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66 CHAPTER 10 Designing AVL Systems for Archived Data Analysis One of the hard lessons learned is that off-line analysis 10.2 Level of Spatial Detail has different data needs than real-time monitoring and that, therefore, AVL systems designed for real-time monitoring As discussed in Sections 2.3.1 and 4.2.1, the choices in may not deliver the type and quality of data needed for off- spatial detail of basic AVL records are polling records (occur- line analysis. Considering the ways off-line data can be used ring at arbitrary locations, when the bus is polled), time- to improve operations and management, and considering point records, and stop records. Collecting data at a finer the way AVL system design affects what data is captured as level is also possible. well as its quality, this chapter presents findings related to AVL system design. 10.2.1 Time-at-Location and Location- The emphasis of this chapter is on AVL rather than APC at-Time Data system design because APCs have always been designed for off-line analysis. To a large extent, this chapter summarizes Polling data can be characterized as location-at-time data, Furth et al. (6). giving bus location at an arbitrary time; stop and timepoint records, on the other hand, have time-at-location data, giving the time at which a bus arrives or departs from a specific loca- 10.1 Off-Vehicle versus On-Vehicle tion. Most off-line analyses, including analyses of running time Data Recording and schedule adherence, require knowledge of departure time There are two options for recording data with AVL systems: from standard locations. Therefore, stop and timepoint records on-vehicle (in an on-board computer) or off-vehicle (by are inherently better suited to off-line analysis of AVL data. sending data messages via radio to a central computer). As Theoretically, time-at-location could be estimated from discussed in Section 2.3, the radio channel capacity limits an polling data by interpolation. This method introduces inter- AVL system's ability to record data over the air. On-vehicle polation errors, whose magnitude can be almost as large as data storage is clearly superior in that it presents no effective the polling interval on segments in which the plausible bus limit on data recording. speed has a wide range (e.g., because of intermittent traffic Where radio-based systems are still contemplated, buyers congestion). During periods of traffic congestion, it can be must learn the capacity of any proposed system to collect difficult with polling data to determine whether a bus report- timepoint and/or stop data, along with random event data. ing coordinates close to a stop is in a queue waiting to reach Capacity depends on the number of radio channels available, the stop, is at the stop, or has already left the stop and is wait- the number of buses instrumented, message length, and ing in a traffic queue. specifics of the technology used. The number of radio chan- The project survey did not find a single case of a transit nels available to a transit system is strictly limited and varies agency routinely using polling data for off-line analysis by location, because radio channels are allocated by govern- except for playback to investigate incidents. Researchers used ment. Radio-based systems have been successfully configured such a data stream from Ann Arbor for some operational to record useful data for off-line analysis; for example, Metro analyses (41), but the process of going from raw poll messages Transit's system makes timepoint records from all buses and to trajectories matched to route and schedule was too involved stop records from about 15% of the fleet by sending messages to become routine. The three case study agencies with round- over the air to a central computer. robin polling data do not use it off line except for incident
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67 investigation using playback. Also, the survey indicates that all ter customer information (both off line, as part of trip plan- of the traditional AVL suppliers, even if they still use polling ning, and in real time, for predicting arrival time), finer con- to support real-time applications, include timepoint records trol, and ability to apply conditional (schedule-based) signal in their data streams as well. priority. Stop-level analysis helps enable operations analysis However, a relatively new entry to the market, stand-alone to identify points of delay, determine the impact of changes "next arrival" systems, uses only polling data to track bus loca- to stop location or traffic control, and analyze bunching. tion. Like earlier AVL systems, they are designed around a real- Stop-level data also permits more customer-oriented service time application and, according to the interviewed vendor, use quality analysis, such as enabling determination of passenger polling data to minimize the amount of equipment installed in waiting time at stops. the vehicles, making such systems less expensive. This vendor With stop-level data, bus arrival and departure times are claims to have obtained good test results using the data from its easier to determine at the end of the line. If the last segment's system for off-line analysis of on-time performance. The next data is unreliable, what is lost becomes much smaller with arrival system's data stream includes predicted arrival time at stop-level data. stops (based on proprietary algorithms); as buses get close to a The practice of making timepoint, but not stop, records stop, predicted arrival time should become a rather accurate appears to be partly a relic of past practice, partly a limitation measure of actual arrival time, especially if the polling cycle is of radio-based data communication, and partly a simplifica- short, and therefore might be used as an approximation. A tion (for example, an agency with timepoint records only has drawback of next arrival systems is that, while their application to make sure its timepoints are mapped correctly, not all its focuses on arrival time, most running time and schedule stops). In today's technology age, with on-board data storage adherence analyses are concerned with departure time. possible at relatively little cost, there seems little reason to set- tle for less than stop-level data. 10.2.2 Timepoint versus Stop-Level Data Recording 10.2.3 Interstop Data Given that obtaining time-at-location data is important, Automatically collected data on what happens between what location detail is needed: stop level or timepoint level? stops is not nearly as important as data about stops. How- Of course, stop-level data is needed for passenger counts; but, ever, there is nearly no marginal cost to making interstop for operations analysis, what is the incremental value from records, which can support some useful applications. Exam- getting data at all stops as well as at timepoints? ples include monitoring maximum speed (both as a check for Because scheduling practice in the United States is based on speeding and as a measure of quality of traffic flow), moni- timepoints, timepoint data is all that is needed for traditional toring time spent below crawl speed (as a measure of delay), running time and schedule adherence analyses. Metro Transit's and treating the bus as a GPS probe for mapping bus paths. AVL-APC system design emphasizes this distinction: on buses Another possibly valuable use, mentioned earlier, is to analyze with APCs, stop records are created; while, on buses with only operations at terminals to help better determine actual arrival AVL, only timepoint records are created. Timepoint data tends and departure times. to be favored by systems that rely primarily on radio transmis- One possible configuration, applied at NJ Transit, permits sion for data recording, because timepoint messages do not records at regular, user-set intervals; for mapping a bus's path, consume much radio channel capacitytimepoint messages are the interval can be made quite small. Eindhoven's configura- not very frequent and tend to be rather short, including only tion, in which a record is made whenever speed crosses a timepoint ID, time and location stamp, and identifiers. (Inter- crawl-speed threshold, can be generalized. By using a few dif- estingly, this issue does not arise in the Netherlands, because ferent thresholds, users could estimate not only delay (time almost every stop is a timepoint there. Also, stop spacing in the spent below crawl speed), but also a speed profile, which Netherlands tends to be about 60% greater than in the United might be used to characterize traffic quality or to monitor States, resulting in fewer stops.) speed in different speed zones. Tri-Met's configuration, in However, stop-level detail offers advantages to a transit which only maximum speed between stops is recorded, is agency willing to go beyond traditional scheduling and oper- partly a concession to limited on-board data storage (which ations analyses. Those advantages stem from (1) finer geo- was an important factor in the mid-1990s). Given the current graphic detail for operations analysis and planning, and availability of low-cost on-board data storage, frequent inter- (2) the fact that stops are where customers meet the system, stop records can easily be accommodated. However, until making stops a natural unit for customer-oriented schedul- now, the value of much interstop detail was not yet proven. ing and service quality analyses. In Section 4.4.5, several Frequent interstop records detail can aid in matching. For advantages of stop-level scheduling were cited, including bet- instance, speed records may help resolve situations such as