Movement Analytics: Current Status, Application to Manufacturing, and Future Prospects from an AI Perspective

Data-driven decision making is becoming an integral part of manufacturing companies. Data is collected and commonly used to improve efficiency and produce high quality items for the customers. IoT-based and other forms of object tracking are an emerging tool for collecting movement data of objects/entities (e.g. human workers, moving vehicles, trolleys etc.) over space and time. Movement data can provide valuable insights like process bottlenecks, resource utilization, effective working time etc. that can be used for decision making and improving efficiency. Turning movement data into valuable information for industrial management and decision making requires analysis methods. We refer to this process as movement analytics. The purpose of this document is to review the current state of work for movement analytics both in manufacturing and more broadly. We survey relevant work from both a theoretical perspective and an application perspective. From the theoretical perspective, we put an emphasis on useful methods from two research areas: machine learning, and logic-based knowledge representation. We also review their combinations in view of movement analytics, and we discuss promising areas for future development and application. Furthermore, we touch on constraint optimization. From an application perspective, we review applications of these methods to movement analytics in a general sense and across various industries. We also describe currently available commercial off-the-shelf products for tracking in manufacturing, and we overview main concepts of digital twins and their applications

Modelling potential movement in constrained travel environments using rough space-time prisms

The widespread adoption of location-aware technologies (LATs) has afforded analysts new opportunities for efficiently collecting trajectory data of moving individuals. These technologies enable measuring trajectories as a finite sample set of time-stamped locations. The uncertainty related to both finite sampling and measurement errors makes it often difficult to reconstruct and represent a trajectory followed by an individual in space-time. Time geography offers an interesting framework to deal with the potential path of an individual in between two sample locations. Although this potential path may be easily delineated for travels along networks, this will be less straightforward for more nonnetwork-constrained environments. Current models, however, have mostly concentrated on network environments on the one hand and do not account for the spatiotemporal uncertainties of input data on the other hand. This article simultaneously addresses both issues by developing a novel methodology to capture potential movement between uncertain space-time points in obstacle-constrained travel environments

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

HUMAN INTERACTIONS IN PHYSICAL AND VIRTUAL SPACES: A GIS-BASED TIME-GEOGRAPHIC EXPLORATORY APPROACH

Information and communication technologies (ICT) such as cell phone and the Internet have extended opportunities of human activities and interactions from physical spaces to virtual spaces. The relaxed spatio-temporal constraints on individual activities may affect human activity-travel patterns, social networks, and many other aspects of society. A challenge for research of human activities in the ICT age is to develop analytical environments that can help visualize and explore individual activities in virtual spaces and their mutual impacts with physical activities. This dissertation focuses on extending the time-geographic framework and developing a spatio-temporal exploratory environment in a space-time geographic information system (GIS) to facilitate research of human interactions in both physical and virtual spaces. In particular, this dissertation study addresses three research questions. First, it extends the time-geographic framework to assess the impacts of phone usage on potential face-to-face (F2F) meeting opportunities, as well as dynamic changes in potential F2F meeting opportunities over time. Secondly, this study extends the time-geographic framework to conceptualize and represent individual trajectories in an online social network space and to explore potential interaction opportunities among people in a virtual space. Thirdly, this study presents a spatio-temporal environment in a space-time GIS to facilitate exploration of the relationships between changes in physical proximity and changes in social closeness in a virtual space. The major contributions of this dissertation include: (1) advancing the time-geographic framework in its ability of exploring processes of virtual communication alerting physical activity opportunities; (2) extending some concepts of the classical time geography from a physical space to a virtual space for representing and exploring virtual interaction patterns; (3) developing a space-time GIS that is useful for exploring patterns of individual activities and interactions in both physical and virtual spaces, as well as the interactions between these two spaces

Temporal GIS Design of an Extended Time-geographic Framework for Physical and Virtual Activities

Recent rapid developments of information and communication technologies (ICT) enable a virtual space, which allows people to conduct activities remotely through tele-presence rather than through conventional physical presence in physical space. ICT offer people additional freedom in space and time to carry out their activities; this freedom leads to changes in the spatio-temporal distributions of activities. Given that activities are the reasons for travel, these changes will impact transportation systems. Therefore, a better understanding of the spatial and temporal characteristics of human activities in today’s society will help researchers study the impact of ICT on transportation. Using an integrated space-time system, Hägerstrand’s time geography provides an effective framework for studying the relationships of various constraints and human activities in physical space, but it does not support activities in virtual space. The present study provides a conceptual model to describe the relationships of physical space and virtual space, extending Hägerstrand’s time geography to handle both physical and virtual activities. This extended framework is used to support investigations of spatial and temporal characteristics of human activities and their interactions in physical and virtual spaces. Using a 3D environment (i.e., 2D space + 1D time), a temporal GIS design is developed to accommodate the extended time-geographic framework. This GIS design supports representations of time-geographic objects (e.g., space-time paths, networkbased space-time prisms, and space-time life paths) and a selected set of analysis functions applied to these objects (e.g., temporal dynamic segmentation and spatiotemporal intersection). A prototype system, with customized functions developed in Visual Basic for Applications (VBA) programs with ArcObjects, is implemented in ArcGIS according to the design. Using a hypothetical activity dataset, the system demonstrates the feasibility of the extended framework and the temporal GIS design to explore physical and virtual activities. This system offers useful tools with which to tackle various real problems related to physical and virtual activities

Spatio-Temporal Context in Agent-Based Meeting Scheduling

Meeting scheduling is a common task for organizations of all sizes. It involves searching for a time and place when and where all the participants can meet. However, scheduling a meeting is generally difficult in that it attempts to satisfy the preferences of all participants. Negotiation tends to be an iterative and time consuming task. Proxy agents can handle the negotiation on behalf of the individuals without sacrificing their privacy or overlooking their preferences. This thesis examines the implications of formalizing meeting scheduling as a spatiotemporal negotiation problem. The “Children in the Rectangular Forest” (CRF) canonical model is applied to meeting scheduling. By formalizing meeting scheduling within the CRF model, a generalized problem emerges that establishes a clear relationship with other spatiotemporal distributed scheduling problems. The thesis also examines the implications of the proposed formalization to meeting scheduling negotiations. A protocol for meeting location selection is presented and evaluated using simulations

T-PickSeer: Visual Analysis of Taxi Pick-up Point Selection Behavior

Taxi drivers often take much time to navigate the streets to look for passengers, which leads to high vacancy rates and wasted resources. Empty taxi cruising remains a big concern for taxi companies. Analyzing the pick-up point selection behavior can solve this problem effectively, providing suggestions for taxi management and dispatch. Many studies have been devoted to analyzing and recommending hot-spot regions of pick-up points, which can make it easier for drivers to pick up passengers. However, the selection of pick-up points is complex and affected by multiple factors, such as convenience and traffic management. Most existing approaches cannot produce satisfactory results in real-world applications because of the changing travel demands and the lack of interpretability. In this paper, we introduce a visual analytics system, T-PickSeer, for taxi company analysts to better explore and understand the pick-up point selection behavior of passengers. We explore massive taxi GPS data and employ an overview-to-detail approach to enable effective analysis of pick-up point selection. Our system provides coordinated views to compare different regularities and characteristics in different regions. Also, our system assists in identifying potential pick-up points and checking the performance of each pick-up point. Three case studies based on a real-world dataset and interviews with experts have demonstrated the effectiveness of our system.Comment: 10 pages, 10 figures; The 10th China Visualization and Visual Analytics Conferenc