本文選題：車載導航 + 車險　； 參考：《國防科學技術大學》2014年碩士論文

【摘要】：隨著信息科學技術的不斷發(fā)展,越來越多的設備可以產生數據,而硬件存儲設備卻越來越便宜,我們因此步入了數據爆炸式增長的時代。大數據如雨后春筍股地出現(xiàn)在各行各業(yè)中,許多行業(yè)已經開始對大數據進行分析,并從分析中得到了驚人的價值,如互聯(lián)網行業(yè)、大型零售超市,等等。然而有很大一部分行業(yè),他們迎來了大數據,卻只是簡單的存儲了數據,還并沒有開展對數據價值的探索。如果能夠有效地使用大數據,無疑將擴大企業(yè)的競爭優(yōu)勢。如果一個企業(yè)忽略了大數據,并將導致在競爭中逐漸落后。伴隨著我國汽車市場的飛速發(fā)展,車載導航軟件近幾年的發(fā)展非常迅速,使用車載導航軟件行車的人越來越多,車載導航迎來了大數據。大數據是機遇,同時也是挑戰(zhàn),如何從導航數據中獲取價值成了車載導航軟件公司的難題。而與此同時,中國車險市場隨著我國汽車市場發(fā)展不斷擴大,競爭也越來越激烈。車險行業(yè)的競爭主要是服務與價格的競爭,歸根到底是風險評估能力的競爭,而目前的廣泛采用的車險定價策略存在難以區(qū)分投保人真實風險的不足。本文結合車險領域風險評估的情況以及車載導航數據的特點,提出通過對車載導航數據的分析,對用戶的統(tǒng)計駕駛情況進行評估,將評估的結果稱作為駕駛統(tǒng)計安全系數(簡稱DSCF)。該系數綜合考慮了用戶駕駛路程、駕駛速度、駕駛區(qū)域、夜間駕駛、疲勞駕駛等情況,是用戶駕駛行為和習慣的真實體現(xiàn)。相對我國目前保險公司所采用的車險費率因子來說,該系數更接近用戶的真實駕駛風險。車險公司可以將該系數作為保費定價的主要費率因子或者將其作為保費調整的次要費率因子,還可以將該系數與傳統(tǒng)的費率因子相結合,對車險服務品種以及定價策略進行改進和創(chuàng)新。本文首先提出了駕駛統(tǒng)計安全系數(DSCF)的概念,然后對DSCF分析方法進行設計,主要包括駕駛統(tǒng)計安全評價模型和導航數據分析處理兩個方面。其中駕駛統(tǒng)計安全評價模型主要包括評價指標體系的構建以及指標權重的分配,本文借助綜合評價法,結合安全駕駛的領域知識以及現(xiàn)有車載導航數據,設計了評估模型的指標體系,然后采用層次分析法對指標的權重進行了分配。導航數據分析處理包括對源數據進行理解、數據選擇、重組、駕駛統(tǒng)計分析等等。最后本文以某車載導航軟件公司提供的真實數據為例,對23752個用戶的DSCF進行了計算,驗證DSCF分析方法的有效性。
[Abstract]:With the continuous development of information science and technology, more and more devices can produce data, while hardware storage devices are becoming cheaper and cheaper. Therefore, we have entered the era of explosive growth of data. Big data has sprung up in a variety of industries, many industries have begun to analyze big data, and from the analysis to get amazing value, such as the Internet industry, large retail supermarkets, and so on. However, a large part of the industry, they welcomed big data, but simply stored data, and has not yet begun to explore the value of the data. If can use big data effectively, will expand the competitive advantage of enterprise undoubtedly. If an enterprise neglects big data, and will lead to gradually fall behind in the competition. With the rapid development of automobile market in China, vehicle navigation software has been developing very rapidly in recent years. More and more people use vehicle navigation software to drive cars, and vehicle navigation ushered in big data. Big data is both an opportunity and a challenge. How to gain value from navigation data has become a problem for vehicle navigation software companies. At the same time, with the development of China's auto market, the competition is becoming more and more fierce. The competition in auto insurance industry is mainly the competition between service and price, and in the final analysis, it is the competition of risk assessment ability. However, the widely used auto insurance pricing strategy is difficult to distinguish the real risks of policy holders. Based on the risk assessment in vehicle insurance field and the characteristics of vehicle navigation data, this paper proposes to evaluate the statistical driving situation of users through the analysis of vehicle navigation data. The results of the assessment are referred to as the driving statistical safety factor (DSCF for short). The coefficient takes into account the driving distance, driving speed, driving area, night driving, fatigue driving and so on, which is the true embodiment of the user's driving behavior and habits. Compared with the car insurance rate factor adopted by the insurance companies in China, the coefficient is closer to the real driving risk of the users. The vehicle insurance company can use the coefficient as the main rate factor for premium pricing or as the secondary premium factor for premium adjustment, and can also combine the coefficient with the traditional rate factor. Improve and innovate the vehicle insurance service and pricing strategy. In this paper, the concept of driving statistical safety factor (DSCF) is proposed, and then the DSCF analysis method is designed, which includes two aspects: driving statistical safety evaluation model and navigation data analysis and processing. The statistical safety evaluation model of driving mainly includes the construction of evaluation index system and the distribution of index weight. This paper combines the domain knowledge of safe driving and the existing vehicle navigation data with the aid of comprehensive evaluation method. The index system of the evaluation model is designed, and then the weight of the index is allocated by the analytic hierarchy process (AHP). Navigation data analysis and processing include understanding of source data, data selection, reorganization, driving statistical analysis, and so on. Finally, taking the real data provided by a vehicle navigation software company as an example, the DSCF of 23752 users is calculated to verify the validity of the DSCF analysis method.
【學位授予單位】：國防科學技術大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：F842.634;TP311.13

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