Editing Mode choice (section)

==Diversion curve techniques==
The CATS had diversion curve techniques available and used them for some tasks.  At first, the CATS studied the diversion of auto traffic from streets and arterial roads to proposed expressways.  Diversion curves were also used for bypasses built around cities to find out what percent of traffic would use the bypass.  The mode choice version of diversion curve analysis proceeds this way:  one forms a ratio, say:

:<math>
\frac{c_\text{transit} }
{c_\text{auto} } = R
</math>

where:
:''c<sub>m</sub>'' = travel time by mode ''m'' and
:''R'' is empirical data in the form:
[[Image:DiversionCurveMC.png|right|500px|Figure: Mode choice diversion curve]]

Given the ''R'' that we have calculated, the graph tells us the percent of users in the market that will choose transit. A variation on the technique is to use costs rather than time in the diversion ratio.  The decision to use a time or cost ratio turns on the problem at hand.  Transit agencies developed diversion curves for different kinds of situations, so variables like income and population density entered implicitly.

Diversion curves are based on empirical observations, and their improvement has resulted from better (more and more pointed) data.  Curves are available for many markets.  It is not difficult to obtain data and array results.  Expansion of transit has motivated data development by operators and planners. [[Yacov Zahavi]]’s UMOT studies, discussed earlier, contain many examples of diversion curves.

In a sense, diversion curve analysis is [[expert system]] analysis.  Planners could "eyeball" neighborhoods and estimate transit ridership by routes and time of day.  Instead, diversion is observed empirically and charts drawn.