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Hybrid Agent Based Simulation With Adaptive Learning Of Travel Mode Choices For University Commuters (Wip), Nagesh Shukla, Albert Munoz, Jun Ma, Nam Huynh
Hybrid Agent Based Simulation With Adaptive Learning Of Travel Mode Choices For University Commuters (Wip), Nagesh Shukla, Albert Munoz, Jun Ma, Nam Huynh
Albert Munoz
This paper presents a methodology for developing a hybrid agent-based micro-simulation model to capture the impacts of commuter travel mode choices on a University campus transport network. The proposed methodology involves: (i) developing realistic population of commuter agents (students and staff); (ii) assigning activity lists and travel mode choices to agents using machine learning method; and, (iii) traffic micro-simulation of the study area transport network. This furthers the understanding of current transport modal distributions, factors affecting the travel mode choice decisions, and, network performance through a number of hypothetical travel scenarios.
Topic And Role Discovery In Social Networks With Experiments On Enron And Academic Email, Andrew Mccallum, Xuerui Wang, Andrés Corrada-Emmanuel
Topic And Role Discovery In Social Networks With Experiments On Enron And Academic Email, Andrew Mccallum, Xuerui Wang, Andrés Corrada-Emmanuel
Andrés Corrada-Emmanuel
Previous work in social network analysis (SNA) has modeled the existence of links from one entity to another, but not the attributes such as language content or topics on those links. We present the Author-Recipient-Topic (ART) model for social network analysis, which learns topic distributions based on the direction-sensitive messages sent between entities. The model builds on Latent Dirichlet Allocation (LDA) and the Author-Topic (AT) model, adding the key attribute that distribution over topics is conditioned distinctly on both the sender and recipient---steering the discovery of topics according to the relationships between people. We give results on both the Enron …