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Traffic Monitoring Research

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Traffic Monitoring Research

Daniel Jenkins, PE
Brad Gudzinas
Steven Jessberger
Patrick Zhang, PE
Office of Highway Policy Information
November 16, 2011

 

 

 

 

Research Areas

  • Motorcycle Data – Danny Jenkins
  • Ramp Data – Danny Jenkins
  • Long Distance Travel/Origin Destination Research – Danny Jenkins/Brad Gudzinas/Patrick Zhang
  • Bicycle / Pedestrian data – Steven Jessberger
  • VMT/VHT Research – Patrick Zhang

 

Research Project Update

Danny Jenkins

  • Motorcycle data
  • Ramp data
  • Long Distance Passenger Origin-Destination Study

 

Motorcycle Data

  • NCHRP Project 08-81 "Improving the Quality of Motorcycle Travel Data Collection"
  • Contractor: Texas Transportation Institute
  • Current Status:
    • Literature Review – complete
    • Field Testing of Promising Technologies – spring 2012
    • Project completion – fall 2012

 

Ramp Data

  • OHPI Project Development of Methods For Obtaining Traffic Data Associated With Interchange Ramps
  • Contractor: AECOM with support from Delcan
  • Major Tasks:
    • Task 2 – Identify Current Ramp Traffic Data Sources (underway- 50% complete)
    • Task 3 – Ramp Configurations and Develop Algorithms To Estimate Ramp Volumes (underway- 10% complete)
    • Task 4 – Validate Estimated Ramp Volume Algorithms (start spring 2012)

 

Long Distance Passenger Origin-Destination Study

  • OHPI Project Long Distance Passenger Origin-Destination Study
  • Contractor: RD Mingo and Associates with significant support from Wilbur Smith Associates and Resource Systems Group
  • Major Tasks:
    • Task 3 – Create 2008 highway (auto and bus), air, and rail passenger trip tables (draft tables complete)
    • Task 4 – Create 2040 highway (auto and bus), air, and rail passenger trip tables
    • Task 5 – Create 2008 and 2040 highway long distance vehicle trip tables

 

Questions?

Danny Jenkins
FHWA Office of Highway Policy Information
daniel.jenkins@dot.gov
202-366-1067

 

Research Project Update

Brad Gudzinas

  • Long-distance travel model exploratory research
  • Long-distance mode choice model
  • Elasticity analysis

 

Long-distance travel model exploratory research

Objectives:

  • Explore methods to modeling long-distance passenger modeling in the U.S.
  • Build on advanced modeling methods being used in regional and statewide modeling for national planning and policy
  • Foundation for modeling national long-distance passenger travel Identify model structure and trip purposes
  • Unique considerations for long-distance trips
  • Useful for external trip inputs to statewide models
  • Also valuable for national transportation infrastructure planning
  • Analogous to FAF, but with people instead of freight

 

Long-distance mode choice model

Objectives:

  • Develop quantitative tools to analyze long-distance travelers make their modal choices
  • Inform policy analyses by better understanding how travelers make these choices
  • Auto is dominant for trips <500 miles but the auto/air transition (trips 500-1000 miles is not well understood
  • Parallel research projects being conducted by Oak Ridge National Laboratory (ORNL) and by Battelle
  • Develop a model of from variables in NHTS that explain travel behavior

 

Elasticity analysis

  • Study the effects of cost changes on auto travel
  • Establish quantitative relationships between auto trips and cost
  • Auto trips shift modes in response to costs
  • Related to model choice model development, since certain factors (e.g. gas prices) may lead to mode shifts

 

Questions?

Brad Gudzinas
FHWA Office of Highway Policy Information
brad.gudzinas@dot.gov
202-366-5024

 

Research Project Update

Steven Jessberger

  • Bicycle and Pedestrian Data

 

Bicycle and Pedestrian Data Collection

  • Task 2 Webinar Results
  • Out of 99 people responding 72% had collected bicycle and pedestrian data for 3 years or less
    • Only 13% of respondents collect 24/7 permanent data
    • 61% of respondents do their pedestrian counts manually
    • 49% of respondents do their bicycle counts manually
    • Only 25% of respondents have independently verified their equipments effectiveness
    • Only 13% of respondents have experience extrapolating bicycle and pedestrian counts temporally
    • Including weather is very important item to include in a National bicycle or pedestrian format

 

Bicycle and Pedestrian Research Findings

  • Task 2 Webinar Results
    • Out of 99 people responding 72% had collected bicycle and pedestrian data for 3 years or less
    • Only 13% of respondents collect 24/7 permanent data
    • 61% of respondents do their pedestrian counts manually 49% of respondents do their bicycle counts manually
    • Only 25% of respondents have independently verified their equipments effectiveness
    • Only 13% of respondents have experience extrapolating bicycle and pedestrian counts temporally Including weather is very important item to include in a National bicycle or pedestrian format

