TW202119395A - Smart scheduling analysis system with instantly collecting driving behavior data and physiological information - Google Patents
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Description
本發明係關於一種智慧排班分析技術,特別是指一種即時蒐集駕駛行為資料與生理資訊之智慧排班分析系統。 The present invention relates to a smart scheduling analysis technology, in particular to a smart scheduling analysis system for real-time collection of driving behavior data and physiological information.
客運、遊覽車、公車、計程車、台鐵、高鐵、捷運等運輸載具是乘客所搭乘之運輸系統的主要交通工具,且運輸載具及其駕駛人之排班表大多由人工所編列且採用固定排班方式。惟,駕駛人可能有生理狀態不佳或影響行車安全之情況,以致影響運輸載具及乘客之行車安全。 Passengers, tourist buses, buses, taxis, Taiwan Railways, high-speed rails, MRT and other transportation vehicles are the main means of transportation in the transportation system that passengers take, and most of the schedules of transportation vehicles and their drivers are compiled by humans. Adopt a fixed schedule method. However, drivers may have poor physical conditions or conditions that affect driving safety, which may affect the driving safety of transportation vehicles and passengers.
在一現有技術中,提出一種車輛自動排班裝置,先由排班模組擷取資料模組所登錄之車輛、人事及路線資料進行排班,並由派遣模組確認排班資料做派車輸出,再由接收模組接收車輛之即時營運狀態及資訊由營運模組進行監控各車輛,追縱駕駛人與車輛情況。然而,此現有技術並無法即時蒐集駕駛人之駕駛行為資料與生理資訊,以致無法依據駕駛人之駕駛行為資料與生理資訊進行智慧排班分析,從而影響運輸載具及乘客之行車安全。 In a prior art, an automatic vehicle scheduling device is proposed. The scheduling module first retrieves the vehicle, personnel, and route data registered in the data module for scheduling, and the scheduling module confirms the scheduling data to dispatch the vehicle. Output, and then the receiving module receives the real-time operating status and information of the vehicle, and the operating module monitors each vehicle to track the driver and vehicle situation. However, this prior art cannot collect the driving behavior data and physiological information of the driver in real time, so that it is impossible to perform intelligent scheduling analysis based on the driving behavior data and physiological information of the driver, thereby affecting the driving safety of transportation vehicles and passengers.
因此,如何提供一種新穎或創新之即時蒐集駕駛行為資料與生理資訊之智慧排班分析技術,已成為本領域技術人員之一大研究課題。 Therefore, how to provide a novel or innovative intelligent scheduling analysis technology for real-time collection of driving behavior data and physiological information has become a major research topic for those skilled in the art.
本發明提供一種新穎或創新之即時蒐集駕駛行為資料與生理資訊之智慧排班分析系統,能即時蒐集駕駛人之駕駛行為資料與生理資訊以進行智慧排班而提高行車安全。 The present invention provides a novel or innovative intelligent scheduling analysis system for real-time collection of driving behavior data and physiological information, which can collect driver's driving behavior data and physiological information in real time to perform smart scheduling and improve driving safety.
本發明中即時蒐集駕駛行為資料與生理資訊之智慧排班分析系統包括:一行動裝置感測器模組與一穿戴式裝置感測器模組其中至少一者,係即時蒐集或感測運輸載具之駕駛人之駕駛行為資料與生理資訊;一機器學習自動分析模組,係將行動裝置感測器模組與穿戴式裝置感測器模組其中至少一者所即時蒐集或感測之駕駛人之駕駛行為資料與生理資訊進行機器學習,以自動分析出駕駛人之駕駛行為資料與生理資訊兩者之關聯結果或關聯模型;以及一智慧排班模組,係依據機器學習自動分析模組所分析之駕駛人之駕駛行為資料與生理資訊兩者之關聯結果或關聯模型進行智慧排班以產生關聯於駕駛人之排班表。 The intelligent scheduling analysis system for real-time collection of driving behavior data and physiological information in the present invention includes: at least one of a mobile device sensor module and a wearable device sensor module, which collects or senses transportation loads in real time Driver’s driving behavior data and physiological information; a machine learning automatic analysis module, which is a driving that is collected or sensed in real time by at least one of the mobile device sensor module and the wearable device sensor module Machine learning is performed on human driving behavior data and physiological information to automatically analyze the correlation result or correlation model between the driver’s driving behavior data and physiological information; and a smart scheduling module based on machine learning automatic analysis module The correlation result or correlation model between the analyzed driving behavior data and physiological information of the driver is used for intelligent scheduling to generate a schedule related to the driver.
