本文選題：車載導(dǎo)航 + 車險(xiǎn)��； 參考：《國防科學(xué)技術(shù)大學(xué)》2014年碩士論文

【摘要】：隨著信息科學(xué)技術(shù)的不斷發(fā)展,越來越多的設(shè)備可以產(chǎn)生數(shù)據(jù),而硬件存儲(chǔ)設(shè)備卻越來越便宜,我們因此步入了數(shù)據(jù)爆炸式增長的時(shí)代。大數(shù)據(jù)如雨后春筍股地出現(xiàn)在各行各業(yè)中,許多行業(yè)已經(jīng)開始對大數(shù)據(jù)進(jìn)行分析,并從分析中得到了驚人的價(jià)值,如互聯(lián)網(wǎng)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(shù)據(jù),卻只是簡單的存儲(chǔ)了數(shù)據(jù),還并沒有開展對數(shù)據(jù)價(jià)值的探索。如果能夠有效地使用大數(shù)據(jù),無疑將擴(kuò)大企業(yè)的競爭優(yōu)勢。如果一個(gè)企業(yè)忽略了大數(shù)據(jù),并將導(dǎo)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(dǎo)航軟件近幾年的發(fā)展非常迅速,使用車載導(dǎo)航軟件行車的人越來越多,車載導(dǎo)航迎來了大數(shù)據(jù)。大數(shù)據(jù)是機(jī)遇,同時(shí)也是挑戰(zhàn),如何從導(dǎo)航數(shù)據(jù)中獲取價(jià)值成了車載導(dǎo)航軟件公司的難題。而與此同時(shí),中國車險(xiǎn)市場隨著我國汽車市場發(fā)展不斷擴(kuò)大,競爭也越來越激烈。車險(xiǎn)行業(yè)的競爭主要是服務(wù)與價(jià)格的競爭,歸根到底是風(fēng)險(xiǎn)評估能力的競爭,而目前的廣泛采用的車險(xiǎn)定價(jià)策略存在難以區(qū)分投保人真實(shí)風(fēng)險(xiǎn)的不足。本文結(jié)合車險(xiǎn)領(lǐng)域風(fēng)險(xiǎn)評估的情況以及車載導(dǎo)航數(shù)據(jù)的特點(diǎn),提出通過對車載導(dǎo)航數(shù)據(jù)的分析,對用戶的統(tǒng)計(jì)駕駛情況進(jìn)行評估,將評估的結(jié)果稱作為駕駛統(tǒng)計(jì)安全系數(shù)(簡稱DSCF)。該系數(shù)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(xí)慣的真實(shí)體現(xiàn)。相對我國目前保險(xiǎn)公司所采用的車險(xiǎn)費(fèi)率因子來說,該系數(shù)更接近用戶的真實(shí)駕駛風(fēng)險(xiǎn)。車險(xiǎn)公司可以將該系數(shù)作為保費(fèi)定價(jià)的主要費(fèi)率因子或者將其作為保費(fèi)調(diào)整的次要費(fèi)率因子,還可以將該系數(shù)與傳統(tǒng)的費(fèi)率因子相結(jié)合,對車險(xiǎn)服務(wù)品種以及定價(jià)策略進(jìn)行改進(jìn)和創(chuàng)新。本文首先提出了駕駛統(tǒng)計(jì)安全系數(shù)(DSCF)的概念,然后對DSCF分析方法進(jìn)行設(shè)計(jì),主要包括駕駛統(tǒng)計(jì)安全評價(jià)模型和導(dǎo)航數(shù)據(jù)分析處理兩個(gè)方面。其中駕駛統(tǒng)計(jì)安全評價(jià)模型主要包括評價(jià)指標(biāo)體系的構(gòu)建以及指標(biāo)權(quán)重的分配,本文借助綜合評價(jià)法,結(jié)合安全駕駛的領(lǐng)域知識以及現(xiàn)有車載導(dǎo)航數(shù)據(jù),設(shè)計(jì)了評估模型的指標(biāo)體系,然后采用層次分析法對指標(biāo)的權(quán)重進(jìn)行了分配。導(dǎo)航數(shù)據(jù)分析處理包括對源數(shù)據(jù)進(jìn)行理解、數(shù)據(jù)選擇、重組、駕駛統(tǒng)計(jì)分析等等。最后本文以某車載導(dǎo)航軟件公司提供的真實(shí)數(shù)據(jù)為例,對23752個(gè)用戶的DSCF進(jìn)行了計(jì)算,驗(yàn)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(xué)位授予單位】：國防科學(xué)技術(shù)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：F842.634;TP311.13

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