Design of a synthetic data generation and simulation framework for mobility on demand applications

Urbanization increases issues such as traffic congestion, lack of parking spots, and underutilized vehicles. In recent years, mobility-on-demand (MOD) concept has been proposed to effectively mitigate these issues. However, a common issue with MOD research is the lack of precise traffic data for conducting transportation-related studies and improving the proficiency of MOD systems. This is mainly because of data privacy concerns, GPS device limitations or errors, and expensive infrastructures for collecting real-time traffic data. Given the constraints, traffic simulations could be a reasonable solution for simulating the dynamic MOD activities such as distributing vehicles in the cities of interest and mimicking their movement behaviours. Despite the features that existing traffic simulators provide, they are not designed to support MOD use cases explicitly. For instance, background traffic generated by these simulators mostly follows random algorithms and the traffic flow is not based on real traffic patterns of the region. Another issue could be the lack of integration APIs to accept user inputs while the simulation is running to adapt the behaviour of the simulation. In this thesis, a synthetic MOD data generation framework is proposed. This framework takes a map region, real traffic data, and service vehicles trip plan as input. Using the ARIMA machine learning algorithm, we could predict demand and generate background traffic, followed by simulating the service vehicles in the region. The proposed framework generates synthetic traffic based on real traffic patterns and then simulates the service vehicles' movements on the map. While the simulation is running, the framework monitors the vehicles and collects real-time trajectory data. This framework leverages the features of SUMO as a microscopic simulation engine. In addition, established HTTP APIs enable third-party integration and allow users to control vehicles and trips on the map before and during the simulation execution. The offered simulation features include and are not limited to, the importation of a trip plan for numerous vehicles and the update of vehicle destinations. In addition to integration APIs, the proposed framework provides a graphical user interface to facilitate simulation setup and execution. The provided user interface enables users to explore a map, specify a region on the map, and then choose it as a simulation boundary. Throughout the simulation, the software core captures and stores real-time data on vehicle movement in a database that might be utilized for mobility-on-demand research. This simulation framework returns comprehensive service vehicle trajectories, departure time, destination time, travel duration, route length, and service vehicle status. The proposed software is open-source and publicly available, and its capabilities could be improved for future study

ARTIFICIAL INTELLIGENCE AND INNOVATION PRACTICES: A CONCEPTUAL INQUIRY

Discourse on artificial intelligence runs noticeably along with the developments of computing technology. At the same time, the business environment become more uncertain, ambiguous, and competitive, which requires organizations to innovate for their sustainability continuously. This paper aims to broaden innovation management knowledge, particularly in applying artificial intelligence. The behavioural Theory of the Firm is a framework for writing this conceptual inquiry. To do so, artificial intelligence can assist organizations in processing information companies need to create incremental and radical innovations. Specifically, artificial intelligence is useful in overcoming barriers to innovation (during information processing and search processes) and generating and developing ideas and solutions. Furthermore, adopting artificial intelligence in innovation management is determined by the level of organizational capability in information processing, which consists of three levels: exploitation, expansion, and exploration. Then, economic, technological and social forces are argued as factors that can push organizations to adopt artificial intelligence. The challenges faced in the adoption process can come from technical aspects of technology, individual aspects, and interaction between technology and humans. The final part of this manuscript describes the future research agenda that can be carried out related to artificial intelligence and innovation management

IS THIS ARTIFICIAL INTELLIGENCE?

Artificial Intelligence (AI) has become one of the most frequently used terms in the technical jargon (and often in not-so-technical jargon). Recent advancements in the field of AI have certainly contributed to the AI hype, and so have numerous applications and results of using AI technology in practice. Still, just like with any other hype, the AI hype has its controversies. This paper critically examines developments in the field of AI from multiple perspectives – research, technological, social and pragmatic. Part of the controversies of the AI hype stem from the fact that people use the term AI differently, often without a deep understanding of the wider context in which AI as a field has been developing since its inception in Mid 1950s

Modeling, Predicting and Capturing Human Mobility

Realistic models of human mobility are critical for modern day applications, specifically for recommendation systems, resource planning and process optimization domains. Given the rapid proliferation of mobile devices equipped with Internet connectivity and GPS functionality today, aggregating large sums of individual geolocation data is feasible. The thesis focuses on methodologies to facilitate data-driven mobility modeling by drawing parallels between the inherent nature of mobility trajectories, statistical physics and information theory. On the applied side, the thesis contributions lie in leveraging the formulated mobility models to construct prediction workflows by adopting a privacy-by-design perspective. This enables end users to derive utility from location-based services while preserving their location privacy. Finally, the thesis presents several approaches to generate large-scale synthetic mobility datasets by applying machine learning approaches to facilitate experimental reproducibility

Generative adversarial networks for sequential learning

Generative modelling aims to learn the data generating mechanism from observations without supervision. It is a desirable and natural approach for learning unlabelled data which is easily accessible. Deep generative models refer to a class of generative models combined with the usage of deep learning techniques, taking advantage of the intuitive principles of generative models as well as the expressiveness and flexibility of neural networks. The applications of generative modelling include image, audio, and video synthesis, text summarisation and translation, and so on. The methods developed in this thesis particularly emphasise on domains involving data of sequential nature, such as video generation and prediction, weather forecasting, and dynamic 3D reconstruction. Firstly, we introduce a new adversarial algorithm for training generative models suitable for sequential data. This algorithm is built on the theory of Causal Optimal Transport (COT) which constrains the transport plans to respect the temporal dependencies exhibited in the data. Secondly, the algorithm is extended to learn conditional sequences, that is, how a sequence is likely to evolve given the observation of its past evolution. Meanwhile, we work with the modified empirical measures to guarantee the convergence of the COT distance when the sequences do not overlap at any time step. Thirdly, we show that state-of-the-art results in the complex spatio-temporal modelling using GANs can be further improved by leveraging prior knowledge in the spatial-temporal correlation in the domain of weather forecasting. Finally, we demonstrate how deep generative models can be adopted to address a classical statistical problem of conditional independence testing. A class of classic approaches for such a task requires computing a test statistic using samples drawn from two unknown conditional distributions. We therefore present a double GANs framework to learn two generative models that approximate both conditional distributions. The success of this approach sheds light on how certain challenging statistical problems can benefit from the adequate learning results as well as the efficient sampling procedure of deep generative models

