Decision-Aid Methodologies in Transportation Choice Theory Matthieu - - PowerPoint PPT Presentation

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Decision-Aid Methodologies in Transportation Choice Theory Matthieu - - PowerPoint PPT Presentation

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Decision-Aid Methodologies in Transportation Choice Theory Matthieu de Lapparent Transport and Mobility Laboratory, School of Architecture, Civil and Environmental Engineering, Ecole Polytechnique F ed erale de Lausanne 26 April 2016

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Decision-Aid Methodologies in Transportation

Choice Theory Matthieu de Lapparent

Transport and Mobility Laboratory, School of Architecture, Civil and Environmental Engineering, Ecole Polytechnique F´ ed´ erale de Lausanne

26 April 2016

Transport and Mobility Laboratory Decision-Aid Methodologies 26 April 2016 1 / 36

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Outline

1

Choice theory foundations

2

Consumer theory

3

Simple example

4

Random utility theory

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Choice theory foundations

Choice theory

Choice: outcome of a sequential decision-making process Definition of the choice problem: How do I get to EPFL? Generation of alternatives: Car as driver, car as passenger, train, bicycle, walk... Evaluation of the attributes of the alternatives: Price, time, flexibility, reliability, comfort Choice: Decision rule Implementation: Travel

Transport and Mobility Laboratory Decision-Aid Methodologies 26 April 2016 3 / 36

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Choice theory foundations

Building the theory

A choice theory defines

1 Decision maker 2 Alternatives 3 Attributes of alternatives 4 Decision rule Transport and Mobility Laboratory Decision-Aid Methodologies 26 April 2016 4 / 36

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Choice theory foundations

Decision maker

Unit of analysis Individual

Socio-economic characteristics: age, gender, income, education, etc.

A group of persons (ignoring within-group peer effects)

Household, firm, government agency Group characteristics

Notation: subscript n

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Choice theory foundations

Alternatives

Choice set Mutually exclusive, finite, exhaustive set of alternatives Universal choice set C Individual n: choice set Cn ⊆ C Availability, awareness, feasibility, consideration Example: Choice of transport mode C = {car, bus, metro, walk} ...traveller has no drivers licence, trip is 12km long Cn = {bus, metro}

Swait, J. (1984) Probabilistic Choice Set Formation in Transportation Demand Models Ph.D. dissertation, Department

  • f Civil Engineering, MIT, Cambridge, Ma.

Transport and Mobility Laboratory Decision-Aid Methodologies 26 April 2016 6 / 36

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Choice theory foundations

Continuous choice set

Microeconomic demand analysis Commodity bundle q1: quantity of milk q2: quantity of bread q3: quantity of butter Unit price: pi Budget: I q1 q2 q3 p1q1 + p2q2 + p3q3 = I

Transport and Mobility Laboratory Decision-Aid Methodologies 26 April 2016 7 / 36

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Choice theory foundations

Discrete choice set

Discrete choice analysis List of alternatives Brand A Brand B Brand C A B C

  • Transport and Mobility Laboratory

Decision-Aid Methodologies 26 April 2016 8 / 36

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Choice theory foundations

Alternative attributes

Characterize each alternative i for each individual n ➜ cost ➜ travel time ➜ walking time ➜ comfort ➜ bus frequency ➜ etc. Nature of the variables ✔ Generic or specific ✔ Quantitative or qualitative ✔ Measured or perceived

Transport and Mobility Laboratory Decision-Aid Methodologies 26 April 2016 9 / 36

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Choice theory foundations

Decision rules

Standard microeconomic decision-maker Full knowledge of options, context and environment Organized and stable system of preferences Evaluates each alternative and assigns precise pay-off (measured through the utility index) Selects alternative with highest pay-off Utility Captures attractiveness of alternative Allows ranking (ordering) of alternatives What decision maker optimizes

Transport and Mobility Laboratory Decision-Aid Methodologies 26 April 2016 10 / 36

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Choice theory foundations

A matter of viewpoints

Individual perspective

Individual possesses perfect information and discrimination capacity

Modeler perspective

Modeler does not have full information about choice process Treats the utility as a random variable At the core of the concept of ’random utility’

Transport and Mobility Laboratory Decision-Aid Methodologies 26 April 2016 11 / 36

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Consumer theory

Consumer theory

Neoclassical consumer theory Underlies mathematical analysis of preferences Allows us to transform ’attractiveness rankings’... into operational demand functions Keep in mind Utility is a latent concept It cannot be directly observed

Figure: Jeremy Bentham

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Consumer theory

Consumer theory

Continuous choice set Consumption bundle Q =    q1 . . . qL    ; p =    p1 . . . pL    Budget constraint

