Archived ITS Data A New Resource for Operations, Planning and Research Robert L. Bertini Portland...

Archived ITS DataArchived ITS DataA New Resource for Operations, A New Resource for Operations,

Planning and ResearchPlanning and Research

Archived ITS DataArchived ITS DataA New Resource for Operations, A New Resource for Operations,

Planning and ResearchPlanning and Research

Robert L. BertiniRobert L. Bertini

Portland State UniversityPortland State University

Robert L. BertiniRobert L. Bertini

Portland State UniversityPortland State University

Archived ITS DataArchived ITS Data

2

IntroductionIntroductionIntroductionIntroduction

““Data are too Data are too valuable to use only valuable to use only

once.”once.”

““Data are too Data are too valuable to use only valuable to use only

once.”once.”


3

ADUS Is BornADUS Is BornADUS Is BornADUS Is Born

• ITS technologies collect data ITS technologies collect data – Real time controlReal time control

» Incident managementIncident management» Traffic signal systemsTraffic signal systems» Traveler informationTraveler information

– Also useful if saved and accessibleAlso useful if saved and accessible– Data already being collected—incentive for storing them for Data already being collected—incentive for storing them for

future use.future use.

• Difficulty not in collecting data but in gaining Difficulty not in collecting data but in gaining access to that dataaccess to that data

• US DOT US DOT Archived Data User Service (ADUS)Archived Data User Service (ADUS)– Managing ITS data beyond ITS Managing ITS data beyond ITS – Careful management of data for various stakeholdersCareful management of data for various stakeholders

• ITS technologies collect data ITS technologies collect data – Real time controlReal time control

» Incident managementIncident management» Traffic signal systemsTraffic signal systems» Traveler informationTraveler information

– Also useful if saved and accessibleAlso useful if saved and accessible– Data already being collected—incentive for storing them for Data already being collected—incentive for storing them for

future use.future use.

• Difficulty not in collecting data but in gaining Difficulty not in collecting data but in gaining access to that dataaccess to that data

• US DOT US DOT Archived Data User Service (ADUS)Archived Data User Service (ADUS)– Managing ITS data beyond ITS Managing ITS data beyond ITS – Careful management of data for various stakeholdersCareful management of data for various stakeholders


4

Who Can Use ITS Data?Who Can Use ITS Data?Who Can Use ITS Data?Who Can Use ITS Data?

• Fourteen Stakeholders IdentifiedFourteen Stakeholders Identified– Transportation planningTransportation planning– Transportation system monitoringTransportation system monitoring– Air quality analysisAir quality analysis– MPO/state freight and intermodal planningMPO/state freight and intermodal planning– Land use/growth management planningLand use/growth management planning– Transportation administrators and policy analysisTransportation administrators and policy analysis– Traffic managementTraffic management– Transit managementTransit management– Construction and maintenanceConstruction and maintenance– Safety planning and administrationSafety planning and administration– CVOCVO– Emergency managementEmergency management– Transportation researchTransportation research– Private sectorPrivate sector

• Fourteen Stakeholders IdentifiedFourteen Stakeholders Identified– Transportation planningTransportation planning– Transportation system monitoringTransportation system monitoring– Air quality analysisAir quality analysis– MPO/state freight and intermodal planningMPO/state freight and intermodal planning– Land use/growth management planningLand use/growth management planning– Transportation administrators and policy analysisTransportation administrators and policy analysis– Traffic managementTraffic management– Transit managementTransit management– Construction and maintenanceConstruction and maintenance– Safety planning and administrationSafety planning and administration– CVOCVO– Emergency managementEmergency management– Transportation researchTransportation research– Private sectorPrivate sector


5

Data Poor to Data RichData Poor to Data RichData Poor to Data RichData Poor to Data Rich

Traffic surveillanceTraffic surveillance

Fare/toll systemsFare/toll systems

Incident managementIncident management

Traffic videoTraffic video

Environmental Environmental

CVOCVO

Traffic controlTraffic control

Highway/railHighway/rail

Emergency responseEmergency response

Traffic surveillanceTraffic surveillance

Fare/toll systemsFare/toll systems

Incident managementIncident management

Traffic videoTraffic video

Environmental Environmental

CVOCVO

Traffic controlTraffic control

Highway/railHighway/rail

Emergency responseEmergency response

ITSITSDataData

ArchivesArchives

ITSITSDataData

ArchivesArchives

Performance MonitoringPerformance Monitoring– National reportingNational reporting– Performance-based planningPerformance-based planning– EvaluationsEvaluations– Public ReactionsPublic Reactions

