INTELLIGENT REAL-TIME TRAFFIC MANAGEMENT SYSTEM

FOR COMPLEX AND BUSY RAILWAY NETWORKS

Andrea D’Ariano1 , Davide Nucci2, Dario Pacciarelli3, Massimo Rosti4

Date: 30 May 2016

Presenter: Andrea D’Ariano

Company: Università degli Studi Roma Tre

Andrea D’Ariano1 , Davide Nucci2, Dario Pacciarelli3, Massimo Rosti4

1,3 Università degli Studi Roma Tre, Roma, Italia2,4 Alstom Ferroviaria S.P.A., Bologna, Italia

* Contact e-mail: dariano@ing.uniroma3.it

� Introduction�Traffic management models

�AGLIBRARY solver

Presentation contentsPresentation contents

�AGLIBRARY solver

�Alstom case study

2

Alstom - Roma Tre Research Collaboration

Context: Keep “service intentions” in case of unexpected events

Aim: Development of novel railway traffic management systems

for a precise, reliable and effective train traffic regulation

in terms of punctuality increase and energy minimization

Tool: Flexible rail operations via advanced models and algorithms

for optimal train routing, sequencing and timing decisions

Application: Recover real-time railway traffic flow disturbances,

such as multiple delayed trains and blocked tracks,

in busy and complex networks (i.e. rail bottlenecks)

Station B

Station C

Station A

Recovery

time

Example : Reference TimetableExample : Reference Timetable

Station D

Station E

Station FTime

Minimum

headway

Buffer

time

4

Station B

Station C

Station A

Conflicts

Initial delayExample : OperationsExample : Operations

Station D

Station E

Station F

Delay

Current Time

5

Time

Station B

Station C

Station A

Initial delay

Consecutive

delay

Timetable

sequenceExample : OperationsExample : Operations

Station D

Station E

Station F

Delay

6

TimeCurrent Time

Station B

Station C

Station A

Consecutive

delay

Initial delayFIFO

sequenceExample : OperationsExample : Operations

Station D

Station E

Station F

Delay

7

TimeCurrent Time

Timetable sequence With rescheduling

A Dutch case study: average initial delay of 1.24 min, and maximum consecutive delay of 16 min

Comulative consecutive delay in all stations is 3093 min when using the timetable sequence,

while this is 1611 min when consecutive delays are minimized by optimal train rescheduling.

Consecutive Delay

No advanced dispatching support tool exists to reschedule

vehicle movements during complex network operations.

In fact, there is still a lack of:

StateState--ofof--thethe--art: Open issuesart: Open issues

• Precision: Models and algorithms must include the variability

of train dynamics and must respect specific problem constraints;

Robustness: Existing dispatching systems are able to provide • Robustness: Existing dispatching systems are able to provide

viable solutions only for small networks and simple disturbances;

• Quality: A set of good solutions can be computed only if global

conflict resolution is considered when optimizing orders, routes

and times of the trains running in the investigated rail network;

• Efficiency: The development of novel optimization algorithms

must consider the limited computation time constraints.

9

� Introduction

�Traffic management models�AGLIBRARY solver

Presentation contentsPresentation contents

�AGLIBRARY solver

�Alstom case study

10

Clearing point

Block sections

Running

Sight & Reaction

time Minimum

headway

time

Headway Time: The blocking time theoryHeadway Time: The blocking time theory

Clearing &

Switching time

Switching

time

Running

time

time

Time

Space

11

Stop

Weight of

fixed arcs

Conflict Detection and Resolution (CDR)Conflict Detection and Resolution (CDR)

Time

Weight of

alternative arcs

Weight of

fixed arcsTime 0

0

Space

Max consecutive delay

n

N = Set of nodes

F = Set of fixed arcs

A= Set of pairs of alternative arcsG = (N,F,A)

The Alternative Graph (AG)The Alternative Graph (AG) [Pacciarelli

EJOR 2002]

13

Selection S = Choose at most

one arc from each pair in A, thus

obtaining a graph G(S)=(N,F∪S)

Time

t0

0

Problem= Find a complete selection

S such that the longest path

from 0 to n in G(S) is minimum

From AG to a MixedFrom AG to a Mixed--Integer Linear ProgramInteger Linear Program

t1

t2t3

t4

t5

t6

t7

t8

t9

t10

tn

w0,1

w0,7

w8,1

w4,9

w9,n

w12,n

14/14

0

t11

t0

X8,1_2,7= 1

X9,2_3,8= 1

X10,3_4,9 = 1

X11,4_5,10 = 1

X12,5_6,11 = 1

Min f(t,x) s.t.

t1≥≥≥≥ w0,1

t7≥≥≥≥ w0,7

t4≥≥≥≥ w0,4

t10≥≥≥≥ w0,10

t2≥≥≥≥ t1 + w1,2

…

t12≥≥≥≥ t11 + w11,12

t1≥≥≥≥ t8 + w8,1 – M (1 – X8,1_2,7)

t7≥≥≥≥ t2 + w2,7 – MX8,1_2,7

…

t12

∈∀−+≥

−−+≥∈∀+≥

AkhjiMxwtt

xMwttFjiwtt

xtf

hkijhkhk

hkijijij

ijij

),(),,(()1(

),(

),(min

,

,

MixedMixed--Integer Linear Programming (MILP):Integer Linear Programming (MILP):

