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Using Archived AVL-APC Data to Improve Transit Performance and Management (2006)

Chapter: Chapter 4 - Uses of AVL-APC Data

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Suggested Citation:"Chapter 4 - Uses of AVL-APC Data." National Academies of Sciences, Engineering, and Medicine. 2006. Using Archived AVL-APC Data to Improve Transit Performance and Management. Washington, DC: The National Academies Press. doi: 10.17226/13907.
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Suggested Citation:"Chapter 4 - Uses of AVL-APC Data." National Academies of Sciences, Engineering, and Medicine. 2006. Using Archived AVL-APC Data to Improve Transit Performance and Management. Washington, DC: The National Academies Press. doi: 10.17226/13907.
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Suggested Citation:"Chapter 4 - Uses of AVL-APC Data." National Academies of Sciences, Engineering, and Medicine. 2006. Using Archived AVL-APC Data to Improve Transit Performance and Management. Washington, DC: The National Academies Press. doi: 10.17226/13907.
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Suggested Citation:"Chapter 4 - Uses of AVL-APC Data." National Academies of Sciences, Engineering, and Medicine. 2006. Using Archived AVL-APC Data to Improve Transit Performance and Management. Washington, DC: The National Academies Press. doi: 10.17226/13907.
×
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Suggested Citation:"Chapter 4 - Uses of AVL-APC Data." National Academies of Sciences, Engineering, and Medicine. 2006. Using Archived AVL-APC Data to Improve Transit Performance and Management. Washington, DC: The National Academies Press. doi: 10.17226/13907.
×
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Suggested Citation:"Chapter 4 - Uses of AVL-APC Data." National Academies of Sciences, Engineering, and Medicine. 2006. Using Archived AVL-APC Data to Improve Transit Performance and Management. Washington, DC: The National Academies Press. doi: 10.17226/13907.
×
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Suggested Citation:"Chapter 4 - Uses of AVL-APC Data." National Academies of Sciences, Engineering, and Medicine. 2006. Using Archived AVL-APC Data to Improve Transit Performance and Management. Washington, DC: The National Academies Press. doi: 10.17226/13907.
×
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Suggested Citation:"Chapter 4 - Uses of AVL-APC Data." National Academies of Sciences, Engineering, and Medicine. 2006. Using Archived AVL-APC Data to Improve Transit Performance and Management. Washington, DC: The National Academies Press. doi: 10.17226/13907.
×
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Suggested Citation:"Chapter 4 - Uses of AVL-APC Data." National Academies of Sciences, Engineering, and Medicine. 2006. Using Archived AVL-APC Data to Improve Transit Performance and Management. Washington, DC: The National Academies Press. doi: 10.17226/13907.
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Suggested Citation:"Chapter 4 - Uses of AVL-APC Data." National Academies of Sciences, Engineering, and Medicine. 2006. Using Archived AVL-APC Data to Improve Transit Performance and Management. Washington, DC: The National Academies Press. doi: 10.17226/13907.
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Suggested Citation:"Chapter 4 - Uses of AVL-APC Data." National Academies of Sciences, Engineering, and Medicine. 2006. Using Archived AVL-APC Data to Improve Transit Performance and Management. Washington, DC: The National Academies Press. doi: 10.17226/13907.
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Suggested Citation:"Chapter 4 - Uses of AVL-APC Data." National Academies of Sciences, Engineering, and Medicine. 2006. Using Archived AVL-APC Data to Improve Transit Performance and Management. Washington, DC: The National Academies Press. doi: 10.17226/13907.
×
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Suggested Citation:"Chapter 4 - Uses of AVL-APC Data." National Academies of Sciences, Engineering, and Medicine. 2006. Using Archived AVL-APC Data to Improve Transit Performance and Management. Washington, DC: The National Academies Press. doi: 10.17226/13907.
×
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Suggested Citation:"Chapter 4 - Uses of AVL-APC Data." National Academies of Sciences, Engineering, and Medicine. 2006. Using Archived AVL-APC Data to Improve Transit Performance and Management. Washington, DC: The National Academies Press. doi: 10.17226/13907.
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Suggested Citation:"Chapter 4 - Uses of AVL-APC Data." National Academies of Sciences, Engineering, and Medicine. 2006. Using Archived AVL-APC Data to Improve Transit Performance and Management. Washington, DC: The National Academies Press. doi: 10.17226/13907.
×
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Suggested Citation:"Chapter 4 - Uses of AVL-APC Data." National Academies of Sciences, Engineering, and Medicine. 2006. Using Archived AVL-APC Data to Improve Transit Performance and Management. Washington, DC: The National Academies Press. doi: 10.17226/13907.
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Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

25 This chapter includes traditional analysis methods as well as new tools developed by agencies using archived AVL-APC data. In addition, part of this project’s survey/interview process included asking for suggestions of how AVL-APC data might be used, and the researchers also developed some concepts. To a large extent, performance analysis can be driven by a performance-monitoring protocol that specifies measures or indicators that are to be reported. Transit agencies have exper- imented with a large number of performance measures. AVL or APC data apply to many of the performance measures listed in TCRP Report 88: A Guidebook for Developing a Transit Performance-Measurement System (26). TCRP Report 100: Transit Capacity and Quality of Service Manual (27) also sug- gests several AVL- and APC-related measures for use as service quality indicators. A list of current and potential uses of AVL-APC data is given in Table 4. They are discussed in detail beginning in Section 4.3, following general discussions of data uses in an era of automatic data collection (Section 4.1) and of data needs for different analyses (Section 4.2). Section 4.3 includes several examples of analysis reports; many more can be found in the accompanying case studies. Both TCRP (14) and the Cana- dian Urban Transit Association (17) have published synthe- ses of practice that include numerous samples of reports generated from archived AVL and APC data. 4.1 Becoming Data Rich: A Revolution in Management Tools The transit industry is in the midst of a revolution from being data poor to data rich. Traditional analysis and deci- sion support tools required little data, not because data has little value, but because traditional management methods had to accommodate a scarcity of data. Automatic data- gathering systems do more than meet traditional data needs; they open the door for new analysis methods that can be used to improve monitoring, planning, performance, and management. Transit agencies—such as Tri-Met, King County Metro, OC Transpo, and HTM—that have good, automatically collected operational data are finding more and more uses for it.At first, agencies may look to an automatic data collection system only to provide the data needed for traditional analyses. But, once they have the larger and richer data stream that AVL and APCs offer, they think of new ways to analyze it, and they want more. Eventually, their whole mode of operation changes as they become data driven. One APC vendor explains, “We’re selling an addiction to data.” Five trends in data use have emerged from the paradigm shift from data poor to data rich: • Focus on extreme values • Customer-oriented service standards and scheduling • Planning for operational control • Solutions to roadway congestion • Discovery of hidden trends 4.1.1 Focus on Extreme Values Traditional methods of scheduling and customer service monitoring generally use mean values of measured quanti- ties, because mean values can be estimated using small sam- ples. However, many management and planning functions are oriented around extreme values and are, therefore, better served by direct analysis of extreme values such as 90th- or 95th-percentile values. These extreme values now can be esti- mated reliably because of the large sample sizes afforded by automatic data collection. Three examples follow: • Recovery time is put into the schedule to limit the proba- bility that a bus finishes one trip so late that its next trip starts late. Therefore, logically, scheduled half-cycle times C H A P T E R 4 Uses of AVL-APC Data

