1. The Transportation & Air Quality Research Group
TRAQ
Modelling GHG Emissions in the GTHA Using Link-Based Operating Mode Distributions as a Proxy for Driving Behaviour
An Wang, Christos Stogios, Yijun Gai, James Vaughan, Gozde Ozonder, SeungJae Lee, Daniel Posen , Eric J. Miller, Marianne Hatzopoulou

2. Research Objectives
Generate a lifecycle emission inventory for transportation in the GTHA
Incorporate “uncertainty” within emission inventory
Quantify the effects of electric vehicles on total emissions 2

3. Methods and data 3

4. Methods and data Based on MOVES and GREET with local inputs
Vehicle registration data from MTO
Various scenarios for electricity generation in Ontario
Hybrid approach using distributions of emission factors derived from AIMSUN and applied within EMME 4

5. Methods and data Based on MOVES and GREET with local inputs
Vehicle registration data from MTO
Various scenarios for electricity generation in Ontario
Hybrid approach using distributions of emission factors derived from AIMSUN and applied within EMME 5

6. MTO vehicle composition (2016)

7. Methods and data Based on MOVES and GREET with local inputs
Vehicle registration data from MTO
Various scenarios for electricity generation in Ontario
Hybrid approach using distributions of emission factors derived from AIMSUN and applied within EMME 7

8. Electricity generation scenarios
Ontario electricity generation mix obtained from the Independent Electricity System Operator (IESO)
Four electricity generation scenarios:
1. Current Ontario mix: 61% nuclear, 23.7% hydro, 8.4% gas/oil, 6.2% wind, 0.3% biofuel, 0.3% solar
2. All fossil mix: 100% natural gas
3. Only dispatchable source mix: 73% hydro, 26% gas/oil, and 1% biofuel
4. Solar and wind mix: 95.3% wind and 4.7% solar 8

9. Methods and data Based on MOVES and GREET with local inputs
Vehicle registration data from MTO
Various scenarios for electricity generation in Ontario
Hybrid approach using distributions of emission factors derived from AIMSUN and applied within EMME 9

10. Two types of EFs in MOVES
Motivation for hybrid approach
Two types of EFs in MOVES
By gram per unit time
Based on single operating mode;
Requires instantaneous speed to estimate emissions
Based on default driving cycles (built-in operating mode distribution);
Only requires average speed to estimate emissions
Various drive cycles can occur at the same average speed
By gram per unit distance
A distribution of emission factors for each average speed makes more sense 10

11. Proposed approach 11 Select n random roads to run microsimulation
Sample of roads includes arterials, ramps and freeways
For link i in each road type, generate an operating mode distribution and calculate total emissions 𝐸 𝑖 based on instantaneous speeds
Generate EF of link i: 𝐸𝐹 𝑖 = 𝐸 𝑖 𝑉𝑀𝑇 𝑖 , 𝑉𝑀𝑇 𝑖 refers to the vehicle miles travelled on link i
Generate distributions of EFs by average speed bins
Test area: City of Toronto
With instantaneous speed:
Link-level total emission;
Link-level EF;
Link average speed; 11

12. AM-Peak Distribution of EFs12

13. PM-Peak Distribution of EFs
No freeway data 13

14. Midday Distribution of EFs
No freeway data 14

15. Evening Distribution of EFs
No freeway data 15

16. Results 16

17. Daily GHG Emissions in GTHA
95% of total emissions 17

18. Comparison with Public Transit
Green bar illustrates the range of private vehicle emission intensities
Colored boxes illustrate the ranges of public transit emission intensities 18

19. Spatial distribution of emissionsAM
Midday 19

20. Daily Fuel-Cycle Stochastic Inventory
From single number to distribution
Daily Fuel-Cycle Emissions (gram) 20

21. Scenario 1: Current electricity mix
Base case
Scenario 1: Current electricity mix 21

22. Base case
Scenario 2: Natural Gas 22

23. Scenario 3: Dispatchable sources
Base case
Scenario 3: Dispatchable sources 23

24. Base case
Scenario 4: Renewables 24

25. BUT: Deep reductions in traffic emissions may not be achieved without large investments in public transit
Emission intensities for electric vehicles are:
12.8, 130.1, 37.2, and 2.8 g CO2eq/km for passenger cars
14.6, 149, 42.6, and 3.2 g CO2eq/km for passenger trucks w
with electricity mix 1 to 4 respectively
Current Ontario mix
All fossil mix: 100% natural gas
Only dispatchable source mix: 73% hydro, 26% gas/oil, and 1% biofuel
Solar and wind mix: 95.3% wind and 4.7% solar
Emission intensity for transit (right now with mostly transit buses which are diesel fuelled) is 18.5 g/PKT (daily average) 25

26. Further improvements 26

27. Limitations of previous approach
Distributions of emissions that we developed for each average speed should be refined
Std. Dev. of speed, delay, other measures?
There is no need to group links by average speed, why not a cluster approach? 27

28. 28 First step: hybrid simulation
Hybrid simulation is composed of two parts: full network mesoscopic simulation and sample area microsimulation.
(see next page) 28

29. Full network mesoscopic + sample microsimulation29

30. 30 Clustering is based on meso traffic variables: average speed,
standard deviation of speed,
average delay,
standard deviation of delay,
Road capacity
Segment density
Vehicle kilometers travelled
After finishing hybrid simulation, two parts of data will be used in the learning step: (see next page) 30

31. Clustering result:
This shows the cluster result. 31

32. The cross validation contains random process, so here in CLEVER, cross-validation will be repeated for 100 times. (next page) 32

33. Distributions of EFs of each cluster33

34. 34 In full network, mesoscopic data are available.
Assign a cluster to each segment in the full network
Apply representative EF and estimate total emissions 34

35. Downtown Toronto GHG estimation: Hybrid VS microscopic model
tons
Microscopic estimation: tons
Mesoscopic estimation: tons 35