This document provides an overview of a traffic forecasting course (CEE320) taught in winter 2006. It discusses the need for traffic forecasting to plan transportation infrastructure and services. It covers topics like traveler decisions, trip generation, and mode choice modeling. Trip generation involves predicting the number and timing of trips, while considering factors like life stage, technology, and land use. Mode choice modeling uses techniques like logit models to predict transportation mode. The document provides examples of models for recreational trip generation and sitcom character preference to illustrate these traffic forecasting techniques.
3. CEE320
Winter2006
Need for Traffic Forecasting
• Impacts of facilities or modes of travel
– Lines on existing roads
– Roads
– Light rail
– Bus service
• Geometric design
• Pavement design
4. CEE320
Winter2006
Traveler Decisions
• Types of decisions
– Time (when do you go?)
– Destination (where do you go?)
– Mode (how do you get there?)
– Route choice (what route do you choose?)
• Influences
– Economic
– Social
5. CEE320
Winter2006
Predicting Travel Decisions
• Collect data on travel behavior
– Observation (number of buses, cars, bikes, etc.)
– Surveys
• Collect data on what travelers have done
• Collect data on their values and choices (utility)
• Inexact nature of prediction
– Incomplete data
– Reporting problems
7. CEE320
Winter2006
Trip Generation
• Purpose
– Predict how many trips will be made
– Predict exactly when a trip will be made
• Approach
– Aggregate decision-making units
– Categorized trip types
– Aggregate trip times (e.g., AM, PM, rush hour)
– Generate Model
8. CEE320
Winter2006
Motivations for Making Trips
• Lifestyle
– Residential choice
– Work choice
– Recreational choice
– Kids, marriage
– Money
• Life stage
• Technology
12. Variable Coefficient Value Product
Constant 0 1 0
Education (undergraduate degree or higher) 0.15 1 0.15
Income 0.00002 45,000 0.9
Whether or not individual owns an SUV 0.1 1 0.1
Whether or not individual owns a sports car 0.05 0 0
Whether or not individual owns a van 0.1 1 0.1
Whether or not individual owns a sedan 0.08 0 0
Whether or not individual uses a bicycle to work 0.02 0 0
Whether or not individual uses the bus to work all the time -0.12 0 0
Number of autos owned in the last ten years 0.06 6 0.36
Gender (female) -0.15 0 0
Age -0.025 40 -1
Internet connection at home -0.06 1 -0.06
Married -0.12 1 -0.12
Number of kids 0.03 2 0.06
Sum = 0.49
λi = 1.632 trips/day
13. CEE320
Winter2006
Example
• Probability of exactly “n” trips using the Poisson model:
• Cumulative probability
– Probability of one trip or less: P(0) + P(1) = 0.52
– Probability of at least two trips: 1 – (P(0) + P(1)) = 0.48
• Confidence level
– We are 52% confident that no more than one recreational or
pleasure trip will be made by the average individual in a day
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eP ( ) ( ) 32.0
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eP
15. CEE320
Winter2006
A Mode Choice Model
• Logit Model
• Final form
mk
n
kmnmnmk zV εβ += ∑
∑
=
s
U
U
mk sk
mk
e
e
P
Specifiable part Unspecifiable part
∑=
n
kmnmnmk zU β
s = all available alternatives
m = alternative being considered
n = traveler characteristic
k = traveler
17. CEE320
Winter2006
Ginger Model
UGinger = 0.0699728 – 0.82331(carg) + 0.90671(mang)
+ 0.64341(pierceg) – 1.08095(genxg)
carg = Number of working vehicles in household
mang = Male indicator (1 if male, 0 if female)
pierceg = Pierce Brosnan indicator for question #11 (1 if Brosnan
chosen, 0 if not)
genxg = generation X indicator (1 if respondent is part of generation
X, 0 if not)
18. CEE320
Winter2006
Mary Anne Model
UMary Anne = 1.83275 – 0.11039(privatem) – 0.0483453(agem)
– 0.85400(sinm) – 0.16781(housem) + 0.67812(seanm)
+ 0.64508(collegem) – 0.71374(llm) + 0.65457(boomm)
privatem = number of years spent in a private school (K – 12)
agem = age in years
sinm = single marital status indicator (1 if single, 0 if not)
housem = number of people in household
seanm = Sean Connery indicator for question #11 (1 if Connery chosen,
0 if not)
collegem = college education indicator (1 if college degree, 0 if not)
llm = long & luxurious hair indicator for question #7 (1 if long, 0 if not)
boomm = baby boom indicator (1 if respondent is a baby boomer, 0 if not)
19. CEE320
Winter2006
No Preference Model
Uno preference = – 9.02430x10-6(incn) – 0.53362(gunsn) + 1.13655(nojames)
+ 0.66619(cafn) + 0.96145(ohairn)
incn = household income
gunsn = gun ownership indicator (1 if any guns owned, 0 if no guns
owned)
nojames = No preference indicator for question #11 (1 if no
preference, 0 if preference for a particular Bond)
cafn = Caffeinated drink indicator for question #5 (1 if
tea/coffee/soft drink, 0 if any other)
ohairn = Other hair style indicator for question #7 (1 if other style
indicated, 0 if any style indicated)
20. CEE320
Winter2006
Results
10. Regarding the TV sitcom “Gilligan’s Island” whom do
your prefer?
29
90
85
30
88 89
7
112
87
0
20
40
60
80
100
120
Ginger Mary Ann No Preference
#ofRespondants
Survey
average
Model
Average probabilities of selection for each choice are shown in yellow.
These average percentages were converted to a hypothetical number of respondents out of a total of
207.
21. CEE320
Winter2006
My Results
∑
=
s
U
U
mk sk
mk
e
e
P
8201.13265.02636.01075.1
=++= −−−
∑ eeee
s
Usk
1815.0
8201.1
1075.1
===
−
∑
e
e
e
P
s
U
U
ginger sk
mk
4221.0
8201.1
2636.0
===
−
∑
e
e
e
P
s
U
U
annemary sk
mk
3964.0
8201.1
3265.0
===
−
∑
e
e
e
P
s
U
U
preferenceno sk
mk
Uginger = – 1.1075
Umary anne = – 0.2636
Uno preference = – 0.3265
22. CEE320
Winter2006
Primary References
• Mannering, F.L.; Kilareski, W.P. and Washburn, S.S. (2005).
Principles of Highway Engineering and Traffic Analysis, Third Edition.
Chapter 8
• Transportation Research Board. (2000). Highway Capacity Manual
2000. National Research Council, Washington, D.C.
Editor's Notes
Saigon
Poisson gives the average number of daily trips but can also calculate the probability of making X number of trips in a day.