Stochastic optimization of a timetable M.E. van Kooten Niekerk.
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Transcript of Stochastic optimization of a timetable M.E. van Kooten Niekerk.
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Stochastic optimization of a timetable
M.E. van Kooten Niekerk
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Outline
• Timetable: theory and reality
• Time Supplements
• Optimization of Time Supplements
• Extension of model
• Theoretical results
• Practical results
• Conclusion
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Timetable: Theory & Reality
• Theoretical: Minimum technical driving times
• Reality is different:– Human factor– Weather– Other
• To cover this, extra driving time is scheduled
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Time Supplements (1)
• In NL: about 5% of MTDT is added as time supplement
• Per trip segment, between important points
• How to assign time supplements?
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Time Supplements (2)
• At every timing point: Actual departure ≥ scheduled departure. If too early, wait. No negative delay.
• D: Delay compared to timetable• s: Time supplement• δ: Actual delay on segment
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Time Supplements (3)
• Spread evenly– 1st intuition: OK– Likely to wait, so total time has larger average than
necessary• All at the start
– Excessive waiting on the trip– No serious option
• All before arrival– Minimal waiting during the trip– Earliest arrival at end of trip– Too late on most timing points
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Time supplements: Optimization
• Distribute time supplements s.t.:– Total supplement = constant– Average delay is minimal
• Problem: non-linearity of delay with respect to applied time supplements
• Solution: Combination of simulation and (I)LP
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Time supplements: Optimization
• 1 Base-timetable
• Number of realizations (set of ‘random’ delays), about 1000
• Goal: minimize average delay in the realizations by making changes to the base-timetable
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Optimization model (1)
• At every timing point: Actual departure ≥ scheduled departure. If too early, wait. No negative delay. Dn ≥ 0
• Formula: RrNtsDD trtrtrt ..1,..1for },0max{ ,,1,
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Optimization model (2)
..Nts
..R..N, rtD
Ss
..R..N, rtDsD
RDwD
t
rt
t
rttrtrt
N
t
R
rrtt
1for 0
10for 0
11for
:toSubject
/min
,
N
1t
,,,1
0 1,
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Results
• Time supplements not evenly spread across trip segments
• Average delay is reduced for the greater part of the trip
• Delay at end of trip is larger
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Extension of model
• Now 1 single line• Reality: complex set
of lines• To model:
– Slow and fast trains on the same track, overtaking is not possible
– Conflicts when trains are crossing
– Single track
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Extension of model
• Overtaking of trains is not possible
• Minimal Headway between trips:
hrhrd ddH ,,1,,2
~~~
hrhra aaH ,,1,,2~~~
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Extension of model
• Possible conflicts on track usage
• Eg. Crossing of trains• Train t2 should wait
until t1 has arrived
0,,1,,2 hrhr ad
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Extension of model
• Trips influence each other, delays can be propagated
• We should keep track of real departure time, only delay is not enough
• We should consider a whole day, not one hour• Change: 21 hrs a day, 20 realizations• Gives LP with 500.000 variables and 400.000
constraints• 16 to 32 hours computation time
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Theoretical results
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Practical results
• Results were applied during 8 weeks in 2006 on the Zaanlijn
• Punctuality went from 79,4% to 86,5%
• Results on corridor Amsterdam-Eindhoven lead to theoretical reduction of average delay of 30%.
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Conclusion
• Optimization of distribution of time supplements leads to a reduction of average delay without extra cost.
• Some stations may have more delays
• Method will be applied to whole network of NS
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Literature
• Kroon, L.G., Dekker, R., Vromans, M.J.C.M., 2007, Cyclic railway timetabling: a stochastic optimization approach. In: Geraets, F., Kroon, L.G., Schöbel, A., Wagner, R., Zaroliagis, C. (Eds.), Algorithmic Methods in Railway Optimization. Lecture notes in Computer Science, vol. 4359. Springer, pp. 41-66
• Kroon, L.G., Maróti, G., Retel Helmrich, M., Vromans, M.J.C.M, Dekker, R., 2007, Stochastic improvement of cyclic railway timetables. In: Transport Research, Part B 42, Elsevier, pp. 553-570.
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Questions?