Utilization of AVL/GPS Technology: Case Studies

MnDOT Contract No. 1026092TPF-5(218)Winter road maintenance accounts for roughly 20 percent of state DOT maintenance budgets. State and local agencies spend over $2.3 billion on winter operations annually. As such, effective winter maintenance operations incorporating smart uses of methods, techniques, technologies, equipment and materials becomes essential. Among various winter maintenance technologies, automated vehicle location (AVL) and global positioning systems (GPS) have been widely used by transportation agencies to monitor vehicle locations and equipment operational status for winter road maintenance operations. This report summarizes the information gathered during the study conducted for the Clear Roads project entitled Utilization of AVL/GPS Technology: Case Studies. The research team surveyed multiple state DOTs on the current state of AVL/GPS system usage for the purpose of gathering information on the planning, processes, steps, and results observed by agencies with their respective systems. Six state DOTs (Utah, Washington State, Michigan, Wisconsin, Nebraska, and Colorado) were selected to conduct detailed case studies. The case studies were performed through in-person interviews with multiple levels of DOT staff involved in AVL/GPS system planning, procurement, implementation, management and operations. This final report summarizes the key results, findings and lessons learned from the case studies. It also identifies best practices and provides a series of recommendations for winter maintenance agencies to consider in the procurement, deployment and integration of an AVL/GPS system for winter maintenance operations

The Impact of Artificial Intelligence on Strategic and Operational Decision Making

openEffective decision making lies at the core of organizational success. In the era of digital transformation, businesses are increasingly adopting data-driven approaches to gain a competitive advantage. According to existing literature, Artificial Intelligence (AI) represents a significant advancement in this area, with the ability to analyze large volumes of data, identify patterns, make accurate predictions, and provide decision support to organizations. This study aims to explore the impact of AI technologies on different levels of organizational decision making. By separating these decisions into strategic and operational according to their properties, the study provides a more comprehensive understanding of the feasibility, current adoption rates, and barriers hindering AI implementation in organizational decision making

From Common Operational Picture to Common Situational Understanding : A Framework for Information Sharing in Multi-Organizational Emergency Management

Complex emergencies such as natural disasters are increasing in frequency and scope, in all regions of the world. These emergencies have devastating impacts on people, property, and the environment. Responding to these events and reducing their impact requires that emergency management organizations (EMOs) collaborate in their operations. Complex emergencies require extraordinary efforts from EMOs and often should be handled beyond ordinary routines and structures. Such operations involving multiple stakeholders are typically characterized by inadequate information sharing, decision-making problems, limited situational awareness (SA), and lack of common situational understanding. Despite a high volume of research on these challenges, evaluations from complex disasters and large-scale exercises document that there are still several unsolved issues related to information sharing and the development of common situational understanding. Examples here include fulfillment of heterogeneous information needs, employment of different communication tools and processes with limited interoperability, and information overload resulting from a lack of mechanisms for filtering irrelevant information. Multi-organizational emergency management is an established area of research focusing on how to successfully collaborate and share information for developing common situational understanding. However, the level of complexity and situational dependencies between the involved EMOs create challenges for researchers. An important element for efficient collaboration and information sharing is building and maintaining a common operational picture (COP). Sharing important information is a key element in emergency management involving several EMOs, and both static and dynamic information must be accessible to perform tasks effectively during emergency response. To be proactive and mitigate the emergency impacts requires up-to-date information, both factual information via the COP and the ability to share interpretations and implications through using a communication system for rapid verbal negotiation. The overall research objective is to investigate how stakeholders perceive and develop SA and COP, and to explore and understand key requirements for stakeholders to develop a common situational understanding in complex multi-organizational emergency management.publishedVersio

Cinematic assemblage: Sinofuturist worldbuilding and the smart city

New forms of digital surveillance have given rise to a data-driven urban condition, one where machine vision increasingly determines mobility and navigation. The ‘cinematic assemblage’, as I term it, refers to a machinic agent that contains many of the sensory, recording, and representational components required to create cinema, but is itself an object of cinematic interest. Framed through surveillance studies, theories of digital cinema, and critical legal frameworks, I investigate how filmmaking practice conducted entirely within a video game engine can embody the logic of two interrelated forms of cinematic assemblage—the smart city and the self-driving car. The resulting feature-length animated film, Death Drive, draws from liberatory practices in non-Western Futurism to formulate a legal fiction about the emergence of electronic personhood within contemporary China. Cinematic assemblage operates through posthuman approaches to distributed agency and embodied vision. This enables an analysis of the smart city and self-driving car as being coconstitutive, with both continually monitoring each other in an enmeshed system of sensing and control. To understand the hierarchy of sensorial regimes in this larger assemblage, I present a particular approach to image production. My practice explores the creation of virtual cinematographic apparatus in video game engines, using filmmaking to embody active and agential characteristics of digital surveillance systems. Based on existing self-driving car imagery, the rendered footage used to compose the film is constructed entirely within the game engine, but also references the coordinates and language of existing data and systems. This builds upon Harun Farocki’s notion of operational images to explore how a reflexive approach to filmmaking can address how surveillance functions in the smart city. In the process of developing the film, I ask how to situate my research without perpetuating either Chinese exceptionalism or Western coloniality. I look to Futurist practices that interrogate the privileged position of the human, reconfiguring narratives from the perspective of the Other. Accordingly, I treat both smart city and self-driving car as nonhuman protagonists. Set in SimBeijing, a fictional research city on the China-Russia border, Death Drive examines the unique conditions of Chinese technological development — as noted by Yuk Hui and Anna Greenspan among others—to speculate on how the social and legal implications of digital surveillance may manifest within a Sinofuturist context. The narrative couples the nonhuman, a key figure in Sinofuturism, with the legal fiction of electronic personhood. This is grounded through problem-centred interviews conducted with legal experts and forensic researchers, drawing together the frameworks of criminal investigation and detective story. By formulating a hypothetical crime involving a selfdriving car, the film circumscribes the nonhuman within the sphere of criminality and liability. This approach challenges humanist conceptions of AI as a disembodied mind, envisioning the electronic Other as a political subject whose legal personhood emerges from the consequences of its corporeal action

