Cover Image

Not for Sale

View/Hide Left Panel
Click for next page ( 69

The National Academies | 500 Fifth St. N.W. | Washington, D.C. 20001
Copyright © National Academy of Sciences. All rights reserved.
Terms of Use and Privacy Statement

Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.

OCR for page 68
68 from in constructing their own scorecard). The original corridor [2.1.10], the level of service can be very different four metrics used by this approach for for-profit assessments when transfer penalties are included in the analysis, which (financial, internal business, customer, and innovation and can only be accomplished via analysis of network models. learning) were adjusted to fit the unique requirements of a In models for public transit usage, the factor representing nonprofit, public service. The Balanced Scorecard approach transit service most often involves the proximity to transit stops, for public use would consider three perspectives: efficiency, either using walking distance buffers around transit routes effectiveness, and impact. Phillips then details the elements or more detailed land use information. These approaches are that go into each of the three perspectives and creates constructs insufficient to examine the effect transit service has on a person's for each. travel mode decision. In work for the Delaware Transportation Institute [2.1.15], factors for transit level of service were developed using ArcInfo network models that more realisti- 2.1.2 Data Envelopment Analysis cally estimate level of service between specified origins and Data envelopment analysis, first put forward by Charnes, destinations taking into account walking distances, transfers, Cooper, and Rhodes in 1978 [2.1.2], is a methodology widely wait times, and park and rides. Methods discussed for travel used for measuring the relative efficiency of decision-making time and distance estimates are applicable for other travel units, which can be business units, government agencies, modes as well. police departments, hospitals, educational institutions, and even people. The underlining assumption is fairly simple even 2.1.4 Summary though the mathematics can get complex. DEA is a multi- criteria approach, capable of handling multiple inputs and In short, performance measurement focuses on whether a outputs that are expressed in different measurement units. system or program has achieved its objectives, expressed as This type of analysis cannot be done with classical statistical measurable performance standards. Because of its ongoing methods. Furthermore, because DEA is not a statistical method, nature, it can serve as an early warning system to management, one is not constrained by the type and the relations of the data as a vehicle for improving accountability to the public [2.1.5], used, as in regression techniques. Inputs and outputs can be as a method to document accomplishments, and as a way to anything, including qualitative measurements. compare similar programs and systems. Chu and Fielding [2.1.3] applied the DEA technique in transit performance measurement by developing DEA models for relative efficiency and effectiveness. DEA can measure 2.2 APM Performance Measurement effectiveness by using consumed service as the output and produced services along with selected external environmental This study has determined from research of numerous variables as inputs. Chang et al. [2.1.1] extended the model industry documents that current performance measurement for measuring the relative effectiveness and changes in effec- of both airport and non-airport APMs is primarily focused tiveness of an organization by merging it with the Malmquist on traditional measures of operating system performance Productivity Approach. The DEA technique may be applied (i.e., reliability, maintainability, and availability). Other to APM performance measures since APM systems have simi- APM performance measures related to economic efficiency, lar input and output variables, even though the quantity and comfort, and convenience, among others, have received sig- configurations may be very different. nificantly less attention in the literature, if any at all. Some of these measures are applied in other industries, however, and are considered in this appendix for their application to an 2.1.3 Network Models airport APM. As the research progresses, we anticipate that Transportation network models were originally developed site visits and surveys will yield more information about these to forecast travel demand and simulate traffic conditions. measures from APM system owners and operators. However, model outputs can often be used as comprehen- The documented methods of system performance measure- sive performance measures for the entire network, individual ment for airport APMs can be broadly divided into two classes: mode, selected areas, or corridors. The flexibility afforded by applied methods and theoretical methods. These classifications transit network analysis often provides the most powerful are described in the following sub-sections. indicators for measuring the efficiency and effectiveness of transportation networks. Such comprehensive evaluation is 2.2.1 Applied Methods extremely important since performance measures of a single mode or isolated sections may distort the results. As demon- In general, there are four applied methods used in airport strated in an early study of the HudsonBergen Light Rail APM performance measurement: the System Dependability