1 intelligent transportation systems: saving lives, time and money portal: transportation data...
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1Intelligent Transportation Systems: Saving Lives, Time and MoneyIntelligent Transportation Systems: Saving Lives, Time and Money
PORTAL: Transportation Data PORTAL: Transportation Data ArchiveArchive
Intelligent Transportation Systems LaboratoryIntelligent Transportation Systems Laboratory
Deena Platman, MetroDeena Platman, Metro
Dr. Kristin Tufte, Portland State UniversityDr. Kristin Tufte, Portland State University
PORTAL: Transportation Data PORTAL: Transportation Data ArchiveArchive
Intelligent Transportation Systems LaboratoryIntelligent Transportation Systems Laboratory
Deena Platman, MetroDeena Platman, Metro
Dr. Kristin Tufte, Portland State UniversityDr. Kristin Tufte, Portland State University
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2Intelligent Transportation Systems: Saving Lives, Time and MoneyIntelligent Transportation Systems: Saving Lives, Time and Money
• PORTAL Background–Developed with CAREER grant from
National Science Foundation with additional financial support from FHWA
–Large investment in developing regional transportation archive
• Funding Situation–Current funding has run out, archive will
wither –Need sustainable funding source
• PORTAL Background–Developed with CAREER grant from
National Science Foundation with additional financial support from FHWA
–Large investment in developing regional transportation archive
• Funding Situation–Current funding has run out, archive will
wither –Need sustainable funding source
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3Intelligent Transportation Systems: Saving Lives, Time and MoneyIntelligent Transportation Systems: Saving Lives, Time and Money
What’s in the PORTAL Database?What’s in the PORTAL Database?What’s in the PORTAL Database?What’s in the PORTAL 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 700 GBAbout 700 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
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4Intelligent Transportation Systems: Saving Lives, Time and MoneyIntelligent Transportation Systems: Saving Lives, Time and Money
PORTAL Web SitePORTAL Web SitePORTAL Web SitePORTAL Web Site
• Graphical display of archived data – Speed, Weather, Incidents, TriMet AVL
• Performance Reports, Traffic Counts, Freight Data, …
• Graphical display of archived data – Speed, Weather, Incidents, TriMet AVL
• Performance Reports, Traffic Counts, Freight Data, …
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5Intelligent Transportation Systems: Saving Lives, Time and MoneyIntelligent Transportation Systems: Saving Lives, Time and Money
Speed Plot & Incident ReportsSpeed Plot & Incident ReportsSpeed Plot & Incident ReportsSpeed Plot & Incident Reports
Incident on NB I-205, Incident on NB I-205, log truck rear-ended a log truck rear-ended a
nursery truck, two nursery truck, two cars also involved, cars also involved,
duration over 4 hours.duration over 4 hours.
11/15/2005 Northbound I-20511/15/2005 Northbound I-205
20,000 20,000 reported reported
incidents/yearincidents/year
92 Database 92 Database fieldsfields
4 Entries per 4 Entries per incidentincident
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6Intelligent Transportation Systems: Saving Lives, Time and MoneyIntelligent Transportation Systems: Saving Lives, Time and Money
Performance Report - ReliabilityPerformance Report - ReliabilityPerformance Report - ReliabilityPerformance Report - Reliability
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7Intelligent Transportation Systems: Saving Lives, Time and MoneyIntelligent Transportation Systems: Saving Lives, Time and Money
Uses of PORTALUses of PORTAL
• Resource for local transportation professionals
• Metro RTP (?) • Projects
– SWARM– Travel Time– Bottleneck Identification– Data Quality Evaluation– Gap Filling– TriMet Data Analysis– Freight Data Display– Incident Autopsy
• Resource for local transportation professionals
• Metro RTP (?) • Projects
– SWARM– Travel Time– Bottleneck Identification– Data Quality Evaluation– Gap Filling– TriMet Data Analysis– Freight Data Display– Incident Autopsy
A
B
C
Bottleneck
Estimated Propagation Speed
A – 25 mphB – 22 mphC – 21 mph
Activation
Deactivation
90% percentile of historicalbottlenecks
8:15 2-vehicles collide8:19 Crash reported8:27 VMS message: CENTER LANES CLSD8:40 COMET requests tow9:10 Tow arrives9:27 Lanes clear 9:30 Traffic starts to clear9:45 Traffic half clear10:00 Traffic all clear
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8Intelligent Transportation Systems: Saving Lives, Time and MoneyIntelligent Transportation Systems: Saving Lives, Time and Money
Metropolitan Mobility the Smart WayMetropolitan Mobility the Smart Way
Deena – Are any of these the RTP slides you
wanted?
