Post on 01-Jan-2016
description
Evaluation of the Effectiveness of Potential ATMIS
Strategies Using Microscopic Simulation
Lianyu Chu, Henry X. Liu, Will Recker
PATH ATMS Center @ UC Irvine
Steve Hague
Traffic operations, Caltrans
Presentation overview
• Background
• Calibration
• ATMIS strategies
• Evaluation studies
• Conclusions
Background
• Caltrans TMS master plan• ATMIS Strategies
– Incident management– Adaptive ramp metering– Adaptive signal control– Traveler information system– Combination / integrated control
I-405 Study network
Scenario description
• northbound of freeway I-405 is highly congested from 7:30 to 8:30 AM
• The merge area of SR-133 and I-405 (on the northbound I-405) is the location where incidents happen most frequently
• Shoulder incident: causes the speed of passing vehicles to be 10 mph for the first ten minutes and 15 mph thereafter
• purpose: evaluate under incident scenario
Calibration: data preparation
– Arterial volume data / cordon traffic counts– Freeway loop detector data– Travel time data– Reference OD matrix (from OCTAM model)– Vehicle performance and characteristics data– Vehicle mix by type
Calibration procedure
• Assumptions– Driver behaviors distribution (awareness and
aggressiveness): normal distribution
– Traffic assignment method: stochastic assignment
• Adjustment of route choice pattern• OD estimation
– Adjustment of the total OD matrix
– Reconstruction of time-dependent OD demands
• Parameter fine-tuning
Adjustmentof route choice pattern
• Route choices: – determined by stochastic assignment, which
calculates shortest path based on speed limits– not affected by traffic signals and ramp
metering (PARAMICS)
• How to adjust:– Adding tolls to entrance ramps– Decreasing the speed limit of arterial links
OD estimation
• an under-defined problem, finding an optimal point in a huge parameter space using limited measurement data
• Our method: two-stage approach– estimation of total OD matrix– profile-based time-dependent OD demands
Total OD matrix (I)
• Reference OD matrix from OCTAM– OCTAM: social-economic data and OD matrix of OC
– sub-extracted OD matrix based on four-step model
– limited to the nearest decennial census year
• Adjustment of the total OD matrix:– traffic counts at all cordon points (i.e. total inbound and
outbound traffic counts )
– balancing the OD table: FURNESS technique
Total OD matrix (II)
• Objective function:– Minimize the difference of estimated traffic flow with
observation – Measurement points: freeway loop stations at on-
ramps, off-ramps and along the mainline freeway, and several important arterial links
– Iterative process: simulation->modify OD->simulation
• overall quality of the calibration: GEH < 5
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Time-dependent OD demand (I)
• Most theoretical methods: only apply to simple network
• Our method: profile-based method– Profile: representation of the variation of OD flow
within the whole study time period, which include multiple sample points(16 points)
– Cordon flow (traffic counts): 15-minute interval
– how many vehicles generated from a zone within each interval: profile of the zone
Time-dependent OD demand (II)
• General case: • For any origin i, profile(i, j) = profile(i) , j =1 to N
• Special cases:• If profile can be roughly determined by loop data• If the corresponding OD flow has strong effects on
the traffic condition
– Special OD profiles: • freeway to freeway, • arterial to freeway, • freeway to arterial
Time-dependent OD demand (III)
Destination Origin 1 2 3 4
total_origin (known)
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Time-dependent OD demand (IV)
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6:00 6:15 6:30 6:45 7:00 7:15 7:30 7:45 8:00 8:15 8:30 8:45 9:00 9:15 9:30 9:45
Time of day
Per
cen
tag
e o
f to
tal
dem
and
a freeway zone to a freeway zone an arterial zone to an industrial zone
a freeway zone to an arterial zone an artertial zone to a freeway zone
Time-dependent OD demand (V)
• Optimization objectives:– Min (difference between the traffic counts of
simulation and observation over all points and periods)– 85% of the GEH value smaller than 5(during
congestion period: 7:30-8:30AM)
• Iteration is required• Pros: reduction in number of parameter to be
estimated:– 30x30x16 -> 30x16– Totally, 30 profiles in the calibrated model
Parameter fine-tuning
• Link specific parameters• Parameters for the car-following and lane-
changing models• Objective:
– Minimize (observed travel time, simulated travel time)
– Minimize the difference between the traffic counts of simulation and observation over all points and periods
Calibration results (I)
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Calibration results (II)
Comparison of observed and simulated travel time of northbound I-405
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simulation observation
Calibration results (III)
• The measure of goodness of fit is the mean abstract percentage error (MAPE):
• MAPE error of traffic counts at selected measurement locations range from 5.8% to 8.7%.
