You are how you travel: A multi-task learning framework for Geodemographic inference using transit smart card data

Geodemographics, providing the information of population's characteristics in the regions on a geographical basis, is of immense importance in urban studies, public policy-making, social research and business, among others. Such data, however, are difficult to collect from the public, which is usually done via census, with a low update frequency. In urban areas, with the increasing prevalence of public transit equipped with automated fare payment systems, researchers can collect massive transit smart card (SC) data from a large population. The SC data record human daily activities at an individual level with high spatial and temporal resolutions. It can reveal frequent activity areas (e.g., residential areas) and travel behaviours of passengers that are intimately intertwined with personal interests and characteristics. This provides new opportunities for geodemographic study. This paper seeks to develop a framework to infer travellers' demographics (such as age, income level and car ownership, et al.) and their residential areas for geodemographic mapping using SC data with a household survey. We first use a decision tree diagram to detect passengers' residential areas. We then represent each individual's spatio-temporal activity pattern derived from multi-week SC data as a 2D image. Leveraging this representation, a multi-task convolutional neural network (CNN) is employed to predict multiple demographics of individuals from the images. Combing the demographics and locations of their residence, geodemographic information is further obtained. The methodology is applied to a large-scale SC dataset provided by Transport for London. Results provide new insights in understanding the relationship between human activity patterns and demographics. To the best of our knowledge, this is the first attempt to infer geodemographics by using the SC data

Impact of the spatial context on human communication activity

Technology development produces terabytes of data generated by hu- man activity in space and time. This enormous amount of data often called big data becomes crucial for delivering new insights to decision makers. It contains behavioral information on different types of human activity influenced by many external factors such as geographic infor- mation and weather forecast. Early recognition and prediction of those human behaviors are of great importance in many societal applications like health-care, risk management and urban planning, etc. In this pa- per, we investigate relevant geographical areas based on their categories of human activities (i.e., working and shopping) which identified from ge- ographic information (i.e., Openstreetmap). We use spectral clustering followed by k-means clustering algorithm based on TF/IDF cosine simi- larity metric. We evaluate the quality of those observed clusters with the use of silhouette coefficients which are estimated based on the similari- ties of the mobile communication activity temporal patterns. The area clusters are further used to explain typical or exceptional communication activities. We demonstrate the study using a real dataset containing 1 million Call Detailed Records. This type of analysis and its application are important for analyzing the dependency of human behaviors from the external factors and hidden relationships and unknown correlations and other useful information that can support decision-making.Comment: 12 pages, 11 figure

Ciudad inteligente y sostenible: Singapore, caso de éxito

The following paper focuses on the qualitative description of the nation city of Singapore and its quantitative evaluation, which are part of an exploratory research for the ORCA and SciBas research group of the Francisco José de Caldas District University. Therefore, during the first semester of 2018, a conceptual review was made on the subject, proposing an action methodology whose snuyources are selected with the category of smart city in the context of sustainability. The search for information, both national and international, was focused on the databases: IEEE Xplore, ScienceDirect, Springer Link, among others; and in the search for the evaluation model.El siguiente trabajo se centra en la descripción cualitativa de la ciudad nación de Singapur y su evaluación cuantitativa, que hacen parte de una investigación exploratoria para el grupo de investigación ORCA y SciBas de la Universidad Distrital Francisco José de Caldas. Por lo tanto, durante el primer semestre de 2018, se realizó una revisión conceptual sobre el tema, proponiendo una metodología de acción cuyas fuentes son seleccionadas con la categoría de ciudad inteligente en el contexto de la sostenibilidad. La búsqueda de información, tanto nacional como internacional, se centró en las bases de datos: IEEE Xplore, ScienceDirect, Springer Link, entre otras; y en la búsqueda del modelo de evaluación

Uncertainty Estimation, Explanation and Reduction with Insufficient Data

Human beings have been juggling making smart decisions under uncertainties, where we manage to trade off between swift actions and collecting sufficient evidence. It is naturally expected that a generalized artificial intelligence (GAI) to navigate through uncertainties meanwhile predicting precisely. In this thesis, we aim to propose strategies that underpin machine learning with uncertainties from three perspectives: uncertainty estimation, explanation and reduction. Estimation quantifies the variability in the model inputs and outputs. It can endow us to evaluate the model predictive confidence. Explanation provides a tool to interpret the mechanism of uncertainties and to pinpoint the potentials for uncertainty reduction, which focuses on stabilizing model training, especially when the data is insufficient. We hope that this thesis can motivate related studies on quantifying predictive uncertainties in deep learning. It also aims to raise awareness for other stakeholders in the fields of smart transportation and automated medical diagnosis where data insufficiency induces high uncertainty. The thesis is dissected into the following sections: Introduction. we justify the necessity to investigate AI uncertainties and clarify the challenges existed in the latest studies, followed by our research objective. Literature review. We break down the the review of the state-of-the-art methods into uncertainty estimation, explanation and reduction. We make comparisons with the related fields encompassing meta learning, anomaly detection, continual learning as well. Uncertainty estimation. We introduce a variational framework, neural process that approximates Gaussian processes to handle uncertainty estimation. Two variants from the neural process families are proposed to enhance neural processes with scalability and continual learning. Uncertainty explanation. We inspect the functional distribution of neural processes to discover the global and local factors that affect the degree of predictive uncertainties. Uncertainty reduction. We validate the proposed uncertainty framework on two scenarios: urban irregular behaviour detection and neurological disorder diagnosis, where the intrinsic data insufficiency undermines the performance of existing deep learning models. Conclusion. We provide promising directions for future works and conclude the thesis

Determinants of public transport use in Perth, Western Australia

This study examines the primary determinants explaining spatial and temporal variations in public transportation use in Perth using 2009 SmartRider data. The developed robust predictive model includes land use, urban form, socio-economic characteristics and public transport availability. Land-use development integrated with sustainable transportation systems, more frequent and densely distributed feeder bus service to train stations, density of low-income resident and university student populations are highlighted as important for policy making encouraging increased public transport use