A Location-Aware Middleware Framework for Collaborative Visual Information Discovery and Retrieval

This work addresses the problem of scalable location-aware distributed indexing to enable the leveraging of collaborative effort for the construction and maintenance of world-scale visual maps and models which could support numerous activities including navigation, visual localization, persistent surveillance, structure from motion, and hazard or disaster detection. Current distributed approaches to mapping and modeling fail to incorporate global geospatial addressing and are limited in their functionality to customize search. Our solution is a peer-to-peer middleware framework based on XOR distance routing which employs a Hilbert Space curve addressing scheme in a novel distributed geographic index. This allows for a universal addressing scheme supporting publish and search in dynamic environments while ensuring global availability of the model and scalability with respect to geographic size and number of users. The framework is evaluated using large-scale network simulations and a search application that supports visual navigation in real-world experiments

Collection and Analysis of Driving Videos Based on Traffic Participants

Autonomous vehicle (AV) prototypes have been deployed in increasingly varied environments in recent years. An AV must be able to reliably detect and predict the future motion of traffic participants to maintain safe operation based on data collected from high-quality onboard sensors. Sensors such as camera and LiDAR generate high-bandwidth data that requires substantial computational and memory resources. To address these AV challenges, this thesis investigates three related problems: 1) What will the observed traffic participants do? 2) Is an anomalous traffic event likely to happen in near future? and 3) How should we collect fleet-wide high-bandwidth data based on 1) and 2) over the long-term? The first problem is addressed with future traffic trajectory and pedestrian behavior prediction. We propose a future object localization (FOL) method for trajectory prediction in first person videos (FPV). FOL encodes heterogeneous observations including bounding boxes, optical flow features and ego camera motions with multi-stream recurrent neural networks (RNN) to predict future trajectories. Because FOL does not consider multi-modal future trajectories, its accuracy suffers from accumulated RNN prediction error. We then introduce BiTraP, a goal-conditioned bidirectional multi-modal trajectory prediction method. BiTraP estimates multi-modal trajectories and uses a novel bi-directional decoder and loss to improve longer-term trajectory prediction accuracy. We show that different choices of non-parametric versus parametric target models directly influence predicted multi-modal trajectory distributions. Experiments with two FPV and six bird's-eye view (BEV) datasets show the effectiveness of our methods compared to state-of-the-art. We define pedestrian behavior prediction as a combination of action and intent. We hypothesize that current and future actions are strong intent priors and propose a multi-task learning RNN encoder-decoder network to detect and predict future pedestrian actions and street crossing intent. Experimental results show that one task helps the other so they together achieve state-of-the-art performance on published datasets. To identify likely traffic anomaly events, we introduce an unsupervised video anomaly detection (VAD) method based on trajectories. We predict locations of traffic participants over a near-term future horizon and monitor accuracy and consistency of these predictions as evidence of an anomaly. Inconsistent predictions tend to indicate an anomaly has happened or is about to occur. A supervised video action recognition method can then be applied to classify detected anomalies. We introduce a spatial-temporal area under curve (STAUC) metric as a supplement to the existing area under curve (AUC) evaluation and show it captures how well a model detects temporal and spatial locations of anomalous events. Experimental results show the proposed method and consistency-based anomaly score are more robust to moving cameras than image generation based methods; our method achieves state-of-the-art performance over AUC and STAUC metrics. VAD and action recognition support event-of-interest (EOI) distinction from normal driving data. We introduce a Smart Black Box (SBB), an intelligent event data recorder, to prioritize EOI data in long-term driving. The SBB compresses high-bandwidth data based on EOI potential and on-board storage limits. The SBB is designed to prioritize newer and anomalous driving data and discard older and normal data. An optimal compression factor is selected based on the trade-off between data value and storage cost. Experiments in a traffic simulator and with real-world datasets show the efficiency and effectiveness of using a SBB to collect high-quality videos over long-term driving.PHDRoboticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/168035/1/brianyao_1.pd

Race, Rhetoric, and Research Methods

Race, Rhetoric, and Research Methods explores multiple antiracist, decolonial forms of study that are relevant to 21st-century knowledge production about language, communication, technology, and culture. The book presents a rare collaboration among scholars representing different racial and ethnic backgrounds, genders, and ranks within the field of Rhetoric, Composition, and Writing Studies (RCWS). In each chapter, the authors examine the significance of their individual experiences with race and racism across contexts. Their research engages the politics of embodiment, institutional critique, multimodal rhetoric, materiality, and public digital literacies. The book merges impassioned storytelling with unflinching analysis, offering a multi-voiced argument that spotlights the field's troubled history with theorizing about race and epistemology. Although the authors directly address aspiring and current RCWS professionals, they model how a comprehensive consideration of race adds legitimacy and integrity to any subject of study. This co-authored work charts uncommon paths forward, demonstrating reflexive engagement with legacies that are personal and transnational, as well as with technologies that are both dehumanizing and liberating