A Choquet Fuzzy Integral Vertical Bagging Classifier for Mobile Telematics Data Analysis

© 2019 IEEE. Mobile app development in recent years has resulted in new products and features to improve human life. Mobile telematics is one such development that encompasses multidisciplinary fields for transportation safety. The application of mobile telematics has been explored in many areas, such as insurance and road safety. However, to the best of our knowledge, its application in gender detection has not been explored. This paper proposes a Choquet fuzzy integral vertical bagging classifier that detects gender through mobile telematics. In this model, different random forest classifiers are trained by randomly generated features with rough set theory, and the top three classifiers are fused using the Choquet fuzzy integral. The model is implemented and evaluated on a real dataset. The empirical results indicate that the Choquet fuzzy integral vertical bagging classifier outperforms other classifiers

A Data-Driven Decision Support System for Mobile Telematics

University of Technology Sydney. Faculty of Engineering and Information Technology.Mobile telematics is an emerging technology that collects data on human behaviour using smartphones. All smartphones have internal sensors with the capability to record and transmit data to an external server. This technology is easy to use, the initial cost is low, and generates a massive amount of data which are noisy, complex, and uncertain. This opens many opportunities for data-driven decision making such as driving behaviour risk analysis, usage-based insurance, remote sensing, and fleet management. Traditional decision-making techniques are not able to work with this type of unstructured data. Thus, new techniques are needed based on advanced analytics to analyze mobile telematics. This research develops a big data-driven decision support system (DSS) for mobile telematics. The research relies on the capabilities of advanced analytics techniques, machine learning, and fuzzy logic. The research presents an innovative analytical system for mobile telematics which consists of four major components: 1) a data preparation component that prepares a trajectory dataset to a new and ready-for-analysis format; 2) a driving style pattern recognition that extracts hidden human patterns in mobile telematics using unsupervised learning and unlabelled data; 3) a fuzzy risk assessment is proposed to assess risk of drivers by fuzzy logic using extracted patterns by unsupervised learning; and 4) a missing data imputation component which is a novel Choquet Fuzzy Integral Vertical Bagging (CFIVB) algorithm to classify large labelled mobile telematics stream datasets. The proposed models were evaluated on two real-world mobile telematics datasets, namely an unlabelled dataset collected by a usage-based insurance company containing 500,000 journeys of 2500 drivers, and an anonymized driving behaviour dataset consisting of streaming data of 408 trips of 310 unique drivers. Various validation measures were used to evaluate the performance of the proposed models. The area under a curve (AUC) and accuracy are used to evaluate the classification algorithms and the Davis-Boulding index, the Calinski-Harabasz index, execution time, and mean square error are utilized to evaluate clustering algorithms and find the optimal number of clusters. The sensitivity analysis results show the proposed model is consistent across different variations of the model. The proposed DSS can be applied on all stream data risk assessments. Moreover, 29 unique driving styles were extracted from mobile telematics data and these patterns can be applied as labels for supervised learning modelling. In addition, performance measures depict the CFIVB algorithm performs well in this domain, and it can be applied for similar problems

Smartphone placement within vehicles

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this recordSmartphone-based driver monitoring is quickly gaining ground as a feasible alternative to competing in-vehicle and aftermarket solutions. Currently the main challenges for data analysts studying smartphone-based driving data stem from the mobility of the smartphone. In this paper, we use kernel-based k-means clustering to infer the placement of smartphones within vehicles. The trip segments are mapped into fifteen different placement clusters. As a part of the presented framework, we discuss practical considerations concerning e.g., trip segmentation, cluster initialization, and parameter selection. The proposed method is evaluated on more than 10 000 kilometers of driving data collected from approximately 200 drivers. To validate the interpretation of the clusters, we compare the data associated with different clusters and relate the results to real-world knowledge of driving behavior. The clusters associated with the label “Held by hand” are shown to display high gyroscope variances, low maximum speeds, low correlations between the measurements from smartphone-embedded and vehicle-fixed accelerometers, and short segment durations

Can Telematics Improve Driving Style? The Use of Behavioural Data in Motor Insurance

