Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.
MODELING THE IMPACT OF TRAFFIC 
CONDITIONS ON THE VARIABILITY OF 
MID-BLOCK ROADSIDE PM2.5 ON AN 
URBAN ARTERIAL 
Adam Moo...
INTRODUCTION 
2 
Portland State University has been studying the 
Powell Boulevard corridor in southeast Portland 
– Busy ...
BACKGROUND 
Exposure to Air Pollution on Roadways 
Vehicle Public Transportation Bicyclist/Pedestrian 
What factors affect...
FINE PARTICULATE MATTER (PM2.5) 
4 
• Cancer 
• Heart disease 
• Increased mortality rates 
• Increased incidence of 
resp...
STUDY LOCATION 
5 
Downtown 
map: bing.com/maps 
Powell Boulevard 
N 
31,500 vehicles daily 
1,500-1,800 peak hours
STUDY SITES 
6 
map: google.com/maps 
May 1, 2013 7:00-9:00am 
Intersection 
“Mid-block” 
N
MID-BLOCK STUDY SITE 
7 
Parking Lot 
Parking Lot 
Powell City Park 
Monitoring 
Location 
SE 24th Ave 
N 
(looking south)
EQUIPMENT 
8
9 
TSI DustTrak DRX 8533 
Fine Particulate 
Matter (PM2.5) 
EQUIPMENT 
photo: www.tsi.com
RM Young Ultrasonic 
Anemometer 81000 
Onset HOBO U12 
Temperature 
Relative Humidity 
10 
EQUIPMENT 
photo: www.onsetcomp...
11 
CountingCars 
CountCam System 
Video Reference 
EQUIPMENT 
photo: www.countingcars.com
12 
Wavetronix 
SmartSensor HD 
Vehicle Volume, Speed, 
Classification, 
Lane Occupancy 
EQUIPMENT
TRAFFIC ACTIVITY 
Vehicles/min 
10 30 50 
Westbound Eastbound 
2:00am 7:00am 12:00pm 5:00pm 10:00pm 
10 30 50 
Speed (mph)...
NEARBY SIGNALS 
14 
Can we see vehicle platooning? 
123 sec 
125 sec 
map: google.com/maps
VEHICLE PLATOONING 
15 
One lag = 10 seconds 
-0.2 0.0 0.2 0.4 
Westbound 
ACF 
Eastbound 
5 10 15 20 
5 10 15 20 
Lag 
-0...
PLATOONING EFFECT ON PM2.5 
16 
-0.10 0.00 0.10 
Westbound CCF 
-20 -10 0 10 20 
Lag 
-0.10 0.00 0.10 
Eastbound CCF 
• We...
EXAMINING CONGESTION 
17 
0 5000 10000 15000 
Westbound 
N(t) 
N(t) 
v0t, v0 = 11.27mph 
Eastbound 
7:30am 8:00am 8:30am 
...
• Pre-congestion, active 
queuing associated 
with lower PM2.5 
concentrations 
• During congestion, 
active queuing 
asso...
REGRESSION FINDINGS 
19 
Vehicles passing when wind blows across 
roadway towards monitoring station 
– Current and previo...
VEHICLE SEMI-ELASTICITIES 
20 
Variable… 
…lagged… 
…changed 
PM2.5 
by… 
…per… 
Occupancy 
0 
sec 
.05% 
% 
occupancy 
in...
CONCLUSIONS 
Exposure to Roadside Fine Particulate Matter 
21 
Data Availability 
Platooning Congestion 
Model Calibration...
Moore, A.; Figliozzi, M.; Bigazzi, A., Modeling the Impact of 
Traffic Conditions on the Variability of Mid-block 
Roadsid...
REGRESSION MODEL 
23 
Dependent variable: ln(PM2.5) 
R2 (R2 
adjusted) .6589 (.6533) 
Residual standard error .06138 on 60...
Upcoming SlideShare
Loading in …5
×

