MIT1_201JF08_lec03

MIT1_201JF08_lec03 - Discrete Choice Analysis I Moshe...

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Unformatted text preview: Discrete Choice Analysis I Moshe Ben-Akiva 1.201 / 11.545 / ESD.210 Transportation Systems Analysis: Demand & Economics Fall 2008 Outline of 2 Lectures on Discrete Choice ● Introduction ● A Simple Example ● The Random Utility Model ● Specification and Estimation ● Forecasting ● IIA Property ● Nested Logit 2 Outline of this Lecture ● Introduction ● A simple example – route choice ● The Random Utility Model – Systematic utility – Random components ● Derivation of the Probit and Logit models – Binary Probit – Binary Logit – Multinomial Logit 3 Continuous vs. Discrete Goods Continuous Goods Discrete Goods x 2 Indifference curves u 1 u 2 u 3 auto x 1 bus 4 Discrete Choice Framework ● Decision-Maker – Individual (person/household) – Socio-economic characteristics (e.g. Age, gender,income, vehicle ownership) ● Alternatives – Decision-maker n selects one and only one alternative from a choice set C n ={1,2,…,i,…,J n } with J n alternatives ● Attributes of alternatives (e.g.Travel time, cost) ● Decision Rule – Dominance, satisfaction, utility etc. 5 Choice: Travel Mode to Work • Decision maker: an individual worker • Choice: whether to drive to work or take the bus to work • Goods: bus, auto • Utility function: U ( X ) = U (bus, auto) • Consumption bundles: {1,0} (person takes bus) {0,1} (person drives) 6 Consumer Choice • Consumers maximize utility – Choose the alternative that has the maximum utility (and falls within the income constraint) If U (bus) > U (auto) choose bus If U (bus) < U (auto) choose auto U (bus)=? U (auto)=? 7 Constructing the Utility Function ● U (bus) = U (walk time, in-vehicle time, fare, …) U (auto) = U (travel time, parking cost, …) ● Assume linear (in the parameters) U (bus) = β 1 × (walk time) + β 2 × (in-vehicle time) + … ● Parameters represent tastes, which may vary over people. – Include socio-economic characteristics (e.g., age, gender, income) – U (bus) = β 1 × (walk time) + β 2 × (in-vehicle time) + β 3 × (cost/income) + … 8 Deterministic Binary Choice ● If U (bus) - U (auto) > 0 , Probability(bus) = 1 If U (bus) - U (auto) < 0 , Probability(bus) = 0 0 U (bus)- U (auto) P (bus) 0 1 9 Probabilistic Choice ● Random utility model U i = V (attributes of i ; parameters) + epsilon i ● What is in the epsilon?...
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This note was uploaded on 12/04/2011 for the course ESD 1.210j taught by Professor Mosheben-akiva during the Fall '08 term at MIT.

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MIT1_201JF08_lec03 - Discrete Choice Analysis I Moshe...

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