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Transcript of Adam moore
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
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
BACKGROUND
Exposure to Air Pollution on Roadways
Vehicle Public Transportation Bicyclist/Pedestrian Public Transportation Bicyclist/Pedestrian
What factors affect exposure to air pollution?
3
Vehicle Activity
Built Environment
Meteorological Conditions
Very High Resolution Data Collection
Vehicle Activity
Meteorological Conditions
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
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
Intersection
“Mid-block”
May 1, 2013 7:00-9:00am
N
MID-BLOCK STUDY SITE
7
Parking Lot
Parking Lot
Powell City Park
Monitoring Location
SE 24th Ave
N
(looking south)
EQUIPMENT
8
9
Fine Particulate Matter (PM2.5)
TSI DustTrak DRX 8533
EQUIPMENT
photo: www.tsi.com
10
Temperature Relative Humidity
Onset HOBO U12
EQUIPMENT
photo: www.onsetcomp.com
Wind Speed Wind Direction
RM Young Ultrasonic Anemometer 81000
photo: www.youngusa.com
11
Video Reference
CountingCars CountCam System
EQUIPMENT
photo: www.countingcars.com
12
Vehicle Volume, Speed, Classification,
Lane Occupancy
Wavetronix SmartSensor HD
EQUIPMENT
Veh
icle
s/m
in
1030
502:00am 7:00am 12:00pm 5:00pm 10:00pm
1030
50
Spe
ed (m
ph)
020
4060
Occ
upan
cy (%
)
2:00am 7:00am 12:00pm 5:00pm 10:00pm
Westbound Eastbound
TRAFFIC ACTIVITY
13
NEARBY SIGNALS
14
123 sec
125 sec
Can we see vehicle platooning?
map: google.com/maps
VEHICLE PLATOONING
15
One lag = 10 seconds
-0.2
0.0
0.2
0.4
Westbound
ACF
Eastbound5 10 15 20
5 10 15 20Lag
-0.2
0.0
0.2
0.4
PACF
130 seconds
Autocorrelation Function
Partial Autocorrelation
Function
PLATOONING EFFECT ON PM2.5
16
-0.1
00.
000.
10
Wes
tbou
nd C
CF
-20 -10 0 10 20
Lag
-0.1
00.
000.
10
East
boun
d CC
F
• 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
EXAMINING CONGESTION
17
050
0010
000
1500
0
Westbound
N(t) N(t)
v0t, v0 = 11.27mph
Eastbound7:30am 8:00am 8:30am
N(t)v0t, v0 = 24.23mph
7:30am 8:00am 8:30am
-500
050
015
00
N(t)-
v 0t
Pre-congestion Congestion
“Active” queuing
Inactive Active
67
89
1011
12
Pre-congestion
mg/m
3
Queuing
Inactive Active
Congestion
mg/m
3
Condition
• Pre-congestion, active queuing associated with lower PM2.5 concentrations
• During congestion, active queuing associated with raised PM2.5 concentrations
18
CONGESTION EFFECT ON PM2.5
0.35% decrease
10.8% increase
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
𝑅↑2 .6589 𝑅↓𝑎𝑑𝑗↑2 .6533
PM2.
5 c
on
ce
ntr
atio
ns
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
CONCLUSIONS
Exposure to Roadside Fine Particulate Matter
21
Data Availability
Congestion Platooning
Model Calibration
Policy Implications to Minimize Exposure
QUESTIONS? Adam Moore [email protected] Dr. Miguel Figliozzi Alexander Bigazzi
Acknowledgements
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.
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