advanced traffic control techniques for freeway systems (and urban networks) by prof. markos...

ADVANCED TRAFFIC CONTROL ADVANCED TRAFFIC CONTROL TECHNIQUES FOR FREEWAY SYSTEMS TECHNIQUES FOR FREEWAY SYSTEMS

(AND URBAN NETWORKS)(AND URBAN NETWORKS)

by Prof. Markos Papageorgiouby Prof. Markos Papageorgiou

Dynamic Systems and Simulation Dynamic Systems and Simulation

Laboratory, Laboratory,

Technical University of Crete, Chania, Technical University of Crete, Chania, GreeceGreece

ADVANCED TRAFFIC CONTROL TECHNIQUES FOR FREEWAY SYSTEMS (AND URBAN NETWORKS), DSSL, TUC, GREECEADVANCED TRAFFIC CONTROL TECHNIQUES FOR FREEWAY SYSTEMS (AND URBAN NETWORKS), DSSL, TUC, GREECE

22

OUTLINEOUTLINE1. Introduction 2. Motorway (Freeway) Traffic Control3. Ramp Metering4. Route Information and Guidance5. Variable Speed Limits6. Urban Signal Control7. Integrated Urban-Freeway Traffic Control8. Motorway Traffic Surveillance9. Other Control Tools


33

Who are we?Who are we?

Dynamic Systems and Simulation Laboratory (DSSL)

(~ 15 Professors, Researchers, PhD/MSc Students) at the Technical University of Crete

also acting occasionally as professional engineers.

1. INTRODUCTION1. INTRODUCTION

What is our Background?What is our Background?• Automatic ControlAutomatic Control• Electrical EngineeringElectrical Engineering• Industrial EngineeringIndustrial Engineering• Civil EngineeringCivil Engineering


44

ExperienceExperience

• 30 years of high-quality research in traffic flow modelling 30 years of high-quality research in traffic flow modelling and controland control

• Numerous successful implementationsNumerous successful implementations

• High number of research or implementation contracts: High number of research or implementation contracts: national, EC, companies, national, EC, companies,

authorities authorities worldwideworldwide. .

• October 2008: October 2008: IEEE Outstanding ITS Research AwardIEEE Outstanding ITS Research Award


55

Concepts Concepts Algorithms Algorithms Software for traffic flow Software for traffic flow

modelling, surveillance, route guidance, traffic control modelling, surveillance, route guidance, traffic control

(freeway + urban)(freeway + urban)

What We DoWhat We Do

What we What we Don’tDon’t Do Do

Our Usual Partners Our Usual Partners

Implementation hardware, System integrationImplementation hardware, System integration

• Road authoritiesRoad authorities

• Local Consultants/University groupsLocal Consultants/University groups

• Local System IntegratorsLocal System Integrators


66

Our PrinciplesOur Principles

• Theoretical soundness: Theoretical soundness: There is nothing more practical than a There is nothing more practical than a

good theorygood theory

• Practical Usefulness.Practical Usefulness.

• General Applicability, InteroperabilityGeneral Applicability, Interoperability

• Highest Efficiency: deep understandingHighest Efficiency: deep understanding

• Practicability: Practicability: As simple as possible, as complex as necessaryAs simple as possible, as complex as necessary..


77

Automatic Control LoopAutomatic Control Loop

Disturbances

Outputs

Measurements

Goals

REAL WORLD

COMPUTER

ProcessInputs

ControlStrategy

DataProcessing

ActuatorsSensors


88

O-D,demand,

environment

Minimizationof total

travel time

URBAN NETWORK

COMPUTER

Flows,Occupancies

Total travel time Urban Traffic Flow

ControlStrategy

A.I.D.,Estimation,Prediction

TrafficLights Loop

Detectors

Example: Urban traffic control system


99

Minimization of Total Time SpentMinimization of Total Time Spent

Maximization of (Early) Exit RatesMaximization of (Early) Exit Rates


1010

Simple Queuing SystemsSimple Queuing Systems• Demand > Capacity Demand > Capacity Queuing Queuing

• Capacity Capacity ≠ f (Queuing)≠ f (Queuing) Delay depends on DDelay depends on D−−C only!C only!

