Macroscopic Relationship between Network-wide Traffic Emissions ...

Macroscopic Relationship between Network-wide Traffic Emissions and Fundamental Properties of the Network

Symposium Celebrating 50 Years of Traffic Flow Theory Portland, Oregon, August 11 – 13, 2014

Eric J. Gonzales Assistant Professor, Civil and Environmental Engineering University of Massachusetts, Amherst [email protected]

Rooholamin (Amin) Shabihkhani Graduate Assistant, Civil and Environmental Engineering Rutgers, The State University of New Jersey [email protected]

Problem of Interest

2 Aug 12, 2014 – Shabihkhani, Gonzales

Electricity32%

Transportation28%

Industry 20%

Residential & Commercial

10%

Agriculture10%

(USEPA, 2013)

Traffic is a major source of air pollutant emissions.

Aggregated greenhouse gas emissions (CO2 equivalent) are of interest for designing networks and managing traffic.

How can macroscopic network-level traffic models be used for making aggregated emissions estimates?

Modeling Traffic and Emissions


EXISTING MODELS

Traffic Emissions

Microscopic Detailed movements of individual vehicles (includes micro-simulation)

Detailed emissions estimates based on second-by-second speeds; requires extensive data

Mesoscopic Traffic streams, accounting for some characteristics (such as heterogeneous driver behavior)

Driving cycles estimated from aggregated inputs; requires speeds and number of stops

Macroscopic Aggregated network-wide traffic conditions; useful for analysis of large networks

Direct estimation of aggregated emissions; not sensitive to changes in driving cycles

Microscopic Emissions Models


EXISTING MODELS

Space

Time

Second-by-second vehicle trajectory data

•  Speed

•  Acceleration

are inputs for microscopic emissions models

•  VT-Micro (Rakha et al., 2000)

•  CMEM (Barth et al., 2000)

•  Project-level MOVES (USEPA, 2010)

Vehicle Trajectory

Driving Cycles and Traffic Flow Theory


EXISTING MODELS

Space

Time

Effective Idling

Analytical models for trajectories with instantaneous acceleration are consistent with aggregate dynamics of real traffic streams.

Driving Cycles and Traffic Flow Theory


EXISTING MODELS

Space

Time

Analytical models for trajectories with instantaneous acceleration are consistent with aggregate dynamics of real traffic streams.

Trajectories can be broken into components of a driving cycle

•  Cruising Time

•  Idling Time

•  Acceleration

•  Deceleration Stopping

Idling Decel.

Accel.

Proposed Model Framework


PROPOSED APPROACH

Network Properties

Demand

Aggregated Emissions Estimate

Cruising

Idling

Stops

Traffic Model

Emission Factors

Driving Cycle Components

(per VMT)

Macroscopic Traffic Model


PROPOSED APPROACH

Network Flow

Network Density

q

k

v

Macroscopic Fundamental Diagram (MFD) relates flow and density for a network based on the properties of the network and traffic.

(Daganzo and Geroliminis, 2008)

•  Free flow speed

•  Saturation Flow

•  Jam Density

•  Block Length

•  Signal Timings

Estimating the Driving Cycle


PROPOSED APPROACH

We want to use the macroscopic traffic state to estimate components of the driving cycle.

Start by considering the implications for idealized trajectories with instantaneous acceleration and deceleration:

Effective Cruising Time e↵ective Tc =1

vf

Effective Idling Time e↵ective Ti =1

v� 1

vf

vf free flow speed



PROPOSED APPROACH

We also need to estimate the number of times that each vehicle stops.

Start with simplest case of homogeneous network with no signal offset, assuming that vehicles stop once per signal cycle:

Number of Stops

signal cycle length

n =1

vC

C

Especially reasonable when effective red signal is longer than the time it takes to travers a block at free-flow speed.



PROPOSED APPROACH

Actual time cruising and idling can be estimated by adjusting the effective time estimates are based on the average duration of acceleration and deceleration associated with each stop.