Long Range PlanningLong Range Planning– TRANSIMSTRANSIMS– IDASIDAS– Four step modelsFour step models– Transit routesTransit routes

Operations PlanningOperations Planning– Incident managementIncident management– ER deploymentER deployment– Signal timingSignal timing– Transit serviceTransit service

Travel Time ForecastingTravel Time Forecasting– Customized route planningCustomized route planning– ATIS AdvisoriesATIS Advisories

Other Stakeholder FunctionsOther Stakeholder Functions– SafetySafety– Land useLand use– Air qualityAir quality– Maintenance managementMaintenance management

Performance MonitoringPerformance Monitoring– National reportingNational reporting– Performance-based planningPerformance-based planning– EvaluationsEvaluations– Public ReactionsPublic Reactions

Long Range PlanningLong Range Planning– TRANSIMSTRANSIMS– IDASIDAS– Four step modelsFour step models– Transit routesTransit routes

Operations PlanningOperations Planning– Incident managementIncident management– ER deploymentER deployment– Signal timingSignal timing– Transit serviceTransit service

Travel Time ForecastingTravel Time Forecasting– Customized route planningCustomized route planning– ATIS AdvisoriesATIS Advisories

Other Stakeholder FunctionsOther Stakeholder Functions– SafetySafety– Land useLand use– Air qualityAir quality– Maintenance managementMaintenance management


6

ADUSADUSADUSADUS

• Development and evaluation of operations Development and evaluation of operations strategiesstrategies

– Detailed data from ADUSDetailed data from ADUS

• Performance monitoringPerformance monitoring– Continuous and direct measurements of actual conditionsContinuous and direct measurements of actual conditions

• Advanced operation productsAdvanced operation products– Sophistication leads to more data requirementsSophistication leads to more data requirements– Short term traffic predictionShort term traffic prediction– Customized route planningCustomized route planning

• Next generation of planning and operations modelsNext generation of planning and operations models– Require more detailed informationRequire more detailed information

• Development and evaluation of operations Development and evaluation of operations strategiesstrategies

– Detailed data from ADUSDetailed data from ADUS

• Performance monitoringPerformance monitoring– Continuous and direct measurements of actual conditionsContinuous and direct measurements of actual conditions

• Advanced operation productsAdvanced operation products– Sophistication leads to more data requirementsSophistication leads to more data requirements– Short term traffic predictionShort term traffic prediction– Customized route planningCustomized route planning

• Next generation of planning and operations modelsNext generation of planning and operations models– Require more detailed informationRequire more detailed information


7

ADUSADUSADUSADUS

• ITS produce continuous dataITS produce continuous data• Continuous data allows measurement of reliabilityContinuous data allows measurement of reliability• Reliability is key to management of transportation Reliability is key to management of transportation

systemsystem• Use of ITS data requires creativityUse of ITS data requires creativity• Requires data to be stored and made accessibleRequires data to be stored and made accessible

• ITS produce continuous dataITS produce continuous data• Continuous data allows measurement of reliabilityContinuous data allows measurement of reliability• Reliability is key to management of transportation Reliability is key to management of transportation

systemsystem• Use of ITS data requires creativityUse of ITS data requires creativity• Requires data to be stored and made accessibleRequires data to be stored and made accessible


8

ADUSADUSADUSADUS

Management of the Management of the transportation system cannot transportation system cannot be done without knowledge of be done without knowledge of

its performanceits performance

Management of the Management of the transportation system cannot transportation system cannot be done without knowledge of be done without knowledge of

its performanceits performance


9

ADUSADUSADUSADUS

• Early involvement of stakeholdersEarly involvement of stakeholders• Design ADUS as original function of ITS Design ADUS as original function of ITS

deploymentdeployment• Build ADUS into ITS from the startBuild ADUS into ITS from the start• National ITS ArchitectureNational ITS Architecture• Few operational examplesFew operational examples• Consider the following set of questions….Consider the following set of questions….