• The comparison of observed and simulated point-to-point travel time for the northbound and the southbound I-405, which have the MAPE errors of 8.5% and 3.1%, respectively.
T
tobssimobs tMtMtM
TMAPE
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ATMIS strategies
• Strategy 1: Incident management– decreasing the response time and clearance time caused
by incidents
• For Caltrans:– no incident management: 33 minutes– existing incident management: 26 minutes– improved incident management: 22 minutes
ATMIS strategies
• Strategy 2: Ramp metering– an effective freeway management strategy to avoid or
ameliorate freeway traffic congestion by limiting vehicles access to the freeway from on-ramps.
• Current implemented ramp metering: fixed-time• Potential improvement: adaptive ramp metering
– local adaptive ramp metering– coordinated ramp metering
ATMIS strategies: ramp metering
• ALINEA: a local feedback ramp metering policy
• maximize the mainline throughput by maintaining a desired occupancy on the downstream mainline freeway.
Downstream detector
On-ramp detector
Queue detector
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ATMIS strategies:ramp metering
• BOTTLENECK, coordinated ramp metering• applied in Seattle, Washington State• Two components:
– a local algorithm computing local-level metering rates based on local conditions,
– a coordination algorithm computing system-level metering rates based on system capacity constraints.
– the more restrictive rate will obey further adjustment
• within the range of the pre-specified minimum and maximum metering rates
• queuing control
ATMIS strategies
• Strategy 3: travel information– all kinds of traveler information systems, including
VMS routing, highway radios, in-vehicle equipment, etc.
– pure traveler information system: no traffic control supports
– how to model in PARAMICS: using dynamic feedback assignment
– assumptions: instantaneous traffic information is used for the calculation of the resulting route choice
ATMIS strategies
• Strategy 4: advanced signal control– adaptive signal control, and
– signal coordination
• Actuated signal coordination: – baseline situation: 11 signal intersections in the study
network are coordinated
• Adaptive signal control: – use SYNCHRO to optimize signal timing of those signals
along major diversion routes during the incident period based on estimated traffic flow
Evaluation: Modeling ATMIS strategies
ATMIS components Scena-
rio Scenario description Ramp Metering Signal Control Traveler Information
Incident Management
0 BASELINE 2000 Fixed time Coordinated N/A N/A
1 Non-incident management Fixed time Coordinated N/A 33 mins
2 Existing incident management Fixed time Coordinated N/A 26 mins
3 Improved incident management Fixed time Coordinated N/A 22 mins
4 Local adaptive ramp metering ALINEA Coordinated N/A 26 mins
5 Coordinated ramp metering BOTTLENECK Coordinated N/A 26 mins
6 Traveler information Fixed time Coordinated 5% compliance 26 mins
7 Combination-1 Fixed time Synchro-Adaptive 5% compliance 26 mins
8 Combination-2 ALINEA Synchro-Adaptive 5% compliance 26 mins
Evaluation: MOEs (I)
• MOE #1 system efficiency measure: average system travel time (weighted mean OD travel time over the whole period)
• MOE #2 system reliability measure: weighted std of mean OD travel time over the whole period
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Evaluation: MOEs (II)
• MOE #3 freeway efficiency measure: average mainline travel speed during the whole period and during the congestion period(7:30-9:30)
• MOE #4 on-ramp efficiency measure– total on-ramp delay– average time percentage of the on-ramp queue spillback
to the local streets
• MOE #5 arterial efficiency measure– average travel time from the upstream end to the
downstream end of an arterial and its std
Evaluation: number of runs
N
Y
Original nine runs
Start
Calculating the mean and its std of each performance measure
Is current # of runs enough?
