Leveraging data from a smart card automatic fare collection system for public transit planning

RÉSUMÉ Le système de transport en commun est une créature artificielle et complexe. L’interaction spatio-temporelle entre l’offre de service par les opérateurs et la demande des usagers est difficile à mesurer et évolue constamment. C’est dans ce contexte que de nombreux efforts sont mis à la recherche de l’information et de la méthodologie qui peuvent contribuer à révéler et à comprendre cette relation dynamique afin que les services répondent aux besoins des voyageurs. Récemment, des changements aux paradigmes remodèlent ce processus. D’une part, les opérateurs de transport en commun adoptent une orientation axée sur la performance et le client. Ceci demande des données qui ne sont pas recueillies par des enquêtes traditionnelles. D’autre part, l’avancement des systèmes automatiques de collecte de données et leur adoption par les opérateurs génèrent une abondance de données dans un environnement où la collecte de données était auparavant limitée. Les systèmes d’analyse et de planification sont souvent adaptés à ces réalités et sont inadéquats pour exploiter de nouvelles sources de données. Au confluent de ces évolutions, se retrouvent un défi et une opportunité : apprivoiser les nouvelles technologies informationnelles dans le but de les réconcilier avec les besoins grandissants de données dans le domaine de transport en commun. Cette recherche se fonde sur un jeu de données de validation provenant d’un système de perception par carte à puce (CAP). Le but de la recherche est de développer de nouvelles méthodologies d’exploitation des données, notamment au niveau de leur traitement, de leur enrichissement et de leur analyse afin de mieux connaître la demande de transport en commun, d’améliorer la planification opérationnelle, de raffiner la gestion du système et de comprendre les comportements de déplacement. Le jeu de données principal provient du système de perception par CAP de la Société de transport de l’Outaouais (STO). Le système est muni de GPS et le jeu contient toutes les validations désagrégées pour le mois de février 2005. Les technologies informationnelles, incluant la base de données relationnelle, le système d’information géographique (SIG), les statistiques spatiales, le data mining et les visualisations, sont des principaux outils de traitement et d’analyse.----------ABSTRACT Public transit system is an artificial and complex creature. The interaction between operators’ supply and users’ demand is at the same time spatial and temporal. It is also difficult to measure and in constant evolution. There is a continuous quest for information and methodology that can help reveal and facilitate the understanding of this dynamic relationship, so that public transit services can be better organized to suit travelers’ needs. Recent paradigm shifts have contributed the reshaping of this process. On the one hand, public transit service has become more performance-driven and customer-oriented. These require data not covered by traditional survey methods. On the other hand, advances in passive data collection methods and their adoption by transit operators progressively transform the industry from data-poor to data-rich. Traditional analysis and planning tools are adapted to past conditions and are not suited to fully leverage new sources of data. At the confluence of these evolutions lies opportunity and challenge: to embrace the data-rich environment with the view of reconciling with the increasingly demanding data needs in public transit. The research is based on a set of validations data from a smart card automatic fare collection (AFC) system. The goal of the research is to develop new methods in data processing, data enrichment and data analysis in order to better quantify transit demand, enhance operations planning, improve system management and understand travel behaviour. The primary dataset comes from the smart card AFC of the Société de transport de l’Outaouais (STO). The system is equipped with GPS and the dataset contains all fare validations in a disaggregate form for the month of February 2005. Information technologies, including relational database, geographic information system (GIS), spatial statistics, data mining and visualization are the main data processing and analysis tools. Three overall principles guide the research: the information-based (data-driven) approach, the totally disaggregated approach and the object-oriented approach. Combined with multi-day smart card data, these principles lead to the multi-day information approach, a new concept used in the proposed data processing and enrichment procedures. The assumption is that each day of data represent partial information of the universe and may contain errors. By synthesizing the correct information from each day, it is possible to reconstruct complete knowledge. The latter is in turn used as a reference to analyze and interpret multi-day data

Validation of design artefacts for blockchain-enabled precision healthcare as a service.

Healthcare systems around the globe are currently experiencing a rapid wave of digital disruption. Current research in applying emerging technologies such as Big Data (BD), Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Augmented Reality (AR), Virtual Reality (VR), Digital Twin (DT), Wearable Sensor (WS), Blockchain (BC) and Smart Contracts (SC) in contact tracing, tracking, drug discovery, care support and delivery, vaccine distribution, management, and delivery. These disruptive innovations have made it feasible for the healthcare industry to provide personalised digital health solutions and services to the people and ensure sustainability in healthcare. Precision Healthcare (PHC) is a new inclusion in digital healthcare that can support personalised needs. It focuses on supporting and providing precise healthcare delivery. Despite such potential, recent studies show that PHC is ineffectual due to the lower patient adoption in the system. Anecdotal evidence shows that people are refraining from adopting PHC due to distrust. This thesis presents a BC-enabled PHC ecosystem that addresses ongoing issues and challenges regarding low opt-in. The designed ecosystem also incorporates emerging information technologies that are potential to address the need for user-centricity, data privacy and security, accountability, transparency, interoperability, and scalability for a sustainable PHC ecosystem. The research adopts Soft System Methodology (SSM) to construct and validate the design artefact and sub-artefacts of the proposed PHC ecosystem that addresses the low opt-in problem. Following a comprehensive view of the scholarly literature, which resulted in a draft set of design principles and rules, eighteen design refinement interviews were conducted to develop the artefact and sub-artefacts for design specifications. The artefact and sub-artefacts were validated through a design validation workshop, where the designed ecosystem was presented to a Delphi panel of twenty-two health industry actors. The key research finding was that there is a need for data-driven, secure, transparent, scalable, individualised healthcare services to achieve sustainability in healthcare. It includes explainable AI, data standards for biosensor devices, affordable BC solutions for storage, privacy and security policy, interoperability, and usercentricity, which prompts further research and industry application. The proposed ecosystem is potentially effective in growing trust, influencing patients in active engagement with real-world implementation, and contributing to sustainability in healthcare

Accountants\u27 index. Twenty-eighth supplement, January-December 1979, volume 2: M-Z

https://egrove.olemiss.edu/aicpa_accind/1034/thumbnail.jp

Accountants\u27 index. Twenty-eighth supplement, January-December 1979, volume 1: A-L

https://egrove.olemiss.edu/aicpa_accind/1033/thumbnail.jp

Task Allocation in Foraging Robot Swarms:The Role of Information Sharing

Autonomous task allocation is a desirable feature of robot swarms that collect and deliver items in scenarios where congestion, caused by accumulated items or robots, can temporarily interfere with swarm behaviour. In such settings, self-regulation of workforce can prevent unnecessary energy consumption. We explore two types of self-regulation: non-social, where robots become idle upon experiencing congestion, and social, where robots broadcast information about congestion to their team mates in order to socially inhibit foraging. We show that while both types of self-regulation can lead to improved energy efficiency and increase the amount of resource collected, the speed with which information about congestion flows through a swarm affects the scalability of these algorithms