Transportation Theory and Applications
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1 Fall MTAT Transportation Theory and Applications Lecture III: Trip Generation Modelling A. Hadachi
2 Definitions Trip or Journey: is a one-way movement from origin to destination. Home-based (HB) trip: is when home is either the origin or destination of the trip. Non-home-based (NHB) trip: is when home is not origin or destination of the trip.
3 Definition Trip production: is defined as the home end of a HB trip or the origin of an NHB trip. Trip Attraction: is the defined as the non-home end of an HB trip or the destination of an NHB trip. HB Production Attraction Home Production Attraction Work NHB Work Production Attraction Attraction Production Shop
4 Definition Trip Generation: is defined as the total number of trips generated by households in a zone, they can be HB or NHB.
5 Characterisation of Journeys case of HB trips: travel to work travel to school or college shopping trips social journeys escort trips other journeys
6 Characterisation of Journeys Example: AM Peak Off Peak
7 Factors affecting trip generation Personal trips income car ownership family size household structure accessibility residential density etc. Professional trips number of employees number of sales total area of firm etc.
8 Growth-factor modelling Regression modelling Cross-Classification modelling Discrete-choice modelling
9 Growth-factor modelling The technique helps to predict the number of journeys using the following equation: future trips in zone i current trips in zone i growth factor.
10 Growth-factor modelling estimation: P: population I: income C: car ownership f: is a direct multiplication function with no parameters d: design c: current year
11 Growth-factor modelling Example 3.1: Check Lecture Note 1
12 Regression modelling Let s consider a test that focuses on observing the values that a certain variable takes for different values of another variable Linear regression where, are parameters or coefficient of the regression equation For Example in our case: Number of trips number of workers number of cars
13 Regression modelling Estimating coefficients for a linear regression a and b can be expressed as follows:
14 Regression modelling Example 3.2: Check Lecture Note 2
15 Cross-Classification modelling the aim is to classify households in homogenous groups. (such as: number of people in the household, number of cars, etc.) Households group 1 group 2 group 3 group 4 trip rate trip rate
16 Cross-Classification modelling advantage grouping is not related to the zones or areas no need for linear relationships groups can have different form of relationships disadvantage necessity of large samples hard to know which grouping is the best
17 Cross-Classification modelling let be the average number of trips with purpose p made by household of type h; and it is expressed in therms of a rate of the total number of trips in cell h by purpose decided by the number of households in it.
18 Cross-Classification modelling Example 3.2 Household No Trips income (euros/mth) cars
19 Cross-Classification modelling Example 3.2 Develop matrices connecting income to available cars (use the provided table), and draw a graph available cars Income/mth or more >15000
20 Cross-Classification modelling Example 3.2 Households number per category available cars Income/mth or more ; ; 18 14; ; 20 16; 17 > ; 12 3; 6; 10
21 Cross-Classification modelling Example 3.2 the average number of trips the household generates in each cell is: available cars Income/mth or more ,0 5, ,0 6,0 9, ,0 7,5 10, ,5 11,5 > ,5 12,7
22 Cross-Classification modelling Example 3.2 A household with 10,000 income and one car per household will make 7.5 trips per day
23 Discrete-choice modelling Binary logit: Alternative 0: do make a trip Alternative 1: make one trip or more Vo= 0 V1=.. Probability of making at least one trip:
24 Discrete-choice modelling Binary logit: person 0 trip 1 trip 1 trip 2 trip 2 trip 3 trip etc n trip
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