Exploring the impact of road surface conditions on truck fleet fuel consumption through Big Data

The thesis presents a novel approach for estimating the impact of road roughness and macro-texture on truck fleet fuel consumption based on Big Data. An extensive literature review was carried out to provide a comprehensive background for the study. Objectives of the study are defined as, primarily, to reduce the uncertainties left by previous studies, which based on experimental data (likely not be representative of real driving conditions) claimed that road roughness and macro-texture could affect the fuel consumption of road vehicles. In fact, this may represent an opportunity for road agencies that, in the light of these results, may wish to review the current maintenance strategies of road pavements to save significant direct and indirect costs for the society. Therefore, instead of carrying out new experiments, by exploring opportunities in the large amount of real-world data available in England, from truck fleet managers, road agencies and weather institutions, the method introduced in the present study promises to be able to provide estimates representative of real driving conditions. The method also differs from those presented in the past for its characteristic repeatability and adaptability to new situations. That includes adaptation of the model to different vehicles and to countries other than England, all without performing any additional field tests. This makes the introduced methodology unique. Advanced statistics and machine learning (ML) techniques have been used to analyse the data. In particular, a multiple linear regression (LR), a support vector regression (SVR), a random forest (RF) and an artificial neural network (ANN) model, including the effect of road roughness and macro-texture as longitudinal profile variance (LPV) and sensor-measured texture depth (SMTD) respectively, have been developed for light, medium and heavy trucks driving along motorways and A roads in England. A parametric analysis was then used to interpret the obtained results and quantify the impact that each of the considered variables, including LPV and SMTD, have on the fuel consumption of the considered truck types. Results show that, although the present study confirms that road surface characteristics, such as roughness and macrotexture, can affect the fuel consumption of trucks, due to the low quality of the data available, that is currently difficult to quantify. A comparison of the results obtained with the findings of studies conducted in the past, shows that there is some match in the order of magnitude of the estimates made, but this is not always the case. For instance, the impact of road roughness, measured as LPV at 3, 10 and 30 metres wavelength, was estimated to be between -2.91% and +6.27% for light trucks, between -8.56% and +3.97% for medium trucks and between -6.55% and +6.28% for heavy trucks, while the effect of macrotexture, measured as SMTD, was estimated to be between +1.04% and +1.21% for light trucks, between -1.21% and +5.98% for medium trucks and between -2.33% and +7.84% for heavy trucks. Variance among the obtained estimates is high and therefore, although results of the present study seem to be promising, it is fair to say that, at the current state of technology, while this approach is feasible, it is difficult to estimate the effect of road surface conditions on truck fleet fuel consumption using the Big Data approach, and further investigation is required to optimise the methodology. Future work will have to consider a wider range of road conditions, increased vehicle reporting frequencies, vehicle speed and vehicle types, including cars. This will extend the applicability of the study and is necessary before estimation of the impact of road surface characteristics on vehicle fuel consumption can be considered reliable and ready to be implemented in the LCA analysis and maintenance programming of road pavements, to support decision making at strategic level

A machine learning approach for the estimation of fuel consumption related to road pavement rolling resistance for large fleets of trucks

There remains a level of uncertainty concerning the methodological assumptions and parameters to consider in the estimation of road vehicle fuel consumption due to the condition of road pavements. 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. This study presents an innovative approach, based on the application of the Boruta algorithm (BA) and neural networks (NN), for the assessment and calculation of the fuel consumption of a large fleet of truck, which can be used to estimate the use phase emissions of road pavements. The study shows that neural networks are suitable to analyse the large quantities of data, coming from fleet and road asset management databases, effectively and that the developed NN model is able to estimate the impact of rolling resistance-related parameters (pavement roughness and macrotexture) on truck fuel consumption

Comparison of truck fuel consumption measurements with results of existing models and implications for road pavement LCA