Deena – Are any of these the RTP slides you
wanted?
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9Intelligent Transportation Systems: Saving Lives, Time and MoneyIntelligent Transportation Systems: Saving Lives, Time and Money
Portal In Action: Metropolitan Congestion Over TimePortal In Action: Metropolitan Congestion Over Time
2005
2006
2004
Winter Spring Summer
Fall
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10Intelligent Transportation Systems: Saving Lives, Time and MoneyIntelligent Transportation Systems: Saving Lives, Time and Money
Cross Section StudyCross Section Study
Speed-Volume Analysis (2005)
2004-05 Speed Comparison
‘04
‘05
Volume
SpeedSpeed
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11Intelligent Transportation Systems: Saving Lives, Time and MoneyIntelligent Transportation Systems: Saving Lives, Time and Money
Cross Section ComparisonCross Section Comparison
Geographic Bottlenecks
Mega-project!
Design Flaws
Good Free-flow performance
Looming Danger
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12Intelligent Transportation Systems: Saving Lives, Time and MoneyIntelligent Transportation Systems: Saving Lives, Time and Money
Ramp Flow ML Flow ML Speed
Metering activated earlier under SWARM
Vehicle-Hours of Delay
Station 28-Sep (P) 21-Sep (S)
Sunnyside 272 161
Johnson Creek 1054 818
Foster 1075 711
Corridor Total 3775 2358
Despite a slightly higher metering rate, SWARM’s earlier activation appeared to delay the onset of congested speeds and allowed for higher and more stable mainline flows.
Note: SWARM Metering Activation Data not collected at Foster
9/21 (SWARM) & 9/28 Pre-Timed9/21 (SWARM) & 9/28 Pre-Timed
Metering Activation
Speeds dropped prior to activation
Pre-Timed
SWARM
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13Intelligent Transportation Systems: Saving Lives, Time and MoneyIntelligent Transportation Systems: Saving Lives, Time and Money
Extras – PORTAL DetailExtras – PORTAL Detail
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14Intelligent Transportation Systems: Saving Lives, Time and MoneyIntelligent Transportation Systems: Saving Lives, Time and Money
What’s Behind the Scenes?What’s Behind the Scenes?What’s Behind the Scenes?What’s Behind the Scenes?
Database ServerDatabase ServerPostgreSQL Relational Database PostgreSQL Relational Database Management System (RDBMS)Management System (RDBMS)
StorageStorage2 Terabyte Redundant Array 2 Terabyte Redundant Array of Independent Disks (RAID)of Independent Disks (RAID)
Web InterfaceWeb Interface
Development ServerDevelopment ServerCentOS Linux distributionCentOS Linux distribution
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15Intelligent Transportation Systems: Saving Lives, Time and MoneyIntelligent Transportation Systems: Saving Lives, Time and Money
Performance Measures UsedPerformance Measures UsedPerformance Measures UsedPerformance Measures Used
VolumeVolume SpeedSpeed
OccupancyOccupancy Vehicle Miles TraveledVehicle Miles Traveled Vehicle Hours TraveledVehicle Hours Traveled
Travel TimeTravel Time DelayDelay
In near future will add: Fuel In near future will add: Fuel Consumption, Emissions, Consumption, Emissions,
Carbon MeasuresCarbon Measures
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16Intelligent Transportation Systems: Saving Lives, Time and MoneyIntelligent Transportation Systems: Saving Lives, Time and Money
Grouped Data – Travel TimeGrouped Data – Travel TimeGrouped Data – Travel TimeGrouped Data – Travel Time
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17Intelligent Transportation Systems: Saving Lives, Time and MoneyIntelligent Transportation Systems: Saving Lives, Time and Money
Performance Report - ReliabilityPerformance Report - ReliabilityPerformance Report - ReliabilityPerformance Report - Reliability
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18Intelligent Transportation Systems: Saving Lives, Time and MoneyIntelligent Transportation Systems: Saving Lives, Time and Money
Monthly ReportMonthly ReportMonthly ReportMonthly Report
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19Intelligent Transportation Systems: Saving Lives, Time and MoneyIntelligent Transportation Systems: Saving Lives, Time and Money
Incident ReportsIncident ReportsIncident ReportsIncident Reports
Incident on NB I-205, Incident on NB I-205, log truck rear-ended a log truck rear-ended a
nursery truck, two nursery truck, two cars also involved, cars also involved,
duration over 4 hours.duration over 4 hours.