Adam moore

453 views

Published on

  • Be the first to comment

  • Be the first to like this

Adam moore

  1. 1. MODELING THE IMPACT OF TRAFFIC CONDITIONS ON THE VARIABILITY OF MID-BLOCK ROADSIDE PM2.5 ON AN URBAN ARTERIAL Adam Moore Miguel Figliozzi Alexander Bigazzi Friday Transpo Seminar 17 January 2014
  2. 2. INTRODUCTION 2 Portland State University has been studying the Powell Boulevard corridor in southeast Portland – Busy arterial linking downtown to suburbs • Investigating variations in PM levels • Incorporating many data sources – Traffic, air quality, meteorology • Utilizing statistical analyses to control for many factors
  3. 3. BACKGROUND Exposure to Air Pollution on Roadways Vehicle Public Transportation Bicyclist/Pedestrian What factors affect exposure to air pollution? 3 Built Environment Meteorological Conditions Vehicle Activity Very High Resolution Data Collection
  4. 4. FINE PARTICULATE MATTER (PM2.5) 4 • Cancer • Heart disease • Increased mortality rates • Increased incidence of respiratory disease (asthma, etc.) image: epa.gov Vehicle emissions, brake wear, tire wear
  5. 5. STUDY LOCATION 5 Downtown map: bing.com/maps Powell Boulevard N 31,500 vehicles daily 1,500-1,800 peak hours
  6. 6. STUDY SITES 6 map: google.com/maps May 1, 2013 7:00-9:00am Intersection “Mid-block” N
  7. 7. MID-BLOCK STUDY SITE 7 Parking Lot Parking Lot Powell City Park Monitoring Location SE 24th Ave N (looking south)
  8. 8. EQUIPMENT 8
  9. 9. 9 TSI DustTrak DRX 8533 Fine Particulate Matter (PM2.5) EQUIPMENT photo: www.tsi.com
  10. 10. RM Young Ultrasonic Anemometer 81000 Onset HOBO U12 Temperature Relative Humidity 10 EQUIPMENT photo: www.onsetcomp.com Wind Speed Wind Direction photo: www.youngusa.com
  11. 11. 11 CountingCars CountCam System Video Reference EQUIPMENT photo: www.countingcars.com
  12. 12. 12 Wavetronix SmartSensor HD Vehicle Volume, Speed, Classification, Lane Occupancy EQUIPMENT
  13. 13. TRAFFIC ACTIVITY Vehicles/min 10 30 50 Westbound Eastbound 2:00am 7:00am 12:00pm 5:00pm 10:00pm 10 30 50 Speed (mph) 0 20 40 60 Occupancy (%) 2:00am 7:00am 12:00pm 5:00pm 10:00pm 13
  14. 14. NEARBY SIGNALS 14 Can we see vehicle platooning? 123 sec 125 sec map: google.com/maps
  15. 15. VEHICLE PLATOONING 15 One lag = 10 seconds -0.2 0.0 0.2 0.4 Westbound ACF Eastbound 5 10 15 20 5 10 15 20 Lag -0.2 0.0 0.2 0.4 PACF 130 seconds Autocorrelation Function Partial Autocorrelation Function
  16. 16. PLATOONING EFFECT ON PM2.5 16 -0.10 0.00 0.10 Westbound CCF -20 -10 0 10 20 Lag -0.10 0.00 0.10 Eastbound CCF • Westbound platooning did not have a significant effect on PM2.5 concentrations • Eastbound platooning did have a significant effect Cross-correlation Function One lag = 10 seconds
  17. 17. EXAMINING CONGESTION 17 0 5000 10000 15000 Westbound N(t) N(t) v0t, v0 = 11.27mph Eastbound 7:30am 8:00am 8:30am v N(t) 0t, v0 = 24.23mph 7:30am 8:00am 8:30am -500 0 500 1500 N(t)-v0t Pre-congestion Congestion “Active” queuing
  18. 18. • Pre-congestion, active queuing associated with lower PM2.5 concentrations • During congestion, active queuing associated with raised PM2.5 concentrations Inactive Active 6 7 8 9 10 11 12 Pre-congestion mg/m3 Queuing Congestion Inactive Active mg/m3 Condition 18 CONGESTION EFFECT ON PM2.5 0.35% decrease 10.8% increase
  19. 19. REGRESSION FINDINGS 19 Vehicles passing when wind blows across roadway towards monitoring station – Current and previous observations Relative humidity increases Congestion worsens Wind blows from the background as vehicles pass Wind speed increases 푅↑2  .6589 푅↓푎푑푗↑2  .6533 PM2.5 concentrations
  20. 20. VEHICLE SEMI-ELASTICITIES 20 Variable… …lagged… …changed PM2.5 by… …per… Occupancy 0 sec .05% % occupancy increase When wind was blowing across roadway towards monitoring sta<on: EB Passenger Vehicle (80, 110, 200) sec (.49%, .46%, .45%) Addi<onal vehicle EB Heavy Vehicle 0 sec 2.45% Addi<onal vehicle When wind was blowing from background neighborhoods: WB Passenger Vehicle 0 sec -­‐.40% Addi<onal vehicle
  21. 21. CONCLUSIONS Exposure to Roadside Fine Particulate Matter 21 Data Availability Platooning Congestion Model Calibration Policy Implications to Minimize Exposure
  22. 22. Moore, A.; Figliozzi, M.; Bigazzi, A., Modeling the Impact of Traffic Conditions on the Variability of Mid-block Roadside Fine Particulate Matter Concentrations on an Urban Arterial. Proceedings of the 93rd Meeting of the Transportation Research Board, January 2014. Paper #14-4970. QUESTIONS? Adam Moore adam.moore@pdx.edu Dr. Miguel Figliozzi Alexander Bigazzi Acknowledgements
  23. 23. REGRESSION MODEL 23 Dependent variable: ln(PM2.5) R2 (R2 adjusted) .6589 (.6533) Residual standard error .06138 on 600 degrees of freedom AICa –1663.29 Variable Lag Coefficient SE p Unit increase effects on dependent variable (Semi-elasticity) Intercept 0 .63261 .05693 <.001 ln(PM2.5) 1 .63829 .03013 <.001 Traffic Conditions WB Occupancy 0 .00046 .00013 <.001 Increase by .05% per percentage point occupancy increase Meteorological Conditions Relative Humidity 0 .00138 .00020 <.001 Increase by .14% per percentage point RH increase Wind Speed 1 –.01274 .00368 .001 Decrease by 1.27% per 1m/s increase 2 .00917 .00368 .013 Increase by .92% per 1m/s increase Vehicle Volume × Wind Wind towards monitoring station EB Passenger Veh 8 .00488 .00214 .023 Increase by .49% per additional vehicle 11 .00455 .00215 .035 Increase by .46% per additional vehicle 20 .00448 .00214 .037 Increase by .45% per additional vehicle EB Heavy Veh 0 .02418 .01075 .025 Increase by 2.45% per additional vehicle Wind away from monitoring station WB Passenger Veh 0 –.00400 .00144 .006 Decrease by .40% per additional vehicle aAkaike Information Criterion 1

×