Typology, network features and damage response in worldwide urban road systems
Mar 1, 2022
Forecasting Reservoir Water Levels Using Deep Neural Networks: A Case Study of Angat Dam in the Philippines
Jan 1, 2022
Generalized radiation model for human migration
Nov 22, 2021
An analysis of network filtering methods to sovereign bond yields during COVID-19
Jul 15, 2021
Phase Transition in Taxi Dynamics and Impact of Ridesharing
Jan 7, 2020
School hazard vulnerability and student learning
Aug 10, 2018
Formulation of a Resilience Index for Metropolitan Rapid Transit Networks
Jan 7, 2018
Characterisation and comparison of spatial patterns in urban systems: A case study of U.S. cities
Jan 1, 2018
Inferring Passenger Types from Commuter Eigentravel Matrices
Here, using an ensemble of machine learning models, a procedure is demonstrated that classifies passengers (Adult, Child/Student, and Senior Citizen) based on their three-month travel patterns. The method proceeds by constructing distinct commuter matrices, we refer to as eigentravel matrices, that capture a commuter's characteristic travel routine. Comparing various classification models, we show that the gradient boosting method gives the best prediction with 76% accuracy, 81% better than the minimum model accuracy (42%) computed using proportional chance criterion.
Feb 28, 2017
Impacts of land use and amenities on public transport use, urban planning and design
In this work, we particularly focus on the complex relationship between land-use and transport offering an innovative approach to the problem by using land-use features at two differing levels of granularity (the more general land-use sector types and the more granular amenity structures) to evaluate their impact on public transit ridership in both time and space. To quantify the interdependencies, we explored three machine learning models and demonstrate that the decision tree model performs best in terms of overall performance—good predictive accuracy, generality, computational efficiency, and “interpretability”.
Nov 30, 2016