TWI774984B - Traffic incident detection system and method - Google Patents
Traffic incident detection system and method Download PDFInfo
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- TWI774984B TWI774984B TW108132056A TW108132056A TWI774984B TW I774984 B TWI774984 B TW I774984B TW 108132056 A TW108132056 A TW 108132056A TW 108132056 A TW108132056 A TW 108132056A TW I774984 B TWI774984 B TW I774984B
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
- G08G1/0129—Traffic data processing for creating historical data or processing based on historical data
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
- G08G1/0133—Traffic data processing for classifying traffic situation
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/065—Traffic control systems for road vehicles by counting the vehicles in a section of the road or in a parking area, i.e. comparing incoming count with outgoing count
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Abstract
Description
本發明是關於一種交通事件偵測技術,特別是指一種交通事件偵測系統及方法。 The present invention relates to a traffic incident detection technology, in particular to a traffic incident detection system and method.
目前之交通事件大部分以人工方式實施,例如使用監視器以人力進行畫面內容之判定或使用路人之通報等,而少部分則依據一般的相機鏡頭所取得的影像由處理單元自動地判定。然而,當使用人工方式進行交通事件之判定或通報時,將可能發生監控人力不足或通報延誤之情況。又,透過影像進行交通事件之判定時,則可能因影像之錯位、陰影、路面之顏色等干擾造成交通事件之誤判。 Most of the current traffic incidents are carried out manually, such as using a monitor to determine the content of the screen manually or using a passerby to notify, etc., while a few are automatically determined by the processing unit based on the image obtained by the ordinary camera lens. However, when using manual methods to determine or report traffic incidents, there may be a shortage of monitoring manpower or delays in reporting. In addition, when judging traffic incidents through images, misjudgment of traffic incidents may be caused by disturbances such as dislocation of images, shadows, and road surface color.
在現有技術中,提出一種道路即時交通事故風險控制方法,是一種基於多類支持向量機(Support Vector Machine;SVM)的道路即時交通事故風險預測及控制方法,可用來預測檢測路段發生交通事故的可能性。對檢測路段建立基於多類支持向量機的事故預測模型,並將採集的即時交通特徵參數帶入事故預測模型,以判斷是否有發生交通事故的風險。但是,此方法需運用多類支持向量機對交通事故學習分類模型進行事故風險判別 及分類,以致需耗費多類支持向量機與交通事故學習分類模型之大量建置時間及成本。 In the prior art, a real-time road traffic accident risk control method is proposed, which is a real-time road traffic accident risk prediction and control method based on a multi-class Support Vector Machine (SVM), which can be used to predict and detect the risk of traffic accidents in road sections. possibility. An accident prediction model based on multi-class support vector machines is established for the detected road sections, and the collected real-time traffic characteristic parameters are brought into the accident prediction model to judge whether there is a risk of a traffic accident. However, this method needs to use a multi-class support vector machine to learn the classification model of traffic accidents to discriminate the accident risk. and classification, so that it takes a lot of time and cost to build multi-class support vector machines and traffic accident learning classification models.
因此,如何提供一種新穎或創新之交通事件偵測技術,實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 traffic incident detection technology 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交通事件偵測系統及方法,能經濟或快速地分析或推估出選定之道路之交通事件。 The present invention provides a novel or innovative traffic incident detection system and method, which can analyze or estimate traffic incidents on selected roads economically or quickly.
