Innovative ITS Approaches for Control of LargeScale Urban Networks

Mehdi Keyvan-EkbataniInternational BinN Research Seminar
University of Tokyo, Tokyo, Japan
11.08.2015
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Introduction
 Despite continuous efforts in the field of traffic control, traffic
congestion in urban road networks persists and extends aroundthe globe.
road capacity (supply)
 Congestion( ) 
traffic demand Road capacity () ⇒ traffic management and signal control

 Practicable and efficient signal control under saturated traffic
conditions remains a challenge.
“No current generally available tool is adequate for optimizing[signal] timing in congested conditions” (FHWA, 2008).
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Introduction
Gating Control??
 Holding traffic back (via prolonged red phases at trafficsignals) at the perimeter or upstream of the zone to beprotected from over-saturation
 Gating :Practical tool against over-saturation
 Usually employed in an ad hoc way

 Based on engineering judgment and manualfine-tuningMay lead to insufficient or unnecessarily strong
gating actions
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Network or Macroscopic
Fundamental Diagram (NFD or MFD)
Verification by real data: Geroliminis & Daganzo, 2008

1. Under-saturated;
minimize delays!
2. Saturated: maximize
capacity!
3. Oversaturated: queue
management, gating!
4. Blocked: call the police orwalk home!

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Combining Gating With NFD
 Protected Network (PN) via gating
q=β qg ( t − τ )in ( t )N ( t ) = qin ( t ) + qd ( t ) − qout ( t ) According to NFD, if N grows beyond certain limits ⇒ PN exitflow ( ) Via gating, PN can be protected from link queue spillovers andgridlocks
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Operational NFD• Operational NFD: based on measurements/ estimates, rather than exact
knowledge,• at links  ⊆ , where z ∈  (complete NFD) or  =  (reduced NFD)
• y-axis: Total Travel Distance (TTD)T ⋅ qz ( k ) ⋅ Lz
TTD==(k ) ∑Tz∈∑ q (k ) ⋅ Lz∈zz(where k =1,2,… are signal cycles)• x-axis: Total Time Spent (TTS)T ⋅ Nˆ z ( k )TTS ( k ) ∑ ==Tz∈Nˆ ( k ) Nˆ ( k )
=
∑z∈

zµz
Nˆ z ( k ) =Lz ⋅⋅ oz ( k − 1)100λUniversity of Tokyo, Tokyo, Japan

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Typical Feedback Loop

 SISO system is a simple control system with one input and one output
 Actuator is the device that can influence the controlled variable ofthe process (In traffic; actuator: traffic light, control variable: TTS)
 Sensor: measures the control variable (in Traffic Control: detectors)

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Feedback Gating Traffic ControlScheme

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Time-Delayed Feedback gatingdΓ=−+−TTStβqt
τqt
F
TTSt())()()()(d g ⋅ A + ε (t )dtBL If gating applied at the border of PN ⇒ τ = 0& qg = qin

Time-delayed nonlinear first-order dynamic system
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Time-Delayed Feedback gating• The model can be linearized around an optimal steady state thatis within the maximum throughput region of NFD

Linearized System:dΓ F′∆TTS  ⋅ A + ε( ∆TTS ) = ∆qg + ∆qd −dtBL• The continuous-time state equation of the PN may be directlytranslated in discrete-time∆TTS ( k + 1)= µ ⋅ ∆TTS ( k ) + ζ ⋅ ∆qin ( k − m ) + γ ⋅ ∆d ( k )ˆ , ∆qg = qg − q g , m  time − delay
Where ∆TTS = TTS − TTSParameters µ , ζ , m can be obtained from using time-series of ( qg, TTS)-measurements via model identification (e.g. least-squaremethod) around the critical TTS-range.
University of Tokyo, Tokyo, JapanChallenge the future11Controller•

A proportional-integral-type (PI) feedback controller issuitableˆ − TTS ( k ) qg ( k=) qg ( k − 1) − K P TTS ( k ) − TTS ( k − 1) + K I TTS• Flow, constrained by pre-specified minimum and maximum
K P and K I gain values• Flow splitting if multiple gating links: Various policies, queue
management (this work, based on link saturation flow)• Conversion of flows to gating signals (appropriatemodification of fixed plans)
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Controller Gain Values
The z-transform function of the process andcontroller:K 
P (z) =ζP
z− K' z ⋅ ( KP + KI ) − KPC ( z== K '⋅ )z −1z−1

K=' K P + K I(z − µ)By closing the loop with C (controller) and P (process):

FCzm

KP 

z−
ζ
K ' ⋅K '⋅   mz−1z⋅z−µ() C ( z)⋅ P( z)=KP 
1+ C ( z) ⋅ P ( z)z−ζK ' ⋅
1 + K '⋅   mzzz−1⋅−µ() 


