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UFTI Seminar
Alex SkabardonisUC Berkeley
Gainesville, FLMarch 24, 2016
Control of Freeway Corridors: Control of Freeway Corridors: Objectives, Performance Measures, StrategiesObjectives, Performance Measures, Strategies
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Freeway Corridor managementBackground/Problem StatementNational Programs: ICM
Signalized Intersections: Performance Measurement
Freeway-Arterial Coordination
Looking Ahead
Outline
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Background: Corridor Management
Cooperative management of freeways and adjacent arterial networks
Los Angeles, Smart Corridor 1988
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Corridor Traffic Management & Information Vision
Background: Corridor Management
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5
USDOT ICM Program (1)
Multimodal operations Complex modeling approaches Operational procedures/plans Institutional constraints Decision support systems Limited field evaluation Limited research
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USDOT ICM Program (2)
US-75 ICM Corridor, Dallas, TX
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I-15 ICM Corridor, San Diego, CA
ManagedLanes
DirectAccess Ramps
Access Road
BRTStation
Parkand-Ride
USDOT ICM Program (3)
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CA CC I-210: Decision Support
PredictionPredictionPredictionPrediction
Heterogeneous data
Historical data
Strategy bank
Measured ρ,v,f
all
Incidents, CHP, etc.
Historical data
Currentρ,v,f state
Demand management
strategy
Supply management
strategy
Predicted OD and routes
Predicted performanceData scopes
KnowledgeKnowledgeKnowledgeKnowledgeActuationActuationActuationActuationDataDataDataData
InterfaceInterfaceInterfaceInterface
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Time (cycle)
Fixed‐Time Plans Time of Day (TOD) No Detection May be actuated
Fixed time plans Traffic responsive plan selection System detection
Traffic responsive control On‐line timing development Approach & system detection
Adaptive control Measure & predict arrivals per cycle Extensive detection
A
B
C
D
Urban Arterials/Networks: Traffic Flow Variability vs. Control
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Most signal systems fixed-time control• Limited data• Out-dated timing plans
Adaptive systems High cost Complex to understand and operate
Arterial Networks: Traffic Control
Source: Alek Stevanovic, NCHRP Synthesis 403
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Improving Signal Timing Plans - Performance derived signal settings - Robust timing plans
Approach: Use of HR data*
Performance measures for operators and travelers - Use of existing infrastructure- No interference with controller operation
*Work with P. Varaiya & Sensys Networks“Management of Urban Traffic with H-R Data” IEEE ITSC 2014
On-Going/Future Work - Traffic volume prediction - Safety (red light running)- Multimodal (pedestrians, bicycles)
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Data Collection System
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Selected Test Site: Beaufort, SC
Leg 1
Leg 2
Leg 3
Leg 4
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Total volume (veh/15 minutes) Total volume (veh/cycle)
2/28/2015, 7AM to 8PM Peak Period, 4-7 PM
Intersection Volume; daily Variation
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Turning Mov -Leg 2 (veh/ 15 min)
Approach Volumes & Turning Movements
Approach Volume (veh/15 min)
Peak Period, 4-7 PM
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Seasonal Volume Variation
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Signal Control Data
Green Times per Phase
MAIN STREET: Phases 1,2,5,6 CROSS STREETS: Phases 4,8
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Signal Phase Operations
Wasted green time: time phase is active with no vehicle present and conflicting phase callVehicle arrivals: % arrivals on green
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Performance: Average Delay (sec/veh) HCM Level of Service (LOS)
Through movement, Leg 1 Through movement, Leg 2
Left turn movement, Leg 1 Right turn movement, Leg 2
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Performance: V/c and LOS
(V/C): (v*C/g*s) S: sat flow (max discharge rate)LOS: Level of Service per HCM
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HR Data and Timing Plan Development
Traditional ApproachLocal adjustments based on spot observations (complaints)
Apply signal optimization software to develop timing plan(s)
Field implementation-fine tuning. Before and after studies (limited)
Field data collection of turning movement counts (one day)
Availability of HR DataAssess existing intersection operations
• Progression (% arrival on green)• Capacity (V/c ratio)• Delay
Develop and Implement Improved Settings
Evaluate performance Aproach/intersection/system
Over time
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Improving Signal Timing Plans• Volume clustering – best set of volumes for the
three timing plans available• New timing plans reduce intersection signal delay by
10% on average*
6am 7am 8am 9am 10am11am12pm 1pm 2pm 3pm 4pm 5pm 6pm0
5
10
15
20
25
30
35
40
time
inte
rsec
tio
n s
ign
al d
elay
Current timing planNew timing plan
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Improving Signal Timing Plans - Performance derived signal settings - Robust timing plans
Summary: Use of HR data
Performance measures for operators and travelers - Use of existing infrastructure- No interference with controller operation
On-Going/Future Work - Traffic volume prediction - Safety (red light running)- Multimodal (pedestrians, bicycles)
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II. Freeway – Arterial Coordination
Important element of corridor management
Existing coordination guidelines mostly address institutional issues (example: FHWA Handbook)
Most approaches consist of scenarios with “flush” signal timing plans on arterials in case of freeway incidents