為讓本發明之上述特徵和優點能更明顯易懂,下文特舉實施例,並配合所附圖式作詳細說明。在以下描述內容中將闡述本發明之額外特徵及優點,且此等特徵及優點將部分自所述描述內容可得而知,或可藉由對本發明之實踐習得。本發明之特徵及優點借助於在申請專利範圍中特別指出的元件及組合來認識到並達到。應理解,前文一般描述與以下詳細描述兩者均僅為例示性及解釋性的,且不欲約束本發明所欲主張之範圍。 In order to make the above-mentioned features and advantages of the present invention more comprehensible, embodiments are specifically described below in conjunction with the accompanying drawings. The additional features and advantages of the present invention will be described in the following description, and these features and advantages will be partly known from the description, or can be learned by practicing the present invention. The features and advantages of the present invention are realized and achieved by means of the elements and combinations specifically pointed out in the scope of the patent application. It should be understood that both the foregoing general description and the following detailed description are only illustrative and explanatory, and are not intended to limit the scope of the present invention.
1‧‧‧智慧排班分析系統 1‧‧‧Smart scheduling analysis system
10‧‧‧行動裝置感測器模組 10‧‧‧Mobile device sensor module
20‧‧‧穿戴式裝置感測器模組 20‧‧‧Wearable device sensor module
30‧‧‧訊號處理模組 30‧‧‧Signal processing module
40‧‧‧訊號相關性評估模組 40‧‧‧Signal Correlation Evaluation Module
50‧‧‧機器學習自動分析模組 50‧‧‧Machine learning automatic analysis module
60‧‧‧特徵分群模組 60‧‧‧Feature Grouping Module
70‧‧‧智慧排班模組 70‧‧‧Smart scheduling module
A1‧‧‧營運資料庫 A1‧‧‧Operation Database
A2‧‧‧行車資料庫 A2‧‧‧Driving Database
A3‧‧‧生理資料庫 A3‧‧‧Physiological Database
B1‧‧‧營運風險 B1‧‧‧Operational risk
B2‧‧‧行車風險 B2‧‧‧Driving Risk
B3‧‧‧醫療生理風險 B3‧‧‧Medical Physiological Risk
D‧‧‧工作績效指標預估值 D‧‧‧Estimated value of work performance indicators
E‧‧‧排班表 E‧‧‧Schedule
第1圖為本發明中即時蒐集駕駛行為資料與生理資訊之智慧排班分析系統之架構示意圖; Figure 1 is a schematic diagram of the structure of the intelligent scheduling analysis system for real-time collection of driving behavior data and physiological information in the present invention;
第2圖為本發明中機器學習自動分析模組之示意圖; Figure 2 is a schematic diagram of the machine learning automatic analysis module of the present invention;
第3圖為本發明中有關營運風險、行車風險與醫療生理風險之演算取得之示意圖; Figure 3 is a schematic diagram of the calculation of operational risks, driving risks and medical physiological risks in the present invention;
第4圖為本發明中有關駕駛人之行駛路線配對分析之一示意圖; Figure 4 is a schematic diagram of the driving route matching analysis of the driver in the present invention;
第5圖為本發明中有關駕駛人之行駛路線配對分析之另一示意圖;以及 Figure 5 is another schematic diagram of the driver's driving route matching analysis in the present invention; and
第6圖為本發明中以智慧排班模組優化駕駛人之排班表之示意圖。 Figure 6 is a schematic diagram of the smart scheduling module used in the present invention to optimize the driver's schedule.
以下藉由特定的具體實施形態說明本發明之實施方式,熟悉此技術之人士可由本說明書所揭示之內容了解本發明之其他優點與功效,亦可因而藉由其他不同的具體等同實施形態加以施行或應用。 The following describes the implementation of the present invention with specific specific embodiments. Those familiar with this technology can understand the other advantages and effects of the present invention from the contents disclosed in this specification, and can also implement other different specific equivalent embodiments. Or apply.
