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Traffic flow theory
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Multi-agent transportation simulation
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Distributed computing and truly
Contents
Some background
Subsections
Traffic flow theory
Introduction
Traffic flow measurements
Speed
Flow
Density
Fundamental diagrams
Car following
Reaction time argument for car following
Discrete space and discrete time: Cellular automata rules
Continuous space and continuous time
Discrete time and continuous space car following
Kinematic waves and fluid-dynamics
The Lighthill-Whitham-Richards equation
Linearization
Macroscopic shocks
The deterministic CA in terms of kinematic waves
More advanced fluid-dynamical models
Capacities, especially at bottlenecks
Cost-flow curves for static assignment
Static assignment
Introduction
Equilibrium principle
Beckmann's mathematical programming formulation
Constrained optimization
Uniqueness
Convexity of
Convexity of the feasible region
A solution method
Summary
Discrete choice theory
Introduction
Contents
Binary choice
Systematic vs random component of utility
Choice based on random utilities
Linear decomposition of systematic part of utility
Simple example
2nd example
Probability distributions, generating functions, etc.
Binary Probit (Randomness is Gaussian)
Gumbel distribution
Combination of Gumbel-distributed variables
Logistic distribution
Binary logit (randomness is Gumbel distributed)
Multinomial choice
Multinomial logit (MNL)
Discussion of modeling assumptions
Independence from irrelevant alternatives (IID)
Maximum likelihood estimation
... for binary choice in general
... for binary logit model
Discussion
The beta parameter from earlier
Summary
Axhausen lecture
Learning and feedback
Introduction
Additional aspects of day-to-day learning
Individualization of knowledge
Classifier System and Agent Database
Individual plans storage
Interpretation as dynamical system
Deterministic systems
Stochastic systems
Transients
Relation to game theory
Relation to machine learning
Smart agents and non-predictability
Conclusion
2004-02-02