• Early involvement of stakeholdersEarly involvement of stakeholders• Design ADUS as original function of ITS Design ADUS as original function of ITS

deploymentdeployment• Build ADUS into ITS from the startBuild ADUS into ITS from the start• National ITS ArchitectureNational ITS Architecture• Few operational examplesFew operational examples• Consider the following set of questions….Consider the following set of questions….


10

Archive CreationArchive CreationArchive CreationArchive Creation

• Question: What data are to be stored?Question: What data are to be stored?– Raw dataRaw data– Summary statisticsSummary statistics– ExamplesExamples

» Volume and lane occupancy, orVolume and lane occupancy, or» Estimated speedEstimated speed

• Question: What data are to be stored?Question: What data are to be stored?– Raw dataRaw data– Summary statisticsSummary statistics– ExamplesExamples

» Volume and lane occupancy, orVolume and lane occupancy, or» Estimated speedEstimated speed

Credit: M. Hallenbeck, Washington DOTCredit: M. Hallenbeck, Washington DOTCredit: M. Hallenbeck, Washington DOTCredit: M. Hallenbeck, Washington DOT


11


• How much data gets stored?How much data gets stored?– All raw dataAll raw data– Only summary statisticsOnly summary statistics– Something in between (e.g., aggregated data)Something in between (e.g., aggregated data)– Samples of the data (raw or summary statistics)Samples of the data (raw or summary statistics)– All variables, or only some (tag IDs)All variables, or only some (tag IDs)

• How much data gets stored?How much data gets stored?– All raw dataAll raw data– Only summary statisticsOnly summary statistics– Something in between (e.g., aggregated data)Something in between (e.g., aggregated data)– Samples of the data (raw or summary statistics)Samples of the data (raw or summary statistics)– All variables, or only some (tag IDs)All variables, or only some (tag IDs)


12


• At what level of aggregationAt what level of aggregation– Lowest level collectedLowest level collected

» Individual vehicle passages (controller)Individual vehicle passages (controller)» 20 second intervals20 second intervals» 5 minute intervals5 minute intervals» 15 minute intervals15 minute intervals» HigherHigher» More than one levelMore than one level

• At what level of aggregationAt what level of aggregation– Lowest level collectedLowest level collected

» Individual vehicle passages (controller)Individual vehicle passages (controller)» 20 second intervals20 second intervals» 5 minute intervals5 minute intervals» 15 minute intervals15 minute intervals» HigherHigher» More than one levelMore than one level


13


• Issues that impact decision:Issues that impact decision:– What use is planned for the data?What use is planned for the data?– How large is storage requirement?How large is storage requirement?– Cost/speed of processing raw data to more Cost/speed of processing raw data to more

useful formuseful form– How much additional data is needed to convert How much additional data is needed to convert

the “raw” data into useful information?the “raw” data into useful information?– Privacy concerns?Privacy concerns?

• Issues that impact decision:Issues that impact decision:– What use is planned for the data?What use is planned for the data?– How large is storage requirement?How large is storage requirement?– Cost/speed of processing raw data to more Cost/speed of processing raw data to more

useful formuseful form– How much additional data is needed to convert How much additional data is needed to convert

the “raw” data into useful information?the “raw” data into useful information?– Privacy concerns?Privacy concerns?


14


• Example: Tag ObservationsExample: Tag Observations• Raw data: Raw data: tag ID, location, time and datetag ID, location, time and date

• Store all of the above?Store all of the above?• Store O/D pairs?Store O/D pairs?• Travel times?Travel times?• Privacy of tag ID?Privacy of tag ID?• Speeds? (distance between readers)Speeds? (distance between readers)

• Example: Tag ObservationsExample: Tag Observations• Raw data: Raw data: tag ID, location, time and datetag ID, location, time and date

• Store all of the above?Store all of the above?• Store O/D pairs?Store O/D pairs?• Travel times?Travel times?• Privacy of tag ID?Privacy of tag ID?• Speeds? (distance between readers)Speeds? (distance between readers)


15


• Example: Fleet AVL InformationExample: Fleet AVL Information– Raw data: Raw data: Vehicle ID, location, time, and dateVehicle ID, location, time, and date

