1. Vermelding onderdeel organisatie February 16, 2016 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

2. February 16, 20162 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

3. February 16, 20163 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 reality We have plenty of loop detector data Possibility to extract microscopic information from them

4. Estimate traffic state using adaptive smoothing method 4 Methodology Treiber et al.,2002; Van Lint et al.,2009.

5. February 16, 20165 Methodology 2 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

6. February 16, 20166 Trajectories and Acceleration 1. Discrete output of filter, i.e. 100m*10s 2. Vehicle speed in each cell is a function of speeds at spatial cell boundaries 3. Reconstruct trajectory by solving:dx/dt=v 4. Acceleration:

7. February 16, 20167 Validation of acceleration estimation Simulation 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

8. February 16, 20168 Estimated v, Detector spacing=500m Estimated a (m/s 2 ) Detector spacing=1000m Estimated v (m/s) Detector spacing=1000m Ground truth a (m/s 2 )Ground truth v (m/s)

9. February 16, 20169 Estimated v, Detector spacing=500m Estimated a (m/s 2 ) Detector spacing=500m Estimated v (m/s) Detector spacing=500m Ground truth a (m/s 2 )Ground truth v (m/s)

10. February 16, 201610 Application Assessing 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

11. February 16, 201611 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

12. February 16, 201612 Results IndicatorsBeforeAfterChange Average Flow (veh/h) 476149915% Average Speed (km/h) 91.291.70.6% Average acceleration (m/s 2 ) 0.0230.022-4% Total Fuel Consumption (l) 10820111783% Total NOx emission (g) 35092366825% Benefits of SPECIALIST on total fuel consumptions and NOx emissions might be compensated by the increase of demand.

13. February 16, 201613 Average fuel consumption rate decreased. Clear benefits of average fuel consumption rate during congestion from 8 to 9 am. Fuel consumption rate (liter/100km/veh) Speed (km/h)

14. February 16, 201614 Average NOx emission rate per vehicle increased by 5% after implementing SPECIALIST. NOx rate (mg/s/veh) Speed (km/h)

15. February 16, 201615 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