Cover Image

Not for Sale



View/Hide Left Panel
Click for next page ( 9


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 8
8 CHAPTER 1 Introduction AVL and APC systems are capable of gathering an enormous inability of AVL to deliver data for off-line planning analy- quantity and variety of operational, spatial, and temporal data sis was a major theme of a 1988 conference sponsored by that--if captured, archived, and analyzed properly--hold sub- the Canadian Urban Transit Association [see, for example, stantial promise for improving transit performance by sup- Furth (1)]. Through the mid-1990s, this situation continued. porting improved management practices in areas such as A report summarizing data analysis practice in 1998 found service planning, scheduling, and service quality monitoring. that, of the seven U.S. transit agencies surveyed that had AVL Historically, however, such data has not been used to its full systems, all relied entirely on manual data collection for run- potential. Many AVL systems, designed primarily for real-time ning time analysis, and only three used AVL data to monitor applications, fail to capture and/or archive data items that schedule adherence (2). would be valuable for off-line analysis. Recent technological Broward County Transit illustrates how AVL systems are advances have created new opportunities for improving the not commonly oriented toward archiving data (3). Its AVL quantity, variety, and quality of captured and archived data and system archived incident messages, but not routine poll data, for analyzing it in meaningful ways. The objective of this which gave vehicle location approximately every 60 s. Because research was to develop guidance for the effective collection, incident messages occur only on an exception basis, they can- archiving, and use of AVL-APC data to improve the perform- not support most running time and schedule adherence ance and management of transit systems. analyses. Although the poll messages were written to an Ora- Automatically collected data can play two important roles cle database as a utility for system maintainers, it was over- in a transit agency's service quality improvement process. As written every 2 min because no permanent use for that data illustrated in Figure 1, there are two quality improvement was foreseen. To archive the poll data, an analyst wrote a pro- cycles: one in real time and one off line. In the real-time loop, gram that copies the contents of the Oracle database to a per- automatically collected data drives operational control, aid- manent database every 2 min. This archive enabled him to ing the transit agency in detecting and responding to devia- plot trajectories and review particular trips. tions from the operational plan; it is also a source of real-time Because real-time data needs differ from those of off-line information that can be conveyed to customers using a vari- analysis, simply warehousing AVL records does not in itself ety of media. In the off-line loop, automatically collected data guarantee a useful data archive. The single greatest problem that has been archived drives analyses that aid the transit with traditional AVL data is that it consists mostly of poll agency in evaluating and improving its operational plan. Ulti- records, in which a vehicle reports its location when polled by mately, good operational performance and high passenger a central computer in round-robin fashion, every 60 seconds or satisfaction follow from having both a good operational plan so. Poll data can be characterized as "location-at-time" data, as and good operational control. distinct from "time-at-location" data such as reporting when a bus arrives at a stop. Either one is adequate for tracking a bus's location in real time, but most off-line analyses (e.g., running 1.1 Historical Background time, headway, or schedule adherence) need records of when Historically, AVL system design has emphasized the real- buses arrive or depart from stops and timepoints. time loop, giving little or no attention to the off-line loop. There are other important differences between real-time Many AVL systems do not archive data in a manner useful for and archived data needs. Some AVL systems transmit data only off-line analysis because they were not designed to do so. The when a bus's schedule deviation is outside a "normal" range