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AbstractAbstractThe value of creating an ITS data archive is somewhat undisputed, and a number exist in states and major metropolitan regions in North America. In addition to providing a secure data storage environment many archives include tools for analyzing the quality of the data and for creating performance measures describing the transportation system both in real time and on a historical basis. An ITS data archive provides a unique opportunity to measure how a transportation system operates over time. An ITS data archive has been developed in Portland, Oregon at relatively low cost, taking advantage of sensors and communications used to operate the system in real time. The value of the data archive is governed by the quality of the analysis tools provided for users. The objective of this paper is to describe the results of a survey conducted to gauge ADUS user needs and experiences from both the planning and operations perspectives. Recommendations for improvements and next steps are provided.
OverviewOverviewWelcome to PORTAL— the Portland Oregon Regional Transportation Archive Listing (http://portal.its.pdx.edu). This system is being developed at Portland State University by students and faculty in the Intelligent Transportation Systems (ITS) Lab under the direction of Dr. Robert L. Bertini. Sponsored by the National Science Foundation, we are working in close cooperation with the Oregon Department of Transportation (ODOT), Metro, the City of Portland, TriMet and other regional partners. The purpose of this project is to implement the archived data user service (ADUS) under the guidance of the National ITS Architecture for the Portland metropolitan region. Since July 2004, PORTAL has been archiving 20-second data from the 485 inductive detectors comprising the Portland region’s Advanced Traffic Management System (ATMS). We also archive area weather data and plan to expand the capabilities of our system and to include multimodal data sources from both Oregon and Washington. We welcome your participation in our project.
What’s in PORTAL Database?What’s in PORTAL Database?
Loop Detector DataLoop Detector Data20 s count, occupancy, 20 s count, occupancy,
speed from 500 detectors speed from 500 detectors (1.2 mi spacing) (1.2 mi spacing)
Incident DataIncident Data
140,000 since 1999140,000 since 1999
Weather DataWeather Data VMS DataVMS Data
19 VMS since 199919 VMS since 1999
Data ArchiveData Archive
Days Since July 2004Days Since July 2004About 300 GBAbout 300 GB
4.2 Million Intervals4.2 Million Intervals
Regional InfrastructureRegional Infrastructure98 CCTV Cameras138 Ramp MetersTriMet Automatic
Vehicle Location and Bus Dispatch
SystemExtensive fiber optics
networkBi-state regionRegional ITS Committee: TransPortData sharing philosophyGigabit ethernet/private ITS networkPSU official data archive entity
Performance MeasuresPerformance MeasuresVolumeSpeedOccupancyDelayVMTVHTTravel TimeReliability
Querying the ArchiveQuerying the ArchiveThe web interface allows users to perform queries on 5-min aggregated data to extract volume, speed, occupancy, vehicle miles traveled, vehicle hours traveled, travel time, and delay. The user can specify a point on the freeway or a full highway segment, the time period and specific travel lanes. Users can display summaries of data from multiple days. The data can be grouped by hour of day, day of week, or week of year. The user can also determine the statistics they wish to see, including, mean, minimum value, maximum value, and standard deviation. The user can also generate congestion-related performance measures for the entire region.
What’s Behind the Scenes?What’s Behind the Scenes?
Database ServerDatabase Server
PostgreSQL RDMSPostgreSQL RDMS
StorageStorage
2 Terabyte RAID2 Terabyte RAIDWeb InterfaceWeb InterfaceDevelopment ServerDevelopment Server
CentOS Linux CentOS Linux distributiondistribution
220 Registered Users220 Registered Users
Future WorkFuture WorkThe Intelligent Transportation Systems Lab (www.its.pdx.edu) has been designated as the regional archiving site for ITS data from Portland and adjacent areas of southwestern Washington. The design and implementation of this archive are being carried out in accordance with the functional requirements for ADUS set forth in the National ITS Architecture. By openly sharing algorithms and experiences, all regions can benefit from past experiences. In Portland, we are emphasizing the development of a multimodal resource that is benefiting all transportation agencies via a unique collaborative environment established by the TransPort committee.
Archiving ITS DataArchiving ITS DataData archiving for ITS refers to the systematic retention and re-use of operational ITS data. Though the original and primary purpose of generating these data is often for real-time management of transportation systems, archiving these otherwise-discarded data offers a rich source of information to various entities and agencies with a heightening need to evaluate system performance and characteristics on a continuing basis. Once retained, the vast amount of ITS-generated data can be used in transportation planning, administration and research by key stakeholders including metropolitan planning organizations (MPOs), state transportation planners, traffic management operators, transit operators and transportation researchers.
