Using Archived ITS Data to Automatically Identify Freeway Bottlenecks in Portland, Oregon

1Using Archived ITS Data to Automatically Identify Freeway Bottlenecks in Portland, OregonUsing Archived ITS Data to Automatically Identify Freeway Bottlenecks in Portland, Oregon

Using Archived ITS Data to Automatically Using Archived ITS Data to Automatically Identify Freeway Bottlenecks in Identify Freeway Bottlenecks in

Portland, OregonPortland, Oregon

Using Archived ITS Data to Automatically Using Archived ITS Data to Automatically Identify Freeway Bottlenecks in Identify Freeway Bottlenecks in

Portland, OregonPortland, Oregon

Robert L. Bertini, Rafael J. Fernández-Moctezuma,Robert L. Bertini, Rafael J. Fernández-Moctezuma,Jerzy WieczorekJerzy Wieczorek, Huan Li, Portland State University , Huan Li, Portland State University

15th World Congress on ITS15th World Congress on ITSNew York City, NYNew York City, NY

November 17, 2008November 17, 2008


PORTAL databasePORTAL databasePORTAL databasePORTAL database

Loop Detector DataLoop Detector Data20 s count, lane occupancy, speed from 20 s count, lane occupancy, speed from

500 detectors (1.2 mi spacing) 500 detectors (1.2 mi spacing)

Incident DataIncident Data140,000 since 1999140,000 since 1999

Weather DataWeather Data VMS DataVMS Data19 VMS since 199919 VMS since 1999

Data ArchiveData Archive

DaysDaysSince July 2004Since July 2004About 300 GBAbout 300 GB

4.2 Million 4.2 Million Detector IntervalsDetector Intervals

Bus DataBus Data1 year stop level data1 year stop level data

140,000,000 rows140,000,000 rows


ObjectivesObjectives

•How can we automate bottleneck detection?

•How can we analyze the resulting detected bottlenecks?

•How can we automate bottleneck detection?

•How can we analyze the resulting detected bottlenecks?


What is a Bottleneck?What is a Bottleneck?

•Queueing upstreamQueueing upstream•Freely-flowing downstreamFreely-flowing downstream•Temporal and spatial variationTemporal and spatial variation

•Queueing upstreamQueueing upstream•Freely-flowing downstreamFreely-flowing downstream•Temporal and spatial variationTemporal and spatial variation

Queued Unqueued

Bottleneck

Detectors


Why study bottlenecks?Why study bottlenecks?

• Find and rank recurrent bottlenecks(via data archive) Planners know where to focus congestion-reduction efforts

• Detect bottlenecks in real time Improve incident detection andtravel time predictions

• Find and rank recurrent bottlenecks(via data archive) Planners know where to focus congestion-reduction efforts

• Detect bottlenecks in real time Improve incident detection andtravel time predictions


Research objectivesResearch objectives

• Refine an algorithm to systematically Refine an algorithm to systematically detect freeway bottlenecks, and detect freeway bottlenecks, and quantify and visualize their impactsquantify and visualize their impacts

• Implement this tool in PORTAL, our Implement this tool in PORTAL, our continuously-updated transportation continuously-updated transportation data archivedata archive

• Refine an algorithm to systematically Refine an algorithm to systematically detect freeway bottlenecks, and detect freeway bottlenecks, and quantify and visualize their impactsquantify and visualize their impacts

• Implement this tool in PORTAL, our Implement this tool in PORTAL, our continuously-updated transportation continuously-updated transportation data archivedata archive


Reading a contour plotReading a contour plotReading a contour plotReading a contour plot

InterstatebridgeMP 308

I-405MP 304

I-405MP 300

OR-217MP 292

I-205MP 288

I-84MP 302




I-405MP 304

I-405MP 300

OR-217MP 292

I-205MP 288

I-84MP 302


Contour plots in real timeContour plots in real timeContour plots in real timeContour plots in real time

?InterstatebridgeMP 308

I-405MP 304

I-405MP 300

OR-217MP 292

I-205MP 288

I-84MP 302


Mockup of desired toolMockup of desired tool

A

B

C

Bottleneck

Estimated Propagation Speed

A – 25 mphB – 22 mphC – 21 mph

Activation

Deactivation

90% percentile of historicalbottlenecks


I-405MP 304

I-405MP 300

OR-217MP 292

I-205MP 288

I-84MP 302


DataData

• I-5 Northbound corridor I-5 Northbound corridor has best loop detector has best loop detector coverage: coverage: 2323 detectors detectors over over 2424 miles, giving miles, giving 1.11.1 mi average detector mi average detector spacingspacing

