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VII Data Characteristics for Traffic Management: Task Overview and Update. 21 June 2006 Karl Wunderlich Fellow, Transportation Analysis. Scope. Examine capability of VII probe data to support (specifically): Signal control Ramp metering Traveler information

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VII Data Characteristics for Traffic Management: Task Overview and Update

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Vii data characteristics for traffic management task overview and update l.jpg

VII Data Characteristics for Traffic Management: Task Overview and Update

21 June 2006

Karl Wunderlich

Fellow, Transportation Analysis


Scope l.jpg

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


Objectives l.jpg

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)


Staffing and coordination l.jpg

Mitretek Systems Team

Michael McGurrin

Karl Wunderlich

Meenakshy Vasudevan

Emily Parkany

Phil Tarnoff, U-Md.

Staffing and Coordination

USDOT Task Manager

Brian Cronin

Key VII-Related Activities

Use Case Development

(BAH)

VII Data Elements

(PB)


Approach l.jpg

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)


Vii data characteristics task l.jpg

VII Data CharacteristicsTask

Planned

Expended

Funding

Completed

Coordination Meetings

Planned

(+ internal draft)

Remaining

Funding

Completed

Completed

Brian/Karl

1

FTE

Width indicates relative Mitretek LOE

Full Team

Deliverables

Bi-Weekly Status Updates

(Scheduled)

4/1

11/1

7/1

3/1

10/1

6/1

9/1

1/1

5/1

8/1

12/1

Coordination/

Progress Briefings

Briefing 2

Briefing 3

Briefing 1

Kickoff

Data Needs White Paper

Assess Needs

Data Needs

Assessment

Prelim

Matrix

Initial Strategy

Development

Initial Strategies

Acquire/Prep Trajectory Data

revisions

Data

Characteristics WP (final)

Observed Data Track

write-up

Preliminary Strategy Evaluation

Draft WP

Analytical Tool

Development and

Evaluation

Build Trajectory

Converter

Derivation Algs. (I)

Downselect

Strategies (II)

Downselect

Strategies (I)

Validate Sim Trajectories

Tradeoff

Analyses

write-up

Simulation

Track

Multi-RSE Strategy Evaluation

Acquire

Traffic Simulation

and Test Networks

Enhance Converter

Der. Algs. (II)

Day 1 Final Report

(draft)

12 June 2006


Key deliverables l.jpg

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


From trajectories to measures l.jpg

1

3

2

Extract Sample

Depending on

Market Penetration

4

Populate

With Snapshots

According to

Message Handling

Strategy

Process

Snapshots

To Estimate

Measures

Queue

Length

Travel

Time

Other

From Trajectories to Measures

Vehicle

Trajectories

Position

Time


Observed data sets floating car strengths and weaknesses l.jpg

Observed Data Sets, Floating Car:Strengths and Weaknesses

Road Weather

Management

  • 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


Observed data sets ngsim l.jpg

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)


Simulated vehicle trajectories strengths and weaknesses l.jpg

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


Sample trajectory conversion columbus ohio route 33 and i 270 l.jpg

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


Columbus ohio expected rse location gps trace l.jpg

Columbus, OhioExpected RSE Location, GPS Trace


Walk through of default vii probe message process l.jpg

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


I 270 route l.jpg

I-270 Route


Time 3133 3244 1 85 min 0 69 miles l.jpg

Time 3133-3244 (1.85 Min)0.69 Miles

43 secs

(7 SS)

Spd 20-28

48 Secs

(1 Start)

Spd 10.5

12 secs

(4 SS)

Spd 0-9

T 3196

(1 Stop)

Periodic 11

Stop 1

Start 1

Capacity 13/30

Deleted

Periodic 0

Stop 0

Start 0


Time 3244 3356 1 87 mins 0 69 miles l.jpg

Time 3244 – 3356 (1.87 mins)0.69 Miles

12 secs

4 SS

Spd 12-19

29 secs

6 SS

Spd 22-32

14 secs

2 SS

Spd 43

20 secs

7 SS

Spd 4-12

1 secs

1 SS

Spd 19

T 3356

(1 Stop)

Buffer is full 3.25 mins after the last vehicle RSE Interaction

Periodic 27

Stop 2

Start 1

Capacity 30/30

Deleted

Periodic 4

Stop 0

Start 0

Deleted from SS from Time 3133- 3151 (0.3 mins)


Time 3356 3448 1 9 mins 0 54 miles l.jpg

Time 3356- 3448 (1.9 mins) 0.54 Miles

4 Secs

(1 Start)

Spd 11.0

20 Secs

(5 SS)

Spd 13-19

Does not report to a RSE for another 4.7 Mins

41 Secs

(10 SS)

Spd 13-19

Periodic 26

Stop 2

Start 2

Capacity 30/30

Periodic 20

Stop 0

Start 0

Deleted

Deleted from SS from Time 3133- 3276 (2.4 mins)


Deleted ss time 3133 3244 1 85 min 0 69 miles l.jpg

Deleted SS Time 3133-3244 (1.85 Min) 0.69 Miles


Deleted ss time 3244 3356 1 87 mins 0 69 miles l.jpg

Deleted SS Time 3244 – 3356 (1.87 mins) 0.69 Miles

95 additional snapshots are deleted before

The vehicle interacts with another RSE


Deleted snapshots by location l.jpg

Deleted Snapshots by Location

First RSE

Interaction

Last RSE

Interaction


Estimating travel time from snapshots l.jpg

Actual = 234 sec

Calculated = 236 sec

Error = 1%

A =100

C = 120

E = 20%

A =200

C = 179

E = 11%

A = 574

C = 242

E = 58%

A = 154

C = 168

E = 9%

A =279

C = 220

E = 21%

A = 120

C = 100

E = 17%

A = 260

C = 262

E = 1%

A =321

C = 159

E = 50%

A =363

C = 292

E = 20%

A = 151

C = 174

E = 15%

A = 460

C = 483

E = 5%

A = 160

C = 195

E = 22%

Estimating Travel Time from Snapshots

OVERALL

Actual = 94 minutes

Calculated = 67 minutes

Error = 29%


Preliminary observations l.jpg

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


Analysis next steps l.jpg

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


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