• • • • • •

T _{0 }= 15 minutes two way auto travel time T_{i1 }= 15 minutes two way auto travel time T_{j0 }= 30 minutes two way transit travel time ε_{i}^{P }=-0.08 parking price elasticity ε_{i}^{T }= -0.225 auto travel time elasticity ε_{i, j }= 0.036 auto travel cross time elasticity with respect to transit

T

• •

i = Auto j = Transit

Next we introduce a trip reduction strategy that reduces transit travel time by 10 minutes and increases SOV parking cost from $6.00 to $9.00 per day. The program details and costs are as follows:

• •

P = $9.00 new daily SOV parking cost T_{j1 }= 20 minutes new two way transit travel time

i1

• • •

$50,000 program implementation cost n = 2 program length (years) i = 6.00% current interest rate[30]

With these initial base case scenario values, the demand for SOV trips is equal to 3,000 round trips per day. Next, we use Equation (5) to estimate the change in vehicle trips brought about by a transit improvement that reduces the overall travel time by 10 minutes, and an increase in car parking costs from $6.00 to $9.00 daily. Following this approach, it suffices to plug in the above values in Equation (5) to obtain the change in vehicle trips per day as a result of the combined effect of transit improvement and car parking policy. The estimation produces a decrease of 138 vehicle trips, or 5 percent,

based on the initial elasticity assumptions:^{18 }

0 0 . 6 0 0 . 9 0 0 0 , 3 0 8 . 0 ⎢ ⎢ ⎣ ⎡ ⎟ ⎠ ⎞ ⎜ ⎝ ⎛ = Δ − i d

1 5 1 5 2 2 5 . 0 ⎟ ⎠ ⎞ ⎜ ⎝ ⎛ −

2 0 3 0 0 3 6 . 0 ⎟ ⎠ ⎞ ⎜ ⎝ ⎛

−1^{⎤}⎥ = −138 ⎦

It is relevant to note that the total number of trips reduced is the result of a multiplicative effect of the two policies affecting parking pricing and transit travel time. Each of these policies has a direct and indirect impact on travel demand, as detailed in Figure 3, which displays the demand for SOV trips.^{19 }Let Tj be the travel time of the alternative mode, in

18 Note that trips do not add up for each day that goes by, as to say that the first day we reduce 138 trips and the next day we reduce another 138 new trips. These reduced trips are recurring reduced daily trips and are to be considered as annual daily vehicle trips or ADDT, if we wish to use the acronym normally employed in transportation demand modeling.

19 For ease of display, the demand curve is represented as a linear function, while the constant elasticity is curvilinear in nature. The graph is not to scale.

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