Water SystemsWater SystemsMore inflow More inflow Higher Pressure Higher Pressure Higher Higher

OutflowOutflow


1111

Traffic NetworksTraffic Networks• Congestion degrades the infrastructure (capacity)Congestion degrades the infrastructure (capacity)

Local link demand exceeds local capacity Local link demand exceeds local capacity

Local congestion degrades local capacityLocal congestion degrades local capacity

Accelerated increase Accelerated increase of congestionof congestion

Further capacity Further capacity degradationdegradation

... until generalized network congestion... until generalized network congestionalthoughalthough

Demand << Nominal network capacity Demand << Nominal network capacity


1212

Conclusion:Conclusion: Generalized traffic congestion is not Generalized traffic congestion is not

only due to high demand.only due to high demand.

Congested Motorway Networks:Congested Motorway Networks: Expensive Expensive

infrastructure capacity not fully available at the infrastructure capacity not fully available at the

only time it is actually needed, i.e. the peak time it is actually needed, i.e. the peak

periods!periods!

Goal: Goal: Operate motorway and urban networks Operate motorway and urban networks

optimallyoptimally

(as (as controllable systems) systems)


1313

Motorways were originally conceived to Motorways were originally conceived to provide virtually unlimited mobility to provide virtually unlimited mobility to road users, but …road users, but …

2. MOTORWAY (FREEWAY) 2. MOTORWAY (FREEWAY) TRAFFIC CONTROLTRAFFIC CONTROL


1414

Man has reached to the moon but …Man has reached to the moon but …

… … even ants were taught by evolution to address even ants were taught by evolution to address their transportation problems more efficiently, their transportation problems more efficiently, seeseeI.D. Couzin and N.R. Franks: “Self-organized lane formation and optimized traffic flow in army ants”, Proc. R. Soc. Lond. B (2003) 270, 139–146


1515

Available Motorway Control MeasuresAvailable Motorway Control Measures Ramp metering (RM):Ramp metering (RM): valuable; limited valuable; limited

storage spacestorage space Variable speed limits (VSL):Variable speed limits (VSL): improved improved

safety; no system improved efficiencysafety; no system improved efficiency Route guidance (RG):Route guidance (RG): best under incident- best under incident-

caused congestioncaused congestion Vehicle-infrastructure integration (VII):Vehicle-infrastructure integration (VII):

promising; emergingpromising; emerging


1616

Prerequisite for efficient traffic control:Prerequisite for efficient traffic control:Understanding the reasons for infrastructure Understanding the reasons for infrastructure degradation!degradation!

(Latent) Motorway bottleneck location:(Latent) Motorway bottleneck location:Capacity upstream > Capacity downstreamCapacity upstream > Capacity downstream

Bottlenecks are candidates for Bottlenecks are candidates for congestion appearance:congestion appearance:– on-ramp mergeon-ramp merge– geometry (lane drop, grade, curvature, geometry (lane drop, grade, curvature,

tunnel, …)tunnel, …)– weavingweaving– speed limitsspeed limits– over-spilling off-rampsover-spilling off-ramps


1717

Two Main Reasons for Motorway Infrastructure Two Main Reasons for Motorway Infrastructure DegradationDegradation

1. Capacity Drop (CD)1. Capacity Drop (CD)

CD not well-understood but is deemed CD not well-understood but is deemed to occur due to vehicle accelerationto occur due to vehicle acceleration


1818

2. Blocking of Off-Ramps (BOR)2. Blocking of Off-Ramps (BOR)

off-ramp flow reduced: vehicles bound off-ramp flow reduced: vehicles bound for the off-ramp contribute to for the off-ramp contribute to accelerated congestion increase!accelerated congestion increase!


