vii data characteristics for traffic management: task overview and update 21 june 2006 karl...
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VII Data Characteristics for Traffic Management:
Task Overview and Update
21 June 2006
Karl Wunderlich
Fellow, Transportation Analysis
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Scope
Examine capability of VII probe data to support (specifically):– Signal control– Ramp metering– Traveler information
This capability must be examined with respect to key variables:– Facility type (arterial/freeway/rural) and geometry– Congestion levels and road/weather conditions– Market penetration– VII probe message management
• In-vehicle• At the roadside and in backhaul communication
Near-term analytical emphasis is on the support of Day 1 applications– For example, off-line periodic signal retiming versus “real-time”
adaptive signal control
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Objectives Identify likely content of collected VII probe messages
passed to traffic managers or traveler information service providers under realistic conditions
Develop (where possible) algorithms that will estimate key measures from the collected probe data, for example:– Vehicle volumes by lane and turning movements– Travel times and intersection delays
Estimate the accuracy of these algorithms with respect to the key variables from previous slide (e.g., market penetration)
Provide USDOT with an understanding of key tradeoffs along a spectrum of issues/conditions (e.g., privacy)
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Staffing and Coordination
Mitretek Systems Team
Michael McGurrinKarl Wunderlich
Meenakshy VasudevanEmily Parkany
Phil Tarnoff, U-Md.
USDOT Task Manager
Brian Cronin
Use Case Development(BAH)
VII Data Elements(PB)
Key VII-Related Activities
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Approach
Data needs assessment– Define the data required by traffic management and traveler
information applications– Qualitative assessment of data produced by VII to meet these
identified needs
Analytical assessment of VII probe data– Develop an analytical tool that takes…
• Vehicle trajectory data• Specific probe message management strategy• Assumed RSE deployment
… and produces the associated VII probe data content– Trajectory data will come from a variety of sources:
• Observed (e.g., NGSIM or floating car data)• Simulated (e.g., from a traffic simulation)
– Develop algorithms to process this probe data into measures of interest (e.g., link travel time)
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VII Data CharacteristicsTask
Data NeedsAssessment
1/1
Data Needs White Paper
3/1 4/1 5/1 6/1 7/1 8/1 9/1 10/1 11/1 12/1
PrelimMatrix
Analytical ToolDevelopment and
Evaluation
Kickoff Briefing 1 Briefing 2
DownselectStrategies (I)
Draft WP
Day 1 Final Report(draft)
Acquire/Prep Trajectory Data
Build TrajectoryConverter
AcquireTraffic Simulationand Test Networks
Enhance Converter
Briefing 3
write-up
Multi-RSE Strategy Evaluation
TradeoffAnalyses
write-up
revisionsData
Characteristics WP (final)
Coordination/Progress Briefings
DownselectStrategies (II)
Initial StrategyDevelopment
Initial Strategies
Assess Needs
Observed Data Track
SimulationTrack
ExpendedFunding
RemainingFunding
Width indicates relative Mitretek LOE Deliverables
Co
mp
lete
d
Pla
nn
ed(+
in
tern
al d
raft
)
1FTE
Der. Algs. (II)
12 June 2006
Planned
Coordination Meetings
Completed
Bi-Weekly Status Updates(Scheduled)
Brian/Karl
Full Team
Completed
Validate Sim Trajectories
Preliminary Strategy Evaluation
Derivation Algs. (I)
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Key Deliverables Data Needs White Paper (completed)
– Broad, qualitative assessment of Day 1 and later needs
Applications Preliminary Requirements Matrix (completed)
– High-level assessment of the capability of VII data to meet the identified short- and long-term needs
Data Characteristics White Paper (1 September 2006)
– Summary of findings, primarily from observed data analysis
– Initial assessment of capability of VII probe data to support Day 1 applications
Draft Day 1 Final Report (1 January 2007)
– Update and expansion of the September white paper
– Results from the analysis of simulated trajectories
– More comprehensive assessment of key tradeoffs
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From Trajectories to Measures
Time
Po
siti
on
VehicleTrajectories
Extract SampleDepending on Market Penetration
12 3
4
PopulateWith SnapshotsAccording to Message HandlingStrategy
ProcessSnapshotsTo EstimateMeasures
TravelTime
QueueLength
Other
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Observed Data Sets, Floating Car:Strengths and Weaknesses Floating car trajectory data
– Strengths:
• Trajectories are long (30+ miles in some cases)
• Arterial, freeway, rural road facilities
• Light to heavy congestion conditions
• Some “other data” collected that looks like VII data elements (e.g., weather or turn signal disposition)
