Modeling the Impact of Key Events on Long-Term Transport Mode Choice Decisions

2005 ◽  
Vol 1926 (1) ◽  
pp. 106-114 ◽  
Author(s):  
Marloes Verhoeven ◽  
Theo Arentze ◽  
Harry J. P. Timmermans ◽  
Peter Van Der Waerden
Keyword(s):  
Mode Choice ◽  
Household Composition ◽  
Bayesian Decision ◽  
Transport Mode ◽  
Decision Network ◽  
Bayesian Decision Network ◽  
Location Change ◽  
Key Events ◽  
The Impact

This paper describes the first phase of a study of the impact of key events on long-term transport mode choice decisions. The suggested complexity of transport mode choice is modeled with a Bayesian decision network. An Internet-based questionnaire was designed to measure the various conditional probability tables and the conditional utility tables of the Bayesian decision network. Seven key events were implemented in the questionnaire: change in residential location, change in household composition, change in work location, change in study location, change in car availability, change in availability of public transport pass, and change in household income. Data from 554 respondents were used to illustrate how the tables can be constructed on the basis of event history data.

scholarly journals Learning to Address Health Inequality in the United States with a Bayesian Decision Network

2019 ◽  
Vol 33 ◽  
pp. 710-717
Author(s):  
Tavpritesh Sethi ◽  
Anant Mittal ◽  
Shubham Maheshwari ◽  
Samarth Chugh
Keyword(s):  
United States ◽  
Web Application ◽  
The United States ◽  
Care Quality ◽  
Joint Probability Distribution ◽  
Bayesian Decision ◽  
Unified Framework ◽  
Decision Network ◽  
Bayesian Decision Network ◽  
The Impact

Life-expectancy is a complex outcome driven by genetic, socio-demographic, environmental and geographic factors. Increasing socio-economic and health disparities in the United States are propagating the longevity-gap, making it a cause for concern. Earlier studies have probed individual factors but an integrated picture to reveal quantifiable actions has been missing. There is a growing concern about a further widening of healthcare inequality caused by Artificial Intelligence (AI) due to differential access to AI-driven services. Hence, it is imperative to explore and exploit the potential of AI for illuminating biases and enabling transparent policy decisions for positive social and health impact. In this work, we reveal actionable interventions for decreasing the longevitygap in the United States by analyzing a County-level data resource containing healthcare, socio-economic, behavioral, education and demographic features. We learn an ensembleaveraged structure, draw inferences using the joint probability distribution and extend it to a Bayesian Decision Network for identifying policy actions. We draw quantitative estimates for the impact of diversity, preventive-care quality and stablefamilies within the unified framework of our decision network. Finally, we make this analysis and dashboard available as an interactive web-application for enabling users and policy-makers to validate our reported findings and to explore the impact of ones beyond reported in this work.

scholarly journals Fog Forecasting for Melbourne Airport Using a Bayesian Decision Network

2015 ◽  
Vol 30 (5) ◽  
pp. 1218-1233 ◽  
Author(s):  
Tal Boneh ◽  
Gary T. Weymouth ◽  
Peter Newham ◽  
Rodney Potts ◽  
John Bally ◽  
...  
Keyword(s):  
Probabilistic Reasoning ◽  
Weather Forecasting ◽  
Weather Prediction ◽  
Aviation Industry ◽  
Bayesian Decision ◽  
Support Tool ◽  
Fog Forecasting ◽  
Decision Network ◽  
Bayesian Decision Network ◽  
The Impact

Abstract Fog events occur at Melbourne Airport, Melbourne, Victoria, Australia, approximately 12 times each year. Unforecast events are costly to the aviation industry, cause disruption, and are a safety risk. Thus, there is a need to improve operational fog forecasting. However, fog events are difficult to forecast because of the complexity of the physical processes and the impact of local geography and weather elements. Bayesian networks (BNs) are a probabilistic reasoning tool widely used for prediction, diagnosis, and risk assessment in a range of application domains. Several BNs for probabilistic weather prediction have been previously reported, but to date none have included an explicit forecast decision component and none have been used for operational weather forecasting. A Bayesian decision network [Bayesian Objective Fog Forecast Information Network (BOFFIN)] has been developed for fog forecasting at Melbourne Airport based on 34 years’ worth of data (1972–2005). Parameters were calibrated to ensure that the network had equivalent or better performance to prior operational forecast methods, which led to its adoption as an operational decision support tool. The current study was undertaken to evaluate the operational use of the network by forecasters over an 8-yr period (2006–13). This evaluation shows significantly improved forecasting accuracy by the forecasters using the network, as compared with previous years. BOFFIN-Melbourne has been accepted by forecasters because of its skill, visualization, and explanation facilities, and because it offers forecasters control over inputs where a predictor is considered unreliable.