1919

3. RAMP METERING3. RAMP METERING


2020

Why Ramp MeteringWhy Ramp Metering1st Answer

qin

d

qcon

no control

nc rmcap cons s

s ncs in con

q qT TT

T q d q

e.g. qcon = 0.95 qcap ; qin+d = 1.2 qcap

sΔT 20%̂

(veh/h)


2121

2nd Answer

q in

d

q cap

)dq(1 cap

q cap d

γ q in

q in

d

q cap

nc

s

rms

ncs

sT

TTT

Note: Note: On-ramp queue should not On-ramp queue should not interfere with surface street interfere with surface street traffic.traffic.


2222

Further reasons

•Influence driver route choiceInfluence driver route choice

•Utilisation of reserve capacity on parallel arterialsUtilisation of reserve capacity on parallel arterials

• Increased traffic safety (less congestion, safer merging)Increased traffic safety (less congestion, safer merging)


2323

When is ramp metering less helpful?

Exit flow problemsExit flow problems


2424

Local Control IssuesLocal Control Issues

q cap

q out

o cr o out

Demand-Capacity(Feedforward)

r

qinoout

qcap

r(k) = qcap - qin (k-1)

Note: oNote: ocr cr less sensitive than less sensitive than qqcapcap (e.g. under adverse (e.g. under adverse weather conditions)weather conditions)


2525

Sample from Glasgow Implementation of ALINEASample from Glasgow Implementation of ALINEA


2626

• Real-time estimation of oReal-time estimation of ocrcr

• Real-time estimation of ramp queueReal-time estimation of ramp queue

• Ramp queue managementRamp queue management

• Various traffic-light policiesVarious traffic-light policies

• Switch on/off logicSwitch on/off logic

. . .. . .

Many additional toolsMany additional tools


2727

Why Coordinated Ramp Metering?Why Coordinated Ramp Metering?

No ramp queue constraints No ramp queue constraints

•Little efficiency improvement via coordination Little efficiency improvement via coordination

•Equity ?Equity ?

•Realistic ?Realistic ?

•DiversionDiversion


2828

• Full ramp Congestion Full ramp Congestion “merely” retarded “merely” retarded

• Equity: badEquity: bad

Limited ramp storage capacity: Limited ramp storage capacity:

significant improvement of both efficiency significant improvement of both efficiency andand equity via equity via appropriate coordinationappropriate coordination


2929

• Rule-based central controlRule-based central control

• employs (modified) ALINEA at each on-rampemploys (modified) ALINEA at each on-ramp

• Master/Slave ramps for increased storage space Master/Slave ramps for increased storage space

• High efficiency High efficiency (depending on available ramp storage space)(depending on available ramp storage space)

HERO: Ramp Metering coordination algorithmHERO: Ramp Metering coordination algorithm


3030

ALINEA Field ImplementationsALINEA Field Implementations

• Boulevard PBoulevard Péériphriphéérique, Paris, Francerique, Paris, France• A6, Ile-de-FranceA6, Ile-de-France• A10, Amsterdam, NetherlandsA10, Amsterdam, Netherlands• Glasgow, ScotlandGlasgow, Scotland• A94, Munich, GermanyA94, Munich, Germany• UK Highways Agency ramp metering roll-outUK Highways Agency ramp metering roll-out• Tel Aviv, IsraelTel Aviv, Israel

. . .. . .

>100 ramps in Europe (even w/o our involvement)>100 ramps in Europe (even w/o our involvement)

StatusStatus: Mature tool, ready for immediate implementation: Mature tool, ready for immediate implementation


3131

HERO/ALINEA Field ImplementationsHERO/ALINEA Field Implementations

• A6, Ile-de-France (simplified)A6, Ile-de-France (simplified)

• Ile-de-France network (~ 80 ramps)Ile-de-France network (~ 80 ramps)

• A10, Amsterdam, Netherlands (~ 40 ramps)A10, Amsterdam, Netherlands (~ 40 ramps)

• Monash Freeway, Melbourne, Australia (6Monash Freeway, Melbourne, Australia (665 ramps)65 ramps)

StatusStatus: Mature tool, ready for immediate implementation: Mature tool, ready for immediate implementation


