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72 In the United States, to the researchers' knowledge, the first and why. Failure patterns can indicate a need for on-board application of third-party software for analyzing AVL data is equipment to be repaired or adjusted or for the base map or under way at CTA for use with a new smart bus system that fea- schedule to be updated. Some transit agencies have developed tures stop announcements and event recording on all buses and semi-automatic correction processes. For example, Houston passenger counting on a sample of the fleet. Called RideCheck Metro's screening program checks operator and run codes Plus because its analysis reports were originally developed for against the dispatch database; if a small discrepancy is found ride check data, it includes many standard analyses (e.g., load that could be explained as a simple keying error, it is either profiles and schedule adherence) and also offers some GIS capa- corrected automatically or brought to the attention of a bilities including links with demographic data and mapping. person monitoring the process who can make or authorize Third-party software for analyzing archived AVL-APC data the correction. has the advantage of modularity, not being tied to any partic- ular brand of equipment or scheduling system. As a stand- 11.2.1 Full and Partial Matching alone product, it is likely to continually improve, unless the product is discontinued. It offers the benefits of standardiza- A large part of entry processing is checking location and tion and replication. A major disadvantage of third-party time stamps for a match against the schedule and base map. software in the United States is that transit agencies' funding A fully matched record will indicate the stop or timepoint ID, mechanisms often forbid them to buy software only for data the scheduled trip ID, and the scheduled departure time from analysis, although such a purpose often can be justified within the stop or timepoint. Matching to stop ensures that records the context of a major AVL or APC system procurement as will be analyzed in the right sequence, and matching to sched- was the case at CTA. uled departure time allows analysis of schedule adherence and selection of trips based on scheduled departure time. In some AVL-APC systems, stop and timepoint records 11.1.5 Software Developed are already matched to stop and scheduled trip; processing for Custom Analysis simply checks for consistency. Other AVL-APC data streams Several specialized AVL analyses by university research teams have to be matched during entry processing. For example, have been reported in the literature, including the previously Tri-Met's AVL data records have only vehicle block ID, with mentioned analyses done at the University of Michigan using time and GPS coordinates. Matching correlates GPS coor- Ann Arbor Transit Authority AVL data and at Morgan State dinates with stops, parses trips, and adds trip ID and sched- University using (Baltimore) MTA data. In both of these cases, uled departure time fields from a table correlating trips the specialized processing required to analyze these datasets left with blocks. When the vehicle block is known, tracking is them inaccessible to staff analysts. In contrast, Tri-Met's APC much easier. database, developed in house, supports analyses by both staff For stop records, matching can include checks for whether analysts and researchers from Portland State University. consecutive stop records should be merged, as when a bus closes its doors and advances a few feet, but then reopens its doors to let some more passengers in or out. In Tri-Met's 11.2 Data Screening and Matching entry processing, multiple stop records for the same stop are As AVL and APC data is retrieved, it usually undergoes some not directly merged; rather, a flag indicates which records are "entry processing" before being entered into the archive data- "primary" (the first stop record for a given stop) versus "sec- base. Entry processing involves screening for and perhaps cor- ondary." Calculation routines are programmed to logically recting errors. If data is not already matched, entry processing merge secondary stop records with their primary record. Stop includes matching data to the schedule and base map. Data that or timepoint record processing may also involve inferring cannot be matched, or is rejected in the screening process, is arrival or departure time by adding or subtracting a constant logically rejected (usually, not by discarding the data, but by travel time from the recorded time, when the recorded time flagging it as unusable). Some AVL-APC databases have flags occurs a known distance from the stop or timepoint. indicating "don't use counts" (for passenger counts that were In many AVL systems, a bus passing a stop or timepoint rejected) and "don't use times" (for invalid time data). without stopping will cause a stop or timepoint record to be Screening involves typical checks for consistency and generated on board. If not, records for stops that were skipped range. For example, passenger count data will be rejected if on can be generated as part of the matching process, as is done at and off totals for a vehicle block differ too much. Tri-Met. While processing AVL-APC raw data is usually automated, If polling data were to be used for more than playback daily monitoring by a skilled analyst is valuable, at least during analysis, matching would be done as part of entry processing the break-in phase of a system, to see what data was rejected to create stop records from it.