Function Tool/Analysis Record TypeNeeded Data Detail or Analysis Capacity Needed Sampling Rate Needed General view Any Incident codes, control messages. 100% Targeted Investigations (complaints, disputes, incidents) Trip recorder view (speed, acceleration) Interstop Maximum speed;records every 2 s or more to measure acceleration/deceleration rates. 100% Route and long segment running time and scheduling Timepoint Endpoint data needed for route running time. Both arrival and departure times are helpful. Stop-level running time and scheduling Stop Running time net of holding time Stop Door open and close times, incident codes, control messages, on and off counts. 10% for mean running time; 100% for analysis and scheduling based on extreme values Speed and traffic delay Stop, interstop 10% Analyzing and Scheduling Running Time Dwell time analysis Stop Door open and close times, on and off counts, farebox transactions, incident codes. 10% Schedule deviation Timepoint Both arrival and departure times are helpful. Waiting time (long headway) Stop Schedule Adherence and Long-Headway Waiting Connection protection Timepoint Arrival and departure times, control messages, farebox transactions. 100% Plotting successive trajectories Timepoint Route-level headway analysis Timepoint Waiting time (short headway) Stop Headway Analysis and Short-Headway Waiting Bunching (load/headway) analysis Stop Current on and off counts. 100% Demand along a route Stop On and off data. Demand over the day and headway/departure time determination Stop On and off data. 10% for mean values; 100% for analysis and scheduling based on extreme crowding Passenger crowding Stop On and off data. Near 100% Route Demand Analysis Pass-ups, special uses, and fare categories Stop Incident codes, farebox transactions. 100% Table 4. Decision support tools and analyses and their data needs.

Geocode stops, verify base map Stop 10% Mapping Map bus path through shopping centers, new subdivisions, etc. Interstop Interface for surveyor-initiated comments. 10% Acceleration and ride smoothness Interstop Records every 2 s or more. 100% Mechanical demand Stop, interstop 100% Terminal movement Interstop 100% Control messages Varies Incident codes and control messages. 100% Miscellaneous Operations Analysis Operator performance Varies 100% Before–after study Special event/weather analysis Trends analysis Varies As required by the type of analysis. varies Aggregation and comparison over routes, including systemwide passenger-miles Varies Multiroute analysis capacity Transfer and linked-trip analysis Stop Farebox transactions with card IDs. 100% Shared route analysis, including headways and load on a trunk Varies Data structures for shared routes. Higher Level Analysis Geographic demand and service quality analysis Stop GIS with stop locations. 100% Note: Items in italics are optional.

(scheduled running time plus recovery time) should be based on an extreme value such as the 95th-percentile run- ning time. However, without enough data to estimate the 95th-percentile running time, traditional practice sets it equal to a fixed percentage (e.g., 15% or 20%) of scheduled running time. Yet, some route-period combinations need more than this standard, and others less, because they do not have the same running time variability. AVL data allows an agency to actually measure 95th-percentile running times and use that to set recovery times. A study for Tri-Met found that basing recovery times on 95th-percentile run- ning times would lead to substantial changes in cycle time that, compared to their current schedule, could reduce annual operating cost by an estimated $7 million (28). • Passenger waiting time is an important measure of serv- ice quality. Studies show that customers are more affected by their 95th-percentile waiting time—for a daily traveler, roughly the largest amount they had to wait in the previ- ous month—than their mean waiting time, because 95th- percentile waiting time is what passengers have to budget in their travel plans to be reasonably certain of arriving on time. • Passenger crowding is also a measure in which extreme values are more important than mean values. Although traditional planning uses mean load at the peak point to set headways and monitor crowding, planners understand that what matters for both passengers and smooth oper- ations is not mean load but how often buses are over- crowded. Therefore, design standards for average peak load are set a considerable margin below the overcrowding threshold. However, load variability is not the same on every route. With a large sample of load measurements, headways can be designed and passenger crowding meas- ured based on 90th-percentile loads, or a similar extreme value, rather than mean loads. 4.1.2 Customer-Oriented Service Standards and Schedules AVL-APC data allows customer-oriented service quality measures to replace (or supplement) operations-oriented service standards. For example, on high-frequency routes, a traditional operations-oriented standard of service quality is the coefficient of variation (cv) in headway. Although such a standard may mean something to service analysts, it means nothing to passengers, and it resists being given a value to pas- sengers (e.g., how much does it benefit passengers if the head- way cv falls from 0.35 to 0.25?). With a large sample size of headway data, one can instead measure the percentage of pas- sengers waiting longer than x minutes, where x is a threshold of unacceptability. Similarly, in place of average load factor as a crowding standard, one could use a standard such as “no more than 5% of our customers should experience a bus whose load exceeds x passengers.” As these examples show, a shift toward customer-oriented measures goes hand-in-hand with the ability to measure extreme values. 4.1.3 Planning for Operational Control One of the questions posed by the explosion of informa- tion technology is how best to use information in real time to control operations, for example, by taking actions such as holding a bus to protect a connection or having a bus turn back early or run express. As agencies experiment with, or use, such actions, they need off-line tools to study the impacts of these control actions in order to improve control practices. For example, AVL-APC data were used to determine the impacts of a Tri-Met experiment in which buses were short- turned to regularize headways during the afternoon peak in the downtown area (29). 4.1.4 Solutions to Roadway Congestion Transit agencies are more actively seeking solutions to traffic congestion, such as signal priority and various traffic management schemes. They need tools to monitor whether countermeasures are effective. For example, a Portland State University study done for Tri-Met using archived AVL-APC data found that while signal priority reduced running time on some routes, it had no positive effect on others (30). In that particular study, only the overall effect on rather long seg- ments was analyzed by comparing before and after running times, making the results hard to correlate with particular intersections. For better diagnosis and fine-tuning of coun- termeasures, agencies need tools to analyze delays on stop-to- stop, or shorter, segments. 4.1.5 Discovery of Hidden Trends Behind a lot of the randomness in transit operations may be some systematic trends that can be discovered only with large data samples. For example, by comparing operators with others running the same routes in the same periods of the day, Tri-Met found that much of the observed variability in running time and schedule deviation is in fact systematic: some operators are slower and some faster. Exploratory analysis might also reveal relationships that can lead to bet- ter end-of-line identification, or to better understanding of terminal circulation needs. It is the nature of exploratory analysis to not have a pre- defined format. To support exploratory analysis, therefore, AVL-APC databases need to be open to standard data analy- sis tools. 28