3232

4. ROUTE 4. ROUTE INFORMATION AND INFORMATION AND

GUIDANCEGUIDANCE Multi-origin, multi-destination, multi-route per O-D pair. Multi-origin, multi-destination, multi-route per O-D pair. Fixed direction signs: shortest path in absence of congestionFixed direction signs: shortest path in absence of congestion Rush hoursRush hours Changing demands, weather conditions, exceptional events, Changing demands, weather conditions, exceptional events,

incidentsincidents

underutilisation of infrastructureunderutilisation of infrastructure

congestion, delays, reduced safety, increased fuel congestion, delays, reduced safety, increased fuel

consumption, environmental pollutionconsumption, environmental pollution


3333


3434

VMS (Variable Message Signs) or two-way communication with VMS (Variable Message Signs) or two-way communication with

equiped vehiclesequiped vehicles Real-time information: Real-time information:

– – Drivers’ knowledgeDrivers’ knowledge

– – Message lengthMessage length

– – Decision efficiencyDecision efficiency

– – System controllabilitySystem controllability

– – Travel time or queue length: drivers’ stress (e.g. BP in Paris) Travel time or queue length: drivers’ stress (e.g. BP in Paris)

but also basis for route choicebut also basis for route choice

– – Instantaneous (estimation) or predicted informationInstantaneous (estimation) or predicted information

Route guidanceRoute guidance

– – Control strategyControl strategy


3535

Our Concept: Simple feedback-based route Our Concept: Simple feedback-based route guidanceguidance


3636

Automatic Control of VMS in Aalborg, DenmarkAutomatic Control of VMS in Aalborg, Denmark

Main goalMain goal: efficient crossing (northbound or southbound) of : efficient crossing (northbound or southbound) of

Limfjorden via BRIDGE (urban) or TUNNEL (motorway)Limfjorden via BRIDGE (urban) or TUNNEL (motorway)

Particularly in presence of incidents, road works etc.Particularly in presence of incidents, road works etc.

116 loop detectors; 14 VMS in front of important bifurcations116 loop detectors; 14 VMS in front of important bifurcations

Sample time: 1minSample time: 1min


3737

Two display modes (no real-time switch) Two display modes (no real-time switch)

– – Delay informationDelay information

– – Route RecommendationRoute Recommendation

Operational constraints:Operational constraints:

– – Information is the most accurate availableInformation is the most accurate available

– – Route recommendation according to user optimum principleRoute recommendation according to user optimum principle

– – No incompatible VMS displaysNo incompatible VMS displays

– – Police report inputPolice report input


3838

Aalborg network with VMS positions indicated.Aalborg network with VMS positions indicated. Bold black lines represent detector equipped segments.Bold black lines represent detector equipped segments.


3939

VMS control modes: Delay information (a) and route guidance (b).VMS control modes: Delay information (a) and route guidance (b).


4040

Automatic Control of VMS in the Interurban Scottish Highway NetworkAutomatic Control of VMS in the Interurban Scottish Highway Network

Motorway/Expressway NetworkMotorway/Expressway Network

Geographically extended (Geographically extended ( predictions necessary) but less predictions necessary) but less

complex in topology.complex in topology.

VMS: Combination of Information and Route RecommendationVMS: Combination of Information and Route Recommendation


4141

VMS Plans pre-approved (some 200)VMS Plans pre-approved (some 200)

Predictive/Feedback Strategy (Smith-Predictor type)Predictive/Feedback Strategy (Smith-Predictor type)

Off-line simulationsOff-line simulations

ImplementationImplementation

Dummy ControlDummy Control

Evaluation/ComparisonEvaluation/Comparison


4242

The Scottish interurban networkThe Scottish interurban network


4343


4444


4545


4646


4747

Glasgow integrated traffic controlGlasgow integrated traffic control

Also involved: Boulevard PAlso involved: Boulevard Péériphriphéérique travel time information rique travel time information

systemsystem

StatusStatus: Mature for implementation: Mature for implementation


4848

5. VARIABLE SPEED 5. VARIABLE SPEED LIMITSLIMITS


4949

Increasingly popular control measure with many applications Increasingly popular control measure with many applications

worldwide.worldwide.

