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AASHTO AQCCES Quarterly CallFriday, March 6, 2020
David Kall, FHWA
FHWA Research
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• Vehicle Activity at Signalized Intersections
• DANA Tool for Traffic Data for Air and Noise Analyses
Outline
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EVALUATION OF METHODS FOR MODELING VEHICLE ACTIVITY AT SIGNALIZED INTERSECTIONS FOR PROJECT SCALE AIR QUALITY ANALYSIS
• Ongoing Research Project– Phase 1 complete – 1st intersection– Phase 2 ongoing – 2nd intersection
• Contractors: Cadmus with UNC Institute for Environment, Kittelson & Associates, NC State Univ., and Zamurs & Associates
• Purpose: compare methods of representing vehicle activity at signalized intersections
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Introduction
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Background
• Modeling emissions is more complicated for signalized intersections than highway traffic, because traffic is interrupted by traffic signals.
• The MOVES emissions model has advanced model options to capture modal vehicle activity (e.g. acceleration, deceleration, cruise, idle).
• Next Generation Simulation (NGSIM) video recordings produced vehicle traces for 100% of the vehicles passing through an intersection.– Used for Detailed Baseline (Method 1)
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Method 1
The study domain with the finalized road network for Method 1.
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Method 2 & 3*
A representation of Method 2 queue links.
Method 2 free flow links. * Method 3 uses the same inputs as Method 2, except it removes idle
from the MOVES default drive cycles for free flow links.
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Methods 4 & 5
Link network for Methods 4 and 5.
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Methods 6 & 7
Link network for Methods 6 and 7.
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Modeling Results
Total Emissions (grams) over the domain
Comparison of CAL3QHC with HCM6
• Predictions of Delayo CAL3QHC & HCM6 close in
predicted delays when the V/C ratio is below 1.00.
o HCM6 delays are significantly lower than CAL3QHC delays when V/C’s exceed 1.00.
• CAL3QHC significantly underestimates queues (as compared to HCM6) when the V/C ratio falls in the range of 0.80 to 1.00. When V/C’s are above 1.10, the situation is reversed with CAL3QHC predicting significantly greater queues than HCM6.
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Conclusions & Recommendations
• Methods 2-7 all underpredict total emissions compared to Method 1. – Phase 2 investigating reasons and creating new
method to address underprediction
• The existing CAL3QHC should allow users to import HCM6 queue and delay results.– Phase 2 adding new method to use HCM6 queue
and delay information
DATABASE FOR AIR QUALITY AND NOISE ANALYSIS (DANA) TOOL
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• Contractors: Cambridge Systematics with ERG and AECOM
• Objective: Utilize FHWA traffic-related data for assisting in air quality and noise applications– Highway Performance Monitoring System (HPMS)– National Performance Management Research
Dataset (NPMRDS)– Travel Monitoring Analysis System (TMAS)
• Result: DANA (Database for Air Quality and Noise Analysis) – a composite database and associated analysis tools
DANA Tool Introduction
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1. Link-Level (Composite) Data Set—For every NPMRDS link for every hour of the year, traffic data and emission rates are compiled
2. MOVES County-Level Input Data—Selected MOVES input files are created
3. Traffic data summaries for TNMAide—Create Hourly distributions over a 24-hour period for TNMAide
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DANA: Main Functions
• High-capacity computer, preferably a workstation with powerful computer processing unit (CPU) (e.g., Intel Xeon)
• Large random access memory (RAM) (e.g., 64 gigabytes (GB) and up).
• Sufficient hard disk space to store output data.
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Recommended Hardware
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DANA Tool Screenshots
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DANA Tool Screenshots
• The following MOVES county-level input files can be created:– Considers VMT from all roadway functional
classes:• Vehicle Type VMT • Road Type Distribution• Hour VMT Fraction• Day VMT Fraction• Month VMT Fraction
– Considers speeds only from the National Highway System (NHS)
• Average Speed Distribution
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MOVES County-Level Input Data
• CRC project A-100 developed speed distributions for the 2014 National Emissions Inventory (NEI) – Used probe vehicle data from vendor StreetLight– Research team compared the results to the NPMRDS data
(vendor Inrix)• Statistical testing on hundreds of paired comparisons• Conducted for Colorado arterials for time period covering September
2015 through August 2016• Overall, 44 percent of the speed distributions for the
arterial combinations were statistically the same– All of the 140 urban cases exhibited similar speed distributions– 38 percent of rural cases had similar speed distributions
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Speed Distribution Comparison
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Example: Similar Speed Distributions
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Example: Different Speed Distributions
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