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Reducing Uncertainty in Structural Safety
Special Session SS6
Ghent, Belgium
28-31 October 2018
A machine learning
approach for the estimation
of fuel consumption related to
road pavement rolling res.
for large fleets ...
Outline HORIZON 2020
 Introduction
 What does impact fuel consumption?
 In terms of money
 A new approach
 Previous s...
Introduction HORIZON 2020
In UK, the road infrastructure is
the most extensive and valuable
asset (House of Commons, 2011)...
What does fuel consumption depends on? HORIZON 2020
In terms of money HORIZON 2020
With accurate fuel consumption estimates and a review of the
current road maintenance strat...
Previous studies HORIZON 2020
What happens instead at the network level under real driving
conditions?
 Experimental appr...
Big Data HORIZON 2020
(SAE J1939 – data from sensors installed
on modern trucks inform fleet managers)
(HAPMS – (Highways ...
A UK case study
~ 300 km of motorway
Considering a week in October ‘16
473 medium trucks
5,423 records
asphalt and concret...
Which variables to consider? HORIZON 2020
From 56variables
initially available
HORIZON 2020
14variables selected
Which variables to consider?
BA (Boruta Algorithm): based on
the theory of decision tree...
Artificial Neural Network (ANN) HORIZON 2020
Grid-search method and cross-validation can be used to
define the optimal str...
Artificial Neural Network (ANN)
Avg. of 10-fold cv
R2 0.88
RMSE 4.02 l/100km
MAE 2.59 l/100km
Measured FC Estimated FC Err...
Parametric analysis HORIZON 2020
Perrotta F., Parry T., Neves L.C., Mesgarpour M., Benbow E. and Viner H., A big
data appr...
Conclusions HORIZON 2020
 BA demonstrated to be able to identify significant variables out of the
large datasets quickly ...
What is next? HORIZON 2020
The answer is in data!
Questions?
Thank you for your
attention!
esr13truss.
blogspot.co.uk
This project has received funding from the European Un...
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"A machine learning approach for the estimation of fuel consumption related to road pavement rolling resitance for large fleets of trucks" presented at IALCCE2018

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Although vehicles emissions have a very significant impact on CO2 emissions, there remains a level of uncertainty concerning the methodological assumptions and parameters to consider in the calculation of greenhouse gas (GHG) emissions coming from the use phase of road pavements (Trupia et al 2016). In fact, recent studies highlighted how existing models can lead to very different results and that because of this, they are not fully ready to be implemented as standard in the life-cycle assessment (LCA) framework (Santero et al 2011; Trupia et al 2016).
This study presents an innovative approach, based on the application of Machine Learning to ‘Big Data’, for the calculation of the use phase emissions of road pavements due to truck fleet fuel consumption. The study shows that the Machine Learning regression technique is suitable to analyse the large quantities of data, coming from fleet and road asset management databases effectively, assessing and estimating the impact of rolling resistance-related parameters (pavement roughness and macrotexture measurements) on the use phase in road pavement LCA.

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"A machine learning approach for the estimation of fuel consumption related to road pavement rolling resitance for large fleets of trucks" presented at IALCCE2018

  1. 1. Reducing Uncertainty in Structural Safety Special Session SS6 Ghent, Belgium 28-31 October 2018
  2. 2. A machine learning approach for the estimation of fuel consumption related to road pavement rolling res. for large fleets of trucks Federico Perrotta, Tony Parry, Luis C. Neves, Mohammad Mesgarpour NTEC Nottingham Transportation Engineering Centre HORIZON 2020 29/10/2018 – IALCCE 2018, Ghent, Belgium
  3. 3. Outline HORIZON 2020  Introduction  What does impact fuel consumption?  In terms of money  A new approach  Previous studies  Big Data  Modelling  A UK case study  Which variables to consider?  Artificial Neural Network (ANN)  Parametric analysis  Conclusions
  4. 4. Introduction HORIZON 2020 In UK, the road infrastructure is the most extensive and valuable asset (House of Commons, 2011):  ~ 295,000 km;  ~ £344 billion;  ~ £4 billion per year for maintenance and repair. “Ensuring a good state of the road infrastructure is critical for our economy and society.” - European Parliament, 2014 However, due to the use of oil derivative fuels, currently, the road transport sector is also a major contributor to the production of greenhouse gases.
  5. 5. What does fuel consumption depends on? HORIZON 2020
  6. 6. In terms of money HORIZON 2020 With accurate fuel consumption estimates and a review of the current road maintenance strategies we can actually make a difference! In the USA every year: 200 billion gal of motor fuel are consumed for e.g. 2% that would be between $5 and $10 billion to re-invest in maintenance* Previous studies (e.g. Chatti and Zaabar 2012) said that road roughness can impact fuel consumption by up to 5%. * Considering the current cost of Diesel
  7. 7. Previous studies HORIZON 2020 What happens instead at the network level under real driving conditions?  Experimental approach;  Limited number of vehicles;  Selected road segments;  Under controlled conditions (e.g. const. speed). ?
  8. 8. Big Data HORIZON 2020 (SAE J1939 – data from sensors installed on modern trucks inform fleet managers) (HAPMS – (Highways Agency Pavement Management System) database containing information for road managers)
  9. 9. A UK case study ~ 300 km of motorway Considering a week in October ‘16 473 medium trucks 5,423 records asphalt and concrete M1 M1 M18 Euro 6 with 4 axles 1 minute or 1 mile Fuel used in 0.001 l
  10. 10. Which variables to consider? HORIZON 2020 From 56variables initially available
  11. 11. HORIZON 2020 14variables selected Which variables to consider? BA (Boruta Algorithm): based on the theory of decision trees and random forests:
  12. 12. Artificial Neural Network (ANN) HORIZON 2020 Grid-search method and cross-validation can be used to define the optimal structure of the network. Using the resilient backpropagation algorithm with backtracking (rprop+):
  13. 13. Artificial Neural Network (ANN) Avg. of 10-fold cv R2 0.88 RMSE 4.02 l/100km MAE 2.59 l/100km Measured FC Estimated FC Error 24.73 l/100km 24.35 l/100km -1.5% Using: 3,904 records for training 1,301 for validation 218 to test the model For the test set: On average:
  14. 14. Parametric analysis HORIZON 2020 Perrotta F., Parry T., Neves L.C., Mesgarpour M., Benbow E. and Viner H., A big data approach for investigating the performance of road infrastructure. Civil Engineering Research in Ireland (CERI) 2018. Dublin, Ireland.
  15. 15. Conclusions HORIZON 2020  BA demonstrated to be able to identify significant variables out of the large datasets quickly and effectively;  NN demonstrated to be able to estimate the fuel consumption of the considered fleet of trucks accurately;  Other studies (Perrotta et al. 2018) showed how a parametric analysis can be used to estimate impacts of each of the variables included in the developed model;  Once fuel consumption is known, it is possible to estimate eqCO2 or quantity of GHGs produced using the emissions factors published by EPA (2018) or other environmental agencies;  Overall it is possible to say that the ‘Big Data’ approach seems to be promising to estimate costs and environmental impact from the use- phase of the road infrastructure;  Further research should focus on more accurate data, a wider range of vehicles, different road materials, weather conditions, urban areas, etc. which will improve applicability of the study and reliability of the developed models.
  16. 16. What is next? HORIZON 2020 The answer is in data!
  17. 17. Questions? Thank you for your attention! esr13truss. blogspot.co.uk This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 642453 NTEC Nottingham Transportation Engineering Centre HORIZON 2020 trussitn.eu Tony.Parry@nottingham.ac.uk Federico.Perrotta@nottingham.ac.uk

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