Download - Evaluation of the Effectiveness of Potential ATMIS Strategies Using Microscopic Simulation Lianyu Chu, Henry X. Liu, Will Recker PATH ATMS Center @ UC.

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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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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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.

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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

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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.