Several evaluations indicate substantial safety improvements Several evaluations indicate substantial safety improvements

(– 30 % accidents)…(– 30 % accidents)…

… … but no efficiency improvements (e.g. no reduction of travel but no efficiency improvements (e.g. no reduction of travel

times)times)

Why?Why?

5. VARIABLE SPEED 5. VARIABLE SPEED LIMITSLIMITS


5050

1) Very simple threshold-based control strategies1) Very simple threshold-based control strategies

An example An example of VSL of VSL switching switching logic.logic.


5151The VSL switching logic at the Monash Freeway.The VSL switching logic at the Monash Freeway.


5252

2) VSL impact on aggregate traffic flow not well known2) VSL impact on aggregate traffic flow not well known slope decreaseslope decrease

oocrcr increase increase

cross-pointscross-points

capacity increase(?)capacity increase(?)

capacity decreasecapacity decrease

(a)

(b)


5353

3) Difficult fine-tuning of thresholds3) Difficult fine-tuning of thresholds


5454

1) Automatic Fine-Tuning AFT21) Automatic Fine-Tuning AFT2

New ToolsNew Tools


5555AFT2 simulated application for VSL control.AFT2 simulated application for VSL control.


5656

2) Different inputs: Slope of flow-occupancy curve2) Different inputs: Slope of flow-occupancy curve

3) Feedback-based VSL upstream of bottlenecks 3) Feedback-based VSL upstream of bottlenecks

as mainstream controlas mainstream control


5757

as ramp metering substituteas ramp metering substitute


5858

No field-tested VSL control strategy availableNo field-tested VSL control strategy available

Discussions with a road authority ongoingDiscussions with a road authority ongoing


5959

6. URBAN SIGNAL 6. URBAN SIGNAL CONTROLCONTROL

Most available signal control strategies are less suitable Most available signal control strategies are less suitable under under

saturated traffic conditionssaturated traffic conditionsQueue spillback Queue spillback – – Wasting of green timeWasting of green time – – Increased delays (all Increased delays (all movements)movements) – – Blocked exitsBlocked exits – – Accelerated queue increaseAccelerated queue increase – – Queue spillbackQueue spillback . . . . . . – – Serious infrastructure Serious infrastructure degradationdegradation . . .. . . – – Gridlock: Infrastructure breaks Gridlock: Infrastructure breaks downdown


6060

Traffic congestion = Traffic congestion =

temporarily and locally d > smaxtemporarily and locally d > smax

++

lack of efficient controllack of efficient control


6161

The TUC strategyThe TUC strategy

TUC Strategy

Controldecisions

Controldecisions

Signal Plans

PriorityPlans

Traffic Lights

URBAN ROAD NETWORK

Mea

sure

men

t dat

a

Data Processing

Offset Control

methodology:decentralized feedbackcontrol law

aim:coordination of mainstages of successivejunctions along arterials

Split Control

methodology:multivariable linear-quadratic control law

aim:minimization of risk ofoversaturation andqueue spillback

Cycle Control

methodology:feedback algorithm(P-regulator)

aim:cycle adjusting to themaximum saturationlevel

Public Transport Priority

methodology:rule-based algorithm

aim:provide priority to publictransport vehicles

Based on modern control and Based on modern control and

optimisation methodsoptimisation methods Efficient under saturated Efficient under saturated

traffic conditions as welltraffic conditions as well Simple, easy to implementSimple, easy to implement One measurement per linkOne measurement per link One communication with One communication with

central computer per cycle.central computer per cycle.


