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3. PROJECT FINDINGS
Pages 35-51

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From page 35...
... This process can be outlined as follows: The increases in single occupant vehicle use we have observed in recent decades are likely to represent a combination of new commuters, disproportionately choosing the private vehicle mode, and existing commuters who have changed" modes. In either case, these changes can be hypothesized to be a function of the following basic factors: · The private vehicle has become an option for more people, or more household members, through increased vehicle availability; · The solo driving option has become relatively more attractive in terms of cost (either because vehicle ownership and operating costs have declined or because incomes have increased, making the costs easier to afford)
From page 36...
... ~.~ it. ~ ~ ~.~.~ ~.~.~.~ ~.~.~ ~ Measures of housing and workplace · °/O suburban households "dispersion" · °/O suburban population · °/O suburban employment · Total central city employment · Age of housing stock Vehicle availability | · vehicles per household · vehicles per capita · vehicles per worker · vehicles per worker not working at home · TO zero vehicle households · °/O 2+ vehicles households Relative vehicle costs · Income · Gasoline prices · State gasoline taxes · Parking costs Levels of service · Average commute time · °/O of commutes >45 minutes · Average transit fare · Peak transit vehicles · Peak transit vehicle miles · Transit vehicle per worker · Transit vehicle miles per worker · HOV lane miles Other characteristics · Household size · Workers per household 36
From page 37...
... The mode] contains four parameters: downtown parking cost, suburban private vehicles available per suburban household, transit vehicle hours per worker not working at home, and central city employment.
From page 38...
... ~ ~ of. ~ Downtown parking cost -0.0127 -3.290 r Private vehicles available per household in 0.1157 1.497 suburbs Transit vehicle hours per worker not working -0.0575 -3.893 at home Central city employed labor force 0.0538 2.069 N Adjusted R2 I -0.09 +0.27 -0.12 +0.03 33 0.53 Source: Charles River Associates, 1997.
From page 39...
... Table 13. 1990 SOV Share Model for Central City to Central City Commute Veritable ~ ~ ~= ~ ~ ~ ~ ~ ~ Parameter ~ t-Stotistic Private vehicles per worker not working at home 0.3215 2.195 Transit vehicle hours per worker not working at -0.0586 -2.965 home Downtown parking cost -0.0053 -1.510 Percent central city housing built prewar -0.0026 -4.103 N Adjusted R2 .
From page 40...
... is somewhat different from the share models as it attempts to explain the average number of persons per vehicle rather than the relative number of total trips by private vehicles in this commute market. The table shows that the variables that best explain mean occupancy include measures of household size and HOV lane availability.
From page 41...
... also contains a vanable measuring the total HOV lane miles for each city in 1990. Given that HOV lanes often provide less congestion and its consequent faster travel times (particularly for the suburb to central city flow of interest here)
From page 42...
... If. ~ ~ ~ .~ ~ .~ ~ ~ Central city percent of MSA labor force 0.0403 1.898 Private vehicles available per household in -0.0823 -7.169 central city Total metropolitan area HOV lane miles 0.0002 2.273 Average household size for central city 0.0183 1.201 households .
From page 43...
... ~ ~ . ~ Private vehicles available per worker not working -0.2738 -3.143 at home Downtown parking cost 0.0053 1.959 Percentage of central city housing units built 0.0015 3.1 40 prewar N Adjusted R2 ~ .
From page 44...
... .~ i. Private vehicles available per household in -0.3594 -9.722 central city Downtown parking cost 0.0097 4.123 Central city percent of MSA labor force 0.1576 2.527 _ Adjusted R Elasticity -3.13 +0.37 +0.31 33 0.83 Source: Charles River Associates, 1997.
From page 45...
... Household sizes continued to decline between 1980-1990, and this result shows that increases in SOV shares are strongly related to shrinking numbers of workers per household. Given that much of the increase in SOV shares in the 19SOs came at the expense of carpooling rather than transit or other modes, this is a logical result: fewer workers per household means fewer opportunities to carpool.
From page 46...
... The positive sign implies that increases in the number of zero vehicle households may have a positive effect on transit share, or alternatively, where these households have declined, so has the market share of public transit. Table 20.1980-1990 Change in Public Transit Share Model i ~ ~ ~ ~ I~ of; ~ ~ ~ ~ ~ ~ .
From page 47...
... Again, since household sizes have been declining, it is most appropn ate to interpret this to mean that falling household sizes are an important factor in the fall in transit shares we have observed during the penod. The percentage of commutes over 45 minutes for 1990 has a positive sign and is also significant at better than the too level.
From page 48...
... The mode] shows that this reorientation of commuting to a less concentrated patterns has had a strong positive effect on private vehicle travel.
From page 49...
... Time Series Model of Private Vehicle Trips Entering Core Area in AM Peak Period for New York City .
From page 50...
... Time Series Model of Private Vehicle Share Entering Core Area in AM Peak Period for New York City If: Variable ~ ~ ~; Parameter Log of average bridge/tunnel toll -0.1866 Log of gasoline price -0.1631 Log of average MTA transit fare 0.2819 . Adjusted R2 ~ - U ~ t-Statistic~ :: :: ::: : :: ::: ::::: :: -3.814 -3.605 4.143 17 0.51 Source: Charles River Associates, 1997 .
From page 51...
... This result is higher than other observed values, and it may reflect the omission of some important variable in the estimation such as parking costs. The result is not the effect of senal correlation, however, as a test for senal correlation was performed, but was rejected at better than the 99% confidence level.


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