Three factors that affect demand for travel

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Three factors that affect demand for travel

• The location and intensity of land use
• The socio-economic characteristics of the people
  living in the area, and
• The extent, cost, and quality of available
  transportation services.

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Definition of terms

• Home-based work (HBW) trip a trip for which the
  purpose is to go from home to work or from work
  to home
• Home-based other (HBO) trip a trip for which
  the purpose is to go from home to another
  location other than work (e.g. shopping, school,
  theater) or from non-work locations to home.
• Non-Home based (NHB) trip a trip for which
  neither trip end is at home

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• Production the ability of a zone to generate
  trip ends. For all non-home based trips,
  productions are synonymous with origins
• Attraction the ability of a zone to generate
  trip ends. For non-home based trips, attractions
  in a zone can be considered synonymous with trip
  destinations in that zone
• Origin point at which a trip begins
• Destination point at which a trip ends

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Source Handbook of Transportation Engineering,
Chapter 7, TRAVEL DEMAND FORECASTING FOR URBAN
TRANSPORTATION PLANNING, by Arun Chatterjee and
Mohan M. Venigalla, McGraw-Hill
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Zone 2
Zone 4
Zone 1
Zone 3
Zone 5
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TRIP GENERATION

• Estimating the number of trips generated by zonal
  activities
• Trip generation estimate by regression analysis
• Trip generation estimates by trip rates/unit
• Trip generation estimates by category analysis
• Method to balance trip productions and attractions

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What is trip generation?

• It is the process by which measures of urban
  activity are converted into numbers of trips.
• In trip generation, the planner attempts to
  quantify the relationship between urban activity
  and travel.

It means both trip productions and trip
attractions.
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A zone produces and attracts trips
Zone i

• of dwelling units

• Shopping center employees
• Etc.

Depending on the activities in the zone, it can
produce and/or attract trips. The planner
estimate these trips.
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Three ways for estimating the number of trips
produced

• Growth Factor Modeling
• Category analysis (cross-classification analysis)
• - Multiple classification Analysis (MCA)

• Trip rates, like of trips/1000ft2, ITEs trip
  generation rates (Fig. 5.10 of the text)

• Regression models

Y dependent var. (trips/household) X1, X2, etc.
independent variables
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Growth Factor Modelling
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Category analysis (cross-classification analysis)

• Less aggregated than trip rates and regression
  models
• Mean for a trip estimation at the home end

HBW Production example
Workers/HH Household size Household size Household size Household size
Workers/HH 1 2 3 4
1 1.418 1.413 1.550 1.655
2 2.855 2.661 2.693
3 3.891 4.154
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Multiple Classification Analysis (MCA)
MCA is an alternative method to define classes
and test the resulting cross-classification which
provides a statistically powerful procedure for
variable selection and classification.
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Trip generation rates
This is an example of trip generation rate
information taken from the ITE Trip Generation
Handbook. Some land use has a lot of data points
like this one, but others (many of them) have
only sparse data points. This handbook is
evolving and every year new data are added.
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Regression models (often, simple or multiple
linear models) advantages and disadvantages

• Easy and relatively inexpensive.
• Correlation among independent (explanatory)
  variables may create estimation problems ? If
  correlated, choose only the variable(s) that has
  the highest correlation with the dependent
  variable. Stepwise regression may help to find
  it.
• The assumptions of linearity and additive
  impacts on trip generation may be wrong.
• Best fit equations may yield counterintuitive
  results.
• By using zonal averages, important socioeconomic
  variations within the zone may be obscured or may
  yield spurious results.

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Regression models (cont) something you want to
be aware

• A high R2 (Coefficient of determination) by
  itself mans little if the t-test is marginal or
  poor,
• Just having a large number of independent
  variables does not mean very much. ? Choose only
  the independent variable that have highest
  correlation with the dependent variable and low
  correlation among the independent variables.
• Check the coefficients are logical or not. Trip
  generation is never negative in reality no
  matter what value the independent variable has.

• See the EXCEL file.
• Then, we will go through Example 2 to get some
  hints.

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Example
Y 318.56 0.883x, R2 0.78
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Trip attraction

• Trip attraction rates can be made by analyzing
  the urban activities that attract trips.
• Trips are attracted to various locations,
  depending on the character of, location, and
  amount of activities taking place in a zone.
• Three tools are used for this end too, but
  obviously types of independent variables used are
  different.

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Example Multiple linear regression modeling
Develop the multiple
linear regression model to estimate the no. of
trips attracted (y) to the cities/municipalities
in Metro Manila using the available office floor
space (x1) and the no. of offstreet parking
spaces (x2).
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• 1st model

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• 2nd model

• Simple regression y -2072.96 0.305x1

• better model because of high R2 and significant
  t-value for x1

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Example
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Seatwork No. 2
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Seatwork
Zone 2
Zone 4
Mall
Church
Zone 1
Joses home
Zone 3
Zone 5
School
Juanas home
Jose and Juana are best friends. After their
classes, they went to the mall to see a movie.
After the movies, Jose went home while Juana
passed by the church to attend the last service
for the day before going home. Identify the trips
generated by the two.
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Control totals (ch5. P.277)

• The area-wide production and attraction must be
  the same. In general they are not the same after
  calculation because trip production and
  attraction are estimated separately by different
  models with different variables.

I-E trips
CTp control total of productions Pz trip
productions for each zone Pe trip productions
at each external station Ae trip attractions at
each external station
I-I trips
Compute the factor used to balance productions
attractions.
E-I trips
(See Figure 5.11 of the text)