6262

TUC field-applicationsTUC field-applications

a) Chania, Greece (23 junctions) a) Chania, Greece (23 junctions)

4A 4B 5 7 8

6

14

9 10 11

D3

O21

L7

O3 D4

L8

L9

L10

L11

L12

O4 D5

L13L14

L15L63

L16

O5D6

O6

D7

D14

L17

L18

L34

L35

L36

O14

D15

D24

O24

L37

L38

O15

D16

O16

L53

L54

L55

D17

D23

L56

L57

O22

L48

L41L40

L39

D20

L50L51

L4

16A

O7D8

L19

L20 16B

O8

13

12

L21

L22

O9D9

L23

L24

L25

O10D10

O11

D11

L27L28 L26

18O12

O13O23D13

D12

2A 2B

3A 3B

17

O2

L6

D2

L58

L59

L60

D19

D18

O17

L49

L47

O18

L43 L42

O1

D22

O20

1A

1B

1C

D1

L1

L2

L62L61

L45 L44

L46

O19D21


6363

b) b) Southampton, Southampton, UK (53 UK (53 junctions)junctions)

6 b5 6 a

1 6

1

4

2

3 7

P 1 0 2 1 4

J 1 0 2 3 1

1 5

J 1 0 2 2 1

J 1 0 2 1 1

J 1 0 2 4 1

9

8

1 0

1 8

1 7

1 9

2 0 a

2 0 b

1 1

1 2

1 4

J 1 0 1 2 1

J 0 9 1 2 1

P 0 9 1 1 4

J 0 9 1 1 1

P 0 9 1 2 4

J 0 9 1 3 2

J 1 0 3 1 1

J 0 9 1 3 1

J 1 0 3 2 1

J 1 0 3 3 1

J 1 0 3 4 1

J 1 0 3 5 1

J 1 0 1 1 1

1 3

J 1 0 3 6 1

P 1 0 1 2 4

1

2

3

45

67

9

8

1 01 1

1 2

1 3

1 4

1 5

1 6

1 7

1 8

1 9

2 0

2 1

2 2

2 3

2 4

2 62 5

2 7 2 8

3 13 0

2 9

3 2

3 3

3 6 3 7

3 5

3 4

3 8 3 9

4 2

4 3

4 0

4 1

4 4

4 54 6

4 7

4 8

5 0

5 5

5 3

5 4

5 6

5 7

5 8

6 2

6 0 6 1

6 7

6 3

6 4 6 5

6 8 6 9

6 6

7 0

7 1 7 2

7 3

7 57 4 7 6

7 8

7 7

7 9

4 9

5 25 1

5 9

( b )