Life Cycle Assessment (LCA) is increasingly used to evaluate the impact of all lifecycle phases of road pavements on the environment. From the late ‘90s, this technique has continuously evolved and improved, however, there are still limitations and uncertainties in the framework. In this regard, Santero et al (2011) showed that gaps still exist in the road pavement LCA methodology. More recently, Trupia et al (2016) highlighted how existing models of the impact of the road pavement condition on vehicle rolling resistance and hence, fuel consumption, can lead to very different results. This study presents a comparison between real measurements of truck fuel consumption from fleet manager’s databases, and results of existing pavement models, MIRAVEC, a model recently developed within an ERA-NET ROAD action, funded by the 6th framework programme of the EU, and HDM-4, one of the most widely used models for estimating vehicle operating costs in road asset management. The paper shows how far results of the considered models can be from reality and opens a discussion of the implications of these differences on pavement LCA and strategic decisions of managers of the road infrastructure

A big data approach to assess the influence of road pavement condition on truck fleet fuel consumption

In Europe, the road network is the most extensive and valuable infrastructure asset. In England, for example, its value has been estimated at around £344 billion and every year the government spends approximately £4 billion on highway maintenance (House of Commons, 2011). Fuel efficiency depends on a wide range of factors, including vehicle characteristics, road geometry, driving pattern and pavement condition. The latter has been addressed, in the past, by many studies showing that a smoother pavement improves vehicle fuel efficiency. A recent study estimated that road roughness affects around 5% of fuel consumption (Zaabar & Chatti, 2010). However, previous studies were based on experiments using few instrumented vehicles, tested under controlled conditions (e.g. steady speed, no gradient etc.) on selected test sections. For this reason, the impact of pavement condition on vehicle fleet fuel economy, under real driving conditions, at network level still remains to be verified. A 2% improvement in fuel efficiency would mean that up to about 720 million liters of fuel (~£1 billion) could be saved every year in the UK. It means that maintaining roads in better condition could lead to cost savings and reduction of greenhouse gas emissions. Modern trucks use many sensors, installed as standard, to measure data on a wide range of parameters including fuel consumption. This data is mostly used to inform fleet managers about maintenance and driver training requirements. In the present work, a ‘Big Data’ approach is used to estimate the impact of road surface conditions on truck fleet fuel economy for many trucks along a motorway in England. Assessing the impact of pavement conditions on fuel consumption at truck fleet and road network level would be useful for road authorities, helping them prioritize maintenance and design decisions

Route level analysis of road pavement surface condition and truck fleet fuel consumption

Experimental studies have estimated the impact of road surface conditions on vehicle fuel consumption to be up to 5% (Beuving et al., 2004). Similar results have been published by Zaabar and Chatti (2010). However, this was established testing a limited number of vehicles under carefully controlled conditions including, for example, steady speed or coast down and no gradient, amongst others. This paper describes a new “Big Data” approach to validate these estimates at truck fleet and route level, for a motorway in the UK. Modern trucks are fitted with many sensors, used to inform truck fleet managers about vehicle operation including fuel consumption. The same measurements together with data regarding pavement conditions can be used to assess the impact of road surface conditions on fuel economy. They are field data collected for thousands of trucks every day, year on year, across the entire network in the UK. This paper describes the data analysis developed and the initial results on the impact of road surface condition on fuel consumption for journeys of 157 trucks over 42.6km of motorway, over a time period of one year. Validation of the relationship between road pavement surface condition and vehicle fuel consumption will increase confidence in results of LCA analyses including the use phase

A big data approach for investigating the performance of road infrastructure

“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

A big data approach for investigating the performance of road infrastructure

“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

The Natural Compound Climacostol as a Prodrug Strategy Based on pH Activation for Efficient Delivery of Cytotoxic Small Agents