4.2 Key Dimensions of Data Needs Along with listing uses of AVL-APC data, this chapter seeks to identify the particular data needs for each use, so that peo- ple involved in AVL-APC system design can better determine what features are needed to support various analyses. As indi- cated in Table 4, the needs of various analysis tools can be examined along three dimensions: basic record type, data detail, and sampling rate. 4.2.1 Basic Record Type Four basic record types, giving vehicle location at regular intervals, are considered. (For more detail on record types, see Sections 2.3.1 and 2.3.2.) Polling records are not suited to stop or timepoint matching and therefore are only suitable for man- ual investigations involving playback. Timepoint records and stop records differ chiefly in level of geographic detail. In addi- tion, stop records, which may or may not include passenger counts, are assumed to include the time at which doors opened and closed, or time of passing a stop if a bus does not stop. Timepoint records are assumed only to have either arrival or departure times. Interstop records are records of speed between stops; the term can also refer to summary data about what occurred between stops that may be part of a stop record. 4.2.2 Data Detail This second dimension indicates what additional data are needed, either as additional items in the basic record type or captured in infrequent event records. Data detail is mainly affected by what devices are integrated into the AVL system. 4.2.3 Sampling Rate As mentioned earlier, analyses involving estimation of mean values require a relatively small sample, while esti- mating extreme values or proportions requires large sample sizes. Therefore, it makes sense, and is consistent with prac- tice, to treat this dimension as binary: either 100% (all vehi- cles equipped) or 10% (a sample of the fleet equipped), noting however that some uses that demand large sample sizes on a subset of routes can be accommodated without instrumenting the entire fleet, if the instrumented percent- age is managed carefully. 4.3 Targeted Investigations Analysis tools may be used in targeted investigations, which may be conducted to support legal, payroll, operations, main- tenance, and other functions. For this category, transit agen- cies use archived AVL data to investigate suspected operator misbehavior and customer complaints, incidents, or accidents. These cases can be investigated using playback (a capability of many AVL systems oriented to real-time applications) in which data from the subject trip is viewed as if it were hap- pening in real time. If stop or timepoint records are stored in a database, cases can be investigated more efficiently using general query capability. For investigating incidents, it is often helpful simply to deter- mine whether the bus in question was really there. Several tran- sit agencies report having refuted accident claims by referring to archived AVL data. GPS-based location systems can indicate whether a bus was off route. Being able to relate the AVL data to operator-initiated event records and to control messages sent by dispatchers can be helpful for some types of investigations. With interstop records, speed and possibly acceleration information can be extracted from an AVL database; however, to date, no agencies are known to sample so frequently as to give their AVL system the “black box”function of trip recorders that have become common for accident investigations in avia- tion and trucking. For investigating customer complaints, simple playback (or better yet, a database query) can identify whether a bus was very early or late, or (with GPS data) off route. However, because being early is often a matter of only 1 or 2 min, time- point records are better than polling records for verifying com- plaints about early buses.Also, because an early arrival does not necessarily mean an early departure, the location data should indicate departure time. Some agencies use their AVL data to investigate operator overtime claims. AVL data has shown, for example, that an operator who reported late to the garage actually finished his last trip on time, suggesting that the late pull-in was inten- tional. Speed data from interstop records can be used to mon- itor speeding; however, experience has shown that speed data can be quite unreliable. 4.4 Running Time Analyzing and scheduling running time is one of the rich- est application areas for archived AVL-APC data.Without AVL data, agencies must set running times based on small manual samples, which simply cannot account for the running time variability that comes with traffic congestion. Buses are scheduled at the timepoint level; therefore, sched- uling demands timepoint data. Because schedules sometimes refer to arrivals as well as departures, it is helpful if timepoint records include both arrival and departure times. Running time analyses that require only estimation of mean values, or that involve only occasional studies (e.g., delay and dwell time analysis), can be conducted with only a sample of the fleet equipped with AVL. However, routine scheduling applica- tions based on extreme values need the entire fleet equipped. 29

As part of this project, a set of improved analysis tools was developed for analyzing running time. They are mentioned in this section, but illustrated and further described in Chapter 5. 4.4.1 Allowed Time, Half-Cycle Time, and Recovery Time A common analysis examines the distribution of observed running time for scheduled trips across the day compared with scheduled running time, also called “allowed time.”An example is given in Figure 2, where the vertical bars show mean observed running time, the short lines show 85th-percentile values, and the arrows indicate maximum observed running times. Heavy horizontal lines show scheduled running time. This figure also distinguishes net and gross trip time, as explained in Section 4.4.4. When the number of observations is not too large, a scat- terplot showing every observation can be useful. Based on the observed distribution of running time for either a single scheduled trip or a set of contiguous trips in a period that will be scheduled as a group, schedule makers can choose a value for allowed time according to their preferred scheduling philosophy. Some schedule makers prefer to base schedules on mean running time. An alternative approach, aimed at improving schedule adherence, is to intentionally put slack into the schedule; this approach has to be coupled with an operating practice of holding at timepoints. With such a schedule, a high percentage of trips depart almost exactly on schedule, and the low percentage of trips that run late are not far behind schedule. The amount of slack put into a schedule is often a simple fraction of mean running time, with ad hoc adjustments based on experience. A more scientific, data-driven approach is to use a percentile value, or “feasibility criterion.” To illus- trate, a feasibility criterion of 85% means setting allowed time equal to 85th-percentile observed running time; such a schedule can be completed on time 85% of the time. This approach is used in Chapter 5, with both the viewpoint of design (“tell me the 85th-percentile running time”) and analysis (“tell me what feasibility I’d get if I added 1 minute to allowed time”). Analysis of running time is also pertinent for determining how much recovery time to schedule at the end of the line. The time from a bus’s departure at one terminal to its next departure in the reverse direction has been called the “half- cycle time”; it is the sum of running time and recovery time. Because the purpose of recovery time is to limit the likeli- hood that delays encountered in one trip will propagate to the next, half-cycle time is based logically on a high-percentile value of running time. Tri-Met has begun to systematically revise its half-cycle times, basing them on a 95% feasibility criterion so that there will be only a 5% chance that a bus will arrive so late that it starts the next trip late. For this applica- 30 Gross and net route section times, mean, 85% and max values Line: Route: 1 1 Company: From: To: Station NS Castilielaan Hermes Departure From: Until: times 07:00 12:00 Dates: 2000/05/01 until 2000/05/26 Mon 4 Tue 4 Wed 4 Thu 4 Fri 4 Sat 0 Sun 0 Total 20 Trips scheduled: Trips used: Trips excluded: 520 427 2 ( ( 82 0 (Count) %) %) 07:00 Source: Hermes (Eindhoven), generated by TriTAPT 08:00 09:00 10:00 11:00 12:00 time [hh:mm] 0 5 10 15 20 25 Count ro u te s ec tio n tim e [m ] Tr ita pt 1 .0 (b 82 ) li ce ns e h old er is Pe ter K no pp ers , T ec hn isc he U niv ers ite it D elf t. C op yri gh t © 1 99 7- 20 06 T U De lft 14 19 15 16 19 15 17 17 16 18 17 15 16 18 12 9 18 18 18 16 18 17 18 18 16 17 Figure 2. Observed running time by scheduled trip.

