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What is the purpose of Trip Distribution in urban transport planning?
Which step of the 4-step Transport Model involves the distribution of travel across available transport modes in a network?
What does the Multinomial Logit Model aim to model in urban transport planning?
In what decision contexts do individuals make mode choices according to the text?
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In utility theory, how can utility be represented?
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What do alternative specific attributes in utility theory refer to?
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What are the three types of parameters in utility theory?
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What do alternative specific constants account for in utility theory?
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What do generic parameters assign weights to in utility theory?
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What is the 'brand effect' referred to in utility theory?
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What is the general thumb rule provided for the application of alternative specific constants and generic parameters?
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What type of survey is the utility theory in urban transport planning practice applied to in the text?
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What is the function of generic parameters in utility theory?
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What is the purpose of alternative specific parameters in utility theory?
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What does the utility theory in urban transport planning practice use an example dataset for?
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How is utility expressed in utility theory?
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In the context of utility theory, what is the significance of the reference category 'SG' in the utility calculations for iPhone, Samsung, and Google phones?
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What impact does an increase in the costs of iPhone, Samsung, and Google phones have on utility, as mentioned in the text?
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According to the text, what is the purpose of alternative specific parameters in utility theory?
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What insight does the text provide about the utility values for iPhone, Samsung, and Google phones when all attributes are zero?
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What does the text mention about the process of obtaining parameter values for utility theory?
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According to the text, what does the Rational Utility Maximization (RUM) concept state?
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What is the significance of the error term in probabilistic choice modeling, as discussed in the text?
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Which distribution is associated with the Logit model in probabilistic choice modeling, as explained in the text?
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What is emphasized regarding the parameters for only J-1 alternatives in utility theory?
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What does the text mention about the utility specifications in utility theory?
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According to the text, what impact does gender have on the utility values for iPhone, Samsung, and Google phones?
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What does the Multinomial Logit (MNL) Model aim to model in urban transport planning?
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In the context of utility theory, how can utility be represented?
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What is the 'brand effect' referred to in utility theory?
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Which distribution is associated with the Logit model in probabilistic choice modeling?
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What is the purpose of Trip Distribution in urban transport planning?
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What impact does an increase in the costs of iPhone, Samsung, and Google phones have on utility, as mentioned in the text?
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What is emphasized regarding the parameters for only J-1 alternatives in utility theory?
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What do alternative specific constants account for in utility theory?
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What does the text mention about the process of obtaining parameter values for utility theory?
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What is the function of generic parameters in utility theory?
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What does the text mention about the utility specifications in utility theory?
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What insight does the text provide about the utility values for iPhone, Samsung, and Google phones when all attributes are zero?
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What is the primary purpose of mode choice analysis in urban transport planning?
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What does the Discrete Choice Modelling Framework aim to achieve?
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What is the role of utility theory in discrete choice modelling?
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What does Random Utility Maximisation (RUM) from consumer theory state?
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What do utility functions express in discrete choice modelling?
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What is an example of discrete choice modelling presented in the text?
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What are the factors affecting mode choice according to the text?
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What does mode choice analysis help to forecast in urban transport planning?
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What is the significance of estimating travel demand for each mode in mode choice analysis?
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What is the primary limitation of linear regression in the context of multinomial variables?
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What does the Discrete Choice Modelling Framework aim to develop?
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What is the role of RUM (Random Utility Maximisation) in consumer theory?
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What is the purpose of Maximum Likelihood Estimation (MLE) in estimating the parameters of a logit model?
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What does Rho-squared assess in the context of the logit model's goodness of fit?
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What is the purpose of the log-likelihood expression in the context of a logit model?
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What software is used to obtain the logit model's estimated parameters?
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What percentage of unobserved variation in the dataset does the sample result indicate the model is able to explain?
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What does the Equivalent Differences Property involve in the context of utilities for different modes of transportation?
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What are the base utilities for auto, rail, and bus, respectively?
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What does the log-likelihood function aim to maximize in the context of a logit model?
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What is the significance of a value closer to 1 for rho-squared in assessing the goodness of fit of the logit model?
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What type of choice does an example of MLE involve in the context of a logit model?
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What does the logit model's estimated parameters estimation involve?
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Study Notes
Utility Theory and Probabilistic Choice Modeling in Urban Transport Planning Practice
- The text discusses utility theory and probabilistic choice modeling in the context of urban transport planning practice.
- It presents mathematical equations and parameters related to utility theory and specific examples of utility value calculations for different alternatives.
- The reference category "SG" is used as a benchmark in the utility calculations for iPhone, Samsung, and Google phones.
- It highlights that even when all attributes are zero, iPhone11 has higher utility compared to Samsung, and Google has lower utility compared to Samsung.
- The text mentions that an increase in the costs of iPhone, Samsung, and Google phones decreases the utility.
- It introduces alternative specific parameters and emphasizes that parameters for only J-1 alternatives can be determined, with the value for one alternative fixed to zero as the reference category.
- It provides a specific example of alternative specific parameters with utility values for iPhone, Samsung, and Google phones and emphasizes the impact of costs on utility.
