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Real Time Train Rescheduling @ SNCF. 1 Agenda Essentials Basic Model Applications Traffic density is...
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Transcript of Real Time Train Rescheduling @ SNCF. 1 Agenda Essentials Basic Model Applications Traffic density is...
![Page 1: Real Time Train Rescheduling @ SNCF. 1 Agenda Essentials Basic Model Applications Traffic density is getting very high in several networks and management.](https://reader035.fdocuments.in/reader035/viewer/2022070308/551b957c550346a6148b61d5/html5/thumbnails/1.jpg)
Real Time Train Rescheduling@ SNCF
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Agenda
• Essentials
• Basic Model
• Applications
Traffic density is getting very high in several networks and management areas also tend to grow. In consequence, traffic management complexity is rising and management system have to evolve.
A major challenge today is to study efficient tools to help experts’ decisions in the rescheduling process of tomorrow.
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Essentials about Real Time Rescheduling
• Essentials– An overview of the problem
– Main challenges
• A Basic Model
• Applications
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An overview of the problem
• Aim : on line recomputing railway schedule following perturbations.
• Method : minimizing the total accumulated delay.
• Nowadays, SNCF has developed 3 off line prototypes working within a train simulator (SiSyFe),
• This allows us to study formulations and optimization techniques.
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Main challenges
Rescheduling requirements:– Tractable - Fast calculations ( < 10 min)– Operational solution – must be immediately applied
on the field– … (good enough solution)
Remark:initial timetable can be used to construct a first feasible solution!
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A brief look at a model formalizing the train rescheduling problem & the railway operations.
• Essentials
• A Basic Model– Network formalization
– Variables
– Constraints
• Applications
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Formalizing:Railway network is a graph.
Station (simple)
Junction, switches, …
Complex station
Track
nodes are stations or switches, and edges are interconnecting tracks.
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Decisions Variables
Rescheduling decisions concern:
• Time of departure and arrival at each node,
(this is equivalent to speed variation considerations )
• Sequencing of trains at nodes,
• Track choice.
• (cancellation)
These decisions have to respect:
operational constraints & commercial constraints.
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Constraints (examples)
The following examples of constraints are associated with each train (c) at each node (n) of the network.
Headways:• In order to prevent conflicts, trains must be spaced. We impose a specific separation time
between departures (D) and/or arrivals (A) of the two trains (c1 & c2 with c1 before c2 ):Min_spacing A(c1,n) - A(c2,n) && Min_spacing D(c1,n) - D(c2,n)
Running times:• Note: considering a minimal and a maximal running time to reach one node from another
is equivalent to imposing speed limits:Min_run A(c,n2) - D(c,n1) Max_run
Stops duration:• Due to commercial and operating factors (maintenance, for example) stopping times
must be bounded: Min_stop D(c,n) - A(c,n) Max_stop
Other specific constraints:• connections between two trains, shuttles, …• we must take into account track choice and sequencing (and cancellation).
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Linear Programming Model
155,1v.1
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Applications @ SNCF
• Essentials
• Model
• Applications– Software system @ SNCF
– 1.Traffic fluidification,
– 2.Traffic control,
– 3.Re-routing.
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Software system implementation
155,1v.1
Train simulator
Takes into account:
•Infrastructure,•Signaling system,•Rolling stock,
•Incidents,•Traffic Control orders,
•Drivers’ behavior
Initial timetable
Control
(positions, …)
Command
Incidents detection
LIPARI Software System
Re-scheduling tools
Timetabling variations monitoring
New Schedule with new :•Routing,•Sequencing,•Timetables.
Translation into commands:•Sequence programming,•Route programming.
Implementation
Sardaigne
• Experimental design,
• Statistical analysis results
incidents
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1- Traffic fluidification
• Aim: manage closely a railway node to prevent conflict between pairs of trains in order to ensure fluidity of the traffic.
• Decisions: speeds, re-sequencing.
155,1v.1155,1v.1
Spac
e
time
First speed limitation(incident)
With fluidification Withoutfluidification
gain
SignalingsystemSecond speed limitation
(consequence)
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1- Rémilly - Baudrecourt
155,1v.1
<1 B>
<2 B>
<2
<2
<1
<1
Poste 2 de Metz-Sablon
SEI de Lamorville
Benestroff
St-Avold
< 2 B >
C3
J4
J1
J2
J3C1
C2
• Management of pairs of predictable conflicts.
• Radius = 50 km
• very heterogeneous traffic (from international freight
to TGV)
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1- Conclusion about traffic fluidification
155,1v.1
Experiments showed 2 problems:
– simplex method vs. robustness of solutions,
– linear programming vs. acceleration modeling.
Real experimentations are not scheduled due to:
– lack of operational equipment (Galileo/GPS, GSM-R, …)
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2- Traffic Control support tool
• Objective: re-compute precisely a new railway schedule following perturbations and help experts in traffic control decisions.
• Scope: minor incident management (e.g. few minutes delays in a heavy traffic area)
• Decisions: timetable, re-sequencing and track choice.
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1- Tours-Bordeaux, Éole, …
155,1v.1
• Incidents:
– delay at the entrance
– delay during a stop
(5-30 min)
• Tours-Bordeaux
– 100.000 var.
– 200.00 const.
– Time limit < 5mn
• ÉOLE project (link between east & west
networks in Paris)
– up to 540 trains
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2- Conclusion about traffic control tools
Studies show:– The sensitivity of solutions: few variations (e.g. 3s) can
lead to problems. (see traffic fluidification)
– Real size of problems were treated.
Perspectives:– Experimenting different models
– Extending the model’s scope (fluidification/routing)
– Integrating in the future control system.
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3- Insertion of re-routed trains
• Scope: major incident (e.g. major line breaks down)
• Principle: trains are to be inserted in a new schedule considering a set of pre-defined routes.
• Uses a less accurate description of the network. (macroscopic)
• Resolution method can be tuned to this specific problem.
Original Schedule
Trainsto be inserted
Original Schedule
Inserted Trains
Cancelled Trains
Before optimization After optimization
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3- Lyon – Paris High Speed Line (LGV 1)
155,1v.1Incident:
– 230 km of “LGV ” is down
– re-routing by Dijon.
Exemple:– Time window: 16h-24h– 80 trains– 30 nodes– 1380 nodes-trains
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3- Conclusion about rerouting
Remarks:
– looks like a “capacity management tool” (i.e. a basic planning tool)
– Macroscopic description leads to refine solutions in a second stage.
Problems:
– Today, cancellation of trains in inhomogeneous traffic is a hard bargain (regulation).
– New developments concern (homogeneous) suburban traffic.
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Global Conclusions
Real Time Train Rescheduling @ SNCF:
Essentials/Model/Applications.
About objective function, optimality,… and robustness!
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Criteria & robustness
What should we optimize?
– Sum of total delays,
– Delays perceived by clients, (including connections, etc ..)
– An economic cost function, (delay fees)
– Capacity management,
– … ?
Criteria may differ, but robustness is the common goal of infrastructure managers.
Not (only) robustness of optimality, but most of all robustness of feasibility!
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Robustness(es)
Indeed, from an industrial point of view, robustness is a key goal:the “best” solution must be operational …
i.e. robust against minor incidents.
Because :
– control system cannot monitor precisely all micro-events,
– great inertia of machines & human factors make precise controlling difficult,
– … life is not predictable !
=> Now, how can we achieve this goal ?
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Thank you for your attention!
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