11/15/2005 Northbound I-20511/15/2005 Northbound I-205
20,000 20,000 reported reported
incidents/yearincidents/year
92 Database 92 Database fieldsfields
4 Entries per 4 Entries per incidentincident
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20Intelligent Transportation Systems: Saving Lives, Time and MoneyIntelligent Transportation Systems: Saving Lives, Time and Money
Mapping – Speed SubtractionMapping – Speed SubtractionMapping – Speed SubtractionMapping – Speed Subtraction
Average Evening Peak Speed (5-6 pm)Average Evening Peak Speed (5-6 pm)
Difference Difference July-December 2005July-December 2005
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21Intelligent Transportation Systems: Saving Lives, Time and MoneyIntelligent Transportation Systems: Saving Lives, Time and Money
Google TrafficGoogle TrafficGoogle TrafficGoogle Traffic
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22Intelligent Transportation Systems: Saving Lives, Time and MoneyIntelligent Transportation Systems: Saving Lives, Time and Money
Incident ReportsIncident Reports
Incident on NB I-205, log truck rear-ended a nursery truck, two cars also involved, duration over 4 hours.
11/15/2005 Northbound I-205
Incident on SB I-205, NB effects visible
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23Intelligent Transportation Systems: Saving Lives, Time and MoneyIntelligent Transportation Systems: Saving Lives, Time and Money
Extras – Other ProjectsExtras – Other Projects
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24Intelligent Transportation Systems: Saving Lives, Time and MoneyIntelligent Transportation Systems: Saving Lives, Time and Money
Ramp Flow ML Flow ML Speed
Vehicle-Hours of Delay
Station1-Oct
(P)
17-Sep (S)
Sunnyside 5 8
Johnson Creek 189 205
Foster 23 61
Corridor Total 262 491
(3) Slightly higher metering rates under SWARM than Pre-Timed
(2) But metering at Sunnyside (and likely Foster) activated later under SWARM than Pre-Timed
(4) SWARM appears to implement a lower metering rate, responding to lower speeds.
10/1 (Pre-Timed)& 9/17 (SWARM)10/1 (Pre-Timed)& 9/17 (SWARM)
(1) SWARM activation matches drop in speed
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25Intelligent Transportation Systems: Saving Lives, Time and MoneyIntelligent Transportation Systems: Saving Lives, Time and Money
SWARM Summary SWARM Summary
• SWARM allows more vehicles onto the freeway at each on-ramp.
» Counter to ODOT’s initial assumptions
• Pilot study on OR-217 SB demonstrated a tradeoff between decreased ramp delay and increased mainline delay
» Could not conclude that higher on-ramp volumes were the sole cause.
» SWARM’s earlier activation times reduce mainline delay under some conditions.
• Adjustment of metering rates and other SWARM parameters is needed to improve performance of the system
• Communications failures impact quality of SWARM operation
»Tradeoff between frequently updating ramp metering plans, and increased need for maintenance and tuning w/adaptive system
• Logging capabilities for SWARM/ATMS would make evaluation efforts easier
» Ramp queue loop detectors, meter activation times, and actual metering rates set by the SWARM system
• SWARM allows more vehicles onto the freeway at each on-ramp.
» Counter to ODOT’s initial assumptions
• Pilot study on OR-217 SB demonstrated a tradeoff between decreased ramp delay and increased mainline delay
» Could not conclude that higher on-ramp volumes were the sole cause.