本發明之交通事件偵測系統包括:一選擇模組、一偵測模組、一計算模組及一分析模組,其中,該選擇模組係選定要偵測交通事件之道路,該偵測模組係透過交通資訊之資料來源偵測或擷取選擇模組所選定之道路現在及過去之旅行時間與車流量,該計算模組係計算偵測模組所偵測或擷取之道路過去之旅行時間與車流量的歷史統計值,再將偵測模組所偵測之道路現在之旅行時間與車流量分別比對計算模組所計算之道路過去之旅行時間與車流量的歷史統計值,以計算出旅行時間之相對值與車流量之相對值,俾由計算模組將旅行時間之相對值與車流量之相對值分別輸入交通事件分析表,而該分析模組係自交通事件分析表中依據旅行時間之相對值與車流量之相對值分析或推估出對應之交通事件。 The traffic incident detection system of the present invention includes: a selection module, a detection module, a calculation module, and an analysis module, wherein the selection module selects a road to detect traffic incidents, and the detection The module detects or retrieves the current and past travel time and traffic flow of the road selected by the selection module through the data source of traffic information. The calculation module calculates the past of the road detected or retrieved by the detection module. The historical statistical values of travel time and traffic flow, and then compare the current travel time and traffic flow of the road detected by the detection module with the historical statistical values of the road's past travel time and traffic flow calculated by the calculation module. , to calculate the relative value of the travel time and the relative value of the traffic flow, so that the relative value of the travel time and the relative value of the traffic flow are respectively input into the traffic event analysis table by the calculation module, and the analysis module is derived from the traffic event analysis In the table, the corresponding traffic events are analyzed or estimated based on the relative value of travel time and the relative value of traffic flow.
本發明之交通事件偵測方法包括:選定要偵測交通事件之道路,以自交通資訊之資料來源中偵測或擷取出所選定之道路現在及過去之旅行時間與車流量;計算所偵測或擷取之道路過去之旅行時間與車流量的歷史統計值,再將所偵測之道路現在之旅行時間與車流量分別比對所計算 之道路過去之旅行時間與車流量的歷史統計值,以計算出旅行時間之相對值與車流量之相對值,俾將旅行時間之相對值與車流量之相對值分別輸入交通事件分析表;以及自交通事件分析表中依據旅行時間之相對值與車流量之相對值分析或推估出對應之交通事件。 The traffic incident detection method of the present invention includes: selecting a road to detect a traffic incident, detecting or retrieving the current and past travel time and traffic flow of the selected road from a data source of traffic information; calculating the detected road Or the historical statistical values of the past travel time and traffic flow of the road are extracted, and then the current travel time and traffic flow of the detected road are compared to calculate the The historical statistic values of the past travel time and traffic flow of the road, to calculate the relative value of the travel time and the relative value of the traffic flow, so as to input the relative value of the travel time and the relative value of the traffic flow into the traffic event analysis table respectively; and From the traffic event analysis table, the corresponding traffic events are analyzed or estimated based on the relative value of travel time and the relative value of traffic flow.
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部分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 obvious and easy to understand, the following specific embodiments are given and described in detail with the accompanying drawings. Additional features and advantages of the present invention will be set forth in part in the description that follows, and in part will be apparent from the description, or may be learned by practice of the invention. The features and advantages of the invention will be realized and attained by means of the elements and combinations particularly pointed out in the scope of the patent application. It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not intended to limit the scope of the invention as claimed.
1‧‧‧交通事件偵測系統 1‧‧‧Traffic Incident Detection System
10‧‧‧選擇模組 10‧‧‧Select Module
11‧‧‧交通事件 11‧‧‧Traffic Incident
20‧‧‧分類模組 20‧‧‧Classification Module
21‧‧‧道路分類 21‧‧‧Road classification
30‧‧‧偵測模組 30‧‧‧Detection Module
30'‧‧‧蒐集模組 30'‧‧‧Collection Module
31‧‧‧旅行時間 31‧‧‧Travel time
32‧‧‧車流量 32‧‧‧Traffic flow
40‧‧‧儲存模組 40‧‧‧Storage Module
41‧‧‧交通資訊 41‧‧‧Traffic Information
50‧‧‧計算模組 50‧‧‧Computing Modules
51‧‧‧歷史統計值 51‧‧‧Historical Statistics
52‧‧‧旅行時間之相對值 52‧‧‧ Relative value of travel time
53‧‧‧車流量之相對值 53‧‧‧Relative value of traffic flow
60‧‧‧分析模組 60‧‧‧Analysis Module
61‧‧‧交通事件分析表 61‧‧‧Analysis of traffic incidents
70‧‧‧連結模組 70‧‧‧Link Module
80‧‧‧排序模組 80‧‧‧Sort module
90‧‧‧發送模組 90‧‧‧Send module
S11至S16、S21至S26‧‧‧步驟 Steps from S11 to S16, S21 to S26‧‧‧
第1圖為本發明之交通事件偵測系統之架構示意圖;第2圖為本發明之交通事件分析表;第3圖為本發明之交通事件偵測方法之流程示意圖;以及第4圖為本發明之交通事件分析表之建立方式之流程示意圖。 Figure 1 is a schematic diagram of the structure of the traffic incident detection system of the present invention; Figure 2 is a traffic incident analysis table of the present invention; Figure 3 is a schematic flowchart of the traffic incident detection method of the present invention; A schematic diagram of the flow chart of the method of establishing the traffic incident analysis table of the invention.