Freeway Cross Section AnalysisFreeway Cross Section Analysis
01
000
200
03
000
400
05
000
0:00 4:00 8:00 12:00 16:00 20:00
Time
Vo
lum
e (v
eh
/hr)
0
10
20
30
40
50
60
70
Sp
eed
(m
ph
)
Vol05
Speed05
10
20
30
40
50
60
70
0:00 4:00 8:00 12:00 16:00 20:00
Time
Sp
eed
(m
ph
)
Speed04
Speed05
Speed - Flow Analysis (2005)2004 - 05 Speed ComparisonCross Section Study
Hwy 26 2004 - 2005
Bus Probe Arterial Speed MapBus Probe Arterial Speed Map
Travel Time ReliabilityTravel Time Reliability
Beyond Archiving: Developing and Attracting Users of an Archived Data User Service
Beyond Archiving: Developing and Attracting Users of an Archived Data User Service
Robert L. Bertini, Portland State University and Jonathan Makler, City of Portland
portal.its.pdx.edu
Sponsor: National Science FoundationSponsor: National Science FoundationSupport from: FHWA, ODOT, City of Support from: FHWA, ODOT, City of Portland, Metro, TriMet, WSDOTPortland, Metro, TriMet, WSDOT
Portland Oregon Regional Portland Oregon Regional Transportation Archive ListingTransportation Archive Listinghttp://portal.its.pdx.eduhttp://portal.its.pdx.edu
Contour Plots - SpeedContour Plots - Speed
Weather PopupWeather Popup
Data Quality PopupData Quality Popup
Data Quality PopupData Quality Popup
Time Series - VolumeTime Series - Volume
Grouped Data – Travel TimeGrouped Data – Travel Time
Performance Report Performance Report
Monthly ReportMonthly Report
Let Us Hear From You!Let Us Hear From You!
Request an AccountRequest an Account
Daily DashboardDaily Dashboard Mapping: Speed SubtractionMapping: Speed Subtraction
Average PM Peak Speed (5-6 pm)
Speed Difference July-Dec 2005
Washington DOT DataWashington DOT Data
Monthly Incident ReportsMonthly Incident Reports
Num
ber o
f Inc
iden
tsTime
Incident Types Number of Lanes Affected
Incident Locations Number of Ongoing Incidents
Incident ReportsIncident Reports
Incident on NB I-205, log Incident on NB I-205, log truck rear-ended nursery truck rear-ended nursery truck, two cars involved, truck, two cars involved, duration >4 hrsduration >4 hrs
11/15/05 NB I-20511/15/05 NB I-205Incident on SB I-205, Incident on SB I-205, NB effects visibleNB effects visible
20,000 reported incidents/year
92 Database fields4 Entries per incident
02
000
500
0
CrashOccurs
All Lanes Clear
All Clear
TowArrives
01
000
300
04
000
08:00 08:30 09:00 09:30 10:00
Flo
w
CrashOccurs
All Lanes Clear
All Clear
TowArrives
10:30
8:15 2 veh crash8:19 Crash reported8:27 VMS message: CENTER LANES CLSD8:40 Tow requested9:10 Tow arrives9:27 Lanes clear 9:30 Traffic starts to clear9:45 Traffic clearing10:00 Traffic all clear
Incident AutopsyI-5 North June 12, 2006
User Survey: What Are People Using?User Survey: What Are People Using?
54%38%
24%32%
41%35%
32%14%
51%14%
8%19%
41%3%
5%
0% 10% 20% 30% 40% 50% 60%
TimeseriesGrouped Data
Data FidelityRaw Data
Monthly DataWeather
Performance
DashboardCongestionSVG Maps
Bivariate PlotsGoogle Traffic
Incident ReportsNoneOther
54%38%
24%32%
41%35%
54%38%
24%32%
41%35%
32%14%
51%14%
8%19%
32%14%
51%14%
8%19%
41%3%
5%
0% 10% 20% 30% 40% 50% 60%
TimeseriesGrouped Data
Data FidelityRaw Data
Monthly DataWeather
Performance
DashboardCongestionSVG Maps
Bivariate PlotsGoogle Traffic
Incident ReportsNoneOther