End
Calculating the required # of runs for each performance measure
Additional one simulation run
22/ )(
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Evaluation results (I): overall performance
Control strategy ASTT (sec) ASTT Saving (%) std_ODTT (sec) Reliability Increase
(%)
Baseline 271.3 51.7
IM-33 297.0 0.0% 139.6 0.0%
IM-26 293.9 1.0% 130.7 6.4%
IM-22 289.1 2.7% 112.6 19.4%
ALINEA 289.7 2.4% 118.9 14.9%
BOTTLENECK 289.2 2.6% 115.5 17.3%
TI 284.4 4.2% 95.3 31.8%
Combination-1 280.5 5.5% 93.2 33.3%
Combination-2 279.6 5.9% 97.2 30.4% ASTT – Average system travel time Std_ODTT— Average standard deviation of OD travel times of the entire simulation period, which represents the reliability of the network
Evaluation results (II):Freeway performance
Scenario AMTS (mph)
AMTS Increase (%)
peak_AMTS (mph)
Increase of peak_AMTS
TOD (hour)
POQS (%)
Baseline 57.3 50.1 55.1 1.8%
IM-33 50.5 0.0% 37.2 0.0% 55.6 1.9%
IM-26 51.4 1.8% 39.4 6.0% 54.6 2.0%
IM-22 51.9 2.8% 40.0 7.5% 54.0 1.8%
ALINEA 51.6 2.1% 39.8 6.9% 57.6 0.9%
BOTTLENECK 51.9 2.7% 39.7 6.7% 89.1 1.9%
TI 51.9 2.8% 39.9 7.3% 58.0 1.8%
Combination-1 52.2 3.3% 41.0 10.1% 59.5 1.9%
Combination-2 52.3 3.5% 40.6 9.1% 60.0 1.0% AMTS – Average mainline travel speed of the entire simulation period (6 – 10 AM) peak_AMTS – Average mainline travel speed of the congestion period (7:30 – 9:30) TOD – Total on-ramp delay POQS – Time percentage of vehicles on the entrance ramps spillback to surface streets
Evaluation results (III):Arterial performance
Westbound ALTON Scenario ATT (sec) std_ATT
Baseline 515.8 70.3
IM-33 515.5 71.0
IM-26 514.1 68.1
IM-22 512.4 68.1
ALINEA 513.6 67.3
BOTTLENECK 518.3 69.0
TI 518.8 70.2
Combination-1 423.5 51.4
Combination-2 423.2 51.0 ATT – Average travel time Std_ATT – Standard deviation of the average travel time
Evaluation results (IV): IM
• Incident management– fast incident response is of particular
importance to freeway traffic management and control
– To achieve this, comprehensive freeway surveillance system and automatic incident detection are both required
Evaluation results (V): ramp metering
• performance improvement introduced by adaptive ramp metering is minor under the incident scenarios
• If the congestion becomes severe, the target LOS could not be maintained by using ramp metering and the effectiveness of ramp control is marginal
• adaptive ramp metering performs worse than the improved incident management scenario
• BOTTLENECK performs a little bit better than ALINEA in term of overall performance, but, BOTTLENECK causes higher on-ramp delay and spillback.
Evaluation results (VI): TI related scenarios
• traveler information– network topology -- one major freeway segment (I405)
with two parallel arterial streets – traveler information systems can greatly improve
overall system performance• Adaptive signal control:
– shorter travel time along diversion route (westbound ALTON parkway)
• Combination scenarios: perform the best– integration of traffic control & traveler information
Conclusions
• Evaluate the effectiveness of potential ATMIS strategies in our API-enhanced PARAMICS environment.
• Findings:– All ATMIS strategies have positive effects on the
improvement of network performance. – Adaptive ramp metering cannot improve the system
performance effectively under incident scenario.– Real-time traveler information systems have the strong
positive effects to the traffic systems if deployed properly
– Proper combination of ATMIS strategies yields greater benefits.