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 embodiments of the present invention will be described below by means of specific embodiments. Those who are familiar with the technology can understand other advantages and effects of the present invention from the contents disclosed in this specification. Therefore, it can also be implemented or applied by other different specific equivalent embodiments.
第1圖為本發明之交通事件偵測系統1之架構示意圖,第2圖為本發明之交通事件分析表61。同時,第1圖之交通事件偵測系統1之主要技術內容如下,其餘技術內容相同於第3圖至第4圖之說明,於此不再重覆敘述。
FIG. 1 is a schematic diagram of the structure of a traffic
如第1圖所示,交通事件偵測系統1可包括一選擇模組10、一分類模組20、一偵測模組30、一蒐集模組30'、一儲存模組40、一計算模組50、一分析模組60、一連結模組70、一排序模組80及一發送模組90。在一些實施例中,選擇模組10可為選擇器或選擇軟體等,分類模組20可為分類器或分類軟體等,偵測模組30可為偵測器或偵測軟體等,蒐集模組30'可為蒐集器或蒐集軟體等,儲存模組40可為資料庫、記憶體(如記憶卡)、硬碟(如雲端硬碟、網路硬碟)、光碟或隨身碟等,計算模組50可為算術邏輯單元(ALU)、計算軟體或統計軟體等,分析模組60可為分析器或分析軟體等,連結模組70可為連結軟體等,排序模組80可為排序軟體等,發送模組90為發送器、收發器、發送軟體或收發軟體等。但是,本發明並不以此為限。
As shown in FIG. 1, the traffic
選擇模組10可選定要偵測交通事件11之道路,分類模組20可依據道路之特性進行道路分類,偵測模組30可透過交通資訊41之資料來源偵測或擷取選擇模組10所選定之道路現在及過去之旅行時間31與車流量32,且儲存模組40可儲存來自資料來源之交通事件11與交通資訊41。在一些實施例中,交通資訊41可包括旅行時間31、車流量32、車速、佔有率、停等長度及/或停等時間,以供偵測模組30、儲存模組40、計算
模組50、分析模組60分別偵測、儲存、計算與分析交通資訊41。
The
計算模組50可計算偵測模組30所偵測或擷取之道路過去之旅行時間31與車流量32的歷史統計值51,再將偵測模組30所偵測之道路現在之旅行時間31與車流量32分別比對計算模組50所計算之道路過去之旅行時間31與車流量32的歷史統計值51,以計算出旅行時間之相對值52與車流量之相對值53,俾由計算模組50將旅行時間之相對值52與車流量之相對值53分別輸入交通事件分析表61。分析模組60可自交通事件分析表61中依據旅行時間之相對值52與車流量之相對值53分析或推估出對應之交通事件11。
The
連結模組70可建立旅行時間之相對值52、車流量之相對值53與交通事件11之對應連結,以在各道路分類21中依據旅行時間之相對值52與車流量之相對值53建立相連結之交通事件11。排序模組80可將各道路分類21中旅行時間之相對值52與車流量之相對值53依序排列,以建立或產生交通事件分析表61。發送模組90可發送分析模組60所分析或推估之交通事件11至道路上之車輛,以將交通事件11通知或告警車輛之駕駛者。
The linking
因此,本發明能利用道路之旅行時間31、車流量32與歷史統計值51等交通資訊41之關係,以快速偵測交通狀況而分析或推估出選定之道路之交通事件11。亦即,本發明能依據道路之旅行時間31與車流量32等交通資訊41之變化觀察交通狀況,以快速判定或推估出交通狀況為何交通事件11,例如嚴重車禍、掉落物、壅塞等。又,在發生交通事件11時,往往造成後方車輛的堵塞,故本發明能自動化偵測交通事件11之持續時間,藉以提供後方駕駛者必要的交通資訊41。
