Ce123-Trip Generation and Attraction (Final)

8/13/2019 Ce123-Trip Generation and Attraction (Final)

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Murillo, Kyle Emmanuel R.

Dela Cruz, Princess J.Golechong, Genevieve Gillian R.Lim, Farrah Mae Aileen M.Santander, Zairel Allen A.


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Why do we live where you do?

Travel is benefit, not a burden!

What we have and what we need


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a key component of the transportation engineerstechnical repertoire.

allows the engineer to predict the volume of traffic

that will use a given transportation element in thefuture


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first stage of the classical first generation aggregatedemand models

aims at predicting the total number of trips

generated and attracted to each zone of the studyarea


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Journey is an out way movement from a point oforigin to a point of destination

Trip denotes an outward and return journey.


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Based on Purpose

Mandatory Trips

Discretionary Trips

Based on Time of the Day Peak Trips

Off Peak Trips

Based on Person Type House Hold Size

Vehicle Ownership

Income level


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Income

Vehicle Ownership

Household Structures


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OD means Origin-Destination

Is used to make more systematic analysis andeasier presentation

Shows the trips from one zone to another within agiven study area


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INTERNAL ZONE

Coverage of the study

Political and administrative boundaries

Barangay, town or a cityEXTERNAL ZONE

Areas outside the study area that might also be

affected by the study


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A number of methods are available for estimatingtrip generation and attraction. The more commonlyused ones may fall under any of the following:

A. Growth rate method

B. Category analysis

C. Regression model


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Growth factor modes tries to predict the numberof trips produced or attracted by a house hold orzone as a linear function of explanatory variables.The models have the following basic equation:

Ti= fiti

Where: where Tiis the number of future trips in the zone

and tiis the number of current trips in that zone and fi

is a growth factor. The growth factor fidepends on theexplanatory variable such as population (P) of the

zone, average house hold income (I), average vehicle

ownership (V).


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The simplest form of fi is represented as follows:

Where the superscript d denotes the design year and

the superscript c denotes the current year.


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Given that a zone has 275 household withcar and 275 household without car and theaverage trip generation rates for each groups

is 5.0 and 2.5 trips per day respectively.Assuming that in the future, all householdwill have a car, find the growth factor and

future trips from that zone, assuming thatthe population and income remainsconstant.


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Solution:

Current trip rate ti= (275 x 2.5) + (275 x 5.0)= 2062.5 trips/day

Growth factor Fi = Vid/ Vi

c = 550/275 = 2.0

Therefore, number of future trips:

Ti= Fiti= 2.0 x 2062.5 = 4125 trips day


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Groups individual HHs according to common

socioeconomic characteristics.

Sample:

Lets say I know that, in zone 101, I have 48 housesthat are in high category income and that half of

them own 3 autos

48*0.5 = 24 HH with income owning 3 autos


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I know that households that are high income and own 3autos make 3.25 trips per day on average.

24 HH each making 3.25 trips/day = 78 trips

I know that a third of trips made from high income

households are work trips.78/3 = 26 work trips

The number of cars is considered the main vehicle in


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Family size

number of Cars/ household1 2

no. of

household no. of tripsno. of

households No. of Trips1 100 200 50 1502 200 500 100 3503 150 450 50 200

More than 3 50 200 10 70

The number of cars is considered the main vehicle indetermining trip making in a certain area. Based onthe present number of households and trip makingactivities, the future trips can be estimated.


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Family size number of Cars/ household1 2

1 2.0 3.02 2.5 3.53 3.0 4.0

More than 3 5.0 7.0


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Family size number of Cars/ householdno. ofhousehold no. of trips no. ofhouseholds No. of Trips

1 200 400 150 4502 500 1250 350 12253 450 1350 200 800

More than 3 250 1000 70 490


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Land use type/activity trip rates Unit1. Residential (6,582 dwellingunits) AM in 1.52per dwelling unit

AM out 3per dwelling unit2. Hotels (178 rooms) AM in 0.85per room

AM out 0.677per room3. Hospitals (262 beds) AM in 1.286per bed

AM out 0.83per bed4. Restaurant (58 establishments)at 25 sq m AM in 0.0548per square meter

AM out 0.0331per square meter5. Commercial (823establishments) at 25 sq m AM in 0.0245per square meter

AM out 0.0192per square meter6. Office (276 establishment) at100 sq m AM in 0.0176per square meter

AM out 0.0027per square meter


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Land use Type

1. Residential in 10,005out 19,746

2. Hotels in 152out 121

3. Hospitals in 337out 217

4. Restaurant in 80out 48

5. Commercial in 505out 396

6. Office in 486out 75


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The different indices or dependentvariables normally considered thatmay influence on trip making areshown in table 8.1.


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Independent

Variable

Indices

Population Daytime, residential, no. of workers,population by industries, etc.

Land Use Land area by usage, floor area, etc.

Economy Retail sales, industries production,etc.

Social Activity Car owners, size of household, etc.

Table 8.1Variables Influencing Trip Making


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In the Metro Manila Urban Transportation Integrationstudy (ALMEC Corp. 1999), the following tripgeneration and attraction models are utilized:


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Where:

- coefficients

- independent variable

- constant


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The general form of a trip generation model is

Where xi's are prediction factor or explanatory variable.The most common form of trip generation model is a linear

function of the form

Where ai's are the coefficient of the regression equationand can be obtained by doing regression analysis. The

above equations are called multiple linear regressionequation, and the solutions are tedious to obtain manually.However for the purpose of illustration, an example withone variable is given.


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For example, to describe to home trips, tripgeneration and attraction regression equations aregiven as follows:

where - population

- workers at workplace

where - workers at residence


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Example 1 :

Let the trip rate of a zone is explained by thehousehold size done from the field survey. It was

found that the household size are 1, 2, 3 and 4. The triprates of the corresponding household is as shown inthe table below.

Fit a linear equation relating trip rate and household

size.


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1 2 3 4

Tripsper

day (y)

1 2 4 6

2 4 5 7

2 3 3 4

5 9 12 17

Household size (x)


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Solution The linear equation will have the formy = bx + a where y is the trip rate, and x is thehousehold size, a and b are the coefficients. For a bestfit, b is given by


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Trip generation forms the first step of four-stage travelmodeling. It gives an idea about the total number oftrips generated to and attracted from different zones in

the study area. Growth factor modeling and regressionmethods can be used to predict the trips.


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Documents

Ce123-Trip Generation and Attraction (Final)