Data Fusion for MaaS: Opportunities and Challenges

© 2018 IEEE. Computer Supported Cooperative Work (CSCW) in design is an essential facilitator for the development and implementation of smart cities, where modern cooperative transportation and integrated mobility are highly demanded. Owing to greater availability of different data sources, data fusion problem in intelligent transportation systems (ITS) has been very challenging, where machine learning modelling and approaches are promising to offer an important yet comprehensive solution. In this paper, we provide an overview of the recent advances in data fusion for Mobility as a Service (MaaS), including the basics of data fusion theory and the related machine learning methods. We also highlight the opportunities and challenges on MaaS, and discuss potential future directions of research on the integrated mobility modelling

The AFIT ENgineer, Volume 2, Issue 4

In this issue: AFMC Spark Tank Semi-finalist New AFIT Patents 2020 Graduate School Award Winners Airmen and Artificial Intelligence Nuclear Treaty Monitorin

The AFIT ENgineer, Volume 2, Issue 4

In this issue: AFMC Spark Tank Semi-finalist New AFIT Patents 2020 Graduate School Award Winners Airmen and Artificial Intelligence Nuclear Treaty Monitorin

Working Notes from the 1992 AAAI Spring Symposium on Practical Approaches to Scheduling and Planning

The symposium presented issues involved in the development of scheduling systems that can deal with resource and time limitations. To qualify, a system must be implemented and tested to some degree on non-trivial problems (ideally, on real-world problems). However, a system need not be fully deployed to qualify. Systems that schedule actions in terms of metric time constraints typically represent and reason about an external numeric clock or calendar and can be contrasted with those systems that represent time purely symbolically. The following topics are discussed: integrating planning and scheduling; integrating symbolic goals and numerical utilities; managing uncertainty; incremental rescheduling; managing limited computation time; anytime scheduling and planning algorithms, systems; dependency analysis and schedule reuse; management of schedule and plan execution; and incorporation of discrete event techniques

Advancing automation and robotics technology for the space station and for the US economy: Submitted to the United States Congress October 1, 1987

In April 1985, as required by Public Law 98-371, the NASA Advanced Technology Advisory Committee (ATAC) reported to Congress the results of its studies on advanced automation and robotics technology for use on the space station. This material was documented in the initial report (NASA Technical Memorandum 87566). A further requirement of the Law was that ATAC follow NASA's progress in this area and report to Congress semiannually. This report is the fifth in a series of progress updates and covers the period between 16 May 1987 and 30 September 1987. NASA has accepted the basic recommendations of ATAC for its space station efforts. ATAC and NASA agree that the mandate of Congress is that an advanced automation and robotics technology be built to support an evolutionary space station program and serve as a highly visible stimulator affecting the long-term U.S. economy

Toward a Bio-Inspired System Architecting Framework: Simulation of the Integration of Autonomous Bus Fleets & Alternative Fuel Infrastructures in Closed Sociotechnical Environments

Cities are set to become highly interconnected and coordinated environments composed of emerging technologies meant to alleviate or resolve some of the daunting issues of the 21st century such as rapid urbanization, resource scarcity, and excessive population demand in urban centers. These cybernetically-enabled built environments are expected to solve these complex problems through the use of technologies that incorporate sensors and other data collection means to fuse and understand large sums of data/information generated from other technologies and its human population. Many of these technologies will be pivotal assets in supporting and managing capabilities in various city sectors ranging from energy to healthcare. However, among these sectors, a significant amount of attention within the recent decade has been in the transportation sector due to the flood of new technological growth and cultivation, which is currently seeing extensive research, development, and even implementation of emerging technologies such as autonomous vehicles (AVs), the Internet of Things (IoT), alternative xxxvi fueling sources, clean propulsion technologies, cloud/edge computing, and many other technologies. Within the current body of knowledge, it is fairly well known how many of these emerging technologies will perform in isolation as stand-alone entities, but little is known about their performance when integrated into a transportation system with other emerging technologies and humans within the system organization. This merging of new age technologies and humans can make analyzing next generation transportation systems extremely complex to understand. Additionally, with new and alternative forms of technologies expected to come in the near-future, one can say that the quantity of technologies, especially in the smart city context, will consist of a continuously expanding array of technologies whose capabilities will increase with technological advancements, which can change the performance of a given system architecture. Therefore, the objective of this research is to understand the system architecture implications of integrating different alternative fueling infrastructures with autonomous bus (AB) fleets in the transportation system within a closed sociotechnical environment. By being able to understand the system architecture implications of alternative fueling infrastructures and AB fleets, this could provide performance-based input into a more sophisticated approach or framework which is proposed as a future work of this research

