Leveraging spatial abstraction in traffic analysis and forecasting with visual analytics

By applying spatio-temporal aggregation to traffic data consisting of vehicle trajectories, we generate a spatially abstracted transportation network, which is a directed graph where nodes stand for territory compartments (areas in geographic space) and links (edges) are abstractions of the possible paths between neighboring areas. From time series of traffic characteristics obtained for the links, we reconstruct mathematical models of the interdependencies between the traffic intensity (a.k.a. traffic flow or flux) and mean velocity. Graphical representations of these interdependencies have the same shape as the fundamental diagram of traffic flow through a physical street segment, which is known in transportation science. This key finding substantiates our approach to traffic analysis, forecasting, and simulation leveraging spatial abstraction. We present the process of data-driven generation of traffic forecasting and simulation models, in which each step is supported by visual analytics techniques

Viewing Visual Analytics as Model Building

To complement the currently existing definitions and conceptual frameworks of visual analytics, which focus mainly on activities performed by analysts and types of techniques they use, we attempt to define the expected results of these activities. We argue that the main goal of doing visual analytics is to build a mental and/or formal model of a certain piece of reality reflected in data. The purpose of the model may be to understand, to forecast or to control this piece of reality. Based on this model-building perspective, we propose a detailed conceptual framework in which the visual analytics process is considered as a goal-oriented workflow producing a model as a result. We demonstrate how this framework can be used for performing an analytical survey of the visual analytics research field and identifying the directions and areas where further research is needed

Interactive, multi-purpose traffic prediction platform using connected vehicles dataset

Traffic congestion is a perennial issue because of the increasing traffic demand yet limited budget for maintaining current transportation infrastructure; let alone expanding them. Many congestion management techniques require timely and accurate traffic estimation and prediction. Examples of such techniques include incident management, real-time routing, and providing accurate trip information based on historical data. In this dissertation, a speech-powered traffic prediction platform is proposed, which deploys a new deep learning algorithm for traffic prediction using Connected Vehicles (CV) data. To speed-up traffic forecasting, a Graph Convolution -- Gated Recurrent Unit (GC-GRU) architecture is proposed and analysis of its performance on tabular data is compared to state-of-the-art models. GC-GRU's Mean Absolute Percentage Error (MAPE) was very close to Transformer (3.16 vs 3.12) while achieving the fastest inference time and a six-fold faster training time than Transformer, although Long-Short-Term Memory (LSTM) was the fastest in training. Such improved performance in traffic prediction with a shorter inference time and competitive training time allows the proposed architecture to better cater to real-time applications. This is the first study to demonstrate the advantage of using multiscale approach by combining CV data with conventional sources such as Waze and probe data. CV data was better at detecting short duration, Jam and stand-still incidents and detected them earlier as compared to probe. CV data excelled at detecting minor incidents with a 90 percent detection rate versus 20 percent for probes and detecting them 3 minutes faster. To process the big CV data faster, a new algorithm is proposed to extract the spatial and temporal features from the CSV files into a Multiscale Data Analysis (MDA). The algorithm also leverages Graphics Processing Unit (GPU) using the Nvidia Rapids framework and Dask parallel cluster in Python. The results show a seventy-fold speedup in the data Extract, Transform, Load (ETL) of the CV data for the State of Missouri of an entire day for all the unique CV journeys (reducing the processing time from about 48 hours to 25 minutes). The processed data is then fed into a customized UNet model that learns highlevel traffic features from network-level images to predict large-scale, multi-route, speed and volume of CVs. The accuracy and robustness of the proposed model are evaluated by taking different road types, times of day and image snippets of the developed model and comparable benchmarks. To visually analyze the historical traffic data and the results of the prediction model, an interactive web application powered by speech queries is built to offer accurate and fast insights of traffic performance, and thus, allow for better positioning of traffic control strategies. The product of this dissertation can be seamlessly deployed by transportation authorities to understand and manage congestions in a timely manner.Includes bibliographical references