• Chose Chose 55 representative representative days for initial testingdays for initial testing

• Averaged data across all Averaged data across all 3 lanes, removed bad 3 lanes, removed bad detectors, and imputed detectors, and imputed missing valuesmissing values

• I-5 Northbound corridor I-5 Northbound corridor has best loop detector has best loop detector coverage: coverage: 2323 detectors detectors over over 2424 miles, giving miles, giving 1.11.1 mi average detector mi average detector spacingspacing

• Chose Chose 55 representative representative days for initial testingdays for initial testing

• Averaged data across all Averaged data across all 3 lanes, removed bad 3 lanes, removed bad detectors, and imputed detectors, and imputed missing valuesmissing values

MP 308

MP 284


Our starting pointOur starting point

• Based on a California field experimentBased on a California field experiment

• Using Using 55-minute aggregated data,-minute aggregated data,declare a bottleneck betweendeclare a bottleneck betweentwo detectors in a given timetwo detectors in a given timeperiod if:period if:

• Speed difference acrossSpeed difference acrossbottleneck is > bottleneck is > 2020 mph, andmph, and

• Upstream speed is < Upstream speed is < 4040 mph mph

• ““Sustained bottlenecks” filter: Sustained bottlenecks” filter:

• Remove outliers with too few “neighbors”Remove outliers with too few “neighbors”

• Fill in any small gaps within bottlenecksFill in any small gaps within bottlenecks

• Based on a California field experimentBased on a California field experiment

• Using Using 55-minute aggregated data,-minute aggregated data,declare a bottleneck betweendeclare a bottleneck betweentwo detectors in a given timetwo detectors in a given timeperiod if:period if:

• Speed difference acrossSpeed difference acrossbottleneck is > bottleneck is > 2020 mph, andmph, and

• Upstream speed is < Upstream speed is < 4040 mph mph

• ““Sustained bottlenecks” filter: Sustained bottlenecks” filter:

• Remove outliers with too few “neighbors”Remove outliers with too few “neighbors”

• Fill in any small gaps within bottlenecksFill in any small gaps within bottlenecks


False Rate Max Upstream Speed

Min Speed Differential

30 35 40 45 50

10 0.23 0.24 0.39 0.48 0.53

15 0.16 0.17 0.38 0.42 0.46

20 0.16 0.17 0.36 0.40 0.47

25 0.10 0.10 0.38 0.44 0.52

30 0.11 0.11 0.44 0.50 0.58

Success and False Alarm Rate TablesSuccess and False Alarm Rate Tables

Success Rate Max Upstream Speed

Min Speed Differential

30 35 40 45 50

10 0.60 0.72 0.77 0.82 0.82

15 0.60 0.72 0.76 0.76 0.76

20 0.60 0.68 0.69 0.69 0.69

25 0.53 0.53 0.54 0.54 0.54

30 0.46 0.46 0.47 0.47 0.47


Success rate over all 5 days(using sustained filter)Success rate over all 5 days(using sustained filter)


False alarm rate over all 5 days(using sustained filter)False alarm rate over all 5 days(using sustained filter)


Bottleneck detection resultsBottleneck detection results

• Optimized parameter values for our Optimized parameter values for our chosen Portland freeway corridorchosen Portland freeway corridor

• Validated this method on Oregon Validated this method on Oregon data as a good start:data as a good start:• It successfully finds 75% of bottlenecksIt successfully finds 75% of bottlenecks

• Only 20% of detections are false alarmsOnly 20% of detections are false alarms

• Optimized parameter values for our Optimized parameter values for our chosen Portland freeway corridorchosen Portland freeway corridor

• Validated this method on Oregon Validated this method on Oregon data as a good start:data as a good start:• It successfully finds 75% of bottlenecksIt successfully finds 75% of bottlenecks

• Only 20% of detections are false alarmsOnly 20% of detections are false alarms


Bottleneck analysis toolsBottleneck analysis tools

• Find entire congested area upstream Find entire congested area upstream of the bottleneck: of the bottleneck: • estimate queue propagation speedestimate queue propagation speed• calculate costs of delay, emissions, etccalculate costs of delay, emissions, etc