第1圖為本發明中即時蒐集駕駛行為資料與生理資訊之智慧排班分析系統1之架構示意圖。如圖所示,智慧排班分析系統1可包括互相連接或通訊之一行動裝置感測器模組10、一穿戴式裝置感測器模組20、一訊號處理模組30、一訊號相關性評估模組40、一機器學習自動分析模組50、一特徵分群模組60與一智慧排班模組70。
Figure 1 is a schematic diagram of the structure of the intelligent
行動裝置感測器模組10可為硬體之感測器或軟體之感測程式(如APP),並安裝於行動裝置(圖未示)中或與行動裝置連接/通訊。穿戴式裝置感測器模組20可為硬體之感測器或軟體之感測程式(如APP),並安裝於穿戴式裝置(圖未示)中或與穿戴式裝置連接/通訊。行動裝置可為手機、智慧手機、平板電腦或個人數位助理(PDA)等,而穿戴式裝置可為手錶、智慧手錶或醫療手錶等。訊號處理模組30可為硬體之訊號處理器或軟體之訊號處理程式,訊號相關性評估模組40可為軟體之訊號相關性評估程式,機器學習自動分析模組50可為軟體之機器學習自動分析程式,特徵分群模組60可為軟體之特徵分群程式,智慧排班模組70可為軟體之智慧排班程式。下述運輸載具可為客運、遊覽車、公車、計程車、台鐵、高鐵、捷運等載具,且伺服器可為後端伺服器、網路伺服器或雲端伺服器等。但是,本發明並不以此為限。
The mobile
行動裝置感測器模組10與穿戴式裝置感測器模組20其中至少一者可即時蒐集或感測運輸載具(圖未示)之駕駛人之駕駛行為資料與生理資訊。機器學習自動分析模組50可將行動裝置感測器模組10與穿戴式裝置感測器模組20其中至少一者所即時蒐集或感測之駕駛人之駕駛行為資料與生理資訊進行機器學習,以自動分析出駕駛人之駕駛行為資料與生理資訊兩者之關聯結果或關聯模型。智慧排班模組70可依據特徵分群模組60之分群結果與機器學習自動分析模組50所分析之駕駛人之駕駛行為資料與生理資訊兩者之關聯結果或關聯模型進行智慧排班以產生關聯於駕駛人之排班表E(見第6圖)。
At least one of the mobile
具體而言,行動裝置感測器模組10及/或穿戴式裝置感測器
模組20可蒐集駕駛人之駕駛行為資料、駕駛人之生理資訊與運輸載具之行車歷史資料等。例如,行動裝置感測器模組10可即時蒐集或感測駕駛人之駕駛行為資料,且穿戴式裝置感測器模組20可即時蒐集或感測駕駛人之生理資訊。同時,本發明可將行動裝置感測器模組10及/或穿戴式裝置感測器模組20所蒐集駕駛人之駕駛行為資料與生理資訊傳送至伺服器(圖未示),再依據駕駛人之駕駛行為資料與生理資訊之特性對行駛情境進行配對或評分,以自動產出高安全係數之排班表E。
Specifically, the mobile
訊號處理模組30可將行動裝置感測器模組10及/或穿戴式裝置感測器模組20所蒐集駕駛人之駕駛行為資料、駕駛人之生理資訊與運輸載具之行車歷史資料等,進行重新採樣、訊號離散化、訊號標準化、濾波分析、事件偵測或資料前處理等。訊號相關性評估模組40可將訊號處理模組30所處理之駕駛人之駕駛行為資料、駕駛人之生理資訊與運輸載具之行車歷史資料等,進行時頻分析或相關性分析。
The
機器學習自動分析模組50可依據訊號相關性評估模組40所分析駕駛人之駕駛行為資料、駕駛人之生理資訊與運輸載具之行車歷史資料等進行機器學習,以自動分析、篩選或評估出駕駛行為資料、駕駛人之生理資訊與運輸載具之行車歷史資料三者之關聯結果或關聯模型(如最佳模型)。智慧排班模組70可利用特徵分群模組60之分群結果與機器學習自動分析模組50之分析結果進行智慧排班,以自動產出高安全係數之排班表E。因此,本發明之智慧排班模組70能納入駕駛人之個體生理差異與實際駕駛情況,以提供適合駕駛人與路線之配對推薦。
The machine learning
詳言之,訊號相關性評估模組40可同時分析駕駛人之駕駛
行為資料與生理資訊(生理訊號)兩者相關聯之至少兩訊號之關係(如定向性或同調性)。駕駛行為資料可為急加減速、急轉彎、超速事件數、定速保持比、駕車時刻比例等,生理訊號可為心率量測指數、血氧飽和度監測指數、壓力指數、心率變異性等。例如,Coh(心率變異性,定速保持比之分數)=0.9,且Coh表示同性調(coherence)。據此,本發明能找出具有高度相關性之資料配對,並提供科學佐證協同行為與個人生理資訊之同調性之科學證據。
In detail, the signal