tion, scheduled recovery time is set to be the difference between 95th-percentile running time and allowed time. The chief engineer for operations analysis at Brussels’ tran- sit agency has recently been working with a scheduling software vendor to develop reports to support statistically based sched- ules. Their approach uses three parameters, as explained in this example: If the three parameters are 95%, 80%, and 5 min, then half-cycle time is set equal to 95th-percentile running time, and allowed time is set equal to 5 min less than 80th-percentile run- ning time. That way, there will be an 80% chance that a trip fin- ishes no more than 5 min late, and a 95% chance that the next trip can start on time. 4.4.2 Segment Running Time Scheduling running time on segments, or equivalently set- ting departure (or arrival) times at timepoints relative to the trip start time, can either precede or follow scheduling route times. One approach, common in U.S. practice, is to first determine segment running time based on mean observed running time by segment, perhaps adding a certain percent- age for slack, and then constructing route time as simply the aggregation over the segments. Simply aggregating over segments will not work with a feasibility-based approach, because the sum of the parts will not yield a valid measure for the whole. For example, the sum of 85th-percentile segment running times does not equal a route’s 85th-percentile running time; the sum will be far greater, in fact. The Delft University of Technology has developed the Pass- ing Moments method of extending statistically based schedul- ing to the segment level (31). Applied at several Dutch transit agencies, the Passing Moments method bases timepoint sched- ules on f-percentile completion time from each timepoint to the end of the line, where completion time is running time from a point to the end of the line, and f is the feasibility cri- terion (e.g., 85%). Segment running times are determined by working backwards from the end of the line, without ever explicitly analyzing observed running time on timepoint-to- timepoint segments. This approach was designed to overcome operators’ resistance to holding, which is the key to good sched- ule adherence. If a schedule is written based on mean running time, operators know that, if they hold at a timepoint, they will have a 50% chance of finishing the trip late and thereby getting a shortened break; therefore, they are reluctant to hold. With 85th-percentile allowed times between each timepoint and the end of the line, operators know that even if they hold, they have a high chance of finishing on time. The Passing Moments method is one of the scheduling tools described in Chapter 5. The Passing Moments method has two advantages compared to setting slack time simply proportional to mean running time. First, it is sensitive to where on the route delays and running time variability occur. Second, it tends to put less slack in the early part of the route and more in the later part of the route, which is a better way to distribute slack than simply applying it proportionately throughout. It results in holding buses less in the early part of the route because they may need that time later in the route. Steve Callas, the manager of Tri-Met’s AVL data analysis program, has suggested that if operating practice is such that buses do not hold at timepoints, it may be better to use a low feasibility criterion because running early is more harmful to passengers than running late. For example, a statistically based approach for segment-level scheduling might be to base departure time at a timepoint on 40th-percentile observed cumulative running time (i.e., from the start of the line to a point). That way, there will be only a 40% chance of a bus departing early, and those that depart early should not be very far ahead of schedule. 4.4.3 Choosing Homogeneous Running Time Periods Another problem in running time analysis is choosing the boundaries of running time periods within which allowed time is constant. Establishing periods of homogeneous running time involves a trade-off between short periods within which scheduled running times match the data well versus longer periods of constant allowed time but greater variability.A com- mon logic for resolving this trade-off is first to determine, for each scheduled trip, an ideal allowed time (e.g., mean running time, or 85th-percentile running time, depending on the desired feasibility criterion) and then to make running time periods as long as possible subject to the restriction that no more than a certain percentage of the scheduled trips in that period have an ideal running time that deviates by more than given tolerance from the suggested running time for that period. 4.4.4 Excluding Holding (Control) Time Ideally, scheduling tools should use net running time, which excludes holding time, also called “control time.” Identifying what part of observed running time is holding time can be tricky, requiring greater data detail, and is done by only a few agencies with AVL-APC data. Ideally, stop records should indi- cate both when doors open and close, and when the wheels start to roll, something provided by at least some APC vendors. If the bus is ahead of schedule, any unusual gap between door close time and departure time can be interpreted as holding. The running time analysis shown earlier in Figure 2 distin- guishes gross from net running time. The gray bars show net running time; their black tops are control time, making the combined height equal to gross running time. Of the short horizontal lines representing 85th-percentile values, those 31

extending to the left of a scheduled start time are gross running times, and those extending to the right are net running times. Because in good weather holding may occur with the doors open, NJ Transit’s system is being upgraded to provide a record of how many passengers boarded and alighted every few sec- onds (the main on-board computer frequently queries the on- board APC analyzer while doors are open and writes records), allowing NJ Transit to recognize periods of inactivity even while the doors are open. Eindhoven’s algorithm looks for unusually long dwell times when the bus is ahead of schedule. Event codes for lift use or a fare dispute can also help iden- tify holding time by explaining the cause for long dwell times. Another version of holding is still harder to detect—“killing time” en route to avoid being early. Data and algorithms that would help detect “killing time” would be valuable and are being developed at NJ Transit. 4.4.5 Stop-Level Scheduling In many European countries including the Netherlands, schedules are written at the stop level. In fact, on many routes, every stop is a timepoint. Making (almost) every stop a time- point has the advantage of replacing occasional large holding actions with frequent small ones, which are less obvious and irritating to passengers. Stop-level schedules fit well with the trend of giving cus- tomers better information. Stops are where customers meet the system, and where they need to know scheduled departure times. Internet-based trip planners need stop-level schedules, as do real-time next-arrival systems (to know stop-to-stop expected running time). In current practice, agencies with cus- tomer information systems like trip planners and real-time information systems estimate stop-level departure and run- ning time by interpolation between timepoints; using stop- level AVL data to develop stop-level schedules offers an obvious improvement. Not providing the public with stop-level sched- ules is a good example of practice being driven by the historic lack of data—practice that should change as advanced tech- nology is deployed in transit. Stop-level schedules are valuable for control even when every stop is not a timepoint. Some AVL systems display schedule deviation to the operator, who can use this infor- mation all along the route to try to adjust bus speed. For example, in Eindhoven a small display for operators shows schedule deviation in units of 10 s. Such a system can be effective, however, only if it is based on a realistic, finely tuned schedule. Simply interpolating between timepoints is too approximate if speed between timepoints is not uni- form, such as when the route passes major intersections. For operators to have confidence in, and therefore use and ben- efit from, a schedule deviation display, they need data-based stop-level schedules. Signal priority is another application that needs stop-level schedules, if priority is conditional on buses being late. Con- ditional priority is a form of operational control, pushing late buses ahead while holding early buses back, and to be effective, it needs a finely tuned schedule at every signalized intersection. This system has been used very effectively in Eindhoven (32). Conditional priority without fine-tuned schedules can easily devolve into either unconditional prior- ity (because buses always arrive late) or no priority (because buses arrive early). To have a good control margin, schedules have to be written so that the probability of arriving late is far from the extremes of 0% and 100%. 4.4.6 Speed and Delay Analysis Speed, delay, and dwell time studies are analyses that help support a transit agency’s efforts to improve commercial speed, something that benefits both operations and passengers. “Speed” in this context is average speed over a segment, not instantaneous or peak speed. A display such as given in Fig- ure 3 showing delay by segment (or, alternatively, average speed by segment) helps a transit agency to identify problem loca- tions, to monitor the impacts of actions that affect speed, and to monitor and document historic trends in operating speed. In that figure, the thin horizontal lines are individual observa- tions of delay by segment; the box height is the 85th-percentile delay, and the bar inside the box indicates mean delay. Analysts will be interested not only in average delay, but also in how vari- able it is, and in the likelihood of extreme values. A report showing delays or speeds between stops offers a richer, more geographically detailed view than one using timepoint segments. Another reason to prefer stop records as the basis of delay analysis is that it allows dwell time and con- trol time (which almost always occur at stops) to be removed, which puts a clearer focus on the effects of the roadway and traffic on bus speed and delay. “Delay” can be defined in several ways. Two definitions of delay are (1) the travel time between stops minus the average travel time measured during non-congested periods such as early morning or late evening and (2) the amount of time spent at speeds below 5 km/h minus the time spent at stops. (Eindhoven uses this second definition.) To support this def- inition of delay, an AVL system needs records of when speed thresholds are crossed. 4.4.7 Dwell Time Analysis Transit agencies also try to improve commercial speed by reducing dwell time, using such measures as low-floor buses or changes to fare collection equipment and practices. Stop records with door open and close times allow agencies to ana- lyze dwell time to determine impacts and trends. 32

