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OCR for page 33
33 Individual delays, mean, 85% Company: USA_H28 Departure times Dates: 2003/03/31 until 2003/04/03 Trips scheduled: 388 (Calc) Line: 1 From: Stop 1 From: 00:00 Mon Tue Wed Thu Fri Sat Sun Total Trips used: 340 (88%) Route: 1 To: Stop 41 Until: 30:00 1 1 1 1 0 0 0 4 Trips excluded: 1 ( 0%) 05:00 Tritapt 1.0 (b82) license holder is Peter Knoppers, Technische Universiteit Delft. Copyright 1997-2006 TU Delft 04:00 delays between stops [mm:ss] 03:00 02:00 01:00 00:00 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 stop Source: Delft University, generated by TriTAPT Figure 3. Delays by segment. Such an analysis should preferably be aided by passenger A distribution of schedule deviations provides full detail. counts, in order to separate out the impact of the number of Such a distribution allows analysts to vary the "early" and boardings and alightings and to identify whether any on- "late" threshold depending on the application, or to deter- vehicle congestion impact arises when vehicles are crowded. mine the percentage of trips with different degrees of lateness. On-off counts, farebox transactions, and incident codes that A profile of schedule deviations along the line is a valuable reveal wheelchair and bicycle use are all useful for giving tool, showing how both the mean and spread of schedule devi- analysts an understanding of dwell time. ation changes from stop to stop. Figure 4 shows two examples taken from Eindhoven. The heavy, black line indicates mean 4.5 Schedule Adherence, schedule deviation; the heavy, gray lines indicate 15th- and Long-Headway Waiting, and 85th-percentile deviation. Thin lines represent individual Connection Protection observed trips. How close the mean deviation is to zero indi- cates whether the scheduled running time is realistic. If the Monitoring schedule adherence is a valuable management mean deviation suddenly jumps, it means the allowed seg- tool, because good schedule adherence demands both realis- ment time is unrealistic. Deviations at the start of the line are tic schedules and good operational control. It is probably the particularly informative: if most trips are starting late, it most common analysis performed with AVL-APC data. might indicate that the route's allowed time is too long, and Schedule adherence can be measured in a summary fash- that operators are starting late to avoid running early. The ion as simply the percentage of departures that were in a spread in deviations, and how much it increases along the defined on-time window, or perhaps as the percentage that line, is a good indicator of operational control. The display in were early, on time, and late. Standard deviation of schedule Figure 4(a) shows a poorly scheduled and poorly controlled deviation is an indicator of how unpredictable and out of route; Figure 4(b), in contrast, shows a route for which most control an operation is; along with schedule adherence, it is schedule deviations remain in the 0- to 2-min band all along part of a daily service quality report in Eindhoven. the line.

OCR for page 33
34 Individual punctuality deviations, 15%, mean and 85% Company: USA_H28 Departure times Dates: 2003/03/31 until 2003/04/04 Trips scheduled: 90 (Count) Line: 1 From: Stop 1 From: 08:00 Mon Tue Wed Thu Fri Sat Sun Total Trips used: 81 (90%) Route: 1 To: Stop 41 Until: 11:00 1 1 1 1 1 0 0 5 Trips excluded: 0 (0%) -10 Early Tritapt 1.0 (b82) license holder is Peter Knoppers, Technische Universiteit Delft. Copyright 1997-2006 TU Delft punctuality deviations [minutes] -5 0 5 Late 10 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 stop (a) Showing Both Systematic and Strong Random Deviation Individual punctuality deviations, 15%, mean and 85% Company: Hermes Departure times Dates: 2000/06/19 until 2000/06/23 Trips scheduled: 60 (Count) Line: 1 From: Station NS From: 07:00 Mon Tue Wed Thu Fri Sat Sun Total Trips used: 50 (83%) Route: 1 To: Castilielaan Until: 09:00 1 1 1 1 1 0 0 5 Trips excluded: 0 ( 0%) -5 Tritapt 1.0 (b82) license holder is Peter Knoppers, Technische Universiteit Delft. Copyright 1997-2006 TU Delft punctuality deviations [minutes] 0 5 10 GL OT GP WC RL AW DW CL HL RL CL SN LS EL CZ GL IO AL EA SW DH CL stop (b) Showing Little Systematic or Random Deviation Source: Hermes (Eindhoven), generated by TriTAPT Figure 4. Schedule deviation along a route.