Cruising Time

Idling Time

vf free-flow speed

Driving Cycle per Vehicle-Distance:

Tc =1

vf� ⌧

2n

Ti =1

v� 1

vf� ⌧

2n

Stops n =1

vCnetwork avg. speed

signal cycle length

avg. duration of accel./decel.

C

⌧

v

Estimating Network-wide Emissions


PROPOSED APPROACH

Aggregated emissions are calculated by multiplying components of the driving cycle by corresponding emissions factors:

Emissions per Vehicle-Distance E = ecTc + eiTi + esn

Estimating Network-wide Emissions


PROPOSED APPROACH

Aggregated emissions are calculated by multiplying components of the driving cycle by corresponding emissions factors:

Emission factors depend on the free flow speed and average from a sample of accelerations and decelerations.

Emissions per Vehicle-Distance E = ecTc + eiTi + esn

vf = 53 km/hr

cruising

idling

vehicle stop

ec = 2.187 gCO2eq/sec

ei = 0.881 gCO2eq/sec

es = 48.876 gCO2eq/stop

⌧ = 22 sec

Using MOVES

(USEPA, 2010; Shabihkhani and Gonzales, 2013)

Analytical Model vs. Simulation


EVALUATION

Simulation is used for comparison with conventional, detailed, microscopic emission analysis.

A simple ring is used to represent a homogeneous network.

vf = 53 km/hr free-flow speed s = 1900 veh/lane-hr capacity kj = 200 veh/lane-km jam density

G/C = 0.5 green ratio C = 60 sec signal cycle length ` = 0.30 km block length

Analytical Model vs. Simulation Network Flow


EVALUATION

0

200

400

600

800

1000

0 50 100 150 200

Network Density, (veh/lane-km) k

Net

wor

k Fl

ow,

(ve

h/la

ne-s

ec)

Analytical Model vs. Simulation Number of Stops


EVALUATION


Stop

s,

(sto

ps/v

eh-k

m)

0

10

20

30

40

50

60

0 50 100 150 200

Analytical Estimate Simulation

Analytical Model vs. Simulation Idling Time


EVALUATION


Idlin

g Ti

me,

(sec

/veh

-km

)

0

500

1000

1500

2000

2500

3000

0 50 100 150 200


Analytical Model vs. Simulation Greenhouse Gas Emissions


EVALUATION


Emis

sion

s,

(g

CO2e

q/ve

h-km

)

0

1000

2000

3000

4000

0 50 100 150 200


Changing the Green Ratio,


EVALUATION

G/C

0

250

500

750

1000

1250

1500

0 50 100 150 200

Net

wor

k Fl

ow,

(ve

h/la

ne-s

ec)


0

500

1000

1500

2000

2500

3000

0 50 100 150 200


Emis

sion

s,

(g

CO2e

q/ve

h-km

)

G/C = 0.50

G/C = 0.25

G/C = 0.75

Accuracy of the Macroscopic Approach


EVALUATION

Percent Error of Analytical Model Relative to Simulation

Insights


CONCLUSIONS

The MFD embodies useful information for estimating driving cycles without the need for extensive trajectory analysis using conventional microscopic methods.

Useful to estimate greenhouse gas emissions, which matter in aggregate, for analysis or monitoring of network traffic at the aggregate level.

Current work is to account for signal offsets and application to more realistic grid networks.

Model is robust across changes in network properties (green ratio, cycle length, block length), except at the most congested traffic states.

Questions

23

Macroscopic Relationship between Network-wide Emissions from Traffic and Fundamental Properties of the Network Symposium Celebrating 50 Years of Traffic Flow Theory

Rooholamin (Amin) Shabihkhani [email protected]

Eric J. Gonzales [email protected]

Aug 12, 2014 – Shabihkhani, Gonzales

Macroscopic Relationship between Network-wide Traffic Emissions ...

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