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"A big data approach for investigating the performance of road infrastructure" presented at CERI2018 by Federico Perrotta

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“Using truck sensors for road pavement performance investigation” is a research project within TRUSS, an innovative training network funded from the EU under the Horizon 2020 programme. The project aims at assessing the impact of the condition of the road pavement unevenness and macrotexture, on the fuel consumption of trucks to reduce uncertainty in the framework of life-cycle assessment of road pavements. In the past, several studies claimed that a road pavement in poor condition can affect the fuel consumption of road vehicles. However, these conclusions are based just on tests performed on a selection of road segments using a few vehicles and this may not be representative of real conditions. That leaves uncertainty in the topic and it does not allow road mangers to review the current road maintenance strategies that could otherwise help in reducing costs and greenhouse gas emissions from the road transport industry. The project investigated an alternative approach that considers large quantities of data from standard sensors installed on trucks combined with information in the database of road agencies that includes measurements of the conditions of the road network. In particular, using advanced regression techniques, a fuel consumption model that can take into consideration these effects has been developed. The paper presents a summary of the findings of the project, it highlights implications for road asset management and the road maintenance strategies and discusses advantages and limitations of the approach used, pointing out possible improvements and future work.

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"A big data approach for investigating the performance of road infrastructure" presented at CERI2018 by Federico Perrotta

  1. 1. Workshop CERI, UCD, Dublin Wednesday 29th August 2018
  2. 2. Federico Perrotta, Tony Parry, Luis C. Neves, Mohammad Mesgarpour, Emma Benbow & Helen Viner A big data approach for investigating the performance of road infrastructure
  3. 3. Introduction In UK, the road infrastructure is the most extensive and valuable asset (House of Commons, 2011):  ~ 295,000 km;  ~ £344 billion;  ~ £4 million 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, roads do not fall on the head of anyone.” - G. Tebaldi, 2013 - and for this reason (?) we do not care much about performing maintenance on roads.
  4. 4. Introduction Due to underinvestment: Which:  Reduces safety (increasing risk of accidents);  Increases vehicle repair costs;  Increases vehicle fuel consumption (and thus GHG emissions). That is not sustainable!!!
  5. 5. Introduction Do poor conditions of the road infrastructure really affect vehicular fuel consumption? How much? 5% ? £££(E. Beuving et al. 2004) In consideration of large fleets or the whole daily traffic in UK (or worldwide) that corresponds to millions.
  6. 6. Previous studies  Experimental approach;  Limited number of vehicles;  Selected road segments;  Under controlled conditions (e.g. const. speed);  Do the results represent real driving? ?
  7. 7. Big Data SAE J1939 HAPMS
  8. 8. First phase  M18 (~ 80 km);  260 trucks;  1420 records;  1 week in October 2016;  Constant speed;  56 measured variables.  Data about trucks come from Microlise Ltd;  Data about road surface conditions come from Highways England (provided by TRL).
  9. 9. Initial results FC = 62.42 + 0.00024 GVW + 14.84 g% – 0.57 s + 0.26 LPV10 + 0.87 SMTD RMSE MAE R2 7.80 5.55 0.68 5 out of 56 variables selected Using:  p-values;  Adjusted-R2;  AIC  and LASSO.
  10. 10. Second phase  Data about trucks come from Microlise Ltd;  Data about road surface conditions come from Highways England (provided by TRL).  M1 and M18 (~ 300 km);  1110 trucks;  14,281 records;  2 weeks in October 2016;  Constant speed;  56 measured variables.
  11. 11. Machine learning Model RMSE MAE R2 Linear reg. 6.02 4.42 0.76 ANN 4.88 3.46 0.85 14 out of 56 variables are considered
  12. 12. Parametric analysis  Fuel consumption increases with GVW: reasonable but not linear…  Fuel consumption increases with road gradient: but it doesn’t if we go downhill…  Fuel consumption increases if the engine works at high revolutions: that tells us about the effect of driver behaviour;  Fuel consumption increases with poor road conditions: in line with what found by previous studies but…
  13. 13. Conclusions  Results show great potential for the Big Data approach to be used in Civil Engineering applications;  In the first phase of the study the impact of road surface characteristics on vehicle fuel consumption has been estimated to be up to 4% which is in line with results of previous studies and gave us confidence in the use of the data available;  In the second phase of the study it was seen that ANNs can outperform traditional methods to estimate the impact of road surface characteristics on vehicle fuel consumption reliably;  The study shows also that machine learning techniques are not just black boxes as there are methods to interpret this type of models which can give sometimes more accurate results;  Currently, we are in a third phase of the study in which validation of results (for a wider range of vehicles, including weather conditions and analysis of a road characteristics representative of the whole infrastructure in UK) will improve applicability of the study.
  14. 14. The TRUSS ITN project (http://trussitn.eu) has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 642453 Thanks for your attention Federico.Perrotta1@nottingham.ac.uk Tony.Parry@nottingham.ac.uk Luis.Neves@nottingham.ac.uk For further information, please, do not hesitate to contact us directly: Or visit: https://esr13truss.blogspot.com

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