– ID may not describe route and runID may not describe route and run» Need schedule information, operations info.Need schedule information, operations info.» Relationships change every dayRelationships change every day» Routes can change every schedule change, Routes can change every schedule change,

need historical informationneed historical information

• Example: Fleet AVL InformationExample: Fleet AVL Information– Raw data: Raw data: Vehicle ID, location, time, and dateVehicle ID, location, time, and date

– ID may not describe route and runID may not describe route and run» Need schedule information, operations info.Need schedule information, operations info.» Relationships change every dayRelationships change every day» Routes can change every schedule change, Routes can change every schedule change,

need historical informationneed historical information


16


• How and why is aggregation performed?How and why is aggregation performed?– Quality controlQuality control– Assumptions madeAssumptions made– Details lostDetails lost– Costs and benefits uncertainCosts and benefits uncertain

• How and why is aggregation performed?How and why is aggregation performed?– Quality controlQuality control– Assumptions madeAssumptions made– Details lostDetails lost– Costs and benefits uncertainCosts and benefits uncertain


17

Quality ControlQuality ControlQuality ControlQuality Control

• Not all collected data is validNot all collected data is valid• Can the archive identify bad or questionable data?Can the archive identify bad or questionable data?• How are these judgments indicated?How are these judgments indicated?• How/are users informed of these conditions? How/are users informed of these conditions? • How are “bad” data identified?How are “bad” data identified?

– Sensor outputSensor output– Checks against historical dataChecks against historical data– Checks against expected rangesChecks against expected ranges– Other comparisonsOther comparisons

• Not all collected data is validNot all collected data is valid• Can the archive identify bad or questionable data?Can the archive identify bad or questionable data?• How are these judgments indicated?How are these judgments indicated?• How/are users informed of these conditions? How/are users informed of these conditions? • How are “bad” data identified?How are “bad” data identified?

– Sensor outputSensor output– Checks against historical dataChecks against historical data– Checks against expected rangesChecks against expected ranges– Other comparisonsOther comparisons


18

Quality ControlQuality ControlQuality ControlQuality Control• What do you do with “questionable” data?What do you do with “questionable” data?

– ConstructionConstruction– WeatherWeather– Major incidentsMajor incidents

• What resources are needed to investigate What resources are needed to investigate “questionable” data?“questionable” data?

• Does this affect willingness to share data?Does this affect willingness to share data?• How do you handle missing/bad data?How do you handle missing/bad data?• Does this change if you areDoes this change if you are

– Storing raw dataStoring raw data– Only storing summary dataOnly storing summary data– Storing bothStoring both

• What do you do with “questionable” data?What do you do with “questionable” data?– ConstructionConstruction– WeatherWeather– Major incidentsMajor incidents

• What resources are needed to investigate What resources are needed to investigate “questionable” data?“questionable” data?

• Does this affect willingness to share data?Does this affect willingness to share data?• How do you handle missing/bad data?How do you handle missing/bad data?• Does this change if you areDoes this change if you are

– Storing raw dataStoring raw data– Only storing summary dataOnly storing summary data– Storing bothStoring both


19

User AccessUser AccessUser AccessUser Access

• Who gets access to the data?Who gets access to the data?• Classes of users and permission processClasses of users and permission process• How do users get access to the data?How do users get access to the data?• How do you communicateHow do you communicate

– What data (variables) are availableWhat data (variables) are available– What geographic locations are availableWhat geographic locations are available– What quality issues existWhat quality issues exist– How the data can (should) and can not (should How the data can (should) and can not (should

not) be usednot) be used

• Who gets access to the data?Who gets access to the data?• Classes of users and permission processClasses of users and permission process• How do users get access to the data?How do users get access to the data?• How do you communicateHow do you communicate

– What data (variables) are availableWhat data (variables) are available– What geographic locations are availableWhat geographic locations are available– What quality issues existWhat quality issues exist– How the data can (should) and can not (should How the data can (should) and can not (should

not) be usednot) be used


20


• Meta DataMeta Data– Data about data (self describing)Data about data (self describing)

• Truth-in-DataTruth-in-Data– The principal that says you will be honest with The principal that says you will be honest with

users about users about » What data are realWhat data are real» What data are interpolatedWhat data are interpolated» What data are missing and have/have not What data are missing and have/have not

been replaced, and how those data were been replaced, and how those data were replacedreplaced

• Meta DataMeta Data– Data about data (self describing)Data about data (self describing)

• Truth-in-DataTruth-in-Data– The principal that says you will be honest with The principal that says you will be honest with

users about users about » What data are realWhat data are real» What data are interpolatedWhat data are interpolated» What data are missing and have/have not What data are missing and have/have not

been replaced, and how those data were been replaced, and how those data were replacedreplaced


21


• Do you trust users to use data correctly?Do you trust users to use data correctly?– At what level of summarization?At what level of summarization?– Site specific data isn’t always representative of Site specific data isn’t always representative of

realityreality• How easy do you make their retrieval of data?How easy do you make their retrieval of data?

