Vermelding onderdeel organisatie

May 4, 2023

1

Estimating Acceleration, Fuel Consumption and Emissions from

Macroscopic Traffic Flow Data

Meng Wang, Winnie Daamen, Serge Hoogendoorn, Bart van Arem

Department of Transport & Planning

May 4, 2023 2

Background• Public concerns on environment and health• Increasing efforts on improving

sustainability through Dynamic Traffic Management (DTM)

• Impacts of DTM on fuel consumption and emissions assessed through emission models

• Emission models:• Macroscopic, v, large network• Microscopic, v and a, link level

May 4, 2023 3

Problem

• Idea for solution:Estimate acceleration of traffic flow through loop detector data, serving as inputs for microscopic emission model

Lack of microscopic traffic data in realityWe have plenty of loop detector dataWe have plenty of loop detector dataPossibility to extract microscopic information from them

Estimate traffic state using adaptive smoothing method

4

Methodology

Treiber et al.,2002;

Van Lint et al.,2009.

May 4, 2023 5

Methodology2

Estimate traffic state using adaptive smoothing method

v(x,t) at any time and position

Take derivative of speed,we get a(x,t) at any time and position

Reconstruct trajectories and approximate acceleration

Calculate fuel consumption & emissions using VT-Micro

May 4, 2023 6

Trajectories and Acceleration1. Discrete output of filter,

i.e. 100m*10s2. Vehicle speed in each cell is a function of speeds at spatial cell boundaries 3. Reconstruct trajectory by solving: dx/dt=v4. Acceleration: exit entry

exit entry

v va

t t

Cell (i,j)

Space

Time

xi

Trajectory vehicle n

xi+2

xi+1

nexitt

nentryt

May 4, 2023 7

Validation of acceleration estimationSimulation experiment:

• 9.5 km Dutch freeway A13• Three-lane section with on-ramps and off-ramps• Afternoon peak 15.00-19.00• Simulated speed and acceleration of individual

vehicle as ground truth• Estimate speed and acceleration from loop

detector data with output gird of 100m*10s

The Hague Rotterdam

Delft-Noord Delft

Delft University of Technology

Delft-Zuid Petrol Station

Rotterdam Airport

Time (s)

Spa

ce(k

m)

Filtered Speed (m/s)

200 400 600 800 1000 1200 1400

1

2

3

4

5

6

7

8

9

0

5

10

15

20

25

30

35

May 4, 2023 8

Estimated v, Detector spacing=500m

Time (s)

Spa

ce(k

m)

Estimated Acceleration (m/s2)

200 400 600 800 1000 1200 1400

1

2

3

4

5

6

7

8

9

-3

-2

-1

0

1

2

3

Estimated a (m/s2)Detector

spacing=1000m

Time (s)

Spa

ce(k

m)

Ground Truth Acceleration (m/s2)

200 400 600 800 1000 1200 1400

1

2

3

4

5

6

7

8

9

-3

-2

-1

0

1

2

3

Time (s)

Spa

ce(k

m)

Ground Truth Speed (m/s)

200 400 600 800 1000 1200 1400

1

2

3

4

5

6

7

8

9

0

5

10

15

20

25

30

35

Estimated v (m/s)Detector

spacing=1000m

Ground truth a (m/s2)Ground truth v (m/s)

Time (s)

Spa

ce(k

m)

Estimated Acceleration (m/s2)

200 400 600 800 1000 1200 1400

1

2

3

4

5

6

7

8

9

-3

-2

-1

0

1

2

3

Time (s)

Spa

ce(k

m)

Filtered Speed (m/s)

200 400 600 800 1000 1200 1400

1

2

3

4

5

6

7

8

9

0

5

10

15

20

25

30

35

May 4, 2023 9

Estimated v, Detector spacing=500m

Estimated a (m/s2)Detector

spacing=500m

Time (s)

Spa

ce(k

m)

Ground Truth Acceleration (m/s2)

200 400 600 800 1000 1200 1400

1

2

3

4

5

6

7

8

9

-3

-2

-1

0

1

2

3

Time (s)

Spa

ce(k

m)

Ground Truth Speed (m/s)

200 400 600 800 1000 1200 1400

1

2

3

4

5

6

7

8

9

0

5

10

15

20

25

30

35

Estimated v (m/s)Detector

spacing=500m

Ground truth a (m/s2)Ground truth v (m/s)

May 4, 2023 10

ApplicationAssessing environmental impacts of a freeway control measure - SPECIALIST

• Control algorithm to reduce shockwaves on freeways

• Detects traffic states and predicts future evolution using Shockwave theory

• Resolves shockwaves by dynamic speed limits at different locations

• Field implementation on 14 km section of A12 from Sep. 2009 to Feb. 2010

May 4, 2023 11

Application - data set

• Double loop detectors with distance of 300 to 600m

• Morning peaks from 6.00 am to 11.00 am• Weekday data from January to May in 2006

as Before-SPECIALIST situation• Weekday data from September to December

in 2009 as After-SPECIALIST situation• Unusual congested days are excluded from

dataset

May 4, 2023 12

Results

Indicators Before After Change

Average Flow (veh/h) 4761 4991 5%Average Speed (km/h) 91.2 91.7 0.6%Average acceleration

(m/s2) 0.023 0.022 -4%

Total Fuel Consumption (l) 10820 11178 3%Total NOx emission (g) 35092 36682 5%Benefits of SPECIALIST on total fuel consumptions

and NOx emissions might be compensated by the increase of demand.

May 4, 2023 13

• Average fuel consumption rate decreased. • Clear benefits of average fuel consumption rate

during congestion from 8 to 9 am.

6 6.5 7 7.5 8 8.5 9 9.5 10 10.5 118.4

8.6

8.8

9

9.2

9.4

9.6

9.8

10

Time fo day

Fuel

rate

l/10

0km

/veh

Before-SPECIALISTAfter-SPECIALIST

Fuel consumption rate(liter/100km/veh)

6 6.5 7 7.5 8 8.5 9 9.5 10 10.5 1175

80

85

90

95

100

105

110

115

Time fo day

Spe

ed k

m/h

Before-SPECIALISTAfter-SPECIALIST

Speed(km/h)

6 6.5 7 7.5 8 8.5 9 9.5 10 10.5 114

5

6

7

8

9

10

11

12

13

Time fo day

NOx

rate

mg/

s/ve

h

Before-SPECIALISTAfter-SPECIALIST

May 4, 2023 14

• Average NOx emission rate per vehicle increased by 5% after implementing SPECIALIST.

NOx rate(mg/s/veh)

6 6.5 7 7.5 8 8.5 9 9.5 10 10.5 1175

80

85

90

95

100

105

110

115

Time fo day

Spe

ed k

m/h

Before-SPECIALISTAfter-SPECIALIST

Speed(km/h)

May 4, 2023 15

Summary and future research• A new method to estimate acceleration from loop detector

data• Provides a way to use microscopic emission model based

on macroscopic traffic data• Potential application includes assessing the impacts of

traffic control measures on sustainability at link level • Certain DTM measure may have different impact on

different indicators of fuel consumption and emissions

Future research• Improve acceleration estimation by using different data

source• Estimation of fuel consumption and emissions with

different emission models