Real-Time Inference of Urban Metrics Applying Machine Learning to an Agent-Based Model Coupling Mobility Mode and Housing Choice

2022 ◽  
pp. 125-138
Author(s):  
Mireia Yurrita ◽  
Arnaud Grignard ◽  
Luis Alonso ◽  
Kent Larson
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Model Coupling ◽  
Housing Choice ◽  
Agent Based Model ◽  
Agent Based ◽  
Urban Metrics

scholarly journals Dynamic Urban Planning: An Agent-Based Model Coupling Mobility Mode and Housing Choice. Use Case Kendall Square

2021 ◽  
pp. 940-951
Author(s):  
Mireia Yurrita ◽  
Arnaud Grignard ◽  
Luis Alonso ◽  
Yan Zhang ◽  
Cristian Ignacio Jara-Figueroa ◽  
...  
Keyword(s):  
Urban Planning ◽  
Model Coupling ◽  
Use Case ◽  
Housing Choice ◽  
Agent Based Model ◽  
Agent Based

An agent-based model for real-time bus stop-skipping and holding schemes

2020 ◽  
pp. 1-33 ◽  
Author(s):  
Lele Zhang ◽  
Jiangyan Huang ◽  
Zhiyuan Liu ◽  
Hai L. Vu
Keyword(s):  
Real Time ◽  
Agent Based Model ◽  
Agent Based ◽  
Bus Stop

Tax optimization in an agent-based model of real-time spectrum secondary market

2016 ◽  
Vol 64 (3) ◽  
pp. 543-558 ◽  
Author(s):  
Juraj Gazda ◽  
Viliam Kováč ◽  
Peter Tóth ◽  
Peter Drotár ◽  
Vladimír Gazda
Keyword(s):  
Real Time ◽  
Time Spectrum ◽  
Secondary Market ◽  
Agent Based Model ◽  
Agent Based

scholarly journals Real-time Analysis of City Scale Transportation Networks in New Orleans Metropolitan Area using an Agent Based Model Approach

2019 ◽  
Vol 271 ◽  
pp. 06007
Author(s):  
Millard McElwee ◽  
Bingyu Zhao ◽  
Kenichi Soga
Keyword(s):  
New Orleans ◽  
Real Time ◽  
High Performance ◽  
Large Scale ◽  
Transportation Networks ◽  
Transportation Network ◽  
Regional Scale ◽  
Agent Based Model ◽  
Agent Based ◽  
Real Time Analysis

The primary focus of this research is to develop and implement an agent-based model (ABM) to analyze the New Orleans Metropolitan transportation network near real-time. ABMs have grown in popularity because of their ability to analyze multifaceted community scale resilience with hundreds of thousands of links and millions of agents. Road closures and reduction in capacities are examples of influences on the weights or removal of edges which can affect the travel time, speed, and route of agents in the transportation model. Recent advances in high-performance computing (HPC) have made modeling networks on the city scale much less computationally intensive. We introduce an open-source ABM which utilizes parallel distributed computing to enable faster convergence to large scale problems. We simulate 50,000 agents on the entire southeastern Louisiana road network and part of Mississippi as well. This demonstrates the capability to simulate both city and regional scale transportation networks near real time.

A Prototype Agent Based Model and Machine Learning Hybrid System for Healthcare Decision Support

2012 ◽  
pp. 230-253 ◽  
Author(s):  
Marek Laskowski
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Decision Process ◽  
Patient Flow ◽  
Policy Decision ◽  
Healthcare Policy ◽  
Learning System ◽  
Agent Based Model ◽  
Agent Based ◽  
Markov Decision

Science is on the verge of practical agent based modeling decision support systems capable of machine learning for healthcare policy decision support. The details of integrating an agent based model of a hospital emergency department with a genetic programming machine learning system are presented in this paper. A novel GP heuristic or extension is introduced to better represent the Markov Decision Process that underlies agent decision making in an unknown environment. The capabilities of the resulting prototype for automated hypothesis generation within the context of healthcare policy decision support are demonstrated by automatically generating patient flow and infection spread prevention policies. Finally, some observations are made regarding moving forward from the prototype stage.

scholarly journals Improving Model Predictions—Integration of Real-Time Sensor Data into a Running Simulation of an Agent-Based Model

2021 ◽  
Vol 13 (13) ◽  
pp. 7000
Author(s):  
Ulfia A. Lenfers ◽  
Nima Ahmady-Moghaddam ◽  
Daniel Glake ◽  
Florian Ocker ◽  
Daniel Osterholz ◽  
...  
Keyword(s):  
Real Time ◽  
Real World ◽  
City Planning ◽  
Sensor Data ◽  
City Management ◽  
Proof Of Concept ◽  
Agent Based Model ◽  
Time Data ◽  
Agent Based ◽  
Real Time Data

The current trend towards living in big cities contributes to an increased demand for efficient and sustainable space and resource allocation in urban environments. This leads to enormous pressure for resource minimization in city planning. One pillar of efficient city management is a smart intermodal traffic system. Planning and organizing the various kinds of modes of transport in a complex and dynamically adaptive system such as a city is inherently challenging. By deliberately simplifying reality, models can help decision-makers shape the traffic systems of tomorrow. Meanwhile, Smart City initiatives are investing in sensors to observe and manage many kinds of urban resources, making up a part of the Internet of Things (IoT) that produces massive amounts of data relevant for urban planning and monitoring. We use these new data sources of smart cities by integrating real-time data of IoT sensors in an ongoing simulation. In this sense, the model is a digital twin of its real-world counterpart, being augmented with real-world data. To our knowledge, this is a novel instance of real-time correction during simulation of an agent-based model. The process of creating a valid mapping between model components and real-world objects posed several challenges and offered valuable insights, particularly when studying the interaction between humans and their environment. As a proof-of-concept for our implementation, we designed a showcase with bike rental stations in Hamburg-Harburg, a southern district of Hamburg, Germany. Our objective was to investigate the concept of real-time data correction in agent-based modeling, which we consider to hold great potential for improving the predictive capabilities of models. In particular, we hope that the chosen proof-of-concept informs the ongoing politically supported trends in mobility—away from individual and private transport and towards—in Hamburg.

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