– Cost implications of that taskCost implications of that task– Political benefits/costs of providing accessPolitical benefits/costs of providing access

• Do you trust users to use data correctly?Do you trust users to use data correctly?– At what level of summarization?At what level of summarization?– Site specific data isn’t always representative of Site specific data isn’t always representative of

realityreality• How easy do you make their retrieval of data?How easy do you make their retrieval of data?

– Cost implications of that taskCost implications of that task– Political benefits/costs of providing accessPolitical benefits/costs of providing access


22


• Mechanism used to provide accessMechanism used to provide access– CD-ROM (Arizona)CD-ROM (Arizona)– Web accessWeb access– File transfer on requestFile transfer on request– Real time data transferReal time data transfer

• Cost to user for access?Cost to user for access?

• Mechanism used to provide accessMechanism used to provide access– CD-ROM (Arizona)CD-ROM (Arizona)– Web accessWeb access– File transfer on requestFile transfer on request– Real time data transferReal time data transfer

• Cost to user for access?Cost to user for access?


23

CommunicationsCommunicationsCommunicationsCommunications

• How do you communicate with potential users?How do you communicate with potential users?– Staff timeStaff time– On-line helpOn-line help– NoneNone– OtherOther

• How do you communicate with potential users?How do you communicate with potential users?– Staff timeStaff time– On-line helpOn-line help– NoneNone– OtherOther


24

PrivacyPrivacyPrivacyPrivacy

• Privacy concerns grow with increased user access Privacy concerns grow with increased user access and sensitivity of data being collectedand sensitivity of data being collected– Personal IDsPersonal IDs

» Vehicle tagsVehicle tags» Driver identification (union issues)Driver identification (union issues)

• Privacy concerns grow with increased user access Privacy concerns grow with increased user access and sensitivity of data being collectedand sensitivity of data being collected– Personal IDsPersonal IDs

» Vehicle tagsVehicle tags» Driver identification (union issues)Driver identification (union issues)


25

Who Pays?Who Pays?Who Pays?Who Pays?

• ITS systems are paid for by those who operate the ITS systems are paid for by those who operate the systemsystem

• Often the greatest use for the archive is a different Often the greatest use for the archive is a different groupgroup– Control of resourcesControl of resources– OwnershipOwnership– Willingness to cooperateWillingness to cooperate

• ITS systems are paid for by those who operate the ITS systems are paid for by those who operate the systemsystem

• Often the greatest use for the archive is a different Often the greatest use for the archive is a different groupgroup– Control of resourcesControl of resources– OwnershipOwnership– Willingness to cooperateWillingness to cooperate


26

Vision for a Portland ADUSVision for a Portland ADUSVision for a Portland ADUSVision for a Portland ADUS


27

Traditional Performance MeasuresTraditional Performance MeasuresTraditional Performance MeasuresTraditional Performance Measures

• Traditional measures Traditional measures – Do not describe the complexity of what is happening on Do not describe the complexity of what is happening on

the roadwaythe roadway– Are not easily understood by most decision makers and/or Are not easily understood by most decision makers and/or

the publicthe public– Examples:Examples:

» V/C Ratios: based on limited data, poor mechanism for V/C Ratios: based on limited data, poor mechanism for showing changing conditions during the dayshowing changing conditions during the day

» LOS: based on limited data, not meaningful over LOS: based on limited data, not meaningful over space, misunderstoodspace, misunderstood

» Travel time and delay: based on limited sample, or Travel time and delay: based on limited sample, or imperfect calculationsimperfect calculations

• Traditional measures Traditional measures – Do not describe the complexity of what is happening on Do not describe the complexity of what is happening on

the roadwaythe roadway– Are not easily understood by most decision makers and/or Are not easily understood by most decision makers and/or

the publicthe public– Examples:Examples:

