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

PAS3-HSID: a Dynamic Bio-Inspired Approach for Real-Time Hot Spot Identification in Data Streams

http://dx.doi.org/10.5902/2236130814684http://dx.doi.org/10.5902/2236130814684O gerenciamento de resíduos municipais é um tema que vem se tornando cada vez mais importante no contexto das preocupações mundiais dos governos, e teve um considerável desenvolvimento nas últimas décadas. Tanto os países desenvolvidos como os “em desenvolvimento” emitiram normativas legais restritivas, visando otimizar seus planos de tratamento e destinação final destes resíduos. O objetivo principal do trabalho é investigar a real situação deste cenário no Brasil e nos países desenvolvidos, demonstrando os resultados obtidos e traçando um paralelo comparativo e critico. São transcritos e analisados os dados obtidos, em cada fase de uma Gestão Integrada de Resíduos Sólidos Urbanos – GIRSU. Conclusões importantes são relatadas, tais como, o alto nível de investimento dos países desenvolvidos em relação às campanhas de conscientização para implantação de uma efetiva GIRSU, assim como contrastes marcantes entre os índices de reciclagem no Brasil e neste bloco diferenciado de países, ou seja, 2% e 20%, respectivamente, no montante dos resíduos totais gerados. A avaliação final é de que o diferencial esta nas ações políticas de incentivo econômico destes países desenvolvidos, em termos de subsídios, se comparados com o caso brasileiro

PAS3-HSID: a Dynamic Bio-Inspired Approach for Real-Time Hot Spot Identification in Data Streams

© 2019, Springer Science+Business Media, LLC, part of Springer Nature. Hot spot identification is a very relevant problem in a wide variety of areas such as health care, energy or transportation. A hot spot is defined as a region of high likelihood of occurrence of a particular event. To identify hot spots, location data for those events is required, which is typically collected by telematics devices. These sensors are constantly gathering information, generating very large volumes of data. Current state-of-the-art solutions are capable of identifying hot spots from big static batches of data by means of variations of clustering or instance selection techniques that pre-process the original input data, providing the most relevant locations. However, these approaches neglect to address changes in hot spots over time. This paper presents a dynamic bio-inspired approach to detect hot spots in big data streams. This computational intelligence method is designed and applied to the transportation sector as a case study to identify incidents in the roads caused by heavy goods vehicles. We adapt an immune-based algorithm to account for the temporary aspect of hot spots inspired by the idea of pheromones, which is then subsequently implemented using Apache Spark Streaming. Experimental results on real datasets with up to 4.5 million data points—provided by a telematics company—show that the algorithm is capable of quickly processing large streaming batches of data, as well as successfully adapting over time to detect hot spots. The outcome of this method is twofold, both reducing data storage requirements and demonstrating resilience to sudden changes in the input data (concept drift)

Vehicle incident hot spots identification: an approach for big data

In this work we introduce a fast big data approach for road incident hot spot identification using Apache Spark. We implement an existing immuno-inspired mechanism, namely SeleSup, as a series of MapReduce-like operations. SeleSup is composed of a number of iterations that remove data redundancies and result in the detection of areas of high likelihood of vehicles incidents. It has been successfully applied to large datasets, however, as the size of the data increases to millions of instances, its performance drops significantly. Our objective therefore is to re-conceptualise the method for big data. In this paper we present the new implementation, the challenges faced when converting the method for the Apache Spark platform as well as the outcomes obtained. For our experiments we employ a large dataset containing hundreds of thousands of Heavy Good Vehicles incidents, collected via telematics. Results show a significant improvement in performance with no detriment to the accuracy of the method

Detecting danger in roads: an immune-inspired technique to identify heavy goods vehicles incident hot spots

We report on the adaptation of an immune-inspired instance selection technique to solve a real-world big data problem of determining vehicle incident hot spots. The technique, which is inspired by the Immune System self-regulation mechanism, was originally conceptualised to eliminate very similar instances in data classification tasks. We adapt the method to detect hot spots from a telematics data set containing hundreds of thousands of data points indicating incident locations involving heavy goods vehicles (HGVs) across the United Kingdom. The objective is to provide HGV drivers with information regarding areas of high likelihood of incidents in order to continuously improve road safety and vehicle economy. The problem presents several challenges and constraints. An accurate view of the hot spots produced in a timely manner is necessary. In addition, the solution is required to be adaptable and dynamic, as thousands of new incidents are included in the database daily. Furthermore, the impact on hot spots after informing drivers about their existence has to be considered. Our solution successfully addresses these constraints. It is fast, robust, and applicable to all different incidents investigated. The method is also self-adjustable, which means that if the hot spots’ configuration changes with time, the algorithm automatically evolves to present the most current topology. Our solution has been implemented by a telematics company to improve HGV safety in the United Kingdom

