Fundamentals of Transportation/Modal Split

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This page describes historical, but no longer standard, practice in Mode Choice models.

The early transportation planning model developed by the Chicago Area Transportation Study (CATS) focused on transit, it wanted to know how much travel would continue by transit. The CATS divided transit trips into two classes: trips to the CBD (mainly by subway/elevated transit, express buses, and commuter trains) and other (mainly on the local bus system). For the latter, increases in auto ownership and use were trade off against bus use; trend data were used. CBD travel was analyzed using historic mode choice data together with projections of CBD land uses. Somewhat similar techniques were used in many studies. Two decades after CATS, for example, the London study followed essentially the same procedure, but first dividing trips into those made in inner part of the city and those in the outer part. This procedure was followed because it was thought that income (resulting in the purchase and use of automobiles) drove mode choice.

Diversion Curve techniques[edit | edit source]

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 to proposed expressways. Diversion curves were also used as bypasses were built around cities to establish what percentage of the traffic would use the bypass. The mode choice version of diversion curve analysis proceeds this way: one forms a ratio, say:


cm = travel time by mode m and
R is empirical data in the form:
Figure: Mode choice diversion curve
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 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 can be drawn.