» V/C Ratios: based on limited data, poor mechanism for V/C Ratios: based on limited data, poor mechanism for showing changing conditions during the dayshowing changing conditions during the day

» LOS: based on limited data, not meaningful over LOS: based on limited data, not meaningful over space, misunderstoodspace, misunderstood

» Travel time and delay: based on limited sample, or Travel time and delay: based on limited sample, or imperfect calculationsimperfect calculations


28

EB Highway 26EB Highway 26EB Highway 26EB Highway 26

STATION 1 - HELVETIA EBSTATION 2 - CORNELIUS Ps Rd EBSTATION 3 - 185 th Ave NB to EBSTATION 4 - 185 th Ave SB to EBSTATION 5 - CORNELL Rd EBSTATION 6 - MURRAY Rd EBSTATION 7 - CEDAR HILLS Blvd EBSTATION 8 - ORE 217 NB to EB - PARKWAY EBSTATION 9 - CANYON Rd EBSTATION 10 - SKYLINE Rd EB

N

LOOP DETECTOR


29

Loop Detector HealthLoop Detector HealthLoop Detector HealthLoop Detector Health


30

Average Daily TrafficAverage Daily TrafficAverage Daily TrafficAverage Daily Traffic

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

160,000

60 62 64 66 68 70 72 74

MILE POST

Data Eastbound ODOT two directions Directional Distribution 50/50-ADT 1999

*

MILEPOST

Station 1 - 61.25 Station 2 - 62.47 Station 3 - 64.50 Station 4 - 64.60 Station 5 - 65.90 Station 6 - 67.40 Station 7 - 68.55 Station 8 - 69.31 Station 9 - 70.90 Station 10 - 71.37


31

Average SpeedAverage SpeedAverage SpeedAverage Speed

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

12:00 AM 3:00 AM 6:00 AM 9:00 AM 12:00 PM 3:00 PM 6:00 PM 9:00 PM 12:00 AMTime

V(x

,t)- v

0 t'

, vo=9

500

mph

per

hou

r

20

30

40

50

60

70

80

90

100

110

120

Spe

ed (m

ph)

60 mph

49 mph

58 mph

49 mph

36 mph

59 mph

7:21:20 am

8:25:00 am

2:55:20 pm

4:01:20 pm

7:36:20

Free flow speed

Best linear approximation on the curve where the slope is the speed

Re-scaled cumulative Speed Average Speed


32

Average Speed + ReliabilityAverage Speed + ReliabilityAverage Speed + ReliabilityAverage Speed + Reliability

0

500

1000

1500

2000

2500

12 AM 2 AM 4 AM 6 AM 8 AM 10 AM 12 PM 2 PM 4 PM 6 PM 8 PM 10 PM

0

10

20

30

40

50

60

70

80

90

100Cong.VPLPH

Estimated Weekday Volume, Speed, and Reliability Conditions (1997)I-405 NE 4th St-NB HOV NB _


33

Percent Lane Miles CongestedPercent Lane Miles CongestedPercent Lane Miles CongestedPercent Lane Miles Congested

0

10

20

30

40

50

60

70

80

90

100

12:00:00 AM 3:00:00 AM 6:00:00 AM 9:00:00 AM 12:00:00 PM 3:00:00 PM 6:00:00 PM 9:00:00 PM 12:00:00 AM

TIME


34

Demand Vs. CapacityDemand Vs. CapacityDemand Vs. CapacityDemand Vs. Capacity

0

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

4,500

1 3 5 7 9 11

13

15

17

19

21

23

TIME (hours)

Station 1 Station 2 Station 3 Station 4 Station 5 Station 6 Station 7 Station 8 Station 9

0.40

0.60

0.80

1.00

0.20

V/C


35

Daily CongestionDaily CongestionDaily CongestionDaily Congestion


36

Frequency of CongestionFrequency of CongestionFrequency of CongestionFrequency of Congestion

