next generation of adaptive traffic signal control

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Next Generation ofAdaptive Traffic Signal Control

Pitu MirchandaniATLAS Research Laboratory

Arizona State University

NSF WorkshopRutgers, New Brunswick, NJ

June 7, 2010

Acknowledgements: FHWA, ADOT, NSF

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Outline – 3-part talk

Conclusions

Current Adaptive Control RHODES RHODES/NG RHODES/VII Conclusion

Current Responsive Traffic Control Practices & Issues

Real-time Adaptive Control

RHODES - Current

RHODES - Next Generation

RHODES - Future with IntelliDrive

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CURRENT PRACTICE – TRAFFIC RESPONSIVE SYSTEMS

UTCS (Urban Traffic Control System, FHWA, US, 1070’s) – 2nd and 3rd generation systems have adaptive features.

SCOOT (Split, Cycle, and Offset Optimization Technique, UK, 1970’s) – Monitor traffic volumes and frequently (every few cycles) develop a new plan based on TRANSYT

– New detectors needed downstream to measure “traffic profiles”

SCATS (Sydney Coordinated Adaptive Traffic System, Australia, 1970’S): – A “degree of saturation” is measured at each approach – Cycle time is increased when average saturation increases, and

– Splits are allocated in proportion to saturation– Adjacent intersections are “grouped” when cycle times are

nearly same, or “ungrouped” for different cycle times demand.


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CURRENT PRACTICE – TRAFFIC RESPONSIVE SYSTEMS

OPAC (Optimization Policies for Adaptive Control, US, early 1980’s by Gartner et al.)

• First to move away from traditional “plans”• Upstream detectors measure approach load• For a given time horizon, various combinations of green phases

are analyzed, and “optimum” durations are selected based on implicit enumeration.

• Current RHODES optimization model uses these ideas.


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CURRENT PRACTICE – ISSUES

Few jurisdictions use adaptive control mainly because • They are hard to implement• Require additional sensors• Improve performance only when system is under saturated

Next generation adaptive control must respond to above concerns.

But note that there is always a “capacity” for a signalized network, and when the load is increased above this capacity there will be unbounded queues no matter what one does.

What the next generation control will do is increase this “capacity” as much as possible.


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Framework For Real-time Decision Systems

DecisionSystem

Sensor media

Real-world

Data Gathering

dataflow

Feedback& decisions

Equipment Processing

Sensors

We will keep coming back to this!


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What is an “Adaptive” Control System?

It is necessarily a “Feedback Control System” that “Adapts”

Measurements:monitoring state of system

Real-time Control System

Feedback& decisions

data

Controls:ActuatorsSignals. …

Actual System


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General Adaptive Control Architecture

Estimator/Predictor

Model

Estimation

Real-World Systems

x(t)

Exogenous inputs

x(t)

OUTPUTS(states of the system.)

Sensors

Measurement noise

y(t)

Measurements

u(t)

Decisions/Controls

(Latency delay)

Comm.delay

Decision/Control Algorithms(using desired objectives)

Model

Optimization

We will keep referring to this architecture


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• Little recognition that traffic state is a non-stationary stochastic process

E.g., RHODES (our adaptive traffic control) does not use “plans” but assumes that some real-time information is available all the time

• “Traffic Adaptive” requires constant monitoring of traffic – this is the cost of adaptive performance

E.g.: A “traffic plan” (cycles, splits and offsets) assumes that the process is stationary

Problems and Issues with Current Traffic Management Paradigms


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“Traffic- Adaptive” Signal Control System

Measurements: detectors &signals

Adaptive Traffic Signal Control System

Feedback& decisions

raw data

Actuators: signals


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Quality Attributes of an “Adaptive” Traffic Signal Control System?

Responsiveness:How fast does it respond to changes in traffic conditions?(including incidents and special events)

Feedback Philosophy:Is it reactive? (the “vanilla” version)Is it proactive? (the “sundae” version)


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“Open-loop”, “Reactive” & “Proactive”(Illustration in following a trajectory)

Time

Position

Actual Trajectory ProactiveReactive

Open Loop


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• Explicitly recognizes that traffic state is anon-stationary stochastic process

• Especially useful for non-recurrent traffic conditionsand major incidents

Adaptive (Real-Time Proactive) Traffic Control

• Requires prediction of short-term future based oncurrent conditions and controls


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Destinations/Origins

Network LoadControl

Network FlowControl

IntersectionControl

Traffic SignalActivation

Detectors andSurveillance

Actual Travel Behavior and Traffic

NetworkLoads

Target Timings

ActualTimings

ControlSignal

Vehicle Flow Prediction

Scenario

Origins/Destinations

Current Capacities, Travel Times,Network Disruptions

(seconds)

(minutes)

(minutes/hours/days)

Platoon Flow Prediction

Network LoadEstimator/Predictor

Network FlowEstimator/Predictor

Intersection FlowEstimator/Predictor

Measurements

y(t)

ATIS

Historical/Infrastructure Data

Reference: Head, Mirchandani, Sheppard, 1992

Hierarchical Architecture for “RHODES” ADAPTIVE TRAFFIC MANAGEMENT

Intersection FeedbackControl

Network levelFeedback

Regional NetworkFeedback


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Simplified Architecture for RHODES

Control SelectionData collection and

prediction of queues and arrivals

processeddata

Feedback& decisions

raw data

Detectors, traffic signals, and communication

CountsStop-bar

Control Actions(phase durations)


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RHODES: THE GENERAL PROACTIVE IDEA

Estimator/Predictor

Model

Estimation

Real-WorldTraffic Systems

x(t)

Exogenous inputs

x(t)

OUTPUTS(traffic volumes,speeds, queues,air quality, etc.)

