TrafficGPT: Viewing, Processing and Interacting with Traffic Foundation Models

With the promotion of chatgpt to the public, Large language models indeed showcase remarkable common sense, reasoning, and planning skills, frequently providing insightful guidance. These capabilities hold significant promise for their application in urban traffic management and control. However, LLMs struggle with addressing traffic issues, especially processing numerical data and interacting with simulations, limiting their potential in solving traffic-related challenges. In parallel, specialized traffic foundation models exist but are typically designed for specific tasks with limited input-output interactions. Combining these models with LLMs presents an opportunity to enhance their capacity for tackling complex traffic-related problems and providing insightful suggestions. To bridge this gap, we present TrafficGPT, a fusion of ChatGPT and traffic foundation models. This integration yields the following key enhancements: 1) empowering ChatGPT with the capacity to view, analyze, process traffic data, and provide insightful decision support for urban transportation system management; 2) facilitating the intelligent deconstruction of broad and complex tasks and sequential utilization of traffic foundation models for their gradual completion; 3) aiding human decision-making in traffic control through natural language dialogues; and 4) enabling interactive feedback and solicitation of revised outcomes. By seamlessly intertwining large language model and traffic expertise, TrafficGPT not only advances traffic management but also offers a novel approach to leveraging AI capabilities in this domain. The TrafficGPT demo can be found in https://github.com/lijlansg/TrafficGPT.git

How to effectively design and create a concept mobile application to aid in the management of type 1 diabetes in adolescents

Diabetes is one of the eight most prevalent chronic health conditions in the World; therefore there is a wide range of diabetes-related mobile applications available to the public to aid in glycaemic control and self-management. Statistically, adherence to medication is extremely low in adolescents with Type 1 Diabetes Mellitus (T1DM), therefore it is crucial that adolescents adhere to their medication from a young age and adopt good medication regimes. This paper focuses on the research and design of an interactive and educational concept mobile application aimed at adolescents, aged 11 to 16 years old, to aid in their understanding of T1DM. As visual elements are an essential part to the design of a mobile application, this research outlines how the visual components of the application were designed specifically for the target audience of adolescents with T1DM

QFD: an interactive algorithm for the prioritization of product's technical characteristics

The paper is concerned with the problem of the ``prioritization' ' of technical design characteristic s of a product. An interactive algorithm has been developed with the aim to better support the engineering design process by means of quality function deployment (QFD). The algorithm tries to soften customer approach to QFD in those situations in which customers are not able to give a ``significant' ' evaluation of the relative importance of their requirements . The method allows determining a ranking order of design characteristic s without the artificia l conversion of symbols contained in the relationshi p matrix, and without the use of explici t information concerning the relative degree of importance of customer requirements. A simple numerical applicatio n is also provide

Structuring the decision process : an evaluation of methods in the structuring the decision process

This chapter examines the effectiveness of methods that are designed to provide structure and support to decision making. Those that are primarily aimed at individual decision makers are examined first and then attention is turned to groups. In each case weaknesses of unaided decision making are identified and how successful the application of formal methods is likely to be in mitigating these weaknesses is assessed

An analysis of the application of AI to the development of intelligent aids for flight crew tasks

This report presents the results of a study aimed at developing a basis for applying artificial intelligence to the flight deck environment of commercial transport aircraft. In particular, the study was comprised of four tasks: (1) analysis of flight crew tasks, (2) survey of the state-of-the-art of relevant artificial intelligence areas, (3) identification of human factors issues relevant to intelligent cockpit aids, and (4) identification of artificial intelligence areas requiring further research