Digital work in the planetary market

Many of the world’s most valuable companies rely on planetary networks of digital work that underpin their products and services. This important book examines implications for both work and workers when jobs are commodified and traded beyond local labor markets. For instance, Amazon’s contractors in Costa Rica, India, and Romania are paid to structure, annotate, and organize conversations captured by ‘Alexa’ to train Amazon’s speech recognition systems. Findings show that despite its planetary connections, labor remains geographically “sticky” and embedded in distinct contexts. The research emphasizes the globe-spanning nature of contemporary networks without resorting to an understanding of “the global” as a place beyond space.Aujourd’hui, de nombreux emplois peuvent être exercés depuis n’importe où. La technologie numérique et la connectivité Internet généralisée permettent à presque n’importe qui, n’importe où, de se connecter à n’importe qui d’autre pour communiquer et interagir à l’échelle planétaire. Ce livre examine les conséquences, tant pour le travail que pour les travailleurs, de la marchandisation et de l’échange des emplois au-delà des marchés du travail locaux. Allant au-delà du discours habituel sur la mondialisation « le monde est plat », les contributeurs examinent à la fois la transformation du travail lui-même et les systèmes, réseaux et processus plus larges qui permettent le travail numérique dans un marché planétaire, en offrant des perspectives empiriques et théoriques. Les contributeurs - des universitaires et des experts de premier plan issus de diverses disciplines - abordent une variété de questions, notamment la modération du contenu, les véhicules autonomes et les assistants vocaux. Ils se penchent d’abord sur la nouvelle expérience du travail et constatent que, malgré ses connexions planétaires, le travail reste géographiquement collé et intégré dans des contextes distincts. Ils examinent ensuite comment les réseaux planétaires de travail peuvent être cartographiés et problématisés, ils discutent de la multiplicité productive et de l’interdisciplinarité de la réflexion sur le travail numérique et ses réseaux et, enfin, ils imaginent comment le travail planétaire pourrait être réglementé. Les directeurs Mark Graham est professeur de géographie de l’Internet à l’Oxford Internet Institute et chargé de cours à l’Alan Turing Institute. Il est l’éditeur du livre Digital Economies at Global Margins (MIT Press et CRDI, 2019). Fabian Ferrari est un candidat au doctorat à l’Oxford Internet Institute

Towards Efficient and Secure Intelligent Transportation Services: AI-driven Traffic Light Controller and Privacy-Preserving Mobility Data Generation

The widespread adoption of artificial intelligence (AI) and Intelligent Transportation Systems (ITS) technologies has led to the increasing application of AI-based ITS controllers, with the Traffic Signal Controller (TSC) being a prominent example. Reinforcement learning (RL) models have shown promising results for adaptively adjusting traffic light schedules in urban environments through RL-based TSCs (RL-TSCs). The real-world deployment of RL-TSCs involves three key aspects: performance, security, and data privacy. In terms of performance, RL-TSC models need to be designed with consideration for various metrics, such as fair traffic scheduling and air quality impact. To address this, our approach takes into account a multi-objective constrained learning formulation to optimize performance. However, the use of RL-TSCs for automation, by leveraging external inputs, introduces security concerns that require active research to mitigate. We address these security challenges by introducing an innovative defense mechanism. Additionally, the training of RL-TSCs relies on real-world mobility datasets, necessitating the protection of data privacy at different levels of granularity. To minimize the constraints associated with limited real data availability or privacy concerns, we introduce two distinct directions: synthetic trajectory data generation using recent generative AI methods, and location privacy models for raw mobility datasets based on differential privacy, which safeguard individual trajectories and aggregated mobility datasets.This research provides a valuable tool for evaluating the practical deployment of RL-TSCs, particularly in real-world settings where the last mile of implementation and security is paramount. By addressing the key challenges of performance, security, and data privacy, this work aims to facilitate the successful real-world deployment of AI-powered ITS controllers

The evolutionary dynamics of the artificial intelligence ecosystem

We analyze the sectoral and national systems of firms and institutions that collectively engage in artificial intelligence (AI). Moving beyond the analysis of AI as a general-purpose technology or its particular areas of application, we draw on the evolutionary analysis of sectoral systems and ask, “Who does what?” in AI. We provide a granular view of the complex interdependency patterns that connect developers, manufacturers, and users of AI. We distinguish between AI enablement, AI production, and AI consumption and analyze the emerging patterns of cospecialization between firms and communities. We find that AI provision is characterized by the dominance of a small number of Big Tech firms, whose downstream use of AI (e.g., search, payments, social media) has underpinned much of the recent progress in AI and who also provide the necessary upstream computing power provision (Cloud and Edge). These firms dominate top academic institutions in AI research, further strengthening their position. We find that AI is adopted by and benefits the small percentage of firms that can both digitize and access high-quality data. We consider how the AI sector has evolved differently in the three key geographies—China, the United States, and the European Union—and note that a handful of firms are building global AI ecosystems. Our contribution is to showcase the evolution of evolutionary thinking with AI as a case study: we show the shift from national/sectoral systems to triple-helix/innovation ecosystems and digital platforms. We conclude with the implications of such a broad evolutionary account for theory and practice