MODEL WITH MODEL WITH FIXEDFIXED ROUTES (ROUTES (CDRFRCDRFR))

15

=

−+≥

selectediskhif

selectedisjiifx

Mxwtt

hkij

hkijhkhk

),(0

),(1,

,

∈∀−−−−−+≥

−−−−−−+≥∀∈∀−−+≥

==∑=

AkhjiyMyMMxwtt

yMyMxMwttsrFjiyMwtt

ntsy

yxtf

uvrshkijijij

rsrsijij

ns

rrs

),(),,(()1()1(

)1()1()1(,,),()1(

,...,11

),,(min

,

1

MixedMixed--Integer Linear Programming (MILP):Integer Linear Programming (MILP):

MODEL WITH MODEL WITH FLEXIBLEFLEXIBLE ROUTES ROUTES ((CDRCDR))[D’Ariano

TRP 2014]

16

=

=

−−−−−+≥

otherwise

selectedisstrainofrrouteify

selectediskhif

selectedisjiifx

yMyMMxwtt

rshkij

uvrshkijhkhk

0

1

),(0

),(1

)1()1(

,

,

ns: number of routes of train s nt: number of trains

CDRFR formulation of a small example with three trains

T

TA

TC

TB

11

2

9 10 5 6

3 4

87

12 13 14

1

Illustrative example (1)Illustrative example (1)

9 13 14

7 8 9 5 6

11 8 9 5 6

n

12

10

10

1 32

0

TA

TB

TC

60

0

40

10 10 10 10 10 10 10

20 20 20 20 20 20

10 10 10 10 10 10-122

-160

-131out

out

out

Each alternative pair is used to order two trains on a block section

17

Optimal CDRFR solution

TA

TC TB

11

2

9 10 5 6

3 4

87

12 13 14

1

9 14121 3TA 10 10 10 10 10 10 10

local rerouting available...

Illustrative example (2)Illustrative example (2)

A conflict-free deadlock-free schedule is a complete consistent selection S

n

-122

9 13 14

7 8 9 5 6

11 8 9 5 6

12

10

10

1 32

0

TA

TB

TC

60

0

40

10 10 10 10 10 10 10

20 20 20 20 20 20

10 10 10 10 10 10

-160

-131out

out

out

Max cons. delay = 8

18

Optimal solution to the compound CDR problem

TA

TCTB

11

2

9 10 5 6

3 4

87

12 13 14

1

4 13 1451 3TA 10 10 10 10 10 10 10 out

New output

sequence!

Illustrative example (3)Illustrative example (3)

A new route for TA and a new complete consistent selection S are shown

4 13 14

7 8 9 5 6

11 8 9 5 6

n

5

10

10

1 32TA

TB

TC

60

0

40

10 10 10 10 10 10 10

20 20 20 20 20 20

10 10 10 10 10 10 -122

-160

-131

0

out

out

out

Max cons. delay = 0

19

� Introduction

�Traffic management models

�AGLIBRARY solver

Presentation contentsPresentation contents

�AGLIBRARY solver

�Alstom case study

20

AGLibrary

(Roma Tre)

Conflict Detection

And Resolution

LOCAL RULES

Conflict Detection

And Resolution

LOCAL RULES

Alternative routes

near the conflicts

detected

Current status New schedule

Alstom StrategyAlstom Strategy

ICONIS RM6

DSS

(Roma Tre)Timetable

Manager

Timetable

Manager

Infrastructure

Manager

Infrastructure

Manager

Rolling Stock

Manager

Rolling Stock

Manager

Current status

21

InfeasibleSchedule

Train (Re)Scheduling

ReroutingAlternatives?

TimetableInfrastructure Data

Train DataPassable Routes

FeasibleSchedule

No Rerouting orTime Limit Reached Optimal Orders

Optimal Routes

CDRFR (Fixed Route) algorithms:

Heuristics (e.g. FCFS, AMCC, JGH)

Branch and Bound (D’Ariano EJOR 2007)

The optimization software: AGLIBRARYThe optimization software: AGLIBRARY

Train routes

Travel times

XML input file:

Xml output:

Train Rerouting

Possible Improvements

New Routes

CDR algorithms:

Local Search

(D’Ariano TS 2008)

Tabu Search

(Corman TRB 2010)

Variable Neigh.Search

(Samà TRB 2016)

22

� Introduction

�Traffic management models

�AGLIBRARY solver

Presentation contentsPresentation contents

�AGLIBRARY solver

�Alstom case study

23

UK railway network : East Coast Main LineUK railway network : East Coast Main Line

