Zimbabwe has witnessed the evolution of Information Communication Technology (ICT). The vehicle population soared to above 1.2 million hence rendering the Transport and Insurance domains complex. Therefore, there is a...Zimbabwe has witnessed the evolution of Information Communication Technology (ICT). The vehicle population soared to above 1.2 million hence rendering the Transport and Insurance domains complex. Therefore, there is a need to look at ways that can augment conventional Vehicular Management Information Systems (VMIS) in transforming business processes through Telematics. This paper aims to contextualise the role that telematics can play in transforming the Insurance Ecosystem in Zimbabwe. The main objective was to investigate the integration of Usage-Based Insurance (UBI) with vehicle tracking solutions provided by technology companies like Econet Wireless in Zimbabwe, aiming to align customer billing with individual risk profiles and enhance the synergy between technology and insurance service providers in the motor insurance ecosystem. A triangulation through structured interviews, questionnaires, and literature review, supported by Information Systems Analysis and Design techniques was conducted. The study adopted a case study approach, qualitatively analyzing the complexities of the Telematics insurance ecosystem in Zimbabwe, informed by the TOGAF framework. A case-study approach was applied to derive themes whilst applying within and cross-case analysis. Data was collected using questionnaires, and interviews. The findings of the research clearly show the importance of Telematics in modern-day insurance and the positive relationship between technology and insurance business performance. The study, therefore revealed how UBI can incentivize positive driver behavior, potentially reducing insurance premiums for safe drivers and lowering the incidence of claims against insurance companies. Future work can be done on studying the role of Telematics in combating highway crime and corruption.展开更多
Vehicle information on high-speed trains can not only determine whether the various parts of the train are working normally,but also predict the train’s future operating status.How to obtain valuable information from...Vehicle information on high-speed trains can not only determine whether the various parts of the train are working normally,but also predict the train’s future operating status.How to obtain valuable information from massive vehicle data is a difficult point.First,we divide the vehicle data of a high-speed train into 13 subsystem datasets,according to the functions of the collection components.Then,according to the gray theory and the Granger causality test,we propose the Gray-Granger Causality(GGC)model,which can construct a vehicle information network on the basis of the correlation between the collection components.By using the complex network theory to mine vehicle information and its subsystem networks,we find that the vehicle information network and its subsystem networks have the characteristics of a scale-free network.In addition,the vehicle information network is weak against attacks,but the subsystem network is closely connected and strong against attacks.展开更多
文摘Zimbabwe has witnessed the evolution of Information Communication Technology (ICT). The vehicle population soared to above 1.2 million hence rendering the Transport and Insurance domains complex. Therefore, there is a need to look at ways that can augment conventional Vehicular Management Information Systems (VMIS) in transforming business processes through Telematics. This paper aims to contextualise the role that telematics can play in transforming the Insurance Ecosystem in Zimbabwe. The main objective was to investigate the integration of Usage-Based Insurance (UBI) with vehicle tracking solutions provided by technology companies like Econet Wireless in Zimbabwe, aiming to align customer billing with individual risk profiles and enhance the synergy between technology and insurance service providers in the motor insurance ecosystem. A triangulation through structured interviews, questionnaires, and literature review, supported by Information Systems Analysis and Design techniques was conducted. The study adopted a case study approach, qualitatively analyzing the complexities of the Telematics insurance ecosystem in Zimbabwe, informed by the TOGAF framework. A case-study approach was applied to derive themes whilst applying within and cross-case analysis. Data was collected using questionnaires, and interviews. The findings of the research clearly show the importance of Telematics in modern-day insurance and the positive relationship between technology and insurance business performance. The study, therefore revealed how UBI can incentivize positive driver behavior, potentially reducing insurance premiums for safe drivers and lowering the incidence of claims against insurance companies. Future work can be done on studying the role of Telematics in combating highway crime and corruption.
基金supported by the Graduate Innovation Project of Beijing Jiaotong University(No.2020YJS098)。
文摘Vehicle information on high-speed trains can not only determine whether the various parts of the train are working normally,but also predict the train’s future operating status.How to obtain valuable information from massive vehicle data is a difficult point.First,we divide the vehicle data of a high-speed train into 13 subsystem datasets,according to the functions of the collection components.Then,according to the gray theory and the Granger causality test,we propose the Gray-Granger Causality(GGC)model,which can construct a vehicle information network on the basis of the correlation between the collection components.By using the complex network theory to mine vehicle information and its subsystem networks,we find that the vehicle information network and its subsystem networks have the characteristics of a scale-free network.In addition,the vehicle information network is weak against attacks,but the subsystem network is closely connected and strong against attacks.
