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

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.

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 + 1.44X1 + 1.07X2

Where Y is the number of trips per household

X1 is the number of workers per household

X2 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:

X1 = 1

X2 = 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.

Ytotal = Y*F*30

Where Ytotal 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)

30 is the total days per month

So the probability to choose GoGet is:

P (x) = Y / Ytotal

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.

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 forx,y andzwe 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

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:

Our results can ended up with four options for the number of trips made by month.

The analysed category can makezero, one, two or more than three 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.