Such an analysis should preferably be aided by passenger counts, in order to separate out the impact of the number of boardings and alightings and to identify whether any on- vehicle congestion impact arises when vehicles are crowded. On-off counts, farebox transactions, and incident codes that reveal wheelchair and bicycle use are all useful for giving analysts an understanding of dwell time. 4.5 Schedule Adherence, Long-Headway Waiting, and Connection Protection Monitoring schedule adherence is a valuable management tool, because good schedule adherence demands both realis- tic schedules and good operational control. It is probably the most common analysis performed with AVL-APC data. Schedule adherence can be measured in a summary fash- ion as simply the percentage of departures that were in a defined on-time window, or perhaps as the percentage that were early, on time, and late. Standard deviation of schedule deviation is an indicator of how unpredictable and out of control an operation is; along with schedule adherence, it is part of a daily service quality report in Eindhoven. A distribution of schedule deviations provides full detail. Such a distribution allows analysts to vary the “early” and “late” threshold depending on the application, or to deter- mine the percentage of trips with different degrees of lateness. A profile of schedule deviations along the line is a valuable tool, showing how both the mean and spread of schedule devi- ation changes from stop to stop. Figure 4 shows two examples taken from Eindhoven. The heavy, black line indicates mean schedule deviation; the heavy, gray lines indicate 15th- and 85th-percentile deviation. Thin lines represent individual observed trips. How close the mean deviation is to zero indi- cates whether the scheduled running time is realistic. If the mean deviation suddenly jumps, it means the allowed seg- ment time is unrealistic. Deviations at the start of the line are particularly informative: if most trips are starting late, it might indicate that the route’s allowed time is too long, and that operators are starting late to avoid running early. The spread in deviations, and how much it increases along the line, is a good indicator of operational control. The display in Figure 4(a) shows a poorly scheduled and poorly controlled route; Figure 4(b), in contrast, shows a route for which most schedule deviations remain in the 0- to 2-min band all along the line. 33 Individual delays, mean, 85% Line: Route: 1 1 Company: From: To: Stop 1 Stop 41 USA_H28 Departure From: Until: times 00:00 30:00 Dates: 2003/03/31 until 2003/04/03 Mon 1 Tue 1 Wed 1 Thu 1 Fri 0 Sat 0 Sun 0 Total 4 Trips scheduled: Trips used: Trips excluded: 388 340 1 ( ( 88 0 (Calc) %) %) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 stop 00:00 Source: Delft University, generated by TriTAPT 01:00 02:00 03:00 04:00 05:00 de la ys b et we en s to ps [m m: ss ] Tr ita pt 1 .0 (b 82 ) li ce ns e h old er is Pe ter K no pp ers , T ec hn isc he U niv ers ite it D elf t. C op yri gh t © 1 99 7- 20 06 T U De lft Figure 3. Delays by segment.

34 Individual punctuality deviations, 15%, mean and 85% Line: Route: 1 1 Company: From: To: Stop 1 Stop 41 USA_H28 Departure From: Until: times 08:00 11:00 Dates: 2003/03/31 until 2003/04/04 Mon 1 Tue 1 Wed 1 Thu 1 Fri 1 Sat 0 Sun 0 Total 5 Trips scheduled: Trips used: Trips excluded: 90 81 0 ( (0%) 90 (Count) %) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 stop -10 -5 0 5 10 pu nc tu al ity d ev ia tio ns [m inu tes ] Tr ita pt 1 .0 (b 82 ) li ce ns e h old er is Pe ter K no pp ers , T ec hn isc he U niv ers ite it D elf t. C op yri gh t © 1 99 7- 20 06 T U De lft Early Late (a) Showing Both Systematic and Strong Random Deviation Individual punctuality deviations, 15%, mean and 85% Line: Route: 1 1 Company: From: To: Station NS Castilielaan Hermes Departure From: Until: times 07:00 09:00 Dates: 2000/06/19 until 2000/06/23 Mon 1 Tue 1 Wed 1 Thu 1 Fri 1 Sat 0 Sun 0 Total 5 Trips scheduled: Trips used: Trips excluded: 60 50 0 ( ( 83 0 (Count) %) %) SN GL LS OT EL GP CZ WC GL RL IO AW AL DW EA CL SW HL DH RL CL CL stop -5 0 5 10 pu nc tu al ity d ev ia tio ns [m inu tes ] Tr ita pt 1 .0 (b 82 ) li ce ns e h old er is Pe ter K no pp ers , T ec hn isc he U niv ers ite it D elf t. C op yri gh t © 1 99 7- 20 06 T U De lft (b) Showing Little Systematic or Random Deviation Source: Hermes (Eindhoven), generated by TriTAPT Figure 4. Schedule deviation along a route.