Visual analytics of location-based social networks for decision support

Recent advances in technology have enabled people to add location information to social networks called Location-Based Social Networks (LBSNs) where people share their communication and whereabouts not only in their daily lives, but also during abnormal situations, such as crisis events. However, since the volume of the data exceeds the boundaries of human analytical capabilities, it is almost impossible to perform a straightforward qualitative analysis of the data. The emerging field of visual analytics has been introduced to tackle such challenges by integrating the approaches from statistical data analysis and human computer interaction into highly interactive visual environments. Based on the idea of visual analytics, this research contributes the techniques of knowledge discovery in social media data for providing comprehensive situational awareness. We extract valuable hidden information from the huge volume of unstructured social media data and model the extracted information for visualizing meaningful information along with user-centered interactive interfaces. We develop visual analytics techniques and systems for spatial decision support through coupling modeling of spatiotemporal social media data, with scalable and interactive visual environments. These systems allow analysts to detect and examine abnormal events within social media data by integrating automated analytical techniques and visual methods. We provide comprehensive analysis of public behavior response in disaster events through exploring and examining the spatial and temporal distribution of LBSNs. We also propose a trajectory-based visual analytics of LBSNs for anomalous human movement analysis during crises by incorporating a novel classification technique. Finally, we introduce a visual analytics approach for forecasting the overall flow of human crowds

A conceptual framework for developing dashboards for big mobility data

Dashboards are an increasingly popular form of data visualization. Large, complex, and dynamic mobility data present a number of challenges in dashboard design. The overall aim for dashboard design is to improve information communication and decision making, though big mobility data in particular require considering privacy alongside size and complexity. Taking these issues into account, a gap remains between wrangling mobility data and developing meaningful dashboard output. Therefore, there is a need for a framework that bridges this gap to support the mobility dashboard development and design process. In this paper we outline a conceptual framework for mobility data dashboards that provides guidance for the development process while considering mobility data structure, volume, complexity, varied application contexts, and privacy constraints. We illustrate the proposed framework’s components and process using example mobility dashboards with varied inputs, end-users and objectives. Overall, the framework offers a basis for developers to understand how informational displays of big mobility data are determined by end-user needs as well as the types of data selection, transformation, and display available to particular mobility datasets

Visual Analytics of Mobility and Transportation: State of the Art and Further Research Directions

Many cities and countries are now striving to create intelligent transportation systems that utilize the current abundance of multisource and multiform data related to the functionality and the use of transportation infrastructure to better support human mobility, interests, and lifestyles. Such intelligent transportation systems aim to provide novel services that can enable transportation consumers and managers to be better informed and make safer and more efficient use of the infrastructure. However, the transportation domain is characterized by both complex data and complex problems, which calls for visual analytics approaches. The science of visual analytics is continuing to develop principles, methods, and tools to enable synergistic work between humans and computers through interactive visual interfaces. Such interfaces support the unique capabilities of humans (such as the flexible application of prior knowledge and experiences, creative thinking, and insight) and couple these abilities with machines' computational strengths, enabling the generation of new knowledge from large and complex data. In this paper, we describe recent developments in visual analytics that are related to the study of movement and transportation systems and discuss how visual analytics can enable and improve the intelligent transportation systems of the future. We provide a survey of literature from the visual analytics domain and organize the survey with respect to the different types of transportation data, movement and its relationship to infrastructure and behavior, and modeling and planning. We conclude with lessons learned and future directions, including social transportation, recommender systems, and policy implications

Deep Learning for Edge Computing Applications: A State-of-the-Art Survey

With the booming development of Internet-of-Things (IoT) and communication technologies such as 5G, our future world is envisioned as an interconnected entity where billions of devices will provide uninterrupted service to our daily lives and the industry. Meanwhile, these devices will generate massive amounts of valuable data at the network edge, calling for not only instant data processing but also intelligent data analysis in order to fully unleash the potential of the edge big data. Both the traditional cloud computing and on-device computing cannot sufficiently address this problem due to the high latency and the limited computation capacity, respectively. Fortunately, the emerging edge computing sheds a light on the issue by pushing the data processing from the remote network core to the local network edge, remarkably reducing the latency and improving the efficiency. Besides, the recent breakthroughs in deep learning have greatly facilitated the data processing capacity, enabling a thrilling development of novel applications, such as video surveillance and autonomous driving. The convergence of edge computing and deep learning is believed to bring new possibilities to both interdisciplinary researches and industrial applications. In this article, we provide a comprehensive survey of the latest efforts on the deep-learning-enabled edge computing applications and particularly offer insights on how to leverage the deep learning advances to facilitate edge applications from four domains, i.e., smart multimedia, smart transportation, smart city, and smart industry. We also highlight the key research challenges and promising research directions therein. We believe this survey will inspire more researches and contributions in this promising field