents some emerging technologies which can be clubbed with robots to take it to another level. Cloud computing is one which is being extensively absorbed in many technologies. This paper discusses some cloud platforms like OpenStack and Google Cloud to deploy path planning applications for robots. This paper tries explicitly to conclude by testing path planning robots using Google cloud platform (using it as Infrastructure as a Service IaaS) citing its advantages and capability to expand its acceptability and assumes to be the future scope for robot path planning.

Complaint handling is essential to Syiah Kuala University’s Integrated Laboratory Technical Management Unit (ILTMU) services. However, manual complaint handling at ILTMU requires more time and energy and increases the staff workload. Complaint handling is necessary to continuously improve the performance efficiency and service quality provided by ILTMU. Therefore, to solve the problem of handling complaints manually, this research intends to design and develop a web-based complaint management system at ILTMU of Syiah Kuala University. The proposed system uses the Goal-Directed Design method, while the development process follows Scrum. The system was developed using Flask and some services from the Google Cloud Platform, namely, the Google App Engine and Google Cloud Datastore. The system is divided into four unique user groups. Each has access privileges and responsibilities: operators, Management, Follow-up, and Customers. Each of these user groups has its distinctive features. The entire features of this application were tested using both Black Box and White Box testing. All system functions were successfully executed using the Black Box testing technique. Meanwhile, the White Box testing returned a value of 100\% passed, indicating that the function being tested is error-free. A usability test was conducted with 20 respondents representing each user group using the USE Questionnaire on a 7-point Likert scale. The usability test results gave an overall average value of 6.13, which suggests that people have a positive attitude toward the system and that it is practical, easy to use, easy to learn, and satisfying.

The rapid advancement of cloud computing has revolutionized how businesses operate, offering scalable resources and cutting - edge technologies. This paper presents a comparative study of the three leading cloud providers?Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP) - focused on their integration of Artificial Intelligence (AI) with DevOps practices. We evaluate their AI service offerings, DevOps toolchains, and the seamless integration of these technologies to enhance automation, scalability, and reliability. The analysis provides insights into the strengths and weaknesses of each provider, helping organizations make informed decisions on their cloud strategy. By analyzing the AI capabilities, DevOps toolchain integration, and overall performance of these cloud providers, this study aims to provide insights into selecting the most suitable platform for AI - driven DevOps workflows. The comparative analysis will focus on factors such as AI services, scalability, automation capabilities, and integration with DevOps tools to help organizations make informed decisions when choosing a cloud provider for their AI and DevOps initiatives.

Cloud computing is growing wide and first-rate promising technology. There are three forms of cloud computing: infrastructure as a service (IaaS), software as a service (SaaS), and platform as a service (PaaS). There are many cloud service providers. Among these, AWS (Amazon Web Service), Azure (Microsoft Cloud), IBM Cloud, Oracle Cloud, and VMware are immensely popular. In this paper, the authors study the Google Cloud Platform (GCP) and a few principally used popular services. Google Cloud Platform could be a cloud provider; they supply servers and services that are used on-demand and at scale. Google Cloud Platform is the fastest growing cloud service supplier now.