This document summarizes a research project analyzing travel demand data from GoGet, a car sharing service, to develop a predictive model of customer trip generation. The researchers used GoGet customer data and transportation demand modeling fundamentals to create a multinomial logistic regression model predicting the number of monthly GoGet trips based on a person's age, income, and car ownership. The results provide GoGet useful information about expected customer demand in different locations to improve service planning and expansion.

1. Summer Research Program UNSW
Data-driven Approach to Demand Modelling in Transport
Professor: Chen Cai
Collaborator: Hoang Nguyen
Authors: Luiza Anselmo Olinto Pavao Xavier and Marinna Pereira Pivatto
20 February 2015

2. Abstract
We studied the travel demand forecast fundamentals so we could apply this
knowledge in provide GoGet improvement. Our work was based in analyse the
GoGet data to make it useful to improve their services. Finding the correct
mathematical model gave us the number of GoGet trips that a person with some
characteristics will do in a month. Using this the company can know how many cars
they should have available.
1. Problem of the research
Now a days, we can realise a lack in travel demand forecast, because they are
usually wrong and this could happen because the data used is not correct, or even
the way of calculation is not right.
However, travel demand is an important information for all transport
companies, because it is a way to estimate the number of trips that will be made in
an area at some future time point. It starts with the calculation of trip generation
that is the number of trips that will be made. This will be influenced by factors as:
number of cars, workers and number of households, for example.
Using this knowledge of travel forecasting we will apply it on GoGet analysis
to study the demand for it. GoGet is a car sharing service that begun in 2003 in
Australia.
The company needs to improve the calculation of how many spots should
have in each area. For this study we will use the data base collected from the
registration forms about their customers. So there are some data available but just
the data does not mean anything for the company. Therefore, the challenge will be
transform this data in information that will be useful for GoGet to improve their
service.

3. 2. Solution
In order to solve our problem, we used the trip generation formula that we extracted
from the document provided by Chen Cai. The document was an important method to
present all the definitions around Transport Area.
We select the example of a household-based model to calculate the number of trips
that one person with a specific job category can generate.
Y = 0.91 + 1.44X​1​+ 1.07X​2
Where Y is the number of trips per household
X​1​ is the number of workers per household
X​2 ​is the number of cars per household
This linear regression model assumes that there is a relationship between the
independent variables (workers and car ownership) and the dependent variable (trips per
household).
We adapted this model to our problem. Assuming Y as a trips per Job Category. So,
our input was:
X​1​= 1
X​2​= Average of car ownership in which Job Category
For example:
A director has an average of 1.2 cars, so the number of trips for this category will be:
Y = 0.91 + 1.44*1 + 1.07*1.2
Y = 3.66
One director will generate on average of 3.7 almost 4 trips per day.
Knowing that, we analysed the GoGet data and we did a relationship between them.
The data showed us how often directors (we will continue using Directors as an
example) use GoGet and also the quantity of trips made by them per month. Using this
inputs we were able to calculate the probability of directors to choose GoGet.
Y​total​= Y*F*30
Where Y​total​is the total monthly trips per category
Y is the number of trips per job category
F is the frequency (how often one job category use GoGet)

4. 30 is the total days per month
So the probability to choose GoGet is:
P (x) = Y / Y​total
By identifying the probability we were able to calculate how many trips one director
will make using GoGet.
G = Y*30*P(x)
Where G is the number of trips using GoGet
P(x) is the probability to choose GoGet
For example:
After that we should create a model to identify how many trips using GoGet a person
with some characteristics like age, income and car ownership will probably make in one
month. To solve this problem we start to analyse the relationship between our independent
variable (age, car ownership and income) with our dependent variable (Trips using GoGet).
The analyse ended up with a multinomial logistic regression model because our dependent
variable has a limited number of possible values.

5. 3. Results
Using the code provide by MatLab for multinomial logistic regression:
X = [Avgage CarOwnership IncoDay];
prob = ordinal(Y,{'1','2','3','4'},[],[0 1 2 3 4]);
[B,dev,stats] = mnrfit(X,prob,'model','ordinal','Interactions','on')
i = 1;
x = exp(B(1,1) + B(2,1)*Avgage(i) + B(3,1)* CarOwnership(i) + B(4,1)*IncoDay(i))
y = exp(B(1,2) + B(2,2)*Avgage(i) + B(3,2)* CarOwnership(i) + B(4,2)*IncoDay(i))
z = exp(B(1,3) + B(2,3)*Avgage(i) + B(3,3)* CarOwnership(i) + B(4,3)*IncoDay(i))
We found the following results:
B =
12.8314 10.3285 14.3284
-0.1505 -0.1821 -0.0366
-17.6728 -4.7472 -19.1064
0.0237 0.0045 0.0215
x =
0.1091
y =
0.2579
z =
4.5673
It means that the three equations of our model will be:
The code is an example of how many trips a director can make in one month.
Analysing the results for ​x​, ​y and ​z ​we can conclude that this category will travel using GoGet
more than 3 times per month because ​x​, ​y ​are less than 1. Also, we can analyse that our
result is reliable using the ​t​and ​p​statistical methods, as we show above.
>> stats.t
ans =
3.1928 3.0076 2.4068
-1.6417 -2.4583 -0.3832
-3.2159 -0.9729 -2.0562
2.8027 0.6066 1.7588
>> stats.p
ans =
0.0014 0.0026 0.0161

6. 0.1007 0.0140 0.7016
0.0013 0.3306 0.0398
0.0051 0.5441 0.0786
To help the interpretation of the results, we made a program using Visual Basic.
The code:
The dashboard to input the independent variables and check the results:

7. Our results can ended up with four options for the number of trips made by month.
The analysed category can make ​zero, one, two or more than thre​e trips. The result depends
on age, income per day and car ownership. The range of results we stipulated by analysing
the data that we had. The maximum trips were four and the minimum were zero. We did
not use four as our maximum value because in the data just a few cases ended up with four
trips. In the future, the data can change and maybe more than five trips can be made, so to
refine the model is necessary increase the number of boundaries in the MatLab code.
4. Implications
The results of the model will be important to improve GoGet services. Knowing how
many trips should a person with some characteristics (age, income per day and car
ownership) use in a month, it will be easy to calculate the demand and also the number of
spots that will be necessary in some area.
Furthermore, this results could also be improved to analyse the data of one specific
zone, so they can calculate the number of trips in each zone and the opportunity of apply
GoGet in new regions. So, the model will provide important demand information for the
company and this will be able to manage it to better serve customers.