1. Chapter 8
Part I
Travel Demand and
Traffic Forecasting
From: Principles of Highway Engineering and Traffic Analysis
Third Edition
Fred Mannering, Walter Kilareski and Scott Washburn

2. Travel Demand & Traffic
Forecasting
 Necessary understand the where to invest in
new facilities and what type of facilities to invest
 Two interrelated elements need to be considered
 Overall regional traffic growth/decline
 Potential traffic diversions

3. Traveler Decisions
 Four key traveler decisions need to be studied
and modeled:
 Temporal decisions – the decision to travel and
when to travel
 Destination decisions – where to travel (shopping
centers, medical centers, etc.)
 Modal decisions – how to travel (auto, transit,
walking, biking, etc)
 Route decisions – which route to travel (I-66 or Rt
50?)

6. Trip Generation
 Objective of this step is to develop a model
which can predict when a trip will be made
 Typical input information
 Aggregate decision making units – we study
households not individual travelers typically
 Segment trips by type – three types 1) work trips 2)
shopping trips and 3) social/recreational trips
 Aggregate temporal decisions – trips per hour or per
day

8. Trip Generation Model
 Typically assume linear form
 Typical variables which influence number of
trips are
 Household income
 Household size
 Number of non-working household members
 Employment rates in the neighborhood
 Etc.

9. Typical Trip Generation Model
Ti = bo + b1 z1i + b2 z 2i + ...bk z ki
where :
Ti = number of veh - based trips of a given type in some
specified time period made by household i
b k = coefficient estimated from traveler survey
data and corresponding to characteristic k
z ki = characteristic k (income, employment in neighborhood,
number of household members) of household i

10. Trip Generation Model
Example Problem
Number of peak hour vehicle-based shopping trips per
household =
0.12 + 0.09 (household size) + 0.011(annual household
income in $1,000s) – 0.15 (employment in the
household’s neighborhood in 100s)
A household with 6 members; annual income of $50k;
current neighborhood has 450 retail employees; new
neighborhood has 150 retail employees.

11. Trip Generation with Count Data
Models
 Linear regression models can produce fractions
of trips which are not realistic
 Poisson regression can be used to estimate trip
generation for a given trip type to address this
problem

12. Poisson Regression Model
e −λi λTi
P (Ti ) = i
Ti !
Where :
Ti = No. of veh - based trips of given type
made in specified time period by household i
P(Ti ) = probability of household i making
exactly Ti trips (where Ti is non negative integer)
e = base of natural logarithm (e = 2.817)
λi = Poisson parameter for household i, which is equal
to household i' s expected number of veh - based
trips in some specified time period, E[Ti ]

13. Estimating Poisson Parameter
λi = e
BZ i
where :
B = vector of estimatable coefficients
Zi = vector of household characteristics
determining trip generation
other terms as explained previously

14. Example 8.4
Given:
BZi= -0.35 + 0.03 (household size) +
(0.004) annual household income in 1,000s –
0.10 (employment in household’s neighborhood in 100s)
Household has 6 members; income of $50k; lives in
neighborhood with 150 retail employment; what is
expected no of peak hour shopping trips? What is prob
household will not make peak hour shopping trip?