5 4

5

6 7

1 8

1 7

1 9

3 4

1 0 a

1 6

1 5

2

1

2 0

8 9

2 1 2 32 2

1 4 1 3 a

1 2 1 1

2 5

2 4

2 7

2 6

2 9

2 8 a 3 2

3 1

3 0

3 4

3 5

3 3

3 7

3 6 a

1

2

34

5

6

7

8

9

1 0

1 1

1 2

1 3

J 0 4 1 2 2

J 0 4 1 2 1

P 0 4 1 3 4 J 0 4 1 3 1

J 0 4 1 4 1

1 0 b

P 0 4 1 5 6

P 0 4 1 5 4

P 0 4 1 5 7

P 0 4 1 5 5

1 4

J 0 7 3 2 2

J 0 7 3 6 1

J 0 7 3 1 1

J 0 7 1 8 1

J 0 7 3 3 1

1 51 6

1 7

1 8

1 9

2 0

2 1

2 2

2 3

2 4

2 5

2 6

1 3 b

2 8 b

3 6 b

J 0 7 3 4 1

J 0 7 1 1 1

J 0 7 1 2 1

J 0 7 1 3 1 J 0 7 1 4 1 J 0 7 1 5 1

J 0 7 1 6 1 J 0 7 1 7 1

P 0 7 1 9 4

P 0 7 1 9 5

J 0 7 3 5 1

J 0 7 3 8 1

P 0 7 3 7 4

P 0 7 4 1 5

P 0 7 4 1 4 J 0 7 4 1 1

P 0 7 4 2 5

J 0 7 4 2 1

P 0 7 4 2 4 J 0 7 2 2 1

2 7

2 8

2 9

3 0

3 1

3 3 3 2

3 4

3 5

3 6

3 7

3 8 3 9 4 0

J 0 7 3 7 1

J 0 7 2 1 1

4 1

J 0 7 3 2 1

4 2

4 3

4 44 54 6

4 7

4 8

4 9

5 05 1

5 2

5 3

5 5

5 6

5 8 5 7

5 9 6 0

6 1

6 2

6 3

6 4

6 5

6 6

6 7

6 8

6 97 0

7 17 2

7 3

7 47 5

7 6

7 77 8

7 9

8 0

8 1

8 2 8 3

8 4

8 5

8 6

8 7

8 8

8 9

9 0

9 1

9 2

9 3

9 4

9 5

9 6

9 7

9 8

9 9

1 0 0

1 0 2 1 0 3 1 0 4 1 0 5

1 0 6

1 0 7

1 0 8

1 0 1

1 0 9

1 1 1 1 1 2

1 1 31 1 4

1 1 5

1 1 6

1 1 7

1 1 8 1 1 9 1 2 0

1 2 1

1 2 2

1 2 3

1 2 4

1 2 5

1 2 6

1 2 7

1 2 8

1 2 9

1 3 0

1 3 4

1 3 5

1 3 3

1 3 1

1 3 2

1 3 6

1 4 0 1 4 1 1 4 2

1 4 3

1 3 9

1 4 5

1 4 6

1 4 7

1 4 8

1 3 7 1 3 8

1 4 4

1 1 0

( a )


6464

c) Munich, Germany (25 junctions)c) Munich, Germany (25 junctions)1 3 1

5

14

13

12

10

11987654 16

1719

20

21

24

22

23

25

26

27

2

Inn

ere

-Wie

ne

r st

r

Orle

an

s

18

Zweibrucken srt.Rosenheimer srt

3a


6565

Independent evaluation report: Independent evaluation report:

The demonstrations proved that TUC is a robust and credible The demonstrations proved that TUC is a robust and credible

signal control strategy, both as a stand-alone system, as insignal control strategy, both as a stand-alone system, as in

Chania and Southampton, and as a hybrid system. Despite Chania and Southampton, and as a hybrid system. Despite

its limited fine tuning, TUC stood up very well against the its limited fine tuning, TUC stood up very well against the

well-established and well-fine-tuned resident systems of the well-established and well-fine-tuned resident systems of the

test networks, which have very different characteristics with test networks, which have very different characteristics with

regard to network layout, detector locations within the links regard to network layout, detector locations within the links

and traffic behaviour.and traffic behaviour.


6666

Glasgow, ScotlandGlasgow, Scotland

MacaMacaé, Brazil (in final implementation phase)é, Brazil (in final implementation phase)

Athens, Greece (planned)Athens, Greece (planned)

Tel Aviv and Jerusalem, Israel (feasibility studies)Tel Aviv and Jerusalem, Israel (feasibility studies)

StatusStatus: Mature tool; ready for immediate implementation: Mature tool; ready for immediate implementation

Other TUC ApplicationsOther TUC Applications


6767

– – Main control measures:Main control measures:

7. INTEGRATED URBAN-7. INTEGRATED URBAN-FREEWAY TRAFFIC CONTROLFREEWAY TRAFFIC CONTROL

Ramp meteringRamp metering Signal controlSignal control VMSVMS

in metropolitan areas.in metropolitan areas.


6868

– – Drivers perceive the overall network as an entity Drivers perceive the overall network as an entity

– – Aims:Aims:

Harmonise actions of various control measuresHarmonise actions of various control measures Avoid antagonistic actionsAvoid antagonistic actions Maximize synergyMaximize synergy

– – Several de facto implementations but very few Several de facto implementations but very few

methodological approaches:methodological approaches:

Optimisation-based (LP)Optimisation-based (LP) Optimal controlOptimal control LQ control: LQ control: IN-TUCIN-TUC