Frequency of Congestion on I-5 at Dearborn - Northbound

0

10

20

30

40

50

60

70

80

90

100

6 AM 8 AM 10 AM 12 PM 2 PM 4 PM 6 PM 8 PM

Time of Day

Pe

rce

nt

of

Tim

e C

on

ge

stio

n O

ccu

rs

1997

1999


37

VHTVHTVHTVHT


38

Travel TimeTravel TimeTravel TimeTravel Time

-10,000

0

10,000

20,000

30,000

40,000

50,000

60,000

12:00 AM 3:00 AM 6:00 AM 9:00 AM 12:00 PM 3:00 PM 6:00 PM 9:00 PM

Time

Cu

mu

lati

ve T

rave

l Tim

e

10

20

30

40

50

60

70

Trav

el T

ime

(min

ute

s)

7:21:20 AM

8:16:20 AM

2:55:20 PM

7:36:20 PMMorning peak

Afternoon peak

Free flow travel time

17 min

25.92 min

11.69 min

11.68 min

11.67 min

Free-flow Travel Time

Free-flow Travel Time

Travel Time


39

Fusing AVL With Loop DataFusing AVL With Loop DataFusing AVL With Loop DataFusing AVL With Loop Data

60

61

62

63

64

65

66

67

68

69

70

71

72

73

10:55:12AM

10:57:12AM

10:59:12AM

11:01:12AM

11:03:12AM

11:05:12AM

11:07:12AM

11:09:12AM

11:11:12AMTIME

MIL

EP

OS

T

STATION 1

STATION 2

STATION 3

STATION 4

STATION 5

STATION 6

STATION 7

STATION 8

STATION 9

STATION 10

Total travel time - Comet = 16.20 minutesTotal travel time -Other vehicles = 11.29 minutes

52.43 mi/hr

14.61mi/hr 143

stop

63.62 mi/hr

58.65 mi/hr

57.01mi/hr

Slope of the curve which represents Average Speed

Other Vehicles Comet


40

Fusing AVL With Travel TimeFusing AVL With Travel TimeFusing AVL With Travel TimeFusing AVL With Travel Time


41

Travel Time ReliabilityTravel Time ReliabilityTravel Time ReliabilityTravel Time Reliability

0:00

0:10

0:20

0:30

0:40

0:50

1:00

12 AM 2 AM 4 AM 6 AM 8 AM 10 AM 12 PM 2 PM 4 PM 6 PM 8 PM 10 PM

Trip Start Time

Est

imate

d A

vera

ge T

ravel Tim

e (

hou

r:m

in)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Con

g.

Freq

uen

cy (

Sp

eed

< 3

5 m

ph

)

Congestion Frequency Avg. GP Travel Time 90th Percentile GP Travel Time

405

90

520

N

Bellevue

Tukwila

5

4


42

Occupancy ContoursOccupancy ContoursOccupancy ContoursOccupancy Contours


43

Contour PlotContour PlotContour PlotContour Plot


44

Contour PlotContour PlotContour PlotContour Plot

Time

182

180

178

176

174

172

170

168

166

Mil

ep

os

t

0 2 4 6 8 10 12 2 4 6 8 10 12

Time

182

180

178

176

174

172

170

168

166

Mil

ep

os

t

0 2 4 6 8 10 12 2 4 6 8 10 12AM PM AM PM

Olive Way

Snohomish County

King County

NE 175th

Northgate Way

405

5

520

522

NE 45th

Uncongested, near speed limit

Restricted movement but near speed limit

More heavily congested, 45 - 55 mph

Extremely congested, unstable flow

NorthboundSouthbound

Interstate 5 North Traffic Profile General Purpose Lanes 1997 Weekday Average


45

Performance MeasuresPerformance MeasuresPerformance MeasuresPerformance Measures

• When truck volume and weight data become When truck volume and weight data become available for freeways, these same matrices (and available for freeways, these same matrices (and some assumptions) can be used to compute:some assumptions) can be used to compute:

– Truck hours of delayTruck hours of delay– Truck miles of delayTruck miles of delay– Ton-miles of delayTon-miles of delay– Value of freight delayValue of freight delay

• When truck volume and weight data become When truck volume and weight data become available for freeways, these same matrices (and available for freeways, these same matrices (and some assumptions) can be used to compute:some assumptions) can be used to compute:

– Truck hours of delayTruck hours of delay– Truck miles of delayTruck miles of delay– Ton-miles of delayTon-miles of delay– Value of freight delayValue of freight delay


