DEMAND FORECASTING
AND UNCERTAINTIES
Recitation 4
Professor Joseph Sussman
Regina Clewlow
EeSD
ESD.00
00
MOTIVATION FOR DEMAND MODELING
ƒ Why forecast demand?
ƒ To estimate future demand levels for planning
p p
purposes.
ƒ To analyze proposed projects or policies.
ƒWho does it?
ƒ Transportation planning agencies.
ƒ Private transportation service providers.
MOTIVATION FOR DEMAND MODELING
ƒ What other large-scale
large scale engineering systems might
have a need to forecast demand?
Mobile Phones
Energy
Image courtesy of janie.hernandez55
on Flickr.
Flickr.
Internet
Image courtesy of Dominik
Syka on Flickr.
Image courtesy of Blaise
Alleyne on Flickr.
Water
Image courtesy of Steven
Depolo on Flickr.
TRAVEL DEMAND FORECASTING METHODS
ƒ Econometric “top
top-down
down” forecasting:
ƒ Often used for national, and regional-level forecasts.
ƒ Examples: total annual air traffic between New York and
Boston, total vehicle miles travelled in the U.S.
ƒ Choice-based
Choice based “bottom
bottom-up
up” forecasting:
ƒ Often used to determine mode choice.
p les: mode share for travel between New York and
ƒ Examp
Boston (auto, air, or rail), mode choice for daily commute
(personal vehicle, transit, biking or walking).
TRAVEL DEMAND FORECASTING METHODS
ƒ Econometric “top-down”
top - down forecasting:
Functional form:
y = β 1 x 1 + β 2 x 2 + … + β nx n + ε
= βx + ε
ƒ Choice-based “bottom-up” forecasting:
ƒ Functional form:
U in = β inx in + ε i
We compare U 1 , U 2 , U 3 , … U n. Select i with highest utility.
DISCRETE CHOICE FRAMEWORK
ƒ Decision
Decision-Maker
Maker (e
(e.g
g. traveler) ƒ Attributes of Decision-Maker (e.g. age, gender, income, etc.)
ƒ Alternatives (e.g. auto, high-speed rail, auto)
ƒ Attributes of Alternatives (e.g. travel time, cost, frequency)
ƒ Choice
ƒ Decisi
D i i on-mak
k er n sell ectt s one and
d onll y one alt
lternati
tive f rom sett
C n = {1,2,…,i…,J n } with J alternatives.
ƒ Decision Rule
ƒ Dominance, satisfaction, utility, etc.
ƒ Utility = happiness
Adapted from Ben-Akiva, 1.201, Fall 2010, Lecture 3
CHOICE EXAMPLE: INTERCIT Y TRAVEL
ƒDecision - maker: an individual traveler.
traveler
ƒChoice: whether to travel between Boston and
g h-sp
p eed rail,, or auto.
New York byy air , hig
ƒGoods: air, high-speed rail, auto.
ƒUtilityy function: U(( X)) = U ( Air, HSR, Auto))
ƒConsumers maximize utility:
ƒIf U(Air) > U(HSR), U(Auto) Æ choose Air
ƒIf U(HSR) > U(Air), U(Auto) Æ choose ?
ƒWhat goes in U(X)?
Adapted from Ben-Akiva, 1.201, Fall 2010, Lecture 3
CONSTRUCTING THE UTILIT Y FUNCTION
ƒ U(Air) = U (travel_time,
(travel time access_time,
access time cost,
cost …))
ƒ Assume linear (in the parameters)
ƒ U(( Air)) = β 1 * travel_time
_
+ β 2 * access_time
_
+…
ƒ Parameters represent tastes, which may vary over
people
ƒ Include socio-economic characteristics (e.g. age, income)
ƒ U(Air) = β 1 * travel_time + β 2 * access_time + β 3 * ( cost// income)) + …
Adapted from Ben-Akiva, 1.201, Fall 2010, Lecture 3
EVALUATING FUTURE CHANGES
Given this framework:
ƒ U(Air) > U(HSR), U(Auto) Æ choose Air
ƒ U(HSR) > U(Air), U(Auto) Æ choose HSR
ƒ U(Auto) > U(Air), U(HSR) Æ choose Auto
How might utility and choice change:
ƒ If air fares go up?
ƒ If cost of traveling by auto goes up?
ƒ If congestion goes up?
ƒ If high-speed rail travel time goes down?
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ESD.00 Introduction to Engineering Systems
Spring 2011
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