7.3. Growth Factor Modeling

Growth factor models try to predict the number of trips produced or attracted by a household or zone as a linear function of explanatory variables.

Growth factor modeling is used in transportation planning to estimate future trips based on current data. It links the number of trips generated or attracted to certain variables like population and income. This model is helpful in understanding how changes in these variables can affect trip numbers in a specific zone over time.

Think of it like predicting how much more food you would need for a party if you knew the current number of guests and how many more people usually join when the number of family members increases.

The models have the following basic equation: T = f_t, where T is the number of future trips in the zone and t is the number of current trips in that zone, and f is a growth factor.

The equation states that the future number of trips (T) in a given area can be calculated from the current number of trips (t) and a growth factor (f). The growth factor serves as a multiplier that adjusts the current trips based on changes in the explanatory variables.

Imagine you're running a small bakery. If you currently sell 100 loaves of bread (current trips) and anticipate that your sales will increase with a new marketing strategy (growth factor), you could estimate that you will sell 120 loaves next month.

The growth factor f depends on explanatory variables such as population (P) of the zone, average household income (I), and average vehicle ownership (V). The simplest form of f is represented as follows: f = (P_d * I_d * V_d) / (P_c * I_c * V_c), where d denotes the design year and c denotes the current year.

The growth factor is determined by comparing the expected future values of population, income, and vehicle ownership to their current values. This ratio allows planners to adjust their trip forecasts based on expected changes in these important demographic factors.

Consider a community where more people are moving in, incomes are rising, and more cars are being bought. This situation can lead to an increase in trips, much like how a new school attracts more students, leading to more traffic around the area.

Given that a zone has 275 households with a car and 275 households without a car, and the average trip generation rates for each group are respectively 5.0 and 2.5 trips per day, assuming that in the future, all households will have a car, find the growth factor and future trips from that zone, assuming that the population and income remain constant.

To find future trips, we first calculate the current trips by multiplying the number of households with their rate. The current trip rate is 2.5 trips/day for those without cars and 5.0 for those with cars, totaling 2062.5 trips/day. The growth factor is then calculated based on the assumption that all households will have cars in the future, leading to a multiplication factor to estimate future trips.

Imagine if you own a small cafe today and serve 100 customers daily, but you expect renovations to boost your capacity to serve 200 customers. You'd estimate future sales based on that expected capacity increase, just like estimating future trips based on changes in household car ownership.

The above example also shows the limitation of growth factor method. If we think intuitively, the trip rate will remain the same in the future. Therefore, the number of trips in the future will be 275 households * 5 trips per day = 2750 trips per day.

The example illustrates that while growth factor models can provide predictions, they rely on the assumption of exponential growth which might not hold true. It is crucial to remember that not all changes in explanatory variables will lead to proportional changes in trip numbers.

Just like predicting the future popularity of a new video game based on early sales might not fully account for potential market saturation, assuming continuous growth in trips might overlook practical limitations, such as road capacity or changing commuter habits.

Therefore growth factor models are normally used in the prediction of external trips where no other methods are available. But for internal trips, regression methods are more suitable.

Growth factor models are particularly useful when there is limited data for internal trips—trips that start and end within the same area. In cases where comprehensive data is available for both internal and external trips, regression methods typically yield better predictions.

Think about a town that lacks traffic data and wants to predict the impact of new developments: using a growth factor model can provide an estimate where detailed data isn't available, similar to making an educated guess about how many more people will shop in a new mall based on general population growth in the area.

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