Low-carbon Energy Transition and Planning for Smart Grids

With the growing concerns of climate change and energy crisis, the energy transition from fossil-based systems to a low-carbon society is an inevitable trend. Power system planning plays an essential role in the energy transition of the power sector to accommodate the integration of renewable energy and meet the goal of decreasing carbon emissions while maintaining the economical, secure, and reliable operations of power systems. In this thesis, a low-carbon energy transition framework and strategies are proposed for the future smart grid, which comprehensively consider the planning and operation of the electricity networks, the emission control strategies with the carbon response of the end-users, and carbon-related trading mechanisms. The planning approach considers the collaborative planning of different types of networks under the smart grid context. Transportation electrification is considered as a key segment in the energy transition of power systems, so the planning of charging infrastructure for electric vehicles (EVs) and hydrogen refueling infrastructure for fuel cell electric vehicles is jointly solved with the electricity network expansion. The vulnerability assessment tools are proposed to evaluate the coupled networks towards extreme events. Based on the carbon footprint tracking technologies, emission control can be realized from both the generation side and the demand side. The operation of the low-carbon oriented power system is modeled in a combined energy and carbon market, which fully considers the carbon emission right trading and renewable energy certificates trading of the market participants. Several benchmark systems have been used to demonstrate the effectiveness of the proposed planning approach. Comparative studies to existing approaches in the literature, where applicable, have also been conducted. The simulation results verify the practical applicability of this method

Knowledge Discovery and Data Mining for Shared Mobility and Connected and Automated Vehicle Applications

The rapid development of shared mobility and connected and automated vehicles (CAVs) has not only brought new intelligent transportation system (ITS) challenges with the new types of mobility, but also brought a huge opportunity to accelerate the connectivity and informatization of transportation systems, particularly when we consider all the new forms of data that is becoming available. The primary challenge is how to take advantage of the enormous amount of data to discover knowledge, build effective models, and develop impactful applications. With the theoretical and experimental progress being made over the last two decades, data mining and machine learning technologies have become key approaches for parsing data, understanding information, and making informed decisions, especially as the rise of deep learning algorithms bringing new levels of performance to the analysis of large datasets. The combination of data mining and ITS can greatly benefit research and advances in shared mobility and CAVs.This dissertation focuses on knowledge discovery and data mining for shared mobility and CAV applications. When considering big data associated with shared mobility operations and CAV research, data mining techniques can be customized with transportation knowledge to initially parse the data. Then machine learning methods can be used to model the parsed data to elicit hidden knowledge. Finally, the discovered knowledge and extracted information can help in the development of effective shared mobility and CAV applications to achieve the goals of a safer, faster, and more eco-friendly transportation systems.In this dissertation, there are four main sections that are addressed. First, new methodologies are introduced for extracting lane-level road features from rough crowdsourced GPS trajectories via data mining, which is subsequently used as the fundamental information for CAV applications. The proposed method results in decimeter level accuracy, which satisfies the positioning needs for many macroscopic and microscopic shared mobility and CAV applications. Second, macroscopic ride-hailing service big data has been analyzed for demand prediction, vehicle operation, and system efficiency monitoring. The proposed deep learning algorithms increase the ride-hailing demand prediction accuracy to 80% and can help the fleet dispatching system reduce 30% of vacant travel distance. Third, microscopic automated vehicle perception data has been analyzed for a real-time computer vision system that can be used for lane change behavior detection. The proposed deep learning design combines the residual neural network image input with time serious control data and reaches 95% of lane change behavior prediction accuracy. Last but not least, new ride sharing and CAV applications have been simulated in a behavior modeling framework to analyze the impact of mobility and energy consumption, which addresses key barriers by quantifying the transportation system-wide mobility, energy and behavior impacts from new mobility technologies using real-world data

Planning multisentential English text using communicative acts

The goal of this research is to develop explanation presentation mechanisms for knowledge based systems which enable them to define domain terminology and concepts, narrate events, elucidate plans, processes, or propositions and argue to support a claim or advocate action. This requires the development of devices which select, structure, order and then linguistically realize explanation content as coherent and cohesive English text. With the goal of identifying generic explanation presentation strategies, a wide range of naturally occurring texts were analyzed with respect to their communicative sttucture, function, content and intended effects on the reader. This motivated an integrated theory of communicative acts which characterizes text at the level of rhetorical acts (e.g., describe, define, narrate), illocutionary acts (e.g., inform, request), and locutionary acts (e.g., ask, command). Taken as a whole, the identified communicative acts characterize the structure, content and intended effects of four types of text: description, narration, exposition, argument. These text types have distinct effects such as getting the reader to know about entities, to know about events, to understand plans, processes, or propositions, or to believe propositions or want to perform actions. In addition to identifying the communicative function and effect of text at multiple levels of abstraction, this dissertation details a tripartite theory of focus of attention (discourse focus, temporal focus, and spatial focus) which constrains the planning and linguistic realization of text. To test the integrated theory of communicative acts and tripartite theory of focus of attention, a text generation system TEXPLAN (Textual EXplanation PLANner) was implemented that plans and linguistically realizes multisentential and multiparagraph explanations from knowledge based systems. The communicative acts identified during text analysis were formalized as over sixty compositional and (in some cases) recursive plan operators in the library of a hierarchical planner. Discourse, temporal, and spatial focus models were implemented to track and use attentional information to guide the organization and realization of text. Because the plan operators distinguish between the communicative function (e.g., argue for a proposition) and the expected effect (e.g., the reader believes the proposition) of communicative acts, the system is able to construct a discourse model of the structure and function of its textual responses as well as a user model of the expected effects of its responses on the reader's knowledge, beliefs, and desires. The system uses both the discourse model and user model to guide subsequent utterances. To test its generality, the system was interfaced to a variety of domain applications including a neuropsychological diagnosis system, a mission planning system, and a knowledge based mission simulator. The system produces descriptions, narrations, expositions, and arguments from these applications, thus exhibiting a broader range of rhetorical coverage than previous text generation systems