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NETWORK EQUILIBRIUM UNDER CUMULATIVE PROSPECT THEORY WITH ENDOGENOUS STOCHASTIC DEMAND & SUPPLY
Dr. Agachai SumaleeHong Kong Polytechnic University
Based on the paper Sumalee et al (2009) presented at ISTTT
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Two-link Network
TT on route 1
TT on route 2
How do drivers choose between routes with uncertain travel times?
Origin Destination
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Approaches
Mean Travel Time ⇒ Route 1Route 1
Route 2
Prob Late Arrival ⇒ Route 2
Mean + stdDevMean + Variance95th Percentile of TT distribution…
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Empirical Evidence leads to PT
• Choice modellers have shown that decisions under uncertainty do not conform to maximising the expected benefit.
• The empirical evidence suggests two major modifications to ‘Expected Utility Maximisation’:
• the carriers of value are gains/losses relative to a reference point
• the value of each outcome is multiplied by a decision weight, not by an additive probability.
This generalisation of EUmax is Prospect Theory
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Context
• A traffic network has many sources of variability in demand and supply: results in Travel Time Variability.
• Drivers are aware that travel times are uncertain and include this TTV in their decision making.
• Senbil, M., & Kitamura, R., (2004). Reference points in commuter departure time choice: a prospect theoretic test of alternative decision frames. Journal of Intelligent Transportation Systems 8, 19–31
• Jou, R.C., Kitamura, R., Weng, M.C., Chen, C.C., (2008). Dynamic commuter departure time choice under uncertainty. Transportation Research Part A 42(5), 774-783.
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Empirical evidence
Senbil and Kitamura (2004)
7/280 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
probability
Decision Weight Transform
Gain WeightsLoss Weights
Prospect Theory Transformations
The fundamental embodiment (and impact) of PT is through the following two transformations:
Value Function g(.) Probability Weighting Function w(.)
Reference Point
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Reference Dependence
Empirical studies show that people’s preferences over finaloutcomes depend on the reference point from which they arejudged:Kahneman & Tversky (1979) for decision under risk
Kahneman et al (1990) for choice among commodity bundles
Loewenstein & Prelec (1992) for inter-temporal choice
Bateman et al (1997) for contingent valuation
Dolan & Robinson (2001) for welfare theory
Bleichrodt & Pinto (2002) for multi-attribute utility
Senbil&Kitamura (2004) for departure time choice
Jou, Kitamura et al (2008) for departure time choice
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Travel Time → Utility
If we have a distribution of path costs
It is convenient to consider the utility ( )( )~ ,k k kC N c σx
( )k dest k dest k kU U C U c ε= − = − −x
Travel Time Distribution
Utility Distribution
LossesGains Losses Gains
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Applying the CPT Transforms
Weighting FunctionMaps Probabilities
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
0.1
0.2
0.3
0.4
0.5
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0.7
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1
probability
Decision Weight Transform
Gain WeightsLoss Weights
Value Function Maps Outcome Utilities
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CPT Transformations
Utility: Cumulative Distribution
Losses Gains
12/28Losses Gains
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Cumulative Prospect Value
( )( ) ( )0u
Udw F ug u du
du−∞
+ ⋅∫( )( ) ( )
0
1 U
u
dw F ucpv g u du
du
∞ −= − ⋅∫
CPV for continuous distribution of outcomes
FU (u) = Path Utility CDF (the outcome distribution)g(.) = Value Functionw(.) = Weight Functionu0 = Reference Point
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Two-link Network
How do drivers choose between routes with uncertain travel times?
Origin Destination
Choose between alternatives according to CPV
CPV = 0.5
CPV = -1.0
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Making Variability Endogenous
• Part B Paper Stochastic link travel times with exogenously defined distribution
Reference point (and other CPT parameters) assumed
Compute equilibrium: show dependence on RefPt and other parameters
• Extension here – to endogenize variability Stochastic OD demand…
…split into path flows via ratio of means f/q hence stochastic path flows
Gives rise to stochastic link flows (with correlations)
Independently stochastic link capacities
Can compute resulting stochastic travel times (with correlations)
CPT applied to route choice
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Stochastic Demand
Lognormal Demand Q ~ LN(μ,σ) with mean q
Path Flows Lognormal Distributed Fk ~ LN(μk,σk)
Conservation condition on means:
Gives stochastic, correlated link flows.k
kq f= ∑
Split Q into path flows Fk = (fk/q)Q
Where fk is mean flow on path k
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Stochastic Supply
Link capacities are assumed to be independent random variables
01
an
aa a
a
XT t bC
= +
With Ca ~ LN(μca,σca) and independent of Xa
We therefore have non-trivial covariance matrices for link flows and link travel times.
Path costs arise from standard link-additive model.
BUT CPV is not link-additive!
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Equilibrium Condition
With cpvk(x) the flow dependent CPV on route k we have that (demand feasible) f* is a CPV-UE if and only if:
( ) ( )* * 0T
F− ≤ ∀ ∈cpv f f f f
( ) min G∈Ωf
f
( ) ( ) ( )( )max T TG∈Ω
= ⋅ − ⋅g
f cpv f g cpv f f
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Equilibrium assignment algorithm
• Generate path set. Find initial feasible mean path flow f
• Compute travel time distribution for each path.
• Compute prospect value for each path & find ‘best’ path
• Assign mean OD flow to best path; gives auxiliary flow g
• Step from f toward g (as in MSA)
• Test convergence
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Nguyen and Dupuis Network
1 12
5 6 4 7 8
9 10 11 2
13 3
1
2
18 17
5 3 9 7
8 6 11 10
14 12
16 13
4
19
15
Destination
Destination
Origin
Orig¥Dest 2 31 1000 (0.2) 800 (0.25)4 1500 (0.2) 1000 (0.25)
OD demand (CoV)
Origin
Link Capacities CoV = 0.1Reference Points = 100 (initially)
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22/280 10 20 30 40 50 60 70 80 90 100300
350
400
450
500
550
600
650
700
750
Iteration k
Mea
n flo
w o
n pa
th 1
Reference point = 100Reference point = 160Reference point = 200
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1 12
5 6 4 7 8
9 10 11 2
13 3
1
2
18 17
5 3 9 7
8 6 11 10
14 12
16 13
4
19
15
Destination
Destination
Origin
Origin
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1 12
5 6 4 7 8
9 10 11 2
13 3
1
2
18 17
5 3 9 7
8 6 11 10
14 12
16 13
4
19
15
Destination
Destination
Origin
Origin
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Summary
Cumulative Prospect Theory as paradigm for route choice with stochastic travel times.
Continuous formulation of cumulative prospect theory presented.Equilibrium condition defined. Potential future studies: Solve for equilibrium efficiently and consider larger networks Path based criterion so needs thought
Network inference (estimation) from the observed link/path data Investigate impact of assuming CPV over E[Umax] or ‘reliability’ terms How is UE recovered from CPV in the limit?
Define reference point via a day-to-day learning process? Initial reference point updated by experience, new alternatives added to choice set
as reference point changes? Does this converge? To a sensible solution? Is this process stable?