Cover Image

Not for Sale

View/Hide Left Panel
Click for next page ( 78

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 77
77 CHAPTER 12 Organizational Issues Effectively capturing, archiving, and using AVL-APC data to the extent that buses are ordered to deviate from their involves overcoming organizational as well as technological schedules, operations control makes schedule matching challenges. This chapter summarizes the chief challenges more difficult. reported by the surveyed agencies. Fortunately, agencies are becoming more aware that col- lecting and later analyzing data can also contribute to improv- 12.1 Raising the Profile ing performance. There is an opportunity to improve data of Archived Data quality by changing control and supervision practices. Exam- ples include detecting and correcting invalid sign-on data, A reason that many AVL systems have failed to deliver their informing the AVL-APC system of a revised schedule when a potential in terms of useful archived data is that those who bus is deviated from its schedule, and standardizing codes for specified and designed the systems either did not emphasize the control messages. importance of archived data or, more likely, did not recognize important differences between the needs of real-time data and 12.3 Staffing and Skill Needs those of archived data (38). Time and again, procurements have focused on real-time applications, with the implicit expec- To date, transit agencies making good use of AVL-APC data tation that archived data analysis would somehow happen. have been able to do so only because of the strong set of staff Some of the lack of appreciation of the character and value of skills they have been able to employ. The lack of available data archived data has been on the part of vendors whose primary analysis tools has meant that agencies have needed the skills to product is real-time information; and some has been on the develop their own database and analysis tools. Only because of part of transit agency staff who managed procurements. There dedicated, qualified, and resourceful staff were Tri-Met and is a need for transit agency staff who are involved in system pro- King County Metro able to make the great strides they did in curement to better understand how system design affects what improving the quality of their AVL-APC data and in develop- data is captured, what the data quality will be, and what off-line ing tools to analyze it. analyses it will be able to support. At the same time, there is a In the future, with the development of third-party analysis need for decision makers to appreciate the importance of software and increasingly relevant and accepted standards for archived data acquisition and analysis for improving system data definitions and interfaces, the need for expertise in infor- management and performance. mation technology (IT) to develop archived AVL-APC data Several studies have looked for quantifiable benefits of AVL systems should decline. However, agencies will still need the systems to justify their cost. Where benefits have been quan- staff and expertise to analyze the data. tified, they most often come from an off-line application, Managing data quality takes considerable staff effort. At namely, revising scheduled running times. This is ironic, con- agencies with good AVL and APC data, one or two staff mem- sidering that off-line analysis has often been an afterthought bers are usually devoted to overseeing that the systems deliver of such systems. the data they should. Identifying and correcting accuracy problems sometimes takes considerable IT expertise, espe- 12.2 Management Practices cially if matching algorithms or data objects have to be changed. With AVL systems, matching, accuracy, and data capture to Support Data Quality issues are often just as much a problem for real-time applica- Control and supervision has traditionally been concerned tions as for archived data, so that little additional work is about performance, not about data collection. In fact, needed for archived data itself.