L

  • ℓ=1

pℓqℓ ≤ I. No attributes, just quantities

Transport and Mobility Laboratory Decision-Aid Methodologies 26 April 2016 13 / 36

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Consumer theory

Preferences

Operators ≻, ∼, and Qa ≻ Qb: Qa is preferred to Qb, Qa ∼ Qb: indifference between Qa and Qb, Qa Qb: Qa is at least as preferred as Qb. To ensure consistent ranking Completeness: for all bundles a and b, Qa ≻ Qb or Qa ≺ Qb or Qa ∼ Qb. Transitivity: for all bundles a, b and c, if Qa Qb and Qb Qc then Qa Qc. “Continuity”: if Qa is preferred to Qb and Qc is arbitrarily “close” to Qa, then Qc is preferred to Qb.

Transport and Mobility Laboratory Decision-Aid Methodologies 26 April 2016 14 / 36

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Consumer theory

Preferences, cont.

further non necessary assumptions to have a “well-behaved” utility function: monotonicity, non satiety, convexity

Transport and Mobility Laboratory Decision-Aid Methodologies 26 April 2016 15 / 36

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Consumer theory

Utility

Utility function Parametrized function:

  • U =

U(q1, . . . , qL; θ) = U(Q; θ) Consistent with the preference indicator:

  • U(Qa; θ) ≥

U(Qb; θ) is equivalent to Qa Qb. Unique up to an order-preserving transformation

Transport and Mobility Laboratory Decision-Aid Methodologies 26 April 2016 16 / 36

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Consumer theory

Optimization problem

Optimization Decision-maker solves the optimization problem maxq∈RL U(q1, . . . , qL) subject to the budget (available income) constraint

L

  • i=1

piqi = I. Demand Quantity is a function of prices and budget q∗ = f (I, p; θ)

Transport and Mobility Laboratory Decision-Aid Methodologies 26 April 2016 17 / 36

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Consumer theory

Optimization problem

max

q1,q2 U = β0qβ1 1 qβ2 2

subject to p1q1 + p2q2 = I. Lagrangian of the problem: L(q1, q2, λ) = β0qβ1

1 qβ2 2 − λ(p1q1 + p2q2 − I).

Necessary optimality condition ∇L(q1, q2, λ) = 0

where λ is the Lagrange multiplier and β’s are the Cobb-Douglas preference parameters

Transport and Mobility Laboratory Decision-Aid Methodologies 26 April 2016 18 / 36

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Consumer theory

Framework

Optimality conditions Lagrangian is differentiated to obtain the first order conditions ∂L/∂q1 = β0β1qβ1−1

1

qβ2

2

− λp1 = ∂L/∂q2 = β0β2qβ1

1 qβ2−1 2

− λp2 = ∂L/∂λ = p1q1 + p2q2 − I = We have β0β1qβ1

1 qβ2 2

− λp1q1 = β0β2qβ1

1 qβ2 2

− λp2q2 = Adding the two and using the third optimality condition λI = β0qβ1

1 qβ2 2 (β1 + β2)

Transport and Mobility Laboratory Decision-Aid Methodologies 26 April 2016 19 / 36

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Consumer theory

Framework

Equivalent to β0qβ1

1 qβ2 2 =

λI (β1 + β2) As β0β2qβ1

1 qβ2 2 = λp2q2, we obtain (assuming λ = 0)

q∗

2 =

Iβ2 p2(β1 + β2) Similarly, we obtain q∗

1 =

Iβ1 p1(β1 + β2)

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Consumer theory

Demand functions

Product 1 q∗

1 = I

p1 β1 β1 + β2 Product 2 q∗

2 = I

p2 β2 β1 + β2 Comments Demand decreases with price Demand increases with budget Demand independent of β0, which does not affect the ranking

Transport and Mobility Laboratory Decision-Aid Methodologies 26 April 2016 21 / 36

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Consumer theory

Marginal rate of substitution

Factoring out λ from first order conditions we get p1 p2 = ∂U(q∗)/∂q1 ∂U(q∗)/∂q2 = MU(q1) MU(q2) MRS Ratio of marginal utilities (right) equals... ratio of prices of the 2 goods (left) Holds if consumer is making optimal choices

Transport and Mobility Laboratory Decision-Aid Methodologies 26 April 2016 22 / 36

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Consumer theory

Discrete goods

Discrete choice set Calculus cannot be used anymore U = U(q1, . . . , qL) with qi = 1 if product i is chosen

  • therwise

and

  • i

qi = 1.

Transport and Mobility Laboratory Decision-Aid Methodologies 26 April 2016 23 / 36

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Consumer theory

Framework

Do not work with demand functions anymore Work with utility functions U is the “global” utility Define Ui the utility associated with product i. It is a function of the attributes of the product (price, quality, etc.) We say that product i is chosen if Ui ≥ Uj ∀j.