訊號相關性評估模組40之時頻分析可利用傅立葉分析或自迴歸模型係數求取駕駛人之駕駛行為資料與生理資訊(生理訊號)兩者相關聯之兩訊號之相關係數。而且,訊號相關性評估模組40之相關性分析可計算轉換後之駕駛人之駕駛行為資料與來自穿戴式裝置感測器模組20之生理資訊(生理訊號),以分析駕駛行為資料與生理資訊(生理訊號)兩者之相關性,例如駕駛行為資料與生理資訊(生理訊號)兩者之相關性是正向關係、反向關係或兩者之數值大小。藉此,透過統計檢定方式分析出駕駛行為資料與生理資訊(生理訊號)兩者之同調性是否有達到顯著的統計結果。
The time-frequency analysis of the signal
第2圖為本發明中機器學習自動分析模組50之示意圖。如圖所示,機器學習自動分析模組50之使用欄位資料係有關駕駛人之駕駛行為資料與生理資訊及行車歷史資料等因子,並可包括[1]心率量測指數、血氧飽和度監測指數、壓力指數、心率變異性;[2]急加減速、急轉彎、超速事件數、定速保持比、駕車時刻比例;[3]已知疾病特徵、問卷結果;[4]定向性、同調性;或[5]路線行駛時間、路線行駛距離、單日路線、往返次數、總塞車次數等。
Figure 2 is a schematic diagram of the machine learning
又,機器學習自動分析模組50可依據前述使用欄位資料(因
子),即有關駕駛人之駕駛行為資料與生理資訊及行車歷史資料等因子,透過因子基本運算組合、裝袋算法分組、敏感度分析或決策樹規則分析等方式自動化產出進階因子,並依據前述進階因子透過逐步迴歸、隨機森林、資訊價值(Information Value;IV值)、遺傳算法、重抽樣算法或彈性網路(Elastic Net)等方式篩選出重要因子,且依據前述重要因子透過羅吉斯迴歸、隨機森林、決策樹、SVM(support vector machine;支援向量機)/SVR(support vector regression;支持向量回歸)、深度學習、K均值分類或貝式網路等方式產生多重模型組合,再依據前述多重模型組合透過混亂矩陣、ROC曲線(receiver operating characteristic curve;接收者操作特徵曲線)、F值(F-Measure)或均方差等篩選或評估出關聯結果或關聯模型(如最佳模型)。
In addition, the machine learning
第3圖為本發明有關營運風險B1、行車風險B2與醫療生理風險B3之演算取得之示意圖。如圖所示,訊號相關性評估模組40、機器學習自動分析模組50、特徵分群模組60可取得運輸載具和駕駛人之營運資料庫A1、行車資料庫A2與駕駛人之生理資料庫A3,以進一步演算取得有關營運風險B1、行車風險B2(駕駛行為資料)與醫療生理風險B3(生理資訊)等資料之因子。例如,營運風險B1之資料包括路線行駛時間、路線行駛距離、單日路線往返次數或單日路線總塞車次數等因子,行車風險B2(駕駛行為資料)之資料包括定速保持比、時段、道路等級、急加減速、急轉彎等因子,醫療生理風險B3(生理資訊)之資料包括問卷結果、睡眠指數(如睡眠效率(Sleep efficiency;Eff))、血氧飽和度監測指數(如血氧飽和度下降指數(Oxvgen desaturation index;ODI))、心率變異率、心率量測指數或生理資訊異常次數等因子。
Figure 3 is a schematic diagram of the calculation of operational risk B1, driving risk B2, and medical physiological risk B3 of the present invention. As shown in the figure, the signal
第4圖為本發明中有關駕駛人之行駛路線配對分析之一示意圖。如圖所示,由第1圖之穿戴式裝置感測器模組20(如醫療手錶)蒐集駕駛人之生理資訊(醫療生理資訊),並由智慧排班模組70依據營運風險B1之資料與相應之行車風險B2(駕駛行為資料)或醫療生理風險B3(生理資訊)之資料進行駕駛人之行駛路線配對分析。例如,營運風險B1之資料包括路線行駛時間、路線行駛距離、日路線往返次數、單日路線總塞車次數或駕駛年齡等,行車風險B2(駕駛行為資料)之資料包括定速保持比、急加減速、急轉彎或超速事件數等,醫療生理風險B3(生理資訊)之資料包括睡眠指數、血氧飽和度監測指數、心率變異性或心率量測指數等。因此,本發明針對不同的營運風險B1、行車風險B2(駕駛行為資料)或醫療生理風險B3(生理資訊)會產生不同的駕駛類型,且不同的駕駛類型需要不同的駕駛特性,也會對駕駛人之行駛路線配對有不同的影響。