• Process historical data and find prior Process historical data and find prior probabilities to improve real-time probabilities to improve real-time detectiondetection

• Find entire congested area upstream Find entire congested area upstream of the bottleneck: of the bottleneck: • estimate queue propagation speedestimate queue propagation speed• calculate costs of delay, emissions, etccalculate costs of delay, emissions, etc

• Process historical data and find prior Process historical data and find prior probabilities to improve real-time probabilities to improve real-time detectiondetection

A

B

C

Bottleneck



Activation

Deactivation



Congestion trackingCongestion tracking


I-405MP 304

I-405MP 300

OR-217MP 292

I-205MP 288

I-84MP 302




I-405MP 304

I-405MP 300

OR-217MP 292

I-205MP 288

I-84MP 302


Queue propagation speedsQueue propagation speeds


I-405MP 304

I-405MP 300

OR-217MP 292

I-205MP 288

I-84MP 302

25.8

mph

14.1

mph

7.66

mph


Delay (in vehicle-hrs)Delay (in vehicle-hrs)


I-405MP 304

I-405MP 300

OR-217MP 292

I-205MP 288

I-84MP 302

1622

veh-hrs

1569

veh-hrs

26403

veh-hrs




I-405MP 304

I-405MP 300

OR-217MP 292

I-205MP 288

I-84MP 302


Congestion Tracking: 90% Of DaysCongestion Tracking: 90% Of Days


I-405MP 304

I-405MP 300

OR-217MP 292

I-205MP 288

I-84MP 302


Congestion Tracking: 75%Congestion Tracking: 75%


I-405MP 304

I-405MP 300

OR-217MP 292

I-205MP 288

I-84MP 302


Congestion Tracking: 50%Congestion Tracking: 50%


I-405MP 304

I-405MP 300

OR-217MP 292

I-205MP 288

I-84MP 302


Congestion tracking: rarest 10%Congestion tracking: rarest 10%


I-405MP 304

I-405MP 300

OR-217MP 292

I-205MP 288

I-84MP 302


Mockup of desired toolMockup of desired tool

A

B

C

Bottleneck



Activation

Deactivation



I-405MP 304

I-405MP 300

OR-217MP 292

I-205MP 288

I-84MP 302


Next stepsNext steps

• Set parameters for remaining corridors; Set parameters for remaining corridors; implement into PORTAL; solicit implement into PORTAL; solicit feedbackfeedback

• Improve detection algorithm: Improve detection algorithm: incorporate weather conditions, incorporate weather conditions, occupancy/flow data, historical occupancy/flow data, historical knowledge, etc.knowledge, etc.

• Distinguish incidents from recurrent Distinguish incidents from recurrent congestion; rank the latter on congestion; rank the latter on Portland’s freewaysPortland’s freeways

• Set parameters for remaining corridors; Set parameters for remaining corridors; implement into PORTAL; solicit implement into PORTAL; solicit feedbackfeedback

• Improve detection algorithm: Improve detection algorithm: incorporate weather conditions, incorporate weather conditions, occupancy/flow data, historical occupancy/flow data, historical knowledge, etc.knowledge, etc.

• Distinguish incidents from recurrent Distinguish incidents from recurrent congestion; rank the latter on congestion; rank the latter on Portland’s freewaysPortland’s freeways


AcknowledgmentsAcknowledgmentsAcknowledgmentsAcknowledgments

• Oregon Department of Transportation Oregon Department of Transportation • Federal Highway AdministrationFederal Highway Administration• TriMetTriMet• The City of Portland, ORThe City of Portland, OR• National Science FoundationNational Science Foundation• CONACYT (Mexico)CONACYT (Mexico)• TransPort ITS CommitteeTransPort ITS Committee

• Oregon Department of Transportation Oregon Department of Transportation • Federal Highway AdministrationFederal Highway Administration• TriMetTriMet• The City of Portland, ORThe City of Portland, OR• National Science FoundationNational Science Foundation• CONACYT (Mexico)CONACYT (Mexico)• TransPort ITS CommitteeTransPort ITS Committee

Visit PORTAL Online:Visit PORTAL Online:http://portal.its.pdx.eduhttp://portal.its.pdx.edu


Thank You!www.its.pdx.edu

Using Archived ITS Data to Automatically Identify Freeway Bottlenecks in Portland, Oregon

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