A data analysis framework to rank HGV drivers

We report on the details of the methodology applied to support shortlisting the nominees for the Microlise Driver of the Year awards. The aim was to recognise the United Kingdom’s most talented heavy goods vehicle (HGV) drivers, with the list of top 46 drivers across 16 different companies determined through the analysis of telematics data. Initial data for the awards was gathered from over 90,000 drivers engaging with Microlise’s telematics solutions. The data was analysed anonymously in order to identify the best criteria to establish top performing drivers. The initial selection was made based on a minimum number of miles driven across each of the four quarters in 2014. Outlier removal and a consensus clustering framework were subsequently employed to the dataset to identify subgroups of drivers. Three categories of drivers were identified: short, medium and long distance drivers. Each qualifying professional belonging to one of the three categories was then assessed using a range of criteria compared to other drivers from the same category. To determine the final winners, questionnaires for further evidence and indicators that might contribute to a driver being named as a winner was sent down to employers and their responses were evaluated

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

An Immune-Inspired Technique to Identify Heavy Goods Vehicles Incident Hot Spots

We report on the adaptation of an immune-inspired instance selection technique to solve a real-world big data problem of determining vehicle incident hot spots. The technique, which is inspired by the Immune System self-regulation mechanism, was originally conceptualised to eliminate very similar instances in data classification tasks. We adapt the method to detect hot spots from a telematics data set containing hundreds of thousands of data points indicating incident locations involving heavy goods vehicles (HGVs) across the United Kingdom. The objective is to provide HGV drivers with information regarding areas of high likelihood of incidents in order to continuously improve road safety and vehicle economy. The problem presents several challenges and constraints. An accurate view of the hot spots produced in a timely manner is necessary. In addition, the solution is required to be adaptable and dynamic, as thousands of new incidents are included in the database daily. Furthermore, the impact on hot spots after informing drivers about their existence has to be considered. Our solution successfully addresses these constraints. It is fast, robust, and applicable to all different incidents investigated. The method is also self-adjustable, which means that if the hot spots’ configuration changes with time, the algorithm automatically evolves to present the most current topology. Our solution has been implemented by a telematics company to improve HGV safety in the United Kingdom

An epidemiological survey of psychiatric disorders in Iran

BACKGROUND: The nation-wide epidemiological survey of psychiatric disorders in term of lifetime prevalence is not adequately known in Iran. The prevalence of lifetime psychiatric disorders was estimated among the population of aged 18 and over on gender, age group, educational level, occupational status, marital status, and residential area. METHODS: The subjects were 25,180 individuals selected through a clustered random sampling method. The psychiatric disorders were diagnosed on the bases of Diagnostic and Statistical Manual of Mental Disorders-IV criteria. It is the first study in which the structured psychiatric interview administered to a representative sample of the Iranian population age 18 and over by the 250 trained clinical psychologist interviewers. The data was entered through EPI-Info software twice in an attempt to prevent any errors and SPSS-11 statistical software was also used for analyses. The odds ratios and their confidence intervals estimated by using logistic regression. RESULTS AND DISCUSSION: The prevalence of psychiatric disorders was 10.81%. It was more common among females than males (14.34% vs. 7.34%, P < 0.001). The prevalence of anxiety and mood disorders were 8.35% and 4.29% respectively. The prevalence of psychotic disorders was 0.89%; neuro-cognitive disorders, 2.78% and dissociative disorders, 0.77%. Among mood disorders, major depressive disorder (2.98%) and among anxiety disorders, phobic disorder (2.05%) had the higher prevalence. The prevalence of psychiatric disorders among divorced and separated 22.31%; residents of urban areas 11.77%; illiterates 13.80%; householders 15.48%; unemployed 12.33% that were more than other groups. CONCLUSION: The mental health pattern in Iran is similar to the western countries, but it seems that the prevalence of psychiatric disorders in Iran may be lower than these countries. It is estimated that at least about 7 millions of Iranian population suffer from one or more of the psychiatric disorders. It shows the importance of the role of the psychiatric disorders in providing preventive and management programs in Iran