Figure 4 is a schematic diagram of the driver's driving route matching analysis in the present invention. As shown in the figure, the wearable device sensor module 20 (such as a medical watch) in Figure 1 collects the physiological information (medical physiological information) of the driver, and the
第5圖為本發明有關駕駛人之行駛路線配對分析之另一示意圖。如第5圖所示,第1圖之訊號相關性評估模組40分析出營運風險B1之因子主要為路線總行駛時間、單日路線往返次數及單日路線總塞車次數,並分析出醫療生理風險B3(生理資訊)之因子主要為睡眠指數及心率量測指數。
Figure 5 is another schematic diagram of the driving route matching analysis of the present invention. As shown in Figure 5, the signal
接著,機器學習自動分析模組50可將訊號相關性評估模組40所分析之營運風險B1、行車風險B2與醫療生理風險B3(生理資訊)進行機器學習,以自動分析出工作績效指標預估值D。例如,機器學習自動分析模組50使用羅吉斯迴歸之方式分析出:工作績效指標= a 1 * x 1 +a 2 * x 2 +a 3 * x 3 +…+a n-1 * x n-1 +a n * x n 。前述ai(如a1至an之任一者)為任意
解釋變數所相應之影響係數,而xi(如x1至xn之任一者)為任意解釋變數之實際值。
Then, the machine learning
第6圖為本發明中以第1圖之智慧排班模組70優化駕駛人之排班表E之示意圖。如圖所示,第5圖之機器學習自動分析模組50分析出:工作績效指標=f(x1,x2,x3,…,xn-1,xn)。前述工作績效指標可為交通運輸公司內部自定之績效指標,如運輸業績達成率、個人產值、個人工作品質達成率或肇事率等公司客觀認定之營運目標,而f(.)函數則是利用機器學習自動分析模組50內所示之多重模型組合,可廣義泛化成任意解釋變數之多重組合函數,該解釋變數x1~n之資料來源為營運風險B1、行車風險B2(駕駛行為資料)與醫療生理風險B3(生理資訊),並透過模型評估方法以找出最佳模型之係數組合(a1~an),藉此可計算未知風險駕駛人之工作績效指標預估值D。
Fig. 6 is a schematic diagram of the
特徵分群模組60可採用無監督式學習法使用營運風險B1、行車風險B2(駕駛行為資料)與醫療生理風險B3(生理資訊)進行集群分析,如K均值分類或層次分析法,以針對駕駛人不同特性(如駕駛人之駕駛行為資料與生理資訊兩者之關聯結果)進行駕駛人之分類或分群,例如長/短路線駕駛人之族群、日/夜間發車駕駛人之族群或各年齡層駕駛人之族群。
The
智慧排班模組70可依據輸入駕駛名單分別計算每一駕駛人於特徵分群模組60定義之每一分群內之f(.)預估值,全部計算完畢後,智慧排班模組70再尋找滿足F(.)最大值且各分群所需名額之最佳組合(同一駕駛僅能被排班到單一分群),其中F(.)是指所有分群內各駕駛人f(.)之預估值總合。然後,利用F(.)值決定各分群之優先人選,以輸出整體最佳之
排班表E。
The
智慧排班模組70透過伺服器(如後端伺服器)進行全體駕駛人之分群配對排序,找出如長/短路線族群或是日/夜間發車族群之最佳配對分數C1,並考量連續天數或工作時數等合規條件,然後輸出一段時間(如當周或當月)之排班表,亦或是出現人力缺口時,由智慧排班模組70找出最佳代理駕駛人。
The
綜上,本發明中即時蒐集駕駛行為資料與生理資訊之智慧排班分析系統可至少具有下列特色、優點或技術功效。 In summary, the intelligent scheduling analysis system for real-time collection of driving behavior data and physiological information in the present invention can at least have the following characteristics, advantages, or technical effects.
