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"Using truck sensors for road pavement performance investigation" presented at ESREL2017 by Federico Perrotta

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Abstract: Considering data from 260 articulated trucks, with ∼12900 cc Euro 6 engines driving along a motorway in England (M18), the study first shows how different approaches lead to the conclusion that road pavement surface conditions influence fuel consumption of the considered truck fleet. Then, a multiple linear regression for the prediction of fuel consumption was generated. The model shows that evenness and macrotexture can impact the truck fuel consumption by up to 3% and 5%, respectively. It is a significant impact which confirms that, although the available funding for pavement maintenance is limited, the importance of limiting GHG emissions, together with the economic benefits of reducing fuel consumption are reasons to improve road condition.

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"Using truck sensors for road pavement performance investigation" presented at ESREL2017 by Federico Perrotta

  1. 1. Using truck sensors for road pavement performance investigation Speaker: Federico Perrotta Federico Perrotta, Tony Parry and Luis C. Neves ESREL 2017, Portoroz, Slovenia | June 18-22, 2017 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
  2. 2. Introduction In England: £344 billion, value of the road infrastructure ~£4 billion, spent in maintenance (per year) ~36 million tons of fuel consumed (per year) ~25% of the whole energy demand in Europe (House of Commons, 2011; Haider et al. 2011) Road roughness can impact fuel economy by up to 5% (Zaabar & Chatti 2010)  significant cost savings;  reduction of GHG emissions. pavement LCA
  3. 3. Previous studies • Experimental approach; • Physical/mechanistic model; • Limited number of vehicles; • Selected road segments; • Under controlled conditions; • Do the results represent real driving? ?
  4. 4. Innovation … in real conditions …
  5. 5. Data Fleet managers constantly monitor the performance of trucks to take decisions about training of drivers and maintenance of vehicles (fuel usage, torque, revs, speed, GPS position, etc.) Road agencies monitor the condition of their network, annually, to make decisions about maintenance with respect to engineering condition and driver safety (geometry, roughness, texture, skid-resistance, etc.)
  6. 6. Methodology Data: • articulated trucks; • with Euro 6 engines; • at constant speed (using gear 12); • a motorway in England (M18, Doncaster); • Data are recorded every 1 min or 1 mile and represent a journey of a vehicle on the considered road; • includes the average roughness (LPV10) and macro- texture (SMTD) along the considered road segment.
  7. 7. Methodology Analysis: • Variable selection: Adjusted-R2; Aikake Information Criterion (AIC); Forward Variable Selection algorithm; And Lasso regression; • Quantification of the impacts: Multiple Linear Regression of the variables.
  8. 8. Results From 56variables available in total
  9. 9. Results FC = 0.14 GVW + 0.87 g% – 0.27 s + 0.02 LPV10 + 0.02 SMTD And from the Lasso regression: Also: Aikake Information Criterion (AIC) and Forward Variable Selection algorithms agree. Adjusted-R2
  10. 10. Results From 56variables available in total Only 5selected Weight Road gradient Speed Roughness (LPV10) Macro-texture (texture)
  11. 11. Results 1420 records from 260 trucks Correlation is ~82% (0.68 R2) More variables need to be considered? And, actually, is a linear model enough? What is considered: Weight Road gradient Speed Roughness (LPV10) Macro-texture (texture)
  12. 12. Discussion • Up to 3% impact of road roughness; • Up to 5% impact of road macrotexture; • The remaining fuel consumption is due to different causes not considered in the study… • What about non-linear relationships in the problem? Similar results to previous studies
  13. 13. What is next - Include more vehicle types; - Investigate a wider range of vehicle speed; - Include road conditions representative of the all network; - Include weather conditions (e.g. temperature, wind speed, etc.) - We are investigating more sophisticated statistics (e.g. Machine Learning) to detect non-linearity and obtain more precise and reliable estimates; - This will improve the predictions and reduce uncertainties in road pavement LCA studies.
  14. 14. esr13truss. blogspot.co.uk Thank you all for your attention! Questions? 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 Federico.Perrotta@nottingham.ac.uk

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