Transcript Evaluation of the Effectiveness of Potential ATMIS

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
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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
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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
Adjustment
of 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
GEH 
M obs (n)  M sim (n) 2
( M obs (n)  M sim (n)) / 2
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)
Origin
1
1
2
3
4
2
Destination
3
4
total_origin
(known)
Time-dependent OD
demand (IV)
Percentage of total demand
12.0%
10.0%
8.0%
6.0%
4.0%
2.0%
0.0%
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
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 lanechanging 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)
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Travel time (sec)
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simulation
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Comparison of observed and simulated
travel time of northbound I-405
Calibration results (III)
• The measure of goodness of fit is the mean
abstract percentage error (MAPE):
1
MAPE 
T
T
((M
obs (t ) 
M sim (t )) / M obs (t ))
t 1
• MAPE error of traffic counts at selected
measurement locations range from 5.8% to 8.7%.
• The comparison of observed and simulated pointto-point travel time for the northbound and the
southbound I-405, which have the MAPE errors of
8.5% and 3.1%, respectively.
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.
r (t )  ~
r (t  t )  K R  (O * O(t ))
Downstream detector
On-ramp detector
Queue detector
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
Scenario Scenario description
0 BASELINE 2000
Non-incident
1 management
Existing incident
2 management
Improved incident
3 management
Local adaptive ramp
4 metering
Coordinated ramp
5 metering
6 Traveler information
7
Combination-1
8
Combination-2
Ramp Metering
Fixed time
ATMIS components
Traveler
Signal Control Information
Coordinated
N/A
Incident
Management
N/A
Fixed time
Coordinated
N/A
33 mins
Fixed time
Coordinated
N/A
26 mins
Fixed time
Coordinated
N/A
22 mins
ALINEA
Coordinated
N/A
26 mins
BOTTLENECK Coordinated
Fixed time
Coordinated
SynchroFixed time
Adaptive
SynchroALINEA
Adaptive
N/A
26 mins
5% compliance 26 mins
5% compliance 26 mins
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)
ASTT 
(T
i, j
 Ni , j )
i , j
N
i, j
i , j
• MOE #2 system reliability measure: weighted std
of mean OD travel time over the whole period
Std _ ODTT 
(Std(T
i , j )  Ni , j )
i , j
N
i , j
i, j
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
Start
Original nine runs
Calculating the mean and its std
of each performance measure
N  (t  / 2 
Calculating the required # of runs
for each performance measure
Is current # of
runs enough?
Y
End
Additional one
simulation run
N

 
)2
Evaluation results (I):
overall performance
Reliability Increase
ASTT Saving (%) std_ODTT (sec)
(%)
51.7
0.0%
139.6
0.0%
1.0%
130.7
6.4%
2.7%
112.6
19.4%
2.4%
118.9
14.9%
2.6%
115.5
17.3%
4.2%
95.3
31.8%
5.5%
93.2
33.3%
5.9%
97.2
30.4%
Control strategy ASTT (sec)
Baseline
271.3
IM-33
297.0
IM-26
293.9
IM-22
289.1
ALINEA
289.7
BOTTLENECK
289.2
TI
284.4
Combination-1
280.5
Combination-2
279.6
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
AMTS
AMTS
peak_AMTS Increase of
TOD
Scenario (mph)
Increase (%)
(mph)
peak_AMTS
(hour)
Baseline
57.3
50.1
55.1
IM-33
50.5
0.0%
37.2
0.0%
55.6
IM-26
51.4
1.8%
39.4
6.0%
54.6
IM-22
51.9
2.8%
40.0
7.5%
54.0
ALINEA
51.6
2.1%
39.8
6.9%
57.6
BOTTLENECK
51.9
2.7%
39.7
6.7%
89.1
TI
51.9
2.8%
39.9
7.3%
58.0
Combination-1
52.2
3.3%
41.0
10.1%
59.5
Combination-2
52.3
3.5%
40.6
9.1%
60.0
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
POQS
(%)
1.8%
1.9%
2.0%
1.8%
0.9%
1.9%
1.8%
1.9%
1.0%
Evaluation results (III):
Arterial performance
Westbound ALTON
ATT (sec)
std_ATT
515.8
70.3
515.5
71.0
514.1
68.1
512.4
68.1
513.6
67.3
518.3
69.0
518.8
70.2
423.5
51.4
423.2
51.0
Scenario
Baseline
IM-33
IM-26
IM-22
ALINEA
BOTTLENECK
TI
Combination-1
Combination-2
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.