35 stations, 800 trains per day, 90 trains in peak hours

24

Example ofExample of

disruptiondisruption

25

Set of 10 Set of 10 smallsmall instancesinstances

ID

Alstom

Time

horizon

Average number of

resources per train

Total number of

alternative routes

Num of

trains

Num of

alt pairs

Num of

arcs

Num of

nodes

1 15 20 22 33 489 2236 1055

2 15 19 9 37 684 2842 1191

3 15 19 21 41 873 3263 1291

4 15 15 14 38 1833 5511 1544

26

4 15 15 14 38 1833 5511 1544

5 15 17 3 40 1003 3569 1327

6 15 18 6 34 1047 3548 1249

7 15 20 6 36 835 3079 1201

8 15 22 8 32 890 3293 1289

9 15 19 11 33 2068 6003 1599

10 15 17 10 37 1638 5179 1566

Set of 9 Set of 9 mediummedium instancesinstances

ID

Alstom

Time

horizon

Average number of

resources per train

Total number of

alternative routes

Num of

trains

Num of

alt pairs

Num of

arcs

Num of

nodes

11 30 18 33 40 2283 6862 1959

12 30 17 31 48 2897 8380 2203

13 30 12 17 54 5475 14088 2641

14 30 13 7 52 3845 10575 2450

27

14 30 13 7 52 3845 10575 2450

15 30 15 4 49 3670 10051 2339

16 30 16 7 42 3007 8473 2106

17 30 17 10 44 2798 8079 2120

18 30 17 11 43 2697 7894 2121

19 30 17 13 41 4003 10691 2305

Set of 10 Set of 10 largelarge instancesinstances

ID

Alstom

Time

horizon

Average number of

resources per train

Total number of

alternative routes

Num of

trains

Num of

alt pairs

Num of

arcs

Num of

nodes

20 55 12 29 58 9405 23149 3726

21 55 12 29 59 9166 22669 3641

22 55 13 26 57 8759 21815 3639

23 60 11 16 64 11642 27993 4058

28

23 60 11 16 64 11642 27993 4058

24 60 12 33 58 10925 26415 3915

25 60 12 31 66 11640 28063 4124

26 60 8 38 90 22188 50682 5483

27 60 12 5 59 11012 26644 3982

28 60 8 19 85 26081 59044 6030

29 60 12 13 61 11450 27550 3998

Computational resultsComputational results

Intel Intel CoreCore 2 Duo E6550 (2.33 2 Duo E6550 (2.33 GHzGHz), 2 GB di RAM, Windows XP), 2 GB di RAM, Windows XP

Train sheduling & routing problem (CDR problem) :

29 practical instances from Network Rail (ECML, UK)

CPLEX (algorithm: 1 hour of computation): CPLEX (algorithm: 1 hour of computation):

[MILP formulation solved by IBM LOG CPLEX MIP 12.0] [MILP formulation solved by IBM LOG CPLEX MIP 12.0]

� 6 fails, 23 optimum, avg comp time (algo) best sol 1011 sec

AGLIBRARY (algorithm: 30 sec of computation): AGLIBRARY (algorithm: 30 sec of computation):

[Branch & Bound (EJOR, 2007) + Tabu Search (TRpartB, 2010)]

� 0 fails, 24 optimum, avg comp time (algo) best sol 9 sec

� ...even better results with the VNS (COR, 2016)

29

CPLEXCPLEX vs AGLIBRARY (scheduling & routing)vs AGLIBRARY (scheduling & routing)

30

CPLEXCPLEX vs AGLIBRARY (scheduling & routing)vs AGLIBRARY (scheduling & routing)

31

Demo at Alstom Ferroviaria S.P.A.

�Return on Experience

Presentation contentsPresentation contents

33

Achieved resultsAchieved results

The first year of collaboration between Roma Tre and Alstom gave

the following very promising results:

• A successful implementation of the coupling of the ICONIS RM6

(Integrated CONtrol and Information System) product, developed

by Alstom Ferroviaria S.P.A. to monitor and control railway traffic in

stations and railway lines, and the AGLIBRARY optimization system

34

stations and railway lines, and the AGLIBRARY optimization system

(Alternative Graph LIBRARY), developed by Roma Tre University to

optimize the real-time performance of railway traffic.

• Computational experiments, based on Network Rail instances, with

multiple train delays and network disruptions, demonstrate that

near-optimal solutions can be found by ICONIS+AGLIBRARY within

a short computation time, compatible with real-time operations.

OnOn--goinggoing & Future & Future ResearchResearch

We are currently investigating a number of possible system

improvements, including:

• Formulation and impact of additional CDR problem constraints;

• Different objective functions (e.g. number of late trains, weight of

broken connections, passenger delay minimization, energy

consumption) and their combinations (multi-objective optimization);

• Advancement of the train scheduling and routing algorithms (e.g.

35

• Advancement of the train scheduling and routing algorithms (e.g.

for dealing with specific disruption scenarios) in terms of reduced

computation time and better solution quality (with respect to various

performance indicators);

• Study of alternative MILP formulations and MILP-based solution

approaches;

• Extensions of the model by incorporating further relevant practical

aspects (e.g. dynamic train speed/position control).