The use of behavioural data in insurance is loaded with promises and unresolved issues. This paper explores the related opportunities and challenges analysing the use of telematics data in third-party liability motor insurance. Behavioural data are used not only to refine the risk profile of policyholders, but also to implement innovative coaching strategies, feeding back to the drivers the aggregated information obtained from the data. The purpose is to encourage an improvement in their driving style. Our research explores the effectiveness of coaching on the basis of an empirical investigation of the dataset of a company selling telematics motor insurance policies. The results of our quantitative analysis show that this effectiveness crucially depends on the propensity of policyholders to engage with the telematics app. We observe engagement as an additional kind of behaviour, producing second-order behavioural data that can also be recorded and strategically used by insurance companies. The conclusions discuss potential advantages and risks connected with this extended interpretation of behavioural data.Comment: Paper sent for publication on a journal. This is a preliminary version, updated versions will be uploade

Growth, Integration and Spillovers in the Central and East European Software Industry

This paper explores growth and competitive advantage in CEE software firms; it looks at the role of strategic partnerships and industry (spillover) effects. The empirical analysis is based on survey data from 224 software firms from six CEE countries (Bulgaria, Czech R, Estonia, Serbia, Slovenia, Romania). The results of the descriptive analysis are interpreted from the perspective of the role of capabilities in industrial development. The analysis shows that the patterns of growth are a mix of sector, region and sub--region specific determinants and show important national differences. This suggests that the CEE software industry cannot be considered as a homogenous phenomenon. There is no general tendency towards an expansion in exports; based on our sample only Romania is developing an export oriented software industry. Research shows that the CEE software industry is populated by young, dedicated, domestic firms, which are independent, and privately owned and which are mainly oriented towards localisation of software. They are strongly dependent for trade and production on alliances and strategic partnerships with foreign partners and a small share of technology based partnerships. There is an extensive process of industry upgrading underway, involving country and sub-region specific changes. The spillover effects are significant, through links with clients and intensive intra-industry knowledge transfer through high employment turnover and potentially high knowledge transfer from foreign to local projects. Differences between central and eastern Europe are strong in terms of degree of diversification of software supply, industrial upgrading and quality of demand. The pattern of software development in CEE differs from that in other emerging markets in the sense that it is domestic market oriented, but with an emerging export market for services. Its further growth and upgrading will be strongly dependent on the acquisition of organisational capabilities by local firms

DrivingBeacon : Driving Behaviour Change Support System Considering Mobile Use and Geo-information

Publisher PD

Elements of scalable data processing

Cooperating objects (COs) is a recently coined term used to signify the convergence of classical embedded computer systems, wireless sensor networks and robotics and control. We present essential elements of a reference architecture for scalable data processing for the CO paradigm

From Actuarial to Behavioural Valuation: The Impact of Telematics on Motor Insurance

Algorithmic predictions are used in insurance to assess the risk exposure of potential customers, for the purpose of improving how the problem of adverse selection is tackled. This paper examines the impact of digital tools in the field of motor insurance, where telematics devices produce data about the behaviour of the insured party. The individual’s resulting behavioural score is combined with their actuarial score to determine the price of the policy or additional incentives. Current experimentation is moving in the direction of proactivity: instead of waiting for a claim to occur, insurance companies engage in coaching and other interventions to mitigate the risk. Such practices may have consequences for the social function of insurance, whose traditional aim has been not to reduce risks, but to make them bearable by socialising them over a pool of insured individuals. The introduction of behavioural variables and the corresponding idea of fairness could instead isolate individuals in their exposure to risk and affect their attitude towards the future

A vehicle classification algorithm based on telematics data

Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 45-46).In the thesis, I develop an algorithm to identify the vehicle model from telematics data. By extracting the features from the accelerometer and GPS data, we obtain the classification features, which then goes through a multiclass random forest classifier. We apply this results into problems of driver and vehicle identification. The result shows that, while the algorithm could identify the vehicle models to some extent, the dominating signal comes from driving style, and an approach running purely unsupervised learning is harder to achieve good classification results compared to supervised methods.by Linh Vuong Nguyen.M. Eng