Therefore, the present invention can utilize the relationship between the
另外,本發明可透過例如交通部高速公路局之交通資料庫、政府資料開放平台之警察廣播即時路況資訊等蒐集道路上之各交通事件11,並透過採用手機基地台為基礎之車輛探偵(Cellular-Based Vehicle Probe;CVP)、固定式車輛偵測器(Vehicle Detector;VD)、採用電子道路收費系統(Electronic Toll Collection;ETC)為基礎之車輛探偵(ETC-Based Vehicle Probe;EVP)、全球定位系統之探偵車(GPS-Based Vehicle Probe;GVP)等取得相關之旅行時間31與車流量32等交通資訊41,再將交通事件11與交通資訊41儲存於儲存模組40(如資料庫)中,進而依據交通資訊41運用大數據分析技術自動化分析判別交通事件11。
In addition, the present invention can collect
第3圖為本發明之交通事件偵測方法之流程示意圖,並請參閱第1圖至第2圖。同時,第3圖之交通事件偵測方法主要包括下列步驟S11至步驟S16之技術內容,其餘技術內容相同於第1圖與第4圖之說明,於此不再重覆敘述。 FIG. 3 is a schematic flowchart of the traffic incident detection method of the present invention, and please refer to FIG. 1 to FIG. 2 . Meanwhile, the traffic incident detection method in FIG. 3 mainly includes the following technical contents of steps S11 to S16 , and the remaining technical contents are the same as those described in FIGS. 1 and 4 , and will not be repeated here.
在第3圖之步驟S11中,由第1圖所示選擇模組10選定要偵測交通事件11之道路,並由分類模組20依據道路之特性進行道路分類。例如,由選擇模組10選定要偵測交通事件11所在地區之道路,並由分類模組20依據行政系統之道路分類21分析或判定該道路屬於何種道路分類21,例如該道路屬於國道、省道、市道、縣道、區道或鄉道等道路分類21。
In step S11 of FIG. 3 , the
舉例而言,由選擇模組10在2019年04月02日選定要偵測交通事件11之道路為國道,如國道1號(中山高速公路)。
For example, the road selected by the
在第3圖之步驟S12中,由偵測模組30透過至少一(如複數)資料來源偵測或擷取選擇模組10所選定之道路現在及過去之旅行時間31
與車流量32等交通資訊41。例如,交通資訊41之至少一資料來源可包括採用手機基地台為基礎之車輛探偵(CVP)、固定式車輛偵測器(VD)、採用電子道路收費系統(ETC)為基礎之車輛探偵(EVP)、全球定位系統之探偵車(GVP)、交通部高速公路局之交通資料庫、政府資料開放平台之警察廣播即時路況資訊等,且交通資訊41可儲存於儲存模組40(如資料庫)中並依照道路分類21以行政系統作分類。
In step S12 of FIG. 3 , the current and
舉例而言,由偵測模組30透過政府資料開放平台之警察廣播即時路況資訊取得2019年04月02日10:00,在國道1號(中山高速公路)南下71公里(km)至83公里之範圍內,該道路之旅行時間31之平均值為43分鐘,且每五分鐘之車流量32為146輛(如國道1號之某一偵測站於此時間範圍內所經過之車流總量)。
For example, at 10:00 on April 02, 2019, the
