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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.”
Archived ITS DataArchived ITS Data
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
Archived ITS DataArchived ITS Data
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
Archived ITS DataArchived ITS Data
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
Archived ITS DataArchived ITS Data
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
Archived ITS DataArchived ITS Data
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
Archived ITS DataArchived ITS Data
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
Archived ITS DataArchived ITS Data
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….
Archived ITS DataArchived ITS Data
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
Archived ITS DataArchived ITS Data
11
Archive CreationArchive CreationArchive CreationArchive Creation
• 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)
Archived ITS DataArchived ITS Data
12
Archive CreationArchive CreationArchive CreationArchive Creation
• 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
Archived ITS DataArchived ITS Data
13
Archive CreationArchive CreationArchive CreationArchive Creation
• 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?
Archived ITS DataArchived ITS Data
14
Archive CreationArchive CreationArchive CreationArchive Creation
• 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)
Archived ITS DataArchived ITS Data
15
Archive CreationArchive CreationArchive CreationArchive Creation
• 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
Archived ITS DataArchived ITS Data
16
Archive CreationArchive CreationArchive CreationArchive Creation
• 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
Archived ITS DataArchived ITS Data
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
Archived ITS DataArchived ITS Data
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
Archived ITS DataArchived ITS Data
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
Archived ITS DataArchived ITS Data
20
User AccessUser AccessUser AccessUser Access
• 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
Archived ITS DataArchived ITS Data
21
User AccessUser AccessUser AccessUser 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
• 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
Archived ITS DataArchived ITS Data
22
User AccessUser AccessUser AccessUser 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?
• 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?
Archived ITS DataArchived ITS Data
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
Archived ITS DataArchived ITS Data
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)
Archived ITS DataArchived ITS Data
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
Archived ITS DataArchived ITS Data
26
Vision for a Portland ADUSVision for a Portland ADUSVision for a Portland ADUSVision for a Portland ADUS
Archived ITS DataArchived ITS Data
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
Archived ITS DataArchived ITS Data
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
Archived ITS DataArchived ITS Data
29
Loop Detector HealthLoop Detector HealthLoop Detector HealthLoop Detector Health
Archived ITS DataArchived ITS Data
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
Archived ITS DataArchived ITS Data
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
Archived ITS DataArchived ITS Data
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 _
Archived ITS DataArchived ITS Data
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
Archived ITS DataArchived ITS Data
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
Archived ITS DataArchived ITS Data
35
Daily CongestionDaily CongestionDaily CongestionDaily Congestion
Archived ITS DataArchived ITS Data
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
Archived ITS DataArchived ITS Data
37
VHTVHTVHTVHT
Archived ITS DataArchived ITS Data
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
Archived ITS DataArchived ITS Data
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
Archived ITS DataArchived ITS Data
40
Fusing AVL With Travel TimeFusing AVL With Travel TimeFusing AVL With Travel TimeFusing AVL With Travel Time
Archived ITS DataArchived ITS Data
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
Archived ITS DataArchived ITS Data
42
Occupancy ContoursOccupancy ContoursOccupancy ContoursOccupancy Contours
Archived ITS DataArchived ITS Data
43
Contour PlotContour PlotContour PlotContour Plot
Archived ITS DataArchived ITS Data
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
Archived ITS DataArchived ITS Data
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
Archived ITS DataArchived ITS Data
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”
Archived ITS DataArchived ITS Data
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)
Archived ITS DataArchived ITS Data
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
Archived ITS DataArchived ITS Data
49
ConclusionConclusionConclusionConclusion
Thank You!Thank You!Thank You!Thank You!