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 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

Verification of the HDM-4 fuel consumption model using a Big data approach: a UK case study

This paper presents an assessment of the accuracy of the HDM-4 fuel consumption model calibrated for the United Kingdom and evaluates the need for further calibration of the model. The study focuses on HGVs and compares estimates made by HDM-4 to measurements from a large fleet of vehicles driving on motorways in England. The data was obtained from the telematic database of truck fleet managers (SAE J1939) and includes three types of HGVs: light, medium and heavy trucks. Some 19,991 records from 1,645 trucks are available in total. These represent records of trucks driving at constant speed along part of the M1 and the M18, two motorways in England

2018 Research & Innovation Day Program

A one day showcase of applied research, social innovation, scholarship projects and activities.https://first.fanshawec.ca/cri_cripublications/1005/thumbnail.jp

The effects of dissection-room experiences and related coping strategies among Hungarian medical students

Background: Students get their first experiences of dissecting human cadavers in the practical classes of anatomy and pathology courses, core components of medical education. These experiences form an important part of the process of becoming a doctor, but bring with them a special set of problems. Methods: Quantitative, national survey (n = 733) among medical students, measured reactions to dissection experiences and used a new measuring instrument to determine the possible factors of coping. Results: Fifty per cent of students stated that the dissection experience does not affect them . Negative effects were significantly more frequently reported by women and students in clinical training (years 3,4,5,6). The predominant factor in the various coping strategies for dissection practicals is cognitive coping (rationalisation, intellectualisation). Physical and emotional coping strategies followed, with similar mean scores. Marked gender differences also showed up in the application of coping strategies: there was a clear dominance of emotional-based coping among women. Among female students, there was a characteristic decrease in the physical repulsion factor in reactions to dissection in the later stages of study. Conclusions: The experience of dissection had an emotional impact on about half of the students. In general, students considered these experiences to be an important part of becoming a doctor. Our study found that students chiefly employed cognitive coping strategies to deal with their experiences. Dissection-room sessions are important for learning emotional as well as technical skills. Successful coping is achieved not by repressing emotions but by accepting and understanding the negative emotions caused by the experience and developing effective strategies to deal with them. Medical training could make better use of the learning potential of these experiences

Defining an ageing-related pathology, disease or syndrome: International Consensus Statement

Around the world, individuals are living longer, but an increased average lifespan does not always equate to an increased health span. With advancing age, the increased prevalence of ageing-related diseases can have a significant impact on health status, functional capacity and quality of life. It is therefore vital to develop comprehensive classification and staging systems for ageing-related pathologies, diseases and syndromes. This will allow societies to better identify, quantify, understand and meet the healthcare, workforce, well-being and socioeconomic needs of ageing populations, whilst supporting the development and utilisation of interventions to prevent or to slow, halt or reverse the progression of ageing-related pathologies. The foundation for developing such classification and staging systems is to define the scope of what constitutes an ageing-related pathology, disease or syndrome. To this end, a consensus meeting was hosted by the International Consortium to Classify Ageing-Related Pathologies (ICCARP), on February 19, 2024, in Cardiff, UK, and was attended by 150 recognised experts. Discussions and voting were centred on provisional criteria that had been distributed prior to the meeting. The participants debated and voted on these. Each criterion required a consensus agreement of ≥ 70% for approval. The accepted criteria for an ageing-related pathology, disease or syndrome were (1) develops and/or progresses with increasing chronological age; (2) should be associated with, or contribute to, functional decline or an increased susceptibility to functional decline and (3) evidenced by studies in humans. Criteria for an ageing-related pathology, disease or syndrome have been agreed by an international consortium of subject experts. These criteria will now be used by the ICCARP for the classification and ultimately staging of ageing-related pathologies, diseases and syndromes

Toolbox, review of functional triggers : Selection of maintenance candidates

ToolBox aims at developing a “concept for proper maintenance planning” to assure the selection of adequate maintenance works (“schemes” or “objects”) to make effective use of the maintenance budget, based on available road condition data, to give minimal negative effects on road users, safety for road workers and the environment. This report covers the first phase of the ToolBox findings and development. In chapter 2 there are a discussion on technical parameters and the use of them. This is followed with a listing of available technical parameters in the partner countries, chapter 4. A state of the art covering how selections of maintenance candidates are done in the respective countries follows including a comprehensive reporting of indicators and parameters available. Finally the chapter 6 covers the selection of models that will be used to evaluate the functional performances decided.ToolBo

Toolbox, Selection of maintenace candidates : Summary report

The ToolBox concept is applicable in the selection of lengths for maintenance (candidates), linked to comfort, safety, durability and the environment, including how the data is used, combined and weighted within current decision tools and models. ToolBox has not developed new models, for the technical parameters that are not currently measured (e.g. fuel consumption) but identified and extracted key tools from existing models used in Europe. The work has considered and developed an understanding of how existing knowledge (data) should be used to account for road user expectations in the selection of object lengths for maintenance. These existing and new concepts have been used to establish a set of functional triggers for selecting lengths (candidates) for maintenance on the network that include road user expectations and combine them to make recommended prioritised treatment objects.ToolBo