Lack of Methodologies for Freeway-Arterial Interactions
Spillbacks to- from ramps
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Background: Freeway Ramp Metering
Control on-ramp flows to preserve freeway capacity
Xi : input flow rate at on-ramp i , N : # on-rampsaij : proportion of traffic entering on-ramp i going through section j Cj : capacity of freeway segment j
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Excessive delays to on-ramp vehiclesSpillback to local streets Queue override –diminishes ramp metering benefits
On Ramp QueuesFreeway Mainline
Freeway Ramp Metering: Impacts
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5.5 6 6.5 7 7.5 8 8.5 9 9.5 10 10.5 110
200
400
600
Time (Hour)
Que
ue L
engt
h (m
)
Queue Override
5.5 6 6.5 7 7.5 8 8.5 9 9.5 10 10.5 110
200
400
600
Time (Hour)
Que
ue L
engt
h (m
)
Queue Override
5.5 6 6.5 7 7.5 8 8.5 9 9.5 10 10.5 110
200
400
600
Que
ue L
engt
h (m
)
Queue Estimation and Control
5.5 6 6.5 7 7.5 8 8.5 9 9.5 10 10.5 110
200
400
600
Que
ue L
engt
h (m
)
Queue Estimation and Control
Queue Override failed to limit the
queue within the limits
large variation in queue length
On-Ramp Queue Control Regulator Queue Override
Queue Estimation & Control
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Application: Los Angeles I-210W
Existing
Improvements: 6% Travel Time/ 16% Delay Reduction
Proposed
“Design, Field Implementation and Evaluation of Adaptive Ramp Metering Strategies,”PATH Research Report UCB-2005-2“Analysis of Queue Estimation Methods Using Wireless Magnetic Sensors, “ TRR 2229, 2011
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Proposed on-Ramp Access Control (1)
Determine the signal settings to avoid queue spillover from ramp metering and result in queue override
Constraints Serve the traffic demand on arterial phases Arterial link storage (arterial spillback)Minimum phase green times
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Minimize the ratio of actual and desired green times per signal phase
Proposed on-Ramp Access Control (2)
Desired green time: minimum green time to serve the traffic demand
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• Minimum green time constraint: ,
• Cycle length constraint: ∑• On-ramp storage constraint: , ·
∈
Freeway
Arte
rial
Phases for on-ramp
access
Total flow onto freeway on‐ramp
Available on-ramp storage space
Constraints
Proposed on-Ramp Access Control (3)
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, · ,
Offset Lost time
Existing queue
,
Available queue storage space
Flow into downstream link
Downstream queue length
Link length
Available queue storage space
Constraint: Arterial link storage
Proposed on-Ramp Access Control (4)
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Application: Test Site
Test Site: I-680, San Jose CA
• AIMSUN Microscopic Simulator
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-10
-5
0
5
10
15
20
FWY DELAY FWY VMT ARTERIAL DELAY ARTERIAL DELAY
SYSTEM /MOE
% C
HA
NG
E
Application: Fidings
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Looking Ahead: Connected Vehicles“Here I am”V2V and V2I
V2I Example: SPaT messageApplication: Dynamic Speed Advisory (source: UC & BMW)
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Field Test Results*
Uninformed Informed Uninformed InformedDriver Driver Driver Driver
&APIV &APIV
Fuel (L/100KM) 10.23 8.84 8.28 7.33
Improvement Base Scenario -13.60% -19.10% -28.40%
*https://www.fhwa.dot.gov/multimedia/research/advancedresearch/index.cfm
Uninformed Driver (Baseline Scenario): no speed recommendation
Informed Driver: follow speed recommendation
Individual Vehicle Priority & Uninformed Driver: no speed recommendation. Intersection adapts timing with individual vehicle priority
Individual Vehicle Priority & Informed Driver: follow speed recommendation. Intersection adapts timing with individual vehicle priority
“ Advanced Signal Control Strategies,” PATH Research Report UCB-2013-3
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Looking Ahead: Beyond Connected Veh
Automation
Connected VehATM
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Back Up Slides
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Measuring Saturation flow
Through/ Left turn shared Left turn lane
Statistics of saturation flow rates.
1200 1400 1600 1800 2000 22000
9
18
27
36
Saturation flow rates [vph]
No
. Cyc
les
Saturation flow rates
1200 1400 1600 1800 2000 22000
0.25
0.5
0.75
1
CD
F
1200 1400 1600 1800 2000 22000
4
8
12
16
Saturation flow rates [vph]
No
. Cyc
les
Saturation flow rates
1200 1400 1600 1800 2000 22000
0.25
0.5
0.75
1
CD
F
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Performance: Delay (Analytical solution)
• d : Delay (sec/veh)• C : Cycle length • g : green time• V/c : Volume to capacity ratio
Leg 2 through movement
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ALINEA Algorithm
r(k) = r(k-1) + KR[Oc –Oout(k)]
r(k) is the metering rate in time step k;r(k-1) is the metering rate in time step k-1KR is the regulator parameter (constant);Oout(k) is the current occupancy measurement
Local traffic-responsive strategy –closed loop
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Key Issues: Freeway Operating conditions (congestion level) Incident characteristics (location, severity) Characteristics of freeway control & freeway surveillance Characteristics of traveler information system Characteristics of parallel arterial(s)
Incident at Bottleneck Incident Upstream of Bottleneck
Non-Recurrent Congestion: Diversion Strategies
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0%
10%
20%
30%
40%
50%
0.70 0.75 0.80 0.85 0.90 0.95VOLUME/CAPACITY
AD
DIT
ION
AL
VOLU
ME
where: DVi : additional traffic volume on approach i (%) Xi : volume/capacity (degree of saturation) on approach i
(%) RCi : reserve capacity on approach i = 1- Xi
Amount of Diverted Volume?
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Inhibit Meteringmaximize flow from arterial into freewayIn case of incidents upstream of the on-ramp
Control Strategies: Non-Recurrent Congestion
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