Civil Engineering Reference
In-Depth Information
Thus when the elasticity of ridership with respect to fares or service is known,
using the above equations it is straight forward to calculate the change in ridership
from a proposed change in fare and/or service.
A fare elasticity of
0.33 has often been used to estimate an average response of
ridership change to a change in transit fares. Thus a 10 % fare
increase
would result
in a 3.3 %
decrease
in ridership.
Arc Elasticity
-
”
This is similar to the Shrinkage Ratio, except that it uses the mid-point of fares or
service, as the denominator, instead of their initial values.
—
A more accurate measure of elasticity is the
“
Arc Elasticity.
Arc Elasticity ¼
½
ð
R2
R1
Þ=
ð
R1
þ
R2
Þ=
2
=
½
ð
X2
X1
Þ=
ð
X1
þ
X2
Þ=
2
ð
23
:
3
Þ
The variables are the same as those for Eq.
23.1
An American Public Transit Association study [
14
] used an
“
Integrated Moving
Average
model to estimate fare elasticity. The following disaggregate values of
fare elasticity were reported:
(a) Overall average =
”
0.40
(b) Systems in urbanized areas greater than one million =
-
0.36
-
(c) Systems in smaller cities =
0.43
-
(d) Average for peak hours =
0.23
-
(e) Average for off-peak =
0.42
-
It should be noted, however, that transit ridership is more responsive to
service
changes than to
fare
changes. Table
23.8
provides an example of cases where this
difference is evident [
15
].
Elasticity estimates are also calculated for different types of service changes
(service expansion or service frequency). It may be seen from Table
23.9
that
ridership is more responsive to changes in service expansion (bus or train miles)
than it is to travel time or transit frequency [
15
,
16
].
23.7 Land Use for New Developments
Land development policies that improve livability and reduce/minimize VMT
growth are desirable societal and environmental goals. Some promising land use
design strategies that can be progressively implemented to mitigate traf
c con-
gestion are described below.