scholarly journals Use of artificial intelligence based models for learning better policy for maternal and child health

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
T Sethi ◽  
R Awasthi
Keyword(s):  
Artificial Intelligence ◽  
Adverse Pregnancy Outcome ◽  
Bayesian Decision ◽  
Learning From Data ◽  
Decision Network ◽  
Sanitation And Hygiene ◽  
Public Health Challenges ◽  
Bayesian Decision Network ◽  
Adverse Pregnancy ◽  
Bayesian Artificial Intelligence

Abstract More than 640,000 babies died of sepsis before they reach the age of one month in India in 2016. Despite a large number of government schemes aimed at reducing this rate, this number still remains high because of the complexity and interplay of factors involved. Finding an optimum policy and solutions to this problem needs learning from data. We integrated diverse sources of data and applied Bayesian Artificial Intelligence methods for learning to mitigate sepsis and adverse pregnancy outcomes in India. In this project, we created models that combine the robustness of ensemble averaged Baeysian Networks with decision learning and impact evaluation by using simulations and counterfactual reasoning respectively. We will demonstrate the process of learning these models and how these led us to infer the pivotal role of Water, Sanitation and Hygiene for reducing Adverse Pregnancy Outcome and neonatal sepsis in the population studied. We will also demonstrate the creation of explainable AI models for complex public health challenges and their deployment with wiseR, our in-house, open source platform for doing end-to-end Bayesian Decision Network learning.

Evaluation of a Bayesian decision network for diagnosing pyloric stenosis

2006 ◽  
Vol 41 (1) ◽  
pp. 155-161 ◽  
Author(s):  
Sonia M. Alvarez ◽  
Beverly A. Poelstra ◽  
Randall S. Burd
Keyword(s):  
Pyloric Stenosis ◽  
Bayesian Decision ◽  
Decision Network ◽  
Bayesian Decision Network

scholarly journals The Seasons of COVID-19: understanding the mood of older adults in the UK through a barometer survey spanning 12 months

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 737-737
Author(s):  
Nicola Palmarini ◽  
Lesley Hall ◽  
Alex Mitchell ◽  
James McNaughton ◽  
Charlotte Nixon
Keyword(s):  
Older Adults ◽  
Social Dynamics ◽  
Fake News ◽  
Policy Makers ◽  
Global Spread ◽  
The Uk ◽  
Key Events ◽  
The Impact ◽  
Political Decisions

Abstract The impact of COVID-19 on older adults has been analysed through different research approaches. However, with its sudden global spread, combined with uncertainty about which countermeasures would be employed, there was a lack of opportunity to systematically and continuously engage in a system of observing the moods of older adults forced to live in unexpected conditions. Ageist narratives, social distancing, the unending barrage of real and fake news, and the lockdowns, have given rise to what we define as a series of “seasons” of life, characterised not by the weather barometer, but by moods of people. How much did these external events, like the impact of weather, affect the mood of older adults? We immediately recognised the pandemic’s long-term nature, and thanks to our position as an "observatory" of social dynamics, and because of our existing community of older adults (VOICE), we could involve our members to provide valuable insights about mood and wellbeing during the pandemic. We initiated a weekly pulse survey, based on the two same questions, starting in week 13 of 2020. Across the 50 weeks which followed, we received 2577 responses. They rated their mood on a scale of 1 (extra-stormy) to 5 (all sunshine), before we collated the data and mapped on key events related to media announcements and political decisions. Our research showed the impact of these events on the mood of participants, and the potential of this approach to identify trends in mood to help policy makers with informed decision-making during unprecedented times.

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