 

Webinar Poll Results

Pedestrian Counts Are Collected For

Pie Chart of Pedestrian Counts
Bullet Safety Analysis
Bullet Project Selection (pre-planning)
Bullet Project Design
Bullet Project Evaluation (before/after studies)
Bullet Modeling – Long Range Planning
Bullet Modeling – Simulation
Bullet Trend Analysis
Bullet Other

 

Webinar Poll Results

Bicycle Counts Are Collected For

Pie Chart of Bicycle Counts
Bullet  Safety Analysis
Bullet Project Selection (pre-planning)
Bullet Project Design
Bullet Project Evaluation (before/after studies)
Bullet Modeling – Long Range Planning
Bullet Modeling – Simulation
Bullet Trend Analysis

 

Questions?

Steven Jessberger
FHWA Office of Highway Policy Information
steven.jessberger@dot.gov
202-366-5052

 

Research Project Update

Patrick Zhang

  • Vehicle and VMT Disaggregate by State and Fuel
  • Vehicle Hours Traveled
  • Vehicle Miles Forecast
  • Design of a New Approach for a National Household Long Distance Travel Survey Instrument
  • OD Study

 

Vehicle and VMT Disaggregate by State and Fuel

  • Gasoline – Conventional
  • Diesel – Conventional
  • Propane
  • Compressed Natural Gas
    • Bio-Diesel
    • B20 (Most Diesel Vehicles)
    • B100 (Vehicles after 1994 without factory warranty)
  • Ethanol
    • E10 (All Gasoline Vehicles)
    • E85 (Flexfuel Vehicles)
  • Electricity
    • All-Electric Vehicles
    • Hybrid Electric Vehicles
    • Plug-in Hybrid Electric Vehicles
      • Parallel plug-in hybrids
      • Series plug-in hybrids

 

Vehicle Hours Traveled

 

  • Historical analysis of trends in VHT
    • Focus on understanding determinants of VHT at urban area level
    • Address objections to TTI focus on delay (vs. total travel time)
  • Develop Forecasting Model
    • Attempt to forecast VHT using TTI historical data
    • Analogous to VMT model and would dovetail nicely
    • Need to determine correspondence between VHT and VMT measures for individual urban areas
  • Develop facility-level model of speed and VHT
    • Focus on single urban area (probably California)

 

Vehicle Miles Forecast

  • The national model forecasts VMT by vehicle types (4, no motorcycle) and road classes (5, verify) at national level.
  • The state model forecasts VMT by vehicle types (4) at state level.
  • Models are updated every 6 months while other agencies release their forecast data such GI and Census of Bureau.
  • Models are used to provide policy support and scenario analysis.

 

Design of a New Approach for a National Household Long Distance Travel Survey Instrument

  • Sampling Frame/Design
  • Data Collection Methods
  • Statistical Estimation and Weighting
  • Resource and Schedule Estimation Exploratory Research on Sampling Frame/Design Topics
  • Exploratory Research on Data Collection Methods
  • Exploratory Research on Estimation and Weighting
  • Survey Design for Long Distance Travel
  • Assist in the Preparation of the OMB Package
  • Pilot Test Demonstration

 

OD Study

Task 1: Evaluate the Impacts of Infrastructure Network, Travel Service Availability, Congestion, Social and Economical Factors, and Other Factors on Long Distance Passenger Travel Demand and Modal Choice

  • develop a framework for analyzing long distance passenger travel decisions.
  • propose a set of research hypotheses regarding the important factors that affect long distance travel decisions.
  • identify data sources useful for evaluating these hypotheses.
  • analyze those data and information that lends credence to or detracts from our working hypotheses.

 

OD Study –continued

Task 2: Develop a Traffic Data Framework

  • Literature Review and Data Identification
  • Network Representation in Database
  • Traffic Assignment Modeling
  • System Implementation and Performance Evaluation

 

Questions?

Patrick Zhang
FHWA Office of Highway Policy Information
Patrick.zhang@dot.gov
202-366-1941

 

Pedestrain Counts Are Collected For 19% - Project Evaluaiton (before/after studies) 18% Project Design 13% Project Selection (pre-planning) 7% Modeling - Long Range Planning 2% Modeling - Simulation 19% Trend Analysis 22% Safety Analysis 20% Trend Analysis 1% Other 1% Other 18% Safety Analysis 16% Project Selection (pre-planning) 16% Project Design 16% Project Design 16% Project Design 21% Project Evaluation (before/after studies) 8% Modeling - Long Range Planning 1% Modeling - Simulation