在第3圖之步驟S13中,由計算模組50計算偵測模組30所偵測或擷取之道路過去之旅行時間31與車流量32等交通資訊41的歷史統計值51。例如,由計算模組50依據儲存模組40(如資料庫)中之交通資訊41計算出該道路過去之每個單位時間的旅行時間31與車流量32等交通資訊41的歷史統計值51,而歷史統計值51可為算術平均值、中位數、眾數或標準差等。
In step S13 of FIG. 3 , the
舉例而言,由計算模組50計算儲存模組40(如資料庫)之交通資訊41中該道路過去之旅行時間31與車流量32等交通資訊41的歷史統計值51,例如2019年3月份10:00~10:05,在國道1號(中山高速公路)南下71公里至83公里之範圍內,該道路過去之旅行時間31之平均值為8分鐘,且五分鐘之車流量32為520輛。
For example, the
在第3圖之步驟S14中,由計算模組50將步驟S12中偵測模組50所偵測之該道路現在之旅行時間31與車流量32分別比對步驟S13中計算模組50所計算之該道路過去之旅行時間31與車流量32的歷史統計值51,以計算出旅行時間之相對值52(包括比例關係)與車流量之相對值53(包括比例關係)。
In step S14 in FIG. 3 , the
舉例而言,旅行時間之相對值52為步驟S12中該道路現在之旅行時間31(如43分鐘)除以步驟S13中該道路過去之旅行時間31(如8分鐘),故本實施例中旅行時間之相對值52為5.375(即43/8=5.375)。同時,車流量之相對值53為步驟S12中該道路現在之車流量32(如146輛)除以步驟S13中該道路過去之車流量32(如520輛),故本實施例中車流量之相對值53為0.28(即146/520=0.28)。
For example, the
在第3圖之步驟S15中,由計算模組50將旅行時間之相對值52與車流量之相對值53分別輸入第2圖所示之交通事件分析表61,再由分析模組60自交通事件分析表61中依據旅行時間之相對值52與車流量之相對值53分析或推估出對應之交通事件11。例如,由分析模組60先自交通事件分析表61中分析出該道路屬於何種道路分類(如國道、省道、市道、縣道、區道或鄉道),再自交通事件分析表61中依據最接近之旅行時間之相對值52與車流量之相對值53兩者的落點取出對應之交通事件11。
In step S15 in FIG. 3, the
舉例而言,由計算模組50將步驟S14中旅行時間之相對值52(如值5.375)與車流量之相對值53(如值0.28)分別輸入第2圖所示之交通事件分析表61,再由分析模組60依據旅行時間之相對值52(如值5.375)與車流量之相對值53(如值0.28)分別比對出最接近之旅行時間之相對值
52(如值5)與車流量之相對值53(如值0.3),以依據最接近之旅行時間之相對值52(如值5)、車流量之相對值53(如值0.3)與道路之道路分類21(如國道)分析或推估出對應之交通事件11為「追撞事故」。
For example, the
在第3圖之步驟S16中,由發送模組90發送分析模組60所分析或推估之交通事件11至該道路上之車輛,以將交通事件11通知或告警車輛之駕駛者。
In step S16 of FIG. 3 , the sending
舉例而言,由發送模組90向車輛發送2019年04月02日10:00,在國道1號(中山高速公路)南下71公里至83公里之範圍內,該道路之交通事件11經分析或推估為「追撞事故」影響,造成嚴重回堵,以通知或告警車輛之駕駛者。
For example, the sending
第4圖為本發明第2圖所示之交通事件分析表61之建立方式之流程示意圖,並請參閱第1圖。同時,交通事件分析表61之建立方式可包括下列步驟S21至步驟S26之技術內容。 FIG. 4 is a schematic flowchart of the method of establishing the traffic incident analysis table 61 shown in FIG. 2 of the present invention, and please refer to FIG. 1 . Meanwhile, the establishment of the traffic event analysis table 61 may include the technical contents of the following steps S21 to S26.