– Weaknesses:
• Only one vehicle tracked
• Ground truth measures can’t be directly observed for aggregate traffic flow – just one vehicle
Will be most valuable for looking at travel time derivation issues over longer links, potentially widely dispersed RSEs
Road Weather Management
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Observed Data Sets: NGSIM
NGSIM data are high-resolution vehicle trajectory data– Processed video images from multiple high-angle cameras– Near 100% of all vehicle positions traced at 0.1 sec intervals– Detailed lane position and disposition to other vehicles– Two freeway data sets, one arterial data set
Strengths: 100% vehicle coverage Weaknesses: Short coverage areas (under 1 km)
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Simulated Vehicle Trajectories:Strengths and Weaknesses Simulated trajectory data
– Strengths:
• Most facilities of interest can be modeled
• 100% tracking of vehicles
• Ground truth measures can be directly obtained
• Congestion levels and other elements can be systematically adjusted
– Weaknesses:
• Validity of detailed trajectories under congestion is poorly understood
• Time and effort to build and calibrate realistic networks
Will be most valuable when attempting to deal with incremental tradeoffs for key issues like market penetration and buffer size
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Sample Trajectory Conversion:Columbus, Ohio: Route 33 and I-270
• Run Type : GPS (Floating Car)• Distance: 62.0 Miles• Travel Time: 93.8 Minutes• Average Speed: 39.6 mph• RSE Spacing : 2.3 miles between RSEs (on average)• Snapshots per Mile: 10.0• Vehicle IDs (Transmit/Produced): 32 / 42• Snapshots per ID (Transmit/Produced): 9.4/13.7• Total number of Snapshots: 618
– Stop Snapshots: 23– Start Snapshots: 13– Periodic Snapshots: 582
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Columbus, OhioExpected RSE Location, GPS Trace
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Walk-Through of Default VII Probe Message Process
• Location:– A congested segment on I-270
• What we will examine:– 50 Snapshots taken right after vehicle RSE interaction
• Time– 3133 to 3448 seconds (5.25 minutes)
• Distance:– 1.9 Miles
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I-270 Route
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Time 3133-3244 (1.85 Min)0.69 Miles
43 secs (7 SS)
Spd 20-28
12 secs (4 SS)
Spd 0-9T 3196
(1 Stop)
48 Secs(1 Start)Spd 10.5
Periodic 11 Stop 1 Start 1 Capacity 13/30Periodic 0 Stop 0 Start 0Deleted
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Time 3244 – 3356 (1.87 mins)0.69 Miles
12 secs 4 SS
Spd 12-19T 3356
(1 Stop)
Periodic 27 Stop 2 Start 1 Capacity 30/30
29 secs 6 SS
Spd 22-32
14 secs 2 SS
Spd 43
1 secs 1 SS
Spd 19
20 secs 7 SS
Spd 4-12
Buffer is full 3.25 mins after the last vehicle RSE Interaction
Periodic 4 Stop 0 Start 0Deleted
Deleted from SS from Time 3133- 3151 (0.3 mins)
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Time 3356- 3448 (1.9 mins) 0.54 Miles
4 Secs(1 Start)Spd 11.0
20 Secs(5 SS)
Spd 13-19
41 Secs(10 SS)
Spd 13-19
Periodic 26 Stop 2 Start 2 Capacity 30/30Periodic 20 Stop 0 Start 0Deleted
Deleted from SS from Time 3133- 3276 (2.4 mins)
Does not report to a RSE for
another 4.7 Mins
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Deleted SS Time 3133-3244 (1.85 Min) 0.69 Miles
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Deleted SS Time 3244 – 3356 (1.87 mins) 0.69 Miles
95 additional snapshots are deleted beforeThe vehicle interacts with another RSE
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Deleted Snapshots by Location
First RSEInteraction
Last RSEInteraction
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Estimating Travel Time from Snapshots
OVERALLActual = 94 minutes
Calculated = 67 minutesError = 29%
A =321C = 159E = 50%
A = 574C = 242E = 58%
A =100C = 120E = 20%
A =279C = 220E = 21%
A = 160C = 195E = 22%
A =363C = 292E = 20%
A = 151C = 174E = 15%
A = 120C = 100E = 17%
A =200C = 179E = 11%
Actual = 234 secCalculated = 236 sec
Error = 1%
A = 260C = 262E = 1%
A = 460C = 483E = 5%
A = 154C = 168E = 9%
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Preliminary Observations
• For uncongested conditions:– the default strategy provides fairly good geographic
coverage and accuracy
• For congested conditions even with relatively closely spaced RSEs:– The default plan results in significant buffer overflow – The deleted snapshots leave significant geographic
gaps– Gaps have impact on accuracy of travel time
estimation
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Analysis: Next Steps
• Evaluate more Data Sources– Columbus, Ohio GPS – Salt Lake City, Utah I-15 GPS runs – Dulles Toll Road GPS runs– I-66/Route 50 GPS runs– NGSIM validation data
• Evaluate VISSIM simulated runs• Test alternative thresholds and strategies for VII
probe message process • Test sensitivity to a range of RSE locations and
densities