Sensors

Measurement noise

y(t)

Measurements

u(t)

Decisions/Controls

(Latency delay)

Comm.delay

Real-time Estimator/Predictor

Decision/Control Algorithms(using desired objectives)

Model

Optimization

Real-time Control/Decision

PREDICT

PREDICT

CAPRI


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PREDICTION & CONTROL IN RHODES

detectors

PREDICTCONTROL

ALGORITHMS

TURN RATIOS

DISCHARGERATES

TRAVEL TIMES

arrivals&

queues

state of traffic network

(CAPRI)


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RHODES: INTERSECTION PREDICTION

cardetector

UNDERRHODESCONTROL

Me Traffic Mgt RHODES Evacuation Dynamic Flows Cases Future

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.5

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1.5

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1.5

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.2L

T

R

1 2 3 4 45 46 47 48 49 50 51 52 Time

And ... PREDICTIONS !

1.5

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1.5

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1.5

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2

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2

1

1

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Next second A little later

RHODES: INTERSECTION PREDICTION

Me Traffic Mgt RHODES Evacuation Dynamic Flows Cases Future

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RHODES INTERSECTION CONTROL

L

T

R

1 2 3 4 45 46 47 48 49 50 51 52 Time

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1.5

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2

1

1

From East

Time

PHASE ORDER: B-C-D-A-B-C-D-A....

1 2 3 4 45 46 47 48 49 50 51 52

B C D A B C

From WestRTL

From SouthRTL

From NorthRTL

We can easily compute total delay and stops from this diagram

RHODES idea is to change Phase durations to minimize “cost”.

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Effectively, a real-time algorithm that determines:for a given Phase Order A,B,C,D,A,B,C,D... what time durations should be given to Phase A, Phase B, ..., etc.allows various objectives (stops, delays, queues) for different classes (cars, buses,...)considers categories of predicted arrivals and their objectives considers a given rolling decision time horizon T, with time increments of D seconds (roll period)

* Categorized Arrivals-based Phase Re-optimization at Intersections.

CAPRI*:INTERSECTION CONTROL LOGIC


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Performance - Simulation (Atlanta)SAC

RHODES


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RHODES InstallationsRHODES


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Additional Features - Transit Priority

PREDICTarrivals

&queues

CAPRI

INTERSECTION CONTROL SUBSYSTEM

detectors

TURN RATIOS

DISCHARGERATES

TRAVEL TIMES

APRES-NETarrivals

&queues

REALBAND

NETWORK FLOW CONTROL SUBSYSTEM

Transit/bus Priority(position and “weight”)


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PREDICTarrivals

&queues

INTERSECTION CONTROL SUBSYTEM

detectors

TURN RATIOS

DISCHARGERATES

TRAVEL TIMES

APRES-NETarrivals

&queues

REALBAND


Emergency vehicles(phase constraints)

Additional Features - Emergency Preemption

CAPRI


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• Traffic signals “pre-empted” based on shortestroute from depot to incident

• Location of incident reported

• Shortest route computed based on real-timetraffic conditions and given to dispatcher

Depot

Additional Features - Emergency Preemption


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PREDICTarrivals

&queues

CONTROL ALGORITHMS


TURN RATIOS

DISCHARGERATES

TRAVEL TIMES

APRES-NETarrivals

&queues

REALBAND


Additional Features - Rail PreemptionTrain movement

(position and schedule)


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2nd Part

Conclusions


Current Traffic Control Practices


RHODES - Current


RHODES – Next Generation with IntelliDrive

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The performance of RHODES is directly related to the accuracy of its queue estimates

Parameters which affect this accuracy:

Turn ProportionsProportion of vehicles on an approach which turn left, turn right or proceed through the intersection

Queue Discharge RatesRate at which vehicles leave an intersection, dependent upon the number of available lanes and the movement involved

Link Travel TimesTime taken by a vehicle to traverse the distance from an upstream peer intersection to a point downstream

RHODES Input Parameters


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RHODES – “Self-Adaptive” Traffic Signal Control

“Self-adaptive” Traffic Signal ControlNext Generation Control Systems Incorporate algorithms that automatically update critical RHODES parameters based on available data

Benefits

Performance of RHODES will be further improved

Significant reduction in calibration and ‘fine-tuning’

Eliminates the need to update parameters periodically

Data and computed parameters will be available to agencies for other purposes, such as regional planning


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PREDICTION & CONTROL IN RHODES-NG

detectors

PREDICTCONTROL

ALGORITHMS

TURN RATIOS

DISCHARGERATES

TRAVEL TIMES

arrivals&

queues

state of traffic network

(CAPRI)