在第4圖之步驟S21中,由第1圖所示分類模組20依據道路之特性進行道路分類。
In step S21 of FIG. 4 , the
舉例而言,由分類模組20依據道路之特性將道路之道路分類21儲存於儲存模組40(如資料庫)中,並依照所在地區之道路以行政系統分類為國道、省道、市道、縣道、區道或鄉道等。
For example, the
在第4圖之步驟S22中,由蒐集模組30'依據道路分類21透過交通資訊41之至少一(如複數)資料來源蒐集交通事件11之道路相關之旅行時間31與車流量32等交通資訊41。例如,由蒐集模組30'透過交通部高速公路局交通資料庫、政府資料開放平台之警察廣播即時路況資訊等
資料來源蒐集道路上各交通事件11,並透過採用手機基地台為基礎之車輛探偵(CVP)、固定式車輛偵測器(VD)、採用電子道路收費系統(ETC)為基礎之車輛探偵(EVP)、全球定位系統之探偵車(GVP)等取得相關之旅行時間31與車流量32等交通資訊41,以儲存交通事件11與交通資訊41於儲存模組40(如資料庫)中。
In step S22 in FIG. 4 , the
舉例而言,由蒐集模組30'蒐集交通事件11之道路(如國道)相關之旅行時間31與車流量32等交通資訊41。例如,在2019年04月01日08:00之交通事件11為外側掉落物,國道1號(中山高速公路)南下71公里至83公里之範圍內,該道路相關之旅行時間31之平均值為30分鐘,且五分鐘之車流量32為208輛。
For example, the
在第4圖之步驟S23中,由計算模組50計算交通事件11之道路過去之旅行時間31與車流量32等交通資訊41的歷史統計值51。例如,由計算模組50依據儲存模組40(如資料庫)中之交通資訊41計算出該道路過去之每個單位時間的旅行時間31與車流量32等交通資訊41的歷史統計值51,而歷史統計值51可為算術平均值、中位數、眾數或標準差等。
In step S23 of FIG. 4 , the
舉例而言,由計算模組50自政府資料開放平台之警察廣播即時路況資訊或交通部高速公路局交通資料庫中取得2019年3月份08:00,在國道1號(中山高速公路)南下71公里至83公里之範圍內,該道路過去之旅行時間31之平均值為7.2分鐘,且五分鐘之車流量32為520輛(如國道1號之某一偵測站於此時範圍內所經過之車流總量),故旅行時間31的歷史統計值為7.2分鐘,且車流量32的歷史統計值為520輛。
For example, at 08:00 in March 2019, the
在第4圖之步驟S24中,由計算模組50將蒐集模組30'所蒐集之交通事件11之道路相關之旅行時間31與車流量32(見步驟S22)分別比對計算模組50所計算之交通事件11之道路過去之旅行時間31與車流量32的歷史統計值51(見步驟S23),以計算出旅行時間之相對值52(包括比例關係)與車流量之相對值53(包括比例關係)。
In step S24 in FIG. 4 , the
舉例而言,旅行時間之相對值52為步驟S22中該道路相關之旅行時間31(如30分鐘)除以步驟S23中該道路過去之旅行時間31(如7.2分鐘),故本實施例中旅行時間之相對值52為4.16(即30/7.2=4.16)。同時,車流量之相對值53為步驟S22中該道路相關之車流量32(如208輛)除以步驟S23中該道路過去之車流量32(如520輛),故本實施例中車流量之相對值53為0.4(即208/520=0.4)。
For example, the
在第4圖之步驟S25中,由連結模組70建立旅行時間之相對值52、車流量之相對值53與交通事件11之對應連結,以在各道路分類21中依據旅行時間之相對值52與車流量之相對值53建立相連結之交通事件11。
In step S25 in FIG. 4 , the
在第4圖之步驟S26中,由排序模組80將各道路分類21中旅行時間之相對值52與車流量之相對值53依序排列,以建立或產生第2圖所示之交通事件分析表61。
In step S26 in FIG. 4 , the
綜上,本發明之交通事件偵測系統及方法可至少具有下列特色、優點或技術功效。 In conclusion, the traffic incident detection system and method of the present invention can at least have the following features, advantages or technical effects.
一、本發明可無須額外增加路測設備,以大幅減少交通基礎建設之建置成本,從而經濟或快速地分析或推估出選定之道路之交通事件。 1. The present invention can greatly reduce the construction cost of traffic infrastructure without the need for additional road testing equipment, so as to analyze or estimate the traffic events of the selected road economically or quickly.