Because schedules are written at the timepoint level, time- point data will support schedule adherence analysis. And because schedule adherence involves estimating proportions and extremes (detecting the percentage of early and late trips), the full fleet should be equipped. Finally, because schedules sometimes refer to arrival time as well as departure time, a data collection system that captures both is preferred. Passenger waiting time on routes with long headways is closely related to schedule adherence. Chapter 6 shows how it is possible to determine excess waiting time from the spread between the 2nd-percentile and 95th-percentile schedule deviation. Passengers are particularly interested in whether they can make their connections. Arriving 4 min late is not a problem if the time allowed for the transfer is 5 min, but it could be a big problem if the allowed time is only 3 min. However, if the departing trip is held—again, the convergence of schedule planning and operations control—other issues arise. AVL data is ideal for determining whether specific connections were met. To analyze connection protection an agency must define the particular connections it wishes to protect or at least analyze. The researchers found one transit agency using its archived AVL data for this purpose. Integrating control message data, which might include requests for holding to help a passenger make a connection, would permit a deeper analysis of oper- ational control. Incorporating demand data, ideally transfer volumes, would make the analysis richer still. Connection protection analysis requires data structures and software that create the capacity to perform analyses across routes. 4.6 Headway Regularity and Short-Headway Waiting On routes with short headways, headway regularity is important to passengers because of its impact on waiting time and crowding. It is also important to the service provider because crowding tends to slow operations and because much of operations control is focused on keeping headways regular. To measure headways, data has to be captured on succes- sive trips, making headway analysis particularly sensitive to the rate of data recovery, as one lost trip means two lost head- ways. Analyzing headway when only part of the bus fleet is instrumented poses the logistical challenge of getting all the buses operating on a route to be instrumented; because of this challenge, Table 4 indicates headway analysis needs 100% of the fleet to be instrumented with AVL. Headways matter all along the route, not only at time- points; therefore, stop records are best suited to headway analysis. (In fact, headways matter most at stops with high boarding rates.) However, because headways at neighboring stops are strongly correlated, timepoints can be thought of as a representative sample of stops, making it possible, although not ideal, to estimate headway-related measures of opera- tional quality from timepoint data. To the degree that opera- tors hold at timepoints, however, using them as representative stops becomes deceptive. 4.6.1 Plotting Trajectories One much-appreciated AVL data analysis tool is a plot of successive trajectories on a route, as illustrated in Figure 5. Its format shows observed versus scheduled trajectories for a line- direction. In the color version of this graph, each bus appears in a different color so that bus-specific trends (e.g., a slow driver) can be spotted. This kind of analysis is helpful for illustrating the dynamics of bunching and overtaking and for showing where delays begin and how they propagate. How- ever, while this tool is helpful for giving the sense of how a route operates, it does not yield any numerical results and is suitable only for analyzing a single day’s data at a time. 4.6.2 Headway Analysis A numerical analysis of headway data applies over multi- ple days, for a route-direction and a period of the day with rel- atively uniform headways. Typical summary results are mean and coefficient of variation (cv, which is standard deviation divided by mean) of headway. On short-headway routes, the Transit Capacity and Quality of Service Manual (27) assigns levels of service for service reliability based on values of head- way cv. Mean headway can be compared with mean scheduled headway to see whether more or less service than scheduled was operated. In place of headway cv, Eindhoven uses a regu- larity index, which is the mean value of the absolute headway deviation divided by mean headway. A distribution of headways is an even richer result than mean and cv of headway, allowing analysts to see how often headways were very short or very long, using any threshold they desire. For its rapid transit routes, the MBTA uses the percentage of headways greater than 1.5 scheduled headways as an indicator of service quality. Analysis procedures have to be careful in dealing with period boundaries. To illustrate, if the morning peak period ends at 9:00, a trip scheduled to pass a timepoint at 8:58 may pass on some days before 9:00 and on other days after 9:00. In an analysis of headways in a period ending at 9:00, that trip will sometimes be counted and sometimes not, introducing variability into the analysis as an artifact. 4.6.3 Passenger Waiting Time On short-headway routes, passengers can be assumed to arrive at random; therefore, passenger waiting time can be 35

determined and analyzed directly from the headway distribu- tion. As an example, transit agencies in both Brussels and Paris calculate, from headway data, the percentage of passen- gers waiting longer than the scheduled headway plus 2 min. As part of this project, analyses of passenger waiting time based on headway data were developed (see Chapter 6). 4.6.4 Headway-Load Analysis Headway and load have a simultaneous effect on each other—longer headways lead to larger loads, and larger loads lead to longer headways. Analyzing headway and load together can lead to interesting insights. By analyzing headway and load together, Tri-Met created a method for determining to what extent an overload is caused by headway variation, as opposed to demand vari- ability (28). The idea behind this approach is that overloads caused by headway variation should be “cured” by better headway control, while overloads that cannot be simply explained by headway variation may require a change in scheduled departure times or headways. Tri-Met estimates, for a given route-direction-period, the slope of the headway- load relationship using a least-squared fit. Then, using that slope, Tri-Met normalizes the loads of individual trips to what the loads would have been if the headway had been as scheduled. 4.7 Demand Analysis The sources of passenger use data are APCs and fare col- lection systems. In this report, the only fare records consid- ered are location-stamped transactions, because analysis of farebox data at the route level or higher is routine. 4.7.1 Demand Along a Route Passenger demand on a route, for a given direction and period of the day, has three dimensions: geographic (i.e., along the route); between scheduled trips (i.e., how is the 7:15 trip different from the 7:30 trip); and between days. Most analysis views aggregate over two dimensions and analyze the remain- ing one; it is also possible to aggregate over only one dimen- sion, showing the other two in the analysis, as the examples will show. 36 Base-file of 2003/04/10: 106 trips scheduled, 96 trips measured (91 %) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 stop 0 1 2 3 4 5 6 7 8 9 10 11 12 distance [mile] 11:11 11:21 11:31 11:41 11:51 12:00 12:10 12:20 12:30 12:40 12:50 13:00 tri p M LT [h h:m m] 11:30 12:00 12:30 13:00 tim e [hh :m m] Tr ita pt 1 .0 (b 82 ) li ce ns e h old er is Pe ter K no pp ers , T ec hn isc he U niv ers ite it D elf t. C op yri gh t © 19 97 -20 06 TU D elf t Source: Hermes (Eindhoven), generated by TriTAPT Figure 5. Observed (solid) versus scheduled (dotted) trajectories.