6969

Urban Traffic Control

methodology:application of linear-quadratic control

aim:balancing of vehicleswithin urban links

Ramp Metering

methodology:application of ALINEAlocal feedback controllaw

aim:maintainance ofmainstream occupancyat desired level

CORRIDOR NETWORK

Urban Traffic Lights VMSsOn-Ramp Traffic

Lights

sign

al p

lans

VM

Ss d

ispl

ays

sign

al p

lans

Sign

Common measurement data

Controldecisions

Controldecisions

IN-TUC Strategy

VMS Control

methodology:application offeedback control

aim:equalization of traveltimes betweenalternative routes

Functional architecture of IN-TUC strategyFunctional architecture of IN-TUC strategy


7070

The Glasgow Site & the GoalThe Glasgow Site & the Goal


7171

Effect of Control on Road Throughput and on Journey TimesEffect of Control on Road Throughput and on Journey Times

Time period: 16:00 – 17:00

Road Throughput Journey Times

veh/hr Percentage Change sec Percentage Change

Base Case

Ramp Metering

UITC UITC and VMS

Base Case

Ramp Metering

UITC UITC and VMS

Motorway 36721 +5 +6 +6 210 5 * *

Urban Diversion Routes

3087

+13

+20

+23

440

+4

*

+1

Urban Network 20157 –3 –10 –6 1174 * 11 15

Total Network 60324 +2 * * 1567 2 –10 –13

** Not statistically significantNot statistically significant


7272

8. MOTORWAY TRAFFIC 8. MOTORWAY TRAFFIC SURVEILLANCESURVEILLANCE

RENAISSANCE RENAISSANCE

State prediction algorithm

Traffic state prediction

Boundary value prediction

.1Kk , ,1k ),(ˆp d

)(ˆ kx

network trafficstate estimation

State estimation algorithmComplete image of the currentnetwork state (estimation)(including non-measured inflows and outflows)

Motorway network( indicatesmeasurement locations)

Real-timemeasurements

)(ky

)(ˆ kx

pKk , ,1k ),(ˆ x Travel-time prediction, queue-tail prediction etc.


7373

Definition of queue(threshold of density)

Parameter estimation forfundamental diagram(yes / no)

Travel timeestimation

Traffic stateprediction

Travel timeprediction

Boundary valueprediction

Traffic measurements(flow, speed, occupancy)

incident report

Prediction horizon (Kp)

Queue tailestimation

Traffic detector specifications: location type (video/loop) kind of measurements sampling rate

Visualization

Queue tailprediction

Historicaldata

Currentqueue tails

Instantaneoustravel times

Future queuetails

Experiencedtravel times

Global user options

Module-orienteduser options

Real-timeinputs

Outputs

Automaticincident

alarmIncident alarm

)(ˆ kx

)(ˆ x

Routespecification

Network specifications: global model parameters network topology link characteristics

Definition of queue(threshold of density)

Operation parameterspecifications: output frequency noise variances

Module-orienteduser options

Traffic stateestimation

Traffic stateprediction

Traffic measurementand user option

processing

Traffic measurement processing: smoothing (yes/no) plausibility limits

pKkk , ,1

Traffic stateestimation

Possible erroror warninginformation

Structure of RENAISSANCEStructure of RENAISSANCE


7474

A92, Munich, Germany (7 km)A92, Munich, Germany (7 km)

A3, Napoli,-Salerno (100 km)A3, Napoli,-Salerno (100 km)

AntwerpAntwerp

StatusStatus: Mature tool (estimation); ready for immediate: Mature tool (estimation); ready for immediate

implementationimplementation

RENAISSANCE ApplicationsRENAISSANCE Applications


7575

Real-time toll plaza controlReal-time toll plaza control

9. OTHER CONTROL TOOLS9. OTHER CONTROL TOOLS


7676


7777

Simulation Example: Work ZoneSimulation Example: Work Zone

M = 3 μ = 1; capq = 2,300 veh/h

advanced traffic control techniques for freeway systems (and urban networks) by prof. markos...

Documents

freeway systems

urban signal control

urban networks

control tools slide

greece slide

automatic control loop

traffic flow modelling

motorway traffic surveillance