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 74
74 of this project involved making the structure of the TriTAPT 11.4 Aggregation Independent database more flexible, allowing an agency to include any of Sequence number of fields in a stop record. Examples are numeric fields for maximum speed and binary fields for whether a particu- Almost all analyses other than incident investigation involve lar event type (e.g., pass-up or drawbridge delay) occurred at aggregation: over multiple days of observation, over multiple the stop or on the segment following. stops or segments, over multiple scheduled trips in a day, over Incorporating interstop summaries in the stop record multiple patterns that make up a line, or over multiple lines. provides adequate geographic detail for many purposes. An important distinction in aggregation is whether an Where an interstop segment does not provide adequate analysis has to follow a sequence of stops or trips. In many geographic detail (e.g., if there are two traffic bottlenecks analyses, stop and trip sequence are irrelevant; once the between stops and the delay at each bottleneck needs to be appropriate stops and trips have been selected, the result is a identified), analysts can simply add a dummy stop to the simple aggregation. Examples include total ons; maximum base map. load; and number of timepoint departures that are early, on If the database's fundamental record is a timepoint rather time, and late. Summary measures that do not involve calcu- than stop record, the length of a timepoint segment creates lations along a sequence of stops can easily be summarized a considerable loss in geographic detail if events that occur over multiple patterns and multiple lines and lend themselves at stops and en route are simply labeled as occurring on also to comparison between lines. a timepoint segment. For some analyses, however, this loss of detail is unimportant. For example, in a running time 11.4.1 Summary Records for Routine analysis, it may be sufficient to know how often the bicycle and Higher Level Analyses rack or wheelchair lift is used on each timepoint segment; where on the segment it was used does not matter. How- Transit agencies often have certain routine analyses that ever, if it does matter, one could query the original event involve this simple type of aggregation. To reduce processing records. time, summary records can be created at the trip level, con- taining such items as total ons; maximum load; and number of timepoint departures that are early, on time, and late. An 11.3.2 Matching Other Record Types analysis such as average or distribution of boardings per trip An alternative to incorporating summaries into stop records on a route, or percentage of early/on-time/late departures, is to associate each event record with a stop (either where the can be performed using those trip summaries. Higher level event occurred or the last stop visited for en route events) and summaries (e.g., aggregating over a week or month, or over a departure time, just like stop records are matched. Tri-Met fol- period of the day, or both) can speed processing for reports lows this approach, adding to event records fields indicating the needing only summaries at that level, such as quarterly route nearest stop and distance from that stop. performance reports and historical trend analysis. Analyses that want to merge stop record information with At higher levels in a transit agency, reports using AVL-APC information from other event record types can select multi- data often involve data from other sources as well, such as data ple record types and use the stop and scheduled trip as keys on revenue, accidents, or customer satisfaction. This kind of to correlate records. Of course, that kind of on-the-fly merg- report is best generated by a general management database. ing of data from multiple record types is more complex and The AVL software's responsibility is to create summary records time consuming than one in which the data was merged dur- that can be exported to the general management database, ing entry processing, but it is also more flexible. If event which also can be used for comparison reports, historical trend records are not labeled with a stop or timepoint, matching reports, and other such higher level reports. At Brussels' tran- and merging them on the fly with stop or timepoint records sit agency, for example, the AVL system generates line-level would be impractical. summaries of schedule adherence and passenger waiting time From the survey, the use of event records other than stops for every 2-week period; those summaries are exported to the and timepoints appears to be only on an ad hoc, analyst- general management database that is used to analyze route per- intensive basis. For example, seeing an unusually large run- formance along many dimensions. Of course, this arrangement ning time might prompt an analyst to query whether there requires a well-developed enterprise database to receive the was an event that caused a major delay on a segment or a spe- AVL summary records. cialized study might query bicycle rack events to get an idea Planning analyses, including those that use a GIS, generally of where they occur. However, to the researchers' knowledge, want to use long-term average passenger count, running time, bicycle rack events and similar event data are not part of rou- and service quality data. AVL-APC systems can supply those tine running time or demand analysis tools. averages and export them to the planning/GIS database.