The geographic dimension of demand is shown in a vol- ume profile or load profile, depending on whether results are expressed in passengers per hour (volume) or passengers per trip (load); another view shows ons and offs by stop. One graphical format, developed by Delft University researchers and illustrated as the lower step line in Figure 6, shows not only mean segment loads, but also mean offs, ons, and through load at each stop in a single profile. The upper, gray step function indicates 85th-percentile segment loads, thus adding the dimension of day-to-day variation. This report has already pointed out the importance of extreme values of load for both passenger service quality monitoring and scheduling. Also shown in Figure 6 for each stop, as well as for the route as a whole, are box and whiskers plots of offs (just to the left of each stop) and ons. The box extends from the 15th percentile to the 85th percentile; a bar indicates the mean value; and the X above the box indicates the maxi- mum observed value. Analysis of demand along a route is necessary for under- standing where along the route high loads occur. It supports decisions about stop relocation and installing stop amenities, and routing and scheduling actions that affect some parts of a route differently from others, such as short turning, zonal service, and limited stop service (33). 4.7.2 Demand Across the Day and Scheduling Headway and Departure Time By abstracting the geographical dimension using trip sum- mary measures such as total boardings, maximum load, and passenger-miles, one can focus on the other two dimensions of demand, variation within a day and between days. Figure 7 shows how demand varies between scheduled trips, with scheduled trips on the horizontal axis and one measure of demand, in this case mean boardings, on the vertical. In other versions of this graph (not shown), day-to-day variation is presented by showing a scatterplot (horizontal whiskers) or selected percentile values, which allows one to see extreme values of load that are important to both scheduling and operational control. Using established thresholds, trips can be categorized and counted by degree of crowding. In Figure 7, four of the scheduled trips in the period ana- lyzed had no valid APC data. They are represented with a large X and a more darkly colored bar whose height is set equal to the average of the nearest trip before and after it with valid counts. The issue of imputing values to missing data is discussed in Chapter 11. Passenger-miles is another summary measure over a route, being the product of the segment load multiplied by the 37 Balanced passenger counts (min, mean, 85% and max) Line: Route: 25 1 Company: From: To: Lozerlaan Station CS uitstap HTM Departure From: Until: times 07:00 09:00 Dates: 2001/11/05 until 2001/11/08 Mon 1 Tue 1 Wed 1 Thu 1 Fri 0 Sat 0 Sun 0 Total 4 Trips scheduled: Trips used: Trips excluded: 60 35 5 ( ( 58 8 (Count) %) %) LL EW MS LL BL VL He DW LW GL AP ZP SP DL SS HP VP WZ Br GM SV AV NH SC Total stop 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 %pmile pa ss en ge rs Tr ita pt 1 .0 (b 82 ) li ce ns e h old er is Pe ter K no pp ers , T ec hn isc he U niv ers ite it D elf t. C op yri gh t © 1 99 7- 20 06 T U De lft 0.3 0.4 0.6 0.8 2.2 2.0 4.1 5.4 4.7 4.1 6.1 6.2 5.6 4.5 6.8 8.9 7.6 5.2 4.1 7.0 2.9 4.0 6.7 Source: Delft University, generated by TriTAPT Figure 6. Load and on/off profile.

segment length. When divided by overall route length, this total indicates the average vehicle occupancy along the route. Special considerations relative to measuring passenger-miles are covered in Chapters 8 and 9. An analysis of demand variation across the day supports scheduling, which, in part, sets headways and departure times so as to achieve target loads. There remains the opportunity to develop design tools for scheduling that take advantage of large APC sample sizes to estimate a demand profile across the day. Using passenger counts combined with measured headways, and averaging over many days, one should be able to derive the passenger arrival rate as a function of time. Combining these arrival rates in small (e.g., 1-min) time slices using a reference frame that moves at the speed of a bus allows one to predict the peak load on a trip based on its departure time and the departure time of its leader. With a minute-by-minute load profile across the day, one valuable tool would be able to find periods of homogeneous demand within which a constant headway can be used, anal- ogous to scheduling tools that seek periods of homogeneous running times. Another valuable tool would not assume con- stant headways at all, but would select departure times that balance loads between trips, accounting for how demand rates vary across the day, as suggested by Ceder (34). Tools of this sort are currently under development for the transit agency of the Hague. In the future, there may be scheduling tools that account for within-day and between-day variation in demand, as well as within-day and between-day variation in running time, in order to design route schedules that respond to how both demand and running times vary across the day, using statis- tical methods to limit the probability of overcrowding and insufficient recovery time. 4.7.3 Passenger Crowding There is a strong relationship between vehicle crowding and passengers’ experience of crowding, but the perspectives are different. For example, if half the trips are empty and half are overcrowded, then only 50% of the trips are overcrowded, yet 100% of the passengers experience an overcrowded trip. Measures of crowding from the passenger perspective are dis- cussed in Chapter 7. 4.7.4 Pass-Ups and Special Uses Operator-initiated incident codes used to register such events as pass-ups, wheelchair customers, and bicycle customers can be used to analyze special demands and events along a route or across the day. Being able to locate them along a route might be useful for load analysis, running time analysis, and facility planning. 38 Balanced unlinked passenger trips per vehicle trip (estimated mean 67.9) Line: Route: 25 1 Company: From: To: Lozerlaan Station CS uitstap HTM Departure From: Until: times 00:00 15:00 Dates: 2001/11/05 until 2001/11/08 Mon 1 Tue 1 Wed 1 Thu 1 Fri 0 Sat 0 Sun 0 Total 4 Trips scheduled: Trips used: Trips excluded: 244 150 19 ( ( 61 8 (Count) %) %) 08:00 09:00 10:00 11:00 12:00 13:00 14:00 time [hh:mm] 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 105 110 115 120 125 130 135 140 145 150 Count pa ss en ge rs Tr ita pt 1 .0 (b 82 ) li ce ns e h old er is Pe ter K no pp ers , T ec hn isc he U niv ers ite it D elf t. C op yri gh t © 1 99 7- 20 06 T U De lft 2 3 2 2 1 1 1 4 2 3 4 3 4 3 3 1 2 4 3 3 4 3 3 3 0 4 0 2 3 3 0 2 2 2 2 2 0 4 2 2 3 Source: Delft University, generated by TriTAPT Figure 7. Boardings by trip across the day.

