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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 34
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.