二、本發明能利用道路之旅行時間、車流量與歷史統計值等交通資訊之關係,以快速偵測交通狀況而分析或推估出選定之道路之交通事件。亦即,本發明能依據道路之旅行時間與車流量等交通資訊之變化觀察交通狀況,以快速判定或推估出交通狀況為何交通事件,例如嚴重車禍、掉落物、壅塞等。 2. The present invention can analyze or estimate the traffic events of the selected road by using the relationship between the travel time of the road, the traffic flow and the historical statistical value and other traffic information to quickly detect the traffic situation. That is, the present invention can observe traffic conditions according to changes in traffic information such as road travel time and traffic flow, so as to quickly determine or estimate what traffic conditions are traffic events, such as serious traffic accidents, falling objects, and congestion.
三、在發生交通事件時,往往造成後方車輛的堵塞,故本發明能自動化偵測交通事件之持續時間,藉以提供後方駕駛者必要的交通資訊。 3. When a traffic incident occurs, the rear vehicles are often blocked, so the present invention can automatically detect the duration of the traffic incident, thereby providing necessary traffic information for the rear driver.
四、本發明能運用大數據分析技術,以自動化分析判別交通事件。亦即,本發明能透過交通部高速公路局之交通資料庫、政府資料開放平台之警察廣播即時路況資訊等蒐集道路上之各交通事件,並透過採用手機基地台為基礎之車輛探偵(CVP)、固定式車輛偵測器(VD)、採用電子道路收費系統(ETC)為基礎之車輛探偵(EVP)、全球定位系統之探偵車(GVP)等取得相關之旅行時間與車流量等交通資訊,並將交通資訊儲存於儲存模組(如資料庫)中,以依據交通資訊運用大數據分析技術自動化分析判別交通事件。 Fourth, the present invention can use big data analysis technology to automatically analyze and judge traffic events. That is, the present invention can collect various traffic events on the road through the traffic database of the Expressway Bureau of the Ministry of Communications, the police broadcast real-time traffic information on the government data open platform, etc. , stationary vehicle detector (VD), vehicle detection (EVP) based on electronic road pricing system (ETC), global positioning system detection vehicle (GVP), etc. to obtain relevant travel time and traffic flow and other traffic information, And the traffic information is stored in a storage module (such as a database), so as to use big data analysis technology to automatically analyze and determine traffic events according to the traffic information.
五、本發明能應用於例如智慧交通、交通控制中心或車輛導航系統等。 Fifth, the present invention can be applied to, for example, intelligent transportation, traffic control center or vehicle navigation system.
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 are only illustrative of the principles, features and effects of the present invention, and are not intended to limit the applicable scope of the present invention. Modifications and changes are made to the implementation form. Any equivalent changes and modifications made by using the contents disclosed in the present invention should still be covered by the scope of the patent application. Therefore, the scope of protection of the right of the present invention should be listed in the scope of the patent application.
1‧‧‧交通事件偵測系統 1‧‧‧Traffic Incident Detection System
10‧‧‧選擇模組 10‧‧‧Select Module
11‧‧‧交通事件 11‧‧‧Traffic Incident
20‧‧‧分類模組 20‧‧‧Classification Module
21‧‧‧道路分類 21‧‧‧Road classification
30‧‧‧偵測模組 30‧‧‧Detection Module
30'‧‧‧蒐集模組 30'‧‧‧Collection Module
31‧‧‧旅行時間 31‧‧‧Travel time
32‧‧‧車流量 32‧‧‧Traffic flow
40‧‧‧儲存模組 40‧‧‧Storage Module
41‧‧‧交通資訊 41‧‧‧Traffic Information
50‧‧‧計算模組 50‧‧‧Computing Modules
51‧‧‧歷史統計值 51‧‧‧Historical Statistics
52‧‧‧旅行時間之相對值 52‧‧‧ Relative value of travel time
53‧‧‧車流量之相對值 53‧‧‧Relative value of traffic flow
60‧‧‧分析模組 60‧‧‧Analysis Module
61‧‧‧交通事件分析表 61‧‧‧Analysis of traffic incidents
70‧‧‧連結模組 70‧‧‧Link Module
80‧‧‧排序模組 80‧‧‧Sort module
90‧‧‧發送模組 90‧‧‧Send module
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