We synthesized and characterized MOMO as a new small molecule analog of the cytotoxic natural product climacostol efficiently activated in mild extracellular acidosis. The synthesis of MOMO had a key step in the Wittig olefination for the construction of the carbon-carbon double bond in the alkenyl moiety of climacostol. The possibility of obtaining the target (Z)-alkenyl MOMO derivative in very good yield and without presence of the less active (E)-diastereomer was favored from the methoxymethyl ether (MOM)-protecting group of hydroxyl functions in aromatic ring of climacostol aldehyde intermediate. Of interest, the easy removal of MOM-protecting group in a weakly acidic environment allowed us to obtain a great quantity of climacostol in biologically active (Z)-configuration. Results obtained in free-living ciliates that share the same micro-environment of the climacostol natural producer Climacostomum virens demonstrated that MOMO is well-tolerated in a physiological environment, while its cytotoxicity is rapidly and efficiently triggered at pH 6.3. In addition, the cytostatic vs. cytotoxic effects of acidified-MOMO can be modulated in a dose-dependent manner. In mouse melanoma cells, MOMO displayed a marked pH-sensitivity since its cytotoxic and apoptotic effects become evident only in mild extracellular acidosis. Data also suggested MOMO being preferentially activated in the unique extra-acidic microenvironment that characterizes tumoural cells. Finally, the use of the model organism Drosophila melanogaster fed with an acidic diet supported the efficient activity and oral delivery of MOMOmolecule in vivo.MOMO affected oviposition ofmating adults and larvae eclosion. Reduced survival of flies was due to lethality during the larval stages while emerging larvae retained their ability to develop into adults. Interestingly, the gut of eclosed larvae exhibited an extended damage (cell death by apoptosis) and the brain tissue was also affected (reduced mitosis), demonstrating that orally activated MOMO efficiently targets different tissues of the developing fly. These results provided a proof-of-concept study on the pHdependence of MOMO effects. In this respect, MOM-protection emerges as a potential prodrug strategy which deserves to be further investigated for the generation of efficient pH-sensitive small organic molecules as pharmacologically active cytotoxic compounds

The successful introduction of an adapted form of the mini extra corporeal circulation used for cardiac surgery in an obese patient

Obese patients undergoing cardiac surgery have been shown to have a high risk of developing postoperative complications, specifically: increased length of hospital stay, readmission to intensive care unit, acute renal failure, deep sternal wound infections and new onset of atrial fibrillation

Acute shock efficacy of the subcutaneous implantable cardioverter-defibrillator according to the implantation technique

Background: The traditional technique for subcutaneous implantable cardioverter defibrillator (S-ICD) implantation involves three incisions and a subcutaneous (SC) pocket. An intermuscular (IM) 2-incision technique has been recently adopted. Aims: We assessed acute defibrillation efficacy (DE) of S-ICD (DE ≤65 J) according to the implantation technique. Methods: We analyzed consecutive patients who underwent S-ICD implantation and DE testing at 53 Italian centers. Regression analysis was used to determine the association between DFT and implantation technique. Results: A total of 805 patients were enrolled. Four groups were assessed: IM + 2 incisions (n = 546), SC + 2 incisions (n = 133), SC + 3 incisions (n = 111), and IM + 3 incisions (n = 15). DE was ≤65 J in 782 (97.1%) patients. Patients with DE ≤65 J showed a trend towards lower body mass index (25.1 vs. 26.5; p = .12), were less frequently on antiarrhythmic drugs (13% vs. 26%; p = .06) and more commonly underwent implantation with the 2-incision technique (85% vs. 70%; p = .04). The IM + 2-incision technique showed the lowest defibrillation failure rate (2.2%) and shock impedance (66 Ohm, interquartile range: 57-77). On multivariate analysis, the 2-incision technique was associated with a lower incidence of shock failure (hazard ratio: 0.305; 95% confidence interval: 0.102-0.907; p = .033). Shock impedance was lower with the IM than with the SC approach (66 vs. 70 Ohm p = .002) and with the 2-incision than the 3-incision technique (67 vs. 72 Ohm; p = .006). Conclusions: In a large population of S-ICD patients, we observed a high defibrillation success rate. The IM + 2-incision technique provides lower shock impedance and a higher likelihood of successful defibrillation