No.

Fifty years ago, when the trip based modeling methodologies were developed, the primary reason was to evaluate highway improvements.  While tolling was in use, the bonding requirements were likely different.  Transit, while extremely important, was not in the public realm (the streetcars were normally privately owned by the area’s electric company).

Now, there are a lot of demands on travel models:

• Tolling/Toll Road analysis at a better level
• Different tolling schemes (area tolling, cordon tolling)
• Travel Demand Management (telecommuting, flex hours, flex time, alternative schedules)
• Better freight modeling (which now is becoming commodity flow and commercial vehicle modeling)
• Varying levels of transit (local bus, express bus, intercity bus, BRT, light rail, and commuter rail

While many of these can be done with trip based models, most of them cannot be done well with trip based models.  There are a number of reasons, but the few that come to mind are aggregation bias, modal inconsistency, and household interrelationships.

Aggregation Bias

Aggregation bias occurs when averages are used to determine an outcome.  For example, using a zonal average vehicles per household, you miss the components that form the average, such as:

20 households, average VPHH = 2.2
2 HH VPHH = 0
5 HH VPHH = 1
4 HH VPHH = 2
6 HH VPHH = 3
3 HH VPHH = 4+

The trip generation and modal choices (car, bus, bike, walk, etc.) among these households are all different, and are even more more different if you look at the number of workers per household.

Modal Inconsistency

In trip based modeling, “people” are not tracked throughout their day.  So, if someone rides the bus to work, there is nothing in the model to ensure that they don’t drive from work to get lunch.  While we don’t want to force people to use the same mode, since many people will use the bus to get to work and then walk to lunch or to go shopping during lunch, we want to make sure that there is some compatibility of modes.

Household Interrelationships

One of the features of of tour based models is determining each person’s daily activity pattern.  During this process, certain person types can determine what another person is doing.  For example, if a preschool age child is staying home, an adult (whether they are a worker or not) HAS to stay home.  Another example is if a school-non-driving-age child is going on a non-mandatory trip, an adult must accompany them.  Trip based models don’t know about the household makeup and the household interaction.

The above are only three of the many reasons why tour-based modeling is important.  There are many more, but I feel these are some of the most important and some of the easiest to understand.

The socioeconomic data for most models includes the following attributes for every zone in the model area:

• Population
• Households or Dwelling Units
• Dwelling Unit Occupancy
• Auto Ownership Information
• Hotel Information
• School Enrollment
• Employment Information (number, type)
• Special Cases (airports, amusement parks, etc)

Many models have several fields that describe these basic attributes.  In some, auto occupancy may be defined as a percentage of households with 0 autos, a percentage with 1 auto, and a percentage with 2 or more autos.  In others, there may be percentages based on the number of vehicles compared to the number of workers in a household.

The simple part of trip generation models is that once the socioeconomic data is in place and trip production factors are in place, trip generation is simply applying factors to determine the number of trips generated by and attracted to each use.  These factors are determined by a trip survey.

Trip generation is normally done with a cross-classification model.  This is where multiple factors are used to determine the rate of trips per unit (household, employee, etc).

This spreadsheet is an example of trip generation calculations. It is a cross-classification model based on household size and auto ownership.  For each purpose, the dwelling units with each criteria of auto ownership is is calculated and then applied to the trip rate in the ‘Production Table’ tab.  For attractions, I simply applied a rate per employee to the employees by sector.  The SEData sheet indicates the inputs and outputs.

The final output is a set of productions by trip purpose and a set of attractions.  These are intentionally close with attractions higher than productions.  The reason why will be discussed in the Trip Distribution post.

A trip purpose is basically a reason for travel.  Most models include home-based work (which are any trip going to or coming from the home and work), home-based school, home-based other (trips for purposes other than school and work), and non-home-based (trips that do not go to or from the home).  Some models may include home-based shopping or home-based social-recreation separately, and some combine it into home-based other.

trip-generation-worksheet