Using control laws,
simplified to:⇓

FC =K 'ζz m ( z − 1) + K ' ζ

Applying the design rules of (Papageorgiou and Messmer, 1985)
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Controller Gain Values• ready design of a PI regulator
mKP

KI

0μ/ζ

(1- μ)/ζ

1μ/(3ζ)(1- μ)/(3ζ)

2μ/(5ζ)(1- μ)/(5ζ)

3μ/(6ζ)(1- μ)/(6ζ)>3μ/(2mζ)(1- μ)/(2mζ)

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Chania (Greece) Urban Network• Microscopic simulator AIMSUN applied.
• Realistic O-D demands and dynamic traffic assignment (DTA)
based routing (4h simulation)• In the middle of every link inside PN, a loop detector has been
installed
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Simulation Result: NFD

 Maximum TTD values in the diagram occurs ina TTS region of 600 to 800 veh∙h per h
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Control Results (90s ControlStep)ˆ
Gating TTS=
750, K=10, =K I 3,=
m 3,=µ 0.7692,=ζ 0.0128p

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NFD after Gating Control

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Time-Delayed Gating with
Higher Control Step (450s)
ˆGating TTS==
K I 20,=m 3,=µ 0.760,=
ζ 0.011750, K
65, =p

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Network-Wide PerformanceIndexes
15 replications per control scenario
Performance indexes (whole network, 4h)• Average vehicle delay per km• Mean speed

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Reduced NFD
Measurements:
 Four groups of measurements collected from: each and every link inside PN (100% of all) signal-controlled links inside PN (35% of all) proportions of controlled links in PN:
 10% of all 5% of all Selection of links: critical links where congestion starts
spreading
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Measurement Location

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Reduced NFD
100%

35%

10%5%

 When TTS(100%) in its critical range, TTS(x%) is in its
own critical range
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Control Results
35%

10%

5%

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Critical Links
5% measurements:
4 early-congested
4 late-congested
maximumTTDTTS-range of[50-90]5%: 8 late-congested

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Multiple Concentric Gating

 After the first core of congestion formed, red perimeter activated If small proves unsuccessful to mitigate the congestion, big
activated additionally
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Test-bed: San Francisco Large-scale urban network ⇒ heterogeneous
spatial distribution of congestion ⇒ different
regions may not reach the critical accumulation(NFD) simultaneously⇓ Red border: first protected network (PN1)
 Pink Border: second protected network (PN2),OVERAL NETWORK measurements collected every 60s (shortestcycle of the traffic lights inside the wholenetwork) 5-hour simulation
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NFDs
NFD for PN2

NFD for PN1

maximum-TTDTTS-range of [3500-4000] maximum-TTDTTS-range of [1700-2000]

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Results: Multiple Concentric
Improvements: No gating (NG); Single Controller (SC); Two Controller (TC)
Avg.
COVNG
1369
0.09

TTS/TTD (s/km)
SC
TC
1227
1166
0.05
0.04

Improvement (%)
SCTC
10.01
14.39
-Avg.
COVNG
563
0.08

Delay (s/km)
SC
442
0.05

Improvement (%)
SCTC
21.15
26.24
-TC
414
0.04

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Combining Gating and Traffic
Responsive Strategies
Two adaptive traffic control
strategies:• Modified SCATS• Volume-based traffic
responsive control strategies
Gating or Perimeter Control

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Control Scenarios• Scenario 1: (no-gating) the traffic lights in the PN arecontrolled applying fix-time control signal plan.
• Scenario 2: (no-gating) “volume-based” traffic responsive
control strategy is implemented to control all the traffic lights
within PN.
• Scenario 3: (no-gating) adaptive traffic control strategy“modified SCATS” is used for controlling the signalized junctions
within PN.
• Scenario 4: Gating at the perimeter and fix-time control insidePN.
• Scenario 5: Gating at the border and “volume-based” for therest of the traffic lights in the PN.
• Scenario 6: Gating at the boundary and “modified SCATS”
within PN.
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NFDs for Different Control
Scenarios

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Density Variation in Different
Scenarios

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Control Results for Six
Scenarios

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Control Results for Six
Scenarios

Performance
Index
delay (sec/km)
speed(Km/h)avg. virtual
queue (veh)max virtual
queue (veh)vehicle outFix-timeVolume-based

SCATS
351
9204

Gating + fixtime
203
13206

Gating +Volume-based
193
14164

Gating +SCATS
203
13194

389
8252

294
10166

728

696

702

965

808

888

12675

12913

12801

12924

12912

12923

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Conclusion and Future WorkA robust feedback controller, by considering timedelay on the system is designed.
It is shown that the feedback gating works properlywith much longer time-steps.
Overall performance of the network improved.
On-going work: comparison and combining withtraffic-responsive signal control strategies.
Future work:
Queue management at gated links
Field test
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Email: m.ekbatani@tudelft.nl

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