TR Circular E-C058_9th LRT Conference_2003.pdf - Florida ...

354 Transportation Research Circular E-C058: 9th National Light Rail Transit Conference
The authors used t-tests to address the possibility of random chance; all regression
coefficients (and all intercept terms except that for BRT) were found significantly different from
zero (5% level). Results for the regression coefficients were very highly significant (0.1% level).
In addition, the three regression coefficients were found to be significantly different from each
other (5% level).
Using the BRT and LRT regression models to predict supply levels commensurate with
given consumption levels, a peak-period volume of 3,000 phd is correlated with an LRT service
supply equivalent to 34 vhd given 23-m (75-ft) vehicles. The BRT service supply is equivalent to
86 standard buses or 58 articulated buses per hour. Implied peak vehicle occupancies are 88 p/v
for LRT, 35 p/v for standard buses, and 52 p/v for articulated buses. The BRT service-supply
level is 36% higher than for LRT at the same consumption level. This comes as no surprise given
the differences in PVO characteristic of each mode. The HRT service level associated with a
peak-period volume of 3,000 phd is 6% greater than for LRT, a difference that is probably not
significant.
The authors sought to avoid problems related to uncertainty and bias by excluding certain
data, presented in Tables 1–3, from the regression models. Inclusion of authors’ personal
observations and authors’ estimates in Tables 1 (BRT) and 3 (HRT) would not result in
significant changes in the regression coefficients. For the LRT regression model, inclusion of
observations and estimates for Baltimore, Maryland, Salt Lake City, Utah, and St. Louis,
Missouri (Table 2) would produce a significantly different, and smaller, regression coefficient
(3.83) and a weaker relationship (R 2 = .74). However, inclusion of these observations and
estimates, together with those for Philadelphia, Pennsylvania and San Francisco (p.m.), would
result in a significantly different, and greater, regression coefficient (4.44) and a stronger
relationship (R 2 = .89). The implication in the latter case is that the peak-period BRT service
level would need to be up to 50% greater than LRT for a given consumption level. It is true that
the Philadelphia and San Francisco networks do not resemble the “typical” suburb-to-downtown
LRT facilities opened in the United States and Canada over the past two decades; however, these
results indicate a need for additional research.
Model Development, Interpretation, and Significance
It is important to understand that the regression models presented in Tables 1–3 are not the
product of “revealed preference” or “revealed choice” consumer surveys, but of direct
observation of consumer behavior—“observed choice.” Therefore, certain issues of statistical
validity that are characteristic of preference and choice surveys do not apply. For example, a
preference survey asking respondents to choose among one’s own auto, a red bus and a blue bus 3
might lead to biased results, for the otherwise-identical transit options are not likely to attract
identical shares of respondents. However, researchers able to demonstrate through direct
observation that red buses do attract larger numbers of consumers than blue buses, all else equal,
face the challenge of explaining why this behavior occurs. With reference to the consumer
behavior documented herein, the models in Tables 1–3 are a tentative first step towards this goal.