4.8 Mapping Equipped vehicles can serve as GPS probes whose archived AVL data is used to improve a transit agency’s base map. For example, if buses stop often at a location not indicated on the base map as a stop, then a stop may be missing on the base map (perhaps because it has been informally added by oper- ators); this data can be used to help locate both permanent and temporary stops. A more explicit use of buses as GPS probes is to intention- ally use them to map a bus’s path through a new shopping center or subdivision. For this application, the on-board com- puter has to be set to make frequent interstop records. An Israeli APC supplier includes a learning mode that allows an on-board surveyor, seated beside the operator and holding a laptop computer, to create geocoded records with codes and comments at points of interest (e.g., where a bus makes a turn) to help map the bus’s path. 4.9 Miscellaneous Operations Analyses The availability of archived AVL-APC data creates oppor- tunities for analysis of many other aspects of operations, of which five are listed in Table 4 and discussed in this section. Other analysis opportunities will undoubtedly be discovered, highlighting the need for AVL-APC databases to support exploratory and new analyses. 4.9.1 Acceleration and Ride Smoothness One aspect of service quality that might be measured with an advanced AVL system is the smoothness of the ride. Pas- sengers value a smooth ride, without jerky accelerations or decelerations, while avoiding unsafe speeds. At present, tran- sit agencies in Paris and Brussels use externally contracted surveyors called “mystery shoppers” to rate quality of service in several categories, including ride smoothness; their ratings are, of course, subjective. Very frequent records of speed would permit an objective measurement of linear speed, acceleration, and deceleration; swerving and bouncing also could be measured if accelerometers in three directions were integrated into the system. 4.9.2 Mechanical Demand AVL data may permit analysts to estimate mechanical demands on buses in order to relate them to vehicle perfor- mance and maintenance. For example, combining measure- ments of vehicle acceleration and passenger load with GIS information on roadway grade allows estimation of the trac- tive and braking forces required, which then could be analyzed to find relationships to fuel consumption, brake wear, or engine maintenance needs. Another suggested measure of mechani- cal demand that could be determined from interstop AVL records is the number of acceleration/deceleration cycles. 4.9.3 Terminal Movements Interstop GPS records might be used to analyze vehicle movements at terminals, which may be of interest at busy terminals with capacity, safety, or efficiency issues. A better understanding of terminal movements can also lead to better determination of arrival and departure times, which are crit- ical for schedule analysis. 4.9.4 Control Messages While operator-initiated messages (e.g., indicating pass- ups or bicycle use) are customarily coded in a manner that permits numerical analysis, control messages sent by radio to bus operators are not customarily so coded. To the extent they could be coded for common commands such as hold for the schedule or hold for a connection, they would allow one to analyze where and when those control messages are used, account for their impact on running time, and analyze their effectiveness. 4.9.5 Operator Performance Finally, published (28) and unpublished studies by Tri- Met using AVL-APC data indicate that much of the variance in running time and schedule adherence can be explained by operator behavior. An analysis of performance by operator could be a valuable tool for training operators and for exper- imenting with different methods of supervision and con- trol. To account for the bus bunching phenomenon, an operator’s performance on short-headway routes should account for the position of its leader. Performance elements can include schedule deviation (especially at dispatch), run- ning time, layover time, headway maintenance and bunch- ing, and more. Correlations between data items may reveal interesting operating patterns. Do operators that are beginning to run early intentionally slow down, and do operators that are getting behind speed up? Do operators drive differently when they have a heavy load or after they depart the terminal late? Being able to identify individual operators may reveal operator- specific patterns or relationships between running time and operator experience (both overall and on the specific route). Uncovering operating patterns like this can be useful for plan- ning both schedules and methods of supervision and training. Operator performance must be analyzed with careful respect for operator acceptance and safety. If used for discipline, data 39

for such analyses may be in danger of sabotage. Agencies may not want to use the AVL data directly to discipline operators, but it can certainly be used to help dispatchers and supervi- sors better target their efforts at conventional discipline. For example, some agencies report that if data indicates a recur- ring problem with a particular trip starting late, a supervisor might be requested to observe. More seriously, safety can be compromised if operators are punished for getting behind schedule. Such concerns do not necessarily mean that operators should not be given feedback on their performance. Experience with data col- lected automatically in Rotterdam’s tram operation shows that operators may enjoy getting a written record of their performance—for the first time operators had written evi- dence to show their family what a good job they were doing in staying on schedule. 4.10 Higher Level Analyses This section discusses analyses of AVL-APC data that cover extended periods of time or multiple routes. 4.10.1 Comparisons and Aggregations By comparing results of analyses done over selected dates, AVL-APC data can be used in before–after studies or to ana- lyze operations during special events or weather conditions. Trends analysis can be seen as simply an extension of the before–after study, but it suggests a need for storing higher level summaries in a separate database. An example is a monthly systemwide report on schedule adherence. A transit agency might specify measures that it wants to follow over time, calculate those measures periodically (e.g., every month) from the detailed AVL-APC data archive, and save those period summaries in a smaller, higher level database where they can be used for trends analysis. Many analyses that involve aggregation or comparison over routes can benefit from AVL-APC data. One example is a periodic route performance comparison, which may include items such as on-time performance or total boardings along with data from other sources such as scheduled vehicle-hours or farebox revenue. Another example is making annual sys- temwide passenger-miles estimates for reporting to the NTD, which can be made by aggregating mean passenger-miles per trip over all the trips in the schedule (see further discussion in Chapter 9). These applications suggest having an automated process of periodically calculating and storing summary measures in higher level tables. 4.10.2 Transfers and Linked Trips While APCs provide the data needed to analyze demand on a route, they do not capture the information needed to iden- tify linked trips or transfers. However, if fare media include unique IDs (as is the case with both magnetic cards and smart cards), stop- and time-stamped farebox transactions permit analysis of transfers and linked trips. Linked-trip analysis is especially important in Canada, where linked trips are the standard measure of transit use. Despite fare systems not capturing alightings by ID code, there have been successful efforts in New York, Chicago, and Dublin to determine transfers and linked-trip origins and destinations by tracking where a fare ID is next used to enter the system (18, 19, and unpublished work). This area is prom- ising for future research. 4.10.3 Headways and Other Measures on Shared Routes Many transit networks have trunks served by multiple lines or multiple patterns (branches) of a line. Some measures of activity on a trunk are simple aggregations of stop-level meas- ures; examples are schedule adherence and passenger load. For these purposes, all that is needed is an interface allowing one to select the appropriate set of stops and patterns. However, headways on a shared route can only be determined by going back to original stop or timepoint records, including data from all the trips serving the trunk, and linking them where their respective route joins and leaves the common trunk. This pro- cedure demands a special data structure for a route trunk, something developed as part of this project (see Section 11.5). 4.10.4 Geographic Analyses Transit agencies often want to do route-independent analy- ses based on geography, including both demand analysis (how many boardings occur in a certain area) and service quality analysis (what is the on-time performance in a certain area). Integrating AVL-APC data with GIS models requires data structures that link geographic locations to stops and route segments, and a process to extract and aggregate results for the selected stops and segments. For demand modeling, methods are needed to convert on- off counts at stops into trip generation rates in small traffic analysis zones. However, this specialized procedure could be driven equally by manual or automatically collected data; the challenge for APC data analysis is to export demand rates by stop for only a selected period of the day. 40

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TRB's Transit Cooperative Research Program (TCRP) Report 113: Using Archived AVL-APC Data to Improve Transit Performance and Management explores the effective collection and use of archived automatic vehicle location (AVL) and automatic passenger counter (APC) data to improve the performance and management of transit systems. Spreadsheet files are available on the web that provide prototype analyses of long and short passenger waiting time using AVL data and passenger crowding using APC data. Case studies on the use of AVL and APC data have previously been published as appendixes to TCRP Web-Only Document 23: Uses of Archived AVL-APC Data to Improve Transit Performance and Management: Review and Potential.

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