Transport and Mobility Laboratory Decision-Aid Methodologies 26 April 2016 24 / 36

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Simple example

Simple example: mode choice

Attributes Attributes Alternatives Travel time (t) Travel cost (c) Car (1) t1 c1 Train (2) t2 c2 Utility

  • U =

U(y1, y2), where we impose the restrictions that, for i = 1, 2, yi = 1 if travel alternative i is chosen,

  • therwise;

and that only one alternative is chosen: y1 + y2 = 1.

Transport and Mobility Laboratory Decision-Aid Methodologies 26 April 2016 25 / 36

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Simple example

Simple example: mode choice

Utility functions U1 = −βtt1 − βcc1, U2 = −βtt2 − βcc2, where βt > 0 and βc > 0 are parameters. Equivalent specification U1 = −(βt/βc)t1 − c1 = −βt1 − c1 U2 = −(βt/βc)t2 − c2 = −βt2 − c2 where β > 0 is a parameter. Choice Alternative 1 is chosen if U1 ≥ U2. Ties are ignored (note: the probability that it occurs is uniformly equal to 0 because Us are continuous functions).

Transport and Mobility Laboratory Decision-Aid Methodologies 26 April 2016 26 / 36

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Simple example

Simple example: mode choice

Choice Alternative 1 is chosen if −βt1 − c1 ≥ −βt2 − c2

  • r

−β(t1 − t2) ≥ c1 − c2 Alternative 2 is chosen if −βt1 − c1 ≤ −βt2 − c2

  • r

−β(t1 − t2) ≤ c1 − c2 Dominated alternative If c2 > c1 and t2 > t1, U1 > U2 for any β > 0 If c1 > c2 and t1 > t2, U2 > U1 for any β > 0

Transport and Mobility Laboratory Decision-Aid Methodologies 26 April 2016 27 / 36

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Simple example

Simple example: mode choice

Trade-off Assume c2 > c1 and t1 > t2. Is the traveler willing to pay the extra cost c2 − c1 to save the extra time t1 − t2? Alternative 2 is chosen if −β(t1 − t2) ≤ c1 − c2

  • r

β ≥ c2 − c1 t1 − t2 β is called the willingness to pay or value of time

Transport and Mobility Laboratory Decision-Aid Methodologies 26 April 2016 28 / 36

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Simple example

Dominated choice example

Obvious cases: c1 ≥ c2 and t1 ≥ t2: 2 dominates 1. c2 ≥ c1 and t2 ≥ t1: 1 dominates 2. Trade-offs in over quadrants

Transport and Mobility Laboratory Decision-Aid Methodologies 26 April 2016 29 / 36

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Simple example

Illustration

  • 4
  • 2

2 4

  • 4
  • 2

2 4 cost by car-cost by train time by car-time by train car is chosen train is chosen car is chosen train is chosen Transport and Mobility Laboratory Decision-Aid Methodologies 26 April 2016 30 / 36

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Simple example

Illustration with real data

  • 4
  • 2

2 4

  • 4
  • 2

2 4 cost by car-cost by train time by car-time by train car is chosen train is chosen Transport and Mobility Laboratory Decision-Aid Methodologies 26 April 2016 31 / 36

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Simple example

Is utility maximization a behaviorally valid assumption?

Assumptions Decision-makers have preferences in line with classical consumer theory are able to process full information have perfect discrimination power have perfect knowledge are perfect maximizer are always consistent

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Random utility theory

Introducing probability

Constant utility Human behavior is inherently random Utility is deterministic Consumer does not maximize utility Probability to use inferior alternative is non zero Random utility Decision-maker are rational maximizers Analysts have no access to the utility used by the decision-maker Utility becomes a random variable Niels Bohr ”Nature is stochastic” Einstein ”God does not throw dice”

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Random utility theory

Assumptions

Sources of uncertainty ☞ Unobserved attributes ☞ Unobserved taste variations ☞ Measurement errors ☞ Instrumental variables

Manski 1973 The structure of Random Utility Models Theory and Decision 8:229–254

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Random utility theory

Random utility maximization

Probabilistic setup Use a probabilistic approach: what is the probability that alternative i is chosen? What is the probability that alternative i is the one that gives maximum utility?

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Random utility theory

Random utility model

Probability model P(i|Cn) = Pr(Uin ≥ Ujn, all j ∈ Cn), Random utility Uin = Vin + εin. Random utility model P(i|Cn) = Pr(Vin + εin ≥ Vjn + εjn, all j ∈ Cn),

  • r

P(i|Cn) = Pr(εjn − εin ≤ Vin − Vjn, all j ∈ Cn).

Transport and Mobility Laboratory Decision-Aid Methodologies 26 April 2016 36 / 36