- It mentions that males get higher utility from iPhone compared to Samsung, and lower utility from Google compared to Samsung.
- The text indicates that the process of obtaining parameter values will be addressed shortly and advises to avoid certain utility specifications.
- It explains the Rational Utility Maximization (RUM) concept, stating that an individual will select an option based on observed utility and an error term.
- It discusses the probabilistic choice modeling and the importance of the error term in differentiating between observed utilities.
- It explains the mathematical form of a probabilistic model, including the Gumbel distribution for the Logit model and the Normal distribution for the Probit model.
Multinomial Logit (MNL) Model for Urban Transport Planning
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RUM (Random Utility Model) states that individual 𝑛𝑛 selects alternative 1 if 𝑼𝑼𝒏𝒏𝟏𝟏 > 𝑼𝑼𝒏𝒏𝟐𝟐, which translates to 𝑽𝑽𝒏𝒏𝟏𝟏 + 𝜺𝜺𝒏𝒏𝟏𝟏 > 𝑽𝑽𝒏𝒏𝟐𝟐 + 𝜺𝜺𝒏𝒏𝟐𝟐.
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The probability that individual 𝑛𝑛 selects alternative 1 is represented as 𝑃𝑃𝑛𝑛 𝑗𝑗 = 1, where 𝑗𝑗 = 1.
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The CDF of Gumbel distribution is used in the formula, where 𝑥𝑥 = 𝑉𝑉𝑛𝑛𝑛 − 𝑉𝑉𝑛𝑛𝑛 + 𝜀𝜀𝑛𝑛𝑛.
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The PDF of Gumbel distribution is represented as 𝑓𝑓 𝜀𝜀𝑛𝑛𝑛, where 𝑥𝑥 = 𝜀𝜀𝑛𝑛𝑛.
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The MNL model is derived using integration by substitution, with 𝑒𝑒 −𝜀𝜀𝑛𝑛𝑛 = 𝑡𝑡 and subsequent calculations.
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The Multinomial Logit (MNL) Model is used for mode split decisions in urban transport planning.
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An example involving choosing between driving a private vehicle (auto) and catching a bus is presented to illustrate the MNL model.
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The utilities of the two alternative modes are represented relative to travel time, with the equation 𝑈𝑈𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 = 𝑉𝑉𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 + 𝜀𝜀𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 = 1 − 2𝑡𝑡𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 + 𝜀𝜀𝑎𝑎𝑎𝑎𝑎𝑎Urban Transport Planning Practice: Multinomial Logit (MNL) Model
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Course: CVEN 9405: Urban Transport Planning Practice
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Model: Multinomial Logit (MNL)
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Equation: 𝑈𝑈𝑏𝑏𝑏𝑏𝑏𝑏 = 𝑉𝑉𝑏𝑏𝑏𝑏𝑏𝑏 + 𝜀𝜀𝑏𝑏𝑏𝑏𝑏𝑏 = −3𝑡𝑡𝑏𝑏𝑏𝑏𝑏𝑏 + 𝜀𝜀𝑏𝑏𝑏𝑏𝑏𝑏
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Parameters: 0.8 P(auto), 1.2, 0.6, 0.4, 0.2
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Utility Difference: Difference in travel time (t_auto - t_bus) = 33
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Property: Equivalent Differences Property
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Options: Consider 2 options A and B with utilities 𝑈𝑈𝐴𝐴 and 𝑈𝑈𝐵𝐵
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Interpretation: -10, 0, 5
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Application: Urban transport planning
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Context: Urban transport planning practice
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Focus: Analysis of travel time and mode choice
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Relevance: Practical implications for urban transport planning
Estimation of Logit Models and Goodness of Fit
- The Equivalent Differences Property involves an increase in utilities for different modes of transportation.
- Base utilities for auto, rail, and bus are 0.690, 0.254, and 0.057, respectively.
- Maximum Likelihood Estimation (MLE) is used to estimate the parameters of a logit model using a given dataset.
- MLE involves finding parameter values that maximize the probability of observing the given data points.
- The log-likelihood function is maximized by changing parameter values using the dataset.
- An example of MLE involves estimating a logit model of binary choice between car and bus based on observed utilities and travel times.
- The log-likelihood expression calculates the joint probability of observed choices made by individuals in the dataset.
- The logit model's estimated parameters are obtained using a software called Biogeme.
- Rho-squared is used to assess the overall goodness of fit of the logit model, similar to R-squared in linear regression.
- A value closer to 1 for rho-squared indicates a better model, showing significant difference relative to the no-information model.
- In the sample result, the model is able to explain 13.6 percent of unobserved variation in the dataset.
- Rho-squared is used to evaluate the model's ability to explain unobserved variation in the dataset.
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Description
Urban Transport Planning and Choice Modeling Quiz: Test your knowledge of utility theory, probabilistic choice modeling, and the Multinomial Logit (MNL) model in the context of urban transport planning practice. Explore concepts such as Rational Utility Maximization, Gumbel distribution, and Maximum Likelihood Estimation (MLE), and their application in analyzing travel time, mode choice, and goodness of fit in transport planning scenarios.