一、本發明能透過行動裝置感測器模組及/或穿戴式裝置感測器模組蒐集駕駛人之駕駛行為資料、駕駛人之生理資訊與運輸載具之行車歷史資料,再依據駕駛人之駕駛行為資料與生理資訊之特性對行駛情境進行配對或評分,以利自動產出高安全係數之排班表。 1. The present invention can collect the driving behavior data of the driver, the physiological information of the driver and the driving history data of the transportation vehicle through the mobile device sensor module and/or the wearable device sensor module, and then according to the driver The characteristics of the driving behavior data and physiological information are matched or scored to the driving situation, so as to automatically produce a high safety schedule.
二、本發明之智慧排班模組能納入駕駛人之個體生理差異與實際駕駛情況,以提供適合駕駛人與路線之配對推薦而提高行車安全。 2. The smart scheduling module of the present invention can incorporate the driver's individual physiological differences and actual driving conditions, so as to provide matching recommendations suitable for the driver and the route to improve driving safety.
三、本發明之特徵分群模組能優化駕駛人之分類或分群,包括針對駕駛人之駕駛行為資料與生理資訊等不同特性進行駕駛人之分類或分群,以提高運輸載具及其駕駛人之行車安全。 3. The feature grouping module of the present invention can optimize the classification or grouping of drivers, including the classification or grouping of drivers for different characteristics such as driving behavior data and physiological information of drivers, so as to improve the transportation vehicle and its driver. Driving safety.
四、有別於傳統運輸載具之固定排班方式,本發明能量化駕駛人實際之駕駛行為資料與生理資訊,針對不同駕駛人之差異進行路線與時段的評分以優化駕駛人與路線之配對,俾提高運輸載具之安全性,亦提供運輸載具之管理端更多的運營資訊。 4. Different from the fixed scheduling method of traditional transportation vehicles, the present invention energizes the actual driving behavior data and physiological information of the driver, and scores the route and time period according to the difference of different drivers to optimize the matching of the driver and the route , In order to improve the safety of transportation vehicles, and also provide more operational information on the management side of transportation vehicles.
上述實施形態僅例示性說明本發明之原理、特點及其功效, 並非用以限制本發明之可實施範疇,任何熟習此項技藝之人士均可在不違背本發明之精神及範疇下,對上述實施形態進行修飾與改變。任何運用本發明所揭示內容而完成之等效改變及修飾,均仍應為申請專利範圍所涵蓋。因此,本發明之權利保護範圍,應如申請專利範圍所列。 The above-mentioned embodiments only exemplify the principles, features and effects of the present invention. It is not intended to limit the applicable scope of the present invention. Anyone familiar with the art can modify and change the above-mentioned embodiments without departing from the spirit and scope of the present invention. Any equivalent changes and modifications made by using the content disclosed in the present invention should still be covered by the scope of the patent application. Therefore, the protection scope of the present invention should be as listed in the scope of the patent application.
1‧‧‧智慧排班分析系統 1‧‧‧Smart scheduling analysis system
10‧‧‧行動裝置感測器模組 10‧‧‧Mobile device sensor module
20‧‧‧穿戴式裝置感測器模組 20‧‧‧Wearable device sensor module
30‧‧‧訊號處理模組 30‧‧‧Signal processing module
40‧‧‧訊號相關性評估模組 40‧‧‧Signal Correlation Evaluation Module
50‧‧‧機器學習自動分析模組 50‧‧‧Machine learning automatic analysis module
60‧‧‧特徵分群模組 60‧‧‧Feature Grouping Module
70‧‧‧智慧排班模組 70‧‧‧Smart scheduling module
A1‧‧‧營運資料庫 A1‧‧‧Operation Database
A2‧‧‧行車資料庫 A2‧‧‧Driving Database
A3‧‧‧生理資料庫 A3‧‧‧Physiological Database
B1‧‧‧營運風險 B1‧‧‧Operational risk
B2‧‧‧行車風險 B2‧‧‧Driving Risk
B3‧‧‧醫療生理風險 B3‧‧‧Medical Physiological Risk
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