46

Performance MeasuresPerformance MeasuresPerformance MeasuresPerformance Measures

• Each time we use our new tools to answer a Each time we use our new tools to answer a question, we develop new ways to display that question, we develop new ways to display that informationinformation

• The goal is to make that information The goal is to make that information – Easier to understandEasier to understand– More accurate of “real life”More accurate of “real life”

• Each time we use our new tools to answer a Each time we use our new tools to answer a question, we develop new ways to display that question, we develop new ways to display that informationinformation

• The goal is to make that information The goal is to make that information – Easier to understandEasier to understand– More accurate of “real life”More accurate of “real life”


47

Example: FASTExample: FASTExample: FASTExample: FAST

FAST system architecture incorporates capability to receive, collect, FAST system architecture incorporates capability to receive, collect, and archive ITS-generated operational data including:and archive ITS-generated operational data including:·· incident dataincident data·· traffic volumestraffic volumes·· vehicle speeds vehicle speeds ·· vehicle classificationvehicle classification·· travel lane occupancytravel lane occupancy

Data will be stored at periodic intervals, and will be remotely accessible Data will be stored at periodic intervals, and will be remotely accessible by partner agencies via communication links. Data flows are defined in by partner agencies via communication links. Data flows are defined in the FAST regional system architecture, which is consistent with the ITS the FAST regional system architecture, which is consistent with the ITS National Architecture. The ADUS implementation will focus on a National Architecture. The ADUS implementation will focus on a centralized concept where relevant data is captured, archived, and centralized concept where relevant data is captured, archived, and provided in a summary format to stakeholders and other FAST ITS provided in a summary format to stakeholders and other FAST ITS subsystems.subsystems.

FAST system architecture incorporates capability to receive, collect, FAST system architecture incorporates capability to receive, collect, and archive ITS-generated operational data including:and archive ITS-generated operational data including:·· incident dataincident data·· traffic volumestraffic volumes·· vehicle speeds vehicle speeds ·· vehicle classificationvehicle classification·· travel lane occupancytravel lane occupancy

Data will be stored at periodic intervals, and will be remotely accessible Data will be stored at periodic intervals, and will be remotely accessible by partner agencies via communication links. Data flows are defined in by partner agencies via communication links. Data flows are defined in the FAST regional system architecture, which is consistent with the ITS the FAST regional system architecture, which is consistent with the ITS National Architecture. The ADUS implementation will focus on a National Architecture. The ADUS implementation will focus on a centralized concept where relevant data is captured, archived, and centralized concept where relevant data is captured, archived, and provided in a summary format to stakeholders and other FAST ITS provided in a summary format to stakeholders and other FAST ITS subsystems.subsystems.

Nevada DOT Archived Data User Service (ADUS)Nevada DOT Archived Data User Service (ADUS)Nevada DOT Archived Data User Service (ADUS)Nevada DOT Archived Data User Service (ADUS)


48

ConclusionsConclusionsConclusionsConclusions

• Archived ITS DataArchived ITS Data• Performance Evaluation and Measurement Performance Evaluation and Measurement

ClearinghouseClearinghouse• Experiment With Different MeasuresExperiment With Different Measures• Freeways as a Starting PointFreeways as a Starting Point• ArterialsArterials• TransitTransit• Integrate Into TMC Decision SupportIntegrate Into TMC Decision Support• PeMS successfully implemented at Caltrans PeMS successfully implemented at Caltrans

Districts 7 & 12Districts 7 & 12

• Archived ITS DataArchived ITS Data• Performance Evaluation and Measurement Performance Evaluation and Measurement

ClearinghouseClearinghouse• Experiment With Different MeasuresExperiment With Different Measures• Freeways as a Starting PointFreeways as a Starting Point• ArterialsArterials• TransitTransit• Integrate Into TMC Decision SupportIntegrate Into TMC Decision Support• PeMS successfully implemented at Caltrans PeMS successfully implemented at Caltrans

Districts 7 & 12Districts 7 & 12


49

ConclusionConclusionConclusionConclusion

Thank You!Thank You!Thank You!Thank You!

Date post:	29-Dec-2015
Category:	Documents
Upload:	cora-shona-ray
View:	216 times
Download:	0 times

Archived ITS Data A New Resource for Operations, Planning and Research Robert L. Bertini Portland...

Documents