Issues Framework RHODES Evacuation Dynamic Flows Cases Future

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PREDICTarrivals

&queues

CAPRI


detectors

TURN RATIOS

DISCHARGERATES

TRAVEL TIMES

FINITE HORIZONDYNAMIC PROGRAM


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PREDICTarrivals

&queues

CAPRI


detectors

TURN RATIOS

DISCHARGERATES

TRAVEL TIMES

GENERALIZED LEAST-SQUARE

ESTIMATION


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PREDICTarrivals

&queues

CAPRI


detectors

TURN RATIOS

DISCHARGERATES

TRAVEL TIMES

REAL-TIME PLATOON TRACKING


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PREDICTarrivals

&queues

CAPRI


detectors

TURN RATIOS

DISCHARGERATES

TRAVEL TIMES

MONITORING ESTIMATED QUEUES &

DETECTOR OCCUPANCIES


THIS IS SUPPORTEDBY AN ON-GOING FHWA CONTRACT

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Adaptive Turn Proportions

Auto configuration based upon intersection geometrics/phasing

Auto adjusts to reflect actual turn proportion variability

Simulation results show an improvement in performance

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Approach-1-through movement

0

0.2

0.4

0.6

0.8

1

1.2

241

754

1187

1664

2155

2600

3269

3787

4433

5030

5929

time

turn

ing

prop

ortio

n

algorithm's predictionthree cycle's average

Adaptive Turn Proportion – Sample Results


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RHODES “Self-adaptive” Traffic Signal Control responds to these issues

1. Changing short-term demand – and in the long run will automatically equilibrate with network flow changes (bi-level dynamic network equilibrium)

2. Saturated traffic conditions (up to a maximum capacity)

3. Accepts and integrates data from IntelliDrive systems


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“Self-adaptive” Traffic Signal ControlAutomatically recognizes various operating regimes


Usually no residual queues

Residual queues keep exploding(over saturation)

Traffic load

Residual queues described by steady-state distribution

Queue size

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(usually no residual queues)

Queue size

Load info provided from

upstream to downstream

Traffic load

(residual queues described by steady-state distribution)

Illustrated this earlier

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Residual queues keep exploding(over saturation)



(usually no residual queues)

Queue size

Load info provided from

upstream to downstream

Traffic load

(residual queues described by steady-state distribution)Queue build info provided from downstream to upstream*

[* info on end of queue to prevent spill-back at upstream intersection]

Illustrated this earlier

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Additional benefit: performance monitoring

Queues, delays and travel times,

Level of congestion – operational regimesUnsaturatedSaturated but stableOver saturated (unstable)

Route travel times

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RHODES Next Generation w/IntelliDrive


RHODES with/IntelliDrive Integration

Scheduling of multiple preemption/priority requests

Data exchange occurs between On Board Units (OBU), Road Side Units (RSU), the signal controller and RHODES. (Currently using DSRC)

RSU

RSU

OBU

OB

U

OBU

RSU

Need DATA FUSION to predict demandfor various signal services

NG-RHODESwill provide appropriate service for various classes of vehicles

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3rd Part

Conclusions


Current Traffic Control Practices


RHODES - Current


RHODES – Next Generation with IntelliDrive

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Concluding Remarks on Next Generation Adaptive Traffic Control

Decrease in traffic operations/planning effortoperators need not “time” signals periodicallyplanners and traffic engineers can concentrate on smaller number of scenarios


Improvement in traffic performance:responds to recurrent congestionresponds to near “oversaturation”responds to non-recurrent conditions and incidents (through “monitor”, “learn”, “predict” and optimally “respond” strategy)

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NEAR FUTURE: Special vehicles will be identified via transponders and detectors, e.g.: Emergency, Transit, HAZMAT, … using IntelliDrive structure

FAR FUTURE: Every vehicle will be tracked. Every vehicle will be require and be provided appropriate service and treated with appropriate priority.

Traffic signals will provide appropriate signal service by scheduling the service within the given time horizon

Signals will provide in-vehicle signal and controls(“STOP or you will have an accident”). Safety will improve.


Concluding Remarks

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Thanks for your attention

Questions???

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atlas ASUCurrent Adaptive Control RHODES RHODES/NG RHODES/VII Conclusion

Without Turning Proportions Estimation

Vehicle Vehicle Vehicle minutes

Travel time Avg. speed Avg. stop

Miles Trips Delay time

(Sec/Veh-Trip) (MPH) (Per Trip)

Period 1 3059 6711 3576 80.3 20.4 .7

Period 2 5474 12304 5737 75.1 21.3 .7

Period 3 8002 18369 8292 73.0 21.5 .6

With Turning Proportions EstimationVehicle Vehicle Vehicle

minutesTravel time Avg. speed Avg. stop

Miles Trips Delay time

(Sec/Veh-Trip) (MPH) (Per Trip)

Period 1 3058 6712 3039 75.4 21.7 .6

Period 2 5467 12284 4760 70.4 22.8 .6

Period 3 7979 18315 7386 70.1 22.4 .6

Simulation Results

next generation of adaptive traffic signal control

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