» SWARM’s earlier activation times reduce mainline delay under some conditions.
• Adjustment of metering rates and other SWARM parameters is needed to improve performance of the system
• Communications failures impact quality of SWARM operation
»Tradeoff between frequently updating ramp metering plans, and increased need for maintenance and tuning w/adaptive system
• Logging capabilities for SWARM/ATMS would make evaluation efforts easier
» Ramp queue loop detectors, meter activation times, and actual metering rates set by the SWARM system
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26Intelligent Transportation Systems: Saving Lives, Time and MoneyIntelligent Transportation Systems: Saving Lives, Time and Money
Travel Time Estimation ErrorTravel Time Estimation Error
• 85% of runs within error threshold of 20%• 85% of runs within error threshold of 20%2.9%
6.1%
14.7%
29.0%
10.3%
2.6% 2.9%
31.4%
0%
5%
10%
15%
20%
25%
30%
35%
< -30% -30% to -20%
-20% to -10%
-10% to0%
0% to10%
10% to20%
20% to30%
> 30%
Percent Error
Pe
rce
nt
of
Ru
ns
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27Intelligent Transportation Systems: Saving Lives, Time and MoneyIntelligent Transportation Systems: Saving Lives, Time and Money
Real Time Travel Time EstimationReal Time Travel Time EstimationReal Time Travel Time EstimationReal Time Travel Time Estimation
• Goal: Assess accuracy of current travel time Goal: Assess accuracy of current travel time estimates and suggest improvementsestimates and suggest improvements
• AnalysisAnalysis
• 500 ground truth runs (GPS-enabled iQue)500 ground truth runs (GPS-enabled iQue)
• Compared ground truth with estimates using Compared ground truth with estimates using PORTAL dataPORTAL data
• ResultsResults
• Average error 11%Average error 11%
• Identified need for additional detectionIdentified need for additional detection
• Methods for evaluating benefits of additional Methods for evaluating benefits of additional detectiondetection
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28Intelligent Transportation Systems: Saving Lives, Time and MoneyIntelligent Transportation Systems: Saving Lives, Time and Money
Sensor Data QualitySensor Data QualitySensor Data QualitySensor Data Quality
• ODOT products (speed map, ramp ODOT products (speed map, ramp metering) are only as good as the metering) are only as good as the input datainput data
• Use PORTAL to identify poorly Use PORTAL to identify poorly performing detectors; prioritize performing detectors; prioritize maintenance on those detectors maintenance on those detectors (improve efficiency)(improve efficiency)
• Key Question: How do data anomalies Key Question: How do data anomalies correlate with problems in the field?correlate with problems in the field?
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29Intelligent Transportation Systems: Saving Lives, Time and MoneyIntelligent Transportation Systems: Saving Lives, Time and Money
Gap FillingGap FillingGap FillingGap Filling
Correlated information can help find mechanisms for filling the data gaps
By looking at available information from nearby stations, models fitted on historical data can provide an online estimate of the missing conditions. Different choices of estimation models exist, some more computationally intensive than others.
A B C
SB SCSA
Direction of flow
),(ˆCAB SSfS
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30Intelligent Transportation Systems: Saving Lives, Time and MoneyIntelligent Transportation Systems: Saving Lives, Time and Money
Bottleneck IdentificationBottleneck IdentificationBottleneck IdentificationBottleneck Identification
A
B
C
Bottleneck
Estimated Propagation Speed
A – 25 mphB – 22 mphC – 21 mph
Activation
Deactivation
90% percentile of historicalbottlenecks
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31Intelligent Transportation Systems: Saving Lives, Time and MoneyIntelligent Transportation Systems: Saving Lives, Time and Money
Incident Autopsy: 6/12/06Incident Autopsy: 6/12/06
8:15 2-vehicles collide8:19 Crash reported8:27 VMS message:
CENTER LANES CLSD8:40 COMET requests tow9:10 Tow arrives9:27 Lanes clear 9:30 Traffic starts to clear9:45 Traffic half clear10:00 Traffic all clear
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Incident Autopsy: 6/12/06Incident Autopsy: 6/12/06
0
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veh
icle
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inu
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Crash
All Lanes Clear
All Traffic Clear
Tow Arrives