Visit the Famous Temples of Dev Bhoomi by Uttarakhand tour Package
Chapter 8 travel demand
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?)
4.
5.
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
7.
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?