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

1. Trip Generation
2. Trip Distribution
3. Mode Choice
4. Trip Assignment

The first step in the process, Trip Generation, uses socioeconomic data to determine the number of trips produced by a traffic analysis zone (or census tract, census blockgroup, or other geographic division).  The socioeconomic data normally includes population, auto ownership, and employment information at the very least.

The second step is trip distribution.  Once the number of trips are known, trip distribution determines where the trips will go.  This normally uses a gravity model, which will be discussed in a future post.  This step takes in several factors, including the number of trip productions, the number of trip attractions, and an impedance value.  The impedance value is the resistance to travel, which could include distance, time, tolls, or a combination of those.  Each model is different in this regard.

The third step is mode choice.  This step determines what vehicle trips will utilize when going from one zone to another.  This step can be particularly complex or extremely simple, depending on the area included in the model.  Some models have very simple mode choice steps because transit isn’t available, or it doesn’t have a significant effect.  Other models can be extremely complex, such as a model for Chicago or New York, which would include autos, bus transit, subway transit, urban rail (such as Chicago’s Metra Rail), and intercity rail transit (such as the Amtrak service that connects New York to Boston, Baltimore, and Washington, DC).

The final step is trip assignment.  This step takes all of the trips from mode choice (which it now knows if they are trips that will drive alone, share a ride, use the bus, or use another mode of transportation) and assigns them to a transportation network.  Prior to the mid-1980s, these networks were largely text based, but with the advent of Geographic Information Systems and personal computers in the mid-1980s and moving forward, these networks became graphical.

These four steps represent the basic building blocks of most transportation models.  These steps are a basic way to ask:

• How many people are going to travel?
• Where are they going to go?
• What transportation mode are they going to use to get there?
• What route will they take to get there?