Poverty and transport modeling: Perspectives offered by Big Data and Machine Learning
Document Type
Lecture
Publication Date
5-4-2023
Abstract
Data and good models are at the forefront of all efficient decision-making processes, especially for poverty alleviation and transport planning. Technological advancement and the recent developments in ‘Big Data’ and machine learning provide useful information and methods that are nice complements to data collected through conventional methods and traditional models. The identification of key challenges and the current knowledge gaps in poverty and transport modeling are explored. Practical examples of how machine learning and big data are combined with statistical and economic models to tackle poverty and transport challenges. Promising areas for future opportunities and research, including new data collection, data analytics, and application development to support and inform policymakers’ decisions are also discussed.
Relational Format
presentation
Recommended Citation
Lonla, Theophile Bougna, "Poverty and transport modeling: Perspectives offered by Big Data and Machine Learning" (2023). Probability & Statistics Seminar. 16.
https://egrove.olemiss.edu/math_statistics/16
