Joseph Chow - Data structures for transportation equity analysis - IPAM at UCLA
Recorded 26 January 2024. Joseph Chow of New York University presents "Data structures for transportation equity analysis" at IPAM's Mathematical Foundations for Equity in Transportation Systems Workshop.
Abstract: Equity analysis requires adequate representation in data to be able to quantify differences in a population. Certain data structures are more conducive to a fair representation than others. For example, Census data for underserved population segments can have higher measures of error, which can be problematic if such data is used for transportation analysis assuming average values are equally accurate for all segments. We derive an algorithm to construct districts to address the Modifiable Areal Unit Problem to ensure fairer representations of different population segments, using population synthesis as a use case. Upon obtaining a synthetic population, further need for equity analysis requires designing behavioral models that can capture these differences in the population without losing computational tractability in integrating with mobility service optimization models. Aggregate mode choice models are proposed that systematically capture k-modal taste heterogeneity down to Census block group OD pairs, such that choice-based minimization of income disparity in mobility service resource allocation can be done as a quadratic program. Policy implications are discussed.
Learn more online at: https://www.ipam.ucla.edu/programs/workshops/mathematical-foundations-for-equity-in-transportation-systems-january-22-26-2024/?tab=overview
Видео Joseph Chow - Data structures for transportation equity analysis - IPAM at UCLA канала Institute for Pure & Applied Mathematics (IPAM)
Abstract: Equity analysis requires adequate representation in data to be able to quantify differences in a population. Certain data structures are more conducive to a fair representation than others. For example, Census data for underserved population segments can have higher measures of error, which can be problematic if such data is used for transportation analysis assuming average values are equally accurate for all segments. We derive an algorithm to construct districts to address the Modifiable Areal Unit Problem to ensure fairer representations of different population segments, using population synthesis as a use case. Upon obtaining a synthetic population, further need for equity analysis requires designing behavioral models that can capture these differences in the population without losing computational tractability in integrating with mobility service optimization models. Aggregate mode choice models are proposed that systematically capture k-modal taste heterogeneity down to Census block group OD pairs, such that choice-based minimization of income disparity in mobility service resource allocation can be done as a quadratic program. Policy implications are discussed.
Learn more online at: https://www.ipam.ucla.edu/programs/workshops/mathematical-foundations-for-equity-in-transportation-systems-january-22-26-2024/?tab=overview
Видео Joseph Chow - Data structures for transportation equity analysis - IPAM at UCLA канала Institute for Pure & Applied Mathematics (IPAM)
Показать
Комментарии отсутствуют
Информация о видео
27 января 2024 г. 2:15:28
00:47:11
Другие видео канала
Jaafar El-Awady - dislocation in high thermomechanical condition in Additive Manufacturing of Alloys
Vikram Gavini - Fast, Accurate and Large-scale Ab-initio Calculations for Materials Modeling
Bistra Dilkina - Machine Learning for MIP Solving - IPAM at UCLA
Amit Acharya - Slow time-scale behavior of fast microscopic dynamics - IPAM at UCLA
Eran Rabani - Stochastic Density Functional Theory - IPAM at UCLA
Deanna Needell - Using Algebraic Factorizations for Interpretable Learning - IPAM at UCLA
Xavier Bresson - Learning to Untangle Genome Assembly Graphs - IPAM at UCLA
Jack Gilbert: "Microbiome of the Built Environment"
John Harrison - Formalization and Automated Reasoning: A Personal and Historical Perspective
Raymond Clay - Machine Learning in Equation of State and Transport Modeling at Extreme Conditions
Rose Yu - Incorporating Symmetry for Learning Spatiotemporal Dynamics - IPAM at UCLA
Yongsoo Yang - Neural network-assisted atomic electron tomography - IPAM at UCLA
Margaret Murnane - Attosecond Quantum Technologies for Advanced Materials Metrologies - IPAM at UCLA
Albert Fannjiang - From Tomographic Phase Retrieval to Projection Tomography - IPAM at UCLA
Thomas Swinburne - Learning uncertainty-aware models of defect kinetics at scale - IPAM at UCLA
Kevin Kelly - Machine Learning Enhanced Compressive Hyperspectral Imaging - IPAM at UCLA
Demetri Psaltis - Machine Learning for 3D Optical Imaging - IPAM at UCLA
Paola Gori-Giorgi - Large-coupling strength expansion in DFT and Hartree-Fock adiabatic connections
Bohua Zhan - Verifying symbolic computation in the HolPy theorem prover - IPAM at UCLA
Xiantao Li - A stochastic algorithm for self-consistent calculations in DFT - IPAM at UCLA
Pascal Van Hentenryck - Fusing Machine Learning and Optimization - IPAM at UCLA