TWI825354B - Route vehicle trajectory analysis system and method thereof and computer readable medium - Google Patents
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Abstract
Description
本發明係有關車輛軌跡分析技術,且特別係有關一種路線車輛軌跡分析系統與方法。 The present invention relates to vehicle trajectory analysis technology, and in particular, to a route vehicle trajectory analysis system and method.
車輛軌跡追蹤與其行駛路線的勾稽及後續延伸分析一直是公共運輸管理單位相當重視的議題。利用全球定位系統(Global Positioning System,GPS)之車輛軌跡追蹤技術已日漸成熟,管理人員可搭配電子地圖即時監控車輛位置及狀態,但當車隊數量龐大時,管理人員就無法依靠人工追蹤所有車輛營運狀況,當車輛有突發狀況偏離既定路線時,也常喪失第一時間處理的機會。此外,對於車輛軌跡資料,目前只採用GPS座標作分析,但GPS座標存在誤差,若用於精密的路線偏移判斷等分析,因資料量大需要較長時間,且有可能發生誤判的狀況。因此,需要一種有效、快速、精確的車輛軌跡分析技術。 Vehicle trajectory tracking and its driving route correlation and subsequent extended analysis have always been issues that public transportation management units attach great importance to. Vehicle trajectory tracking technology using the Global Positioning System (GPS) has become increasingly mature. Managers can use electronic maps to monitor the location and status of vehicles in real time. However, when the number of fleets is huge, managers cannot rely on manual tracking of all vehicle operations. situation, when a vehicle deviates from the established route due to an unexpected situation, the opportunity to deal with it immediately is often lost. In addition, for vehicle trajectory data, only GPS coordinates are currently used for analysis, but GPS coordinates have errors. If used for precise analysis such as route deviation judgment, it will take a long time due to the large amount of data, and misjudgments may occur. Therefore, an effective, fast, and accurate vehicle trajectory analysis technology is needed.
為解決上述問題,本發明提供一種路線車輛軌跡分析系統,包括:資料收集模組,用於接收行駛於路線之車輛之動態資料,其中,該動態資 料包括該車輛行駛於該路線時之複數座標;以及網格建構模組,用於根據該動態資料建構該路線之標準網格模型,其中,該標準網格模型包括切割該等座標之分布範圍所得之複數網格。 In order to solve the above problems, the present invention provides a route vehicle trajectory analysis system, including: a data collection module for receiving dynamic data of vehicles traveling on the route, wherein the dynamic data The data includes the plurality of coordinates when the vehicle travels on the route; and a grid construction module is used to construct a standard grid model of the route based on the dynamic data, where the standard grid model includes the distribution range of the cut coordinates The resulting complex grid.
本發明另提供一種路線車輛軌跡分析方法,包括:接收行駛於路線之車輛之動態資料,其中,該動態資料包括該車輛行駛於該路線時之複數座標;以及根據該動態資料建構該路線之標準網格模型,其中,該標準網格模型包括切割該等座標之分布範圍所得之複數網格。 The present invention also provides a route vehicle trajectory analysis method, which includes: receiving dynamic data of vehicles traveling on the route, wherein the dynamic data includes plural coordinates of the vehicle when traveling on the route; and constructing standards for the route based on the dynamic data. A grid model, wherein the standard grid model includes a complex grid obtained by cutting the distribution range of the coordinates.
本發明復提供一種電腦可讀媒介,應用於計算裝置或電腦中,係儲存有指令,以執行上述之路線車輛軌跡分析方法。 The present invention further provides a computer-readable medium, which is used in a computing device or a computer and stores instructions to execute the above-mentioned route vehicle trajectory analysis method.
上述之標準網格模型係用於車輛軌跡資料之融合分析。藉此,本發明可減少車輛軌跡之資料量,並快速判斷車輛是否偏離路線及是否發生異常狀況,以即時通報管理人員,作為管理人員處理之依據。 The above standard grid model is used for fusion analysis of vehicle trajectory data. In this way, the present invention can reduce the amount of data on the vehicle trajectory, and quickly determine whether the vehicle deviates from the route and whether an abnormal situation occurs, so as to immediately notify the management personnel as a basis for management personnel's processing.
1~10,6’~10’:軌跡資料 1~10,6’~10’: trajectory data
100:路線車輛軌跡分析系統 100:Route vehicle trajectory analysis system
110:資料收集模組 110:Data collection module
120:網格建構模組 120:Grid construction module
130:資料融合模組 130:Data fusion module
140:資料儲存模組 140:Data storage module
150:資料分析模組 150:Data analysis module
170:車輛 170:Vehicle
180:行動裝置 180:Mobile device
300:路線 300:Route
301~303:站點 301~303: Site
500:標準網格模型 500: Standard mesh model
550:基準點 550: base point
800:軌跡 800:Trajectory
S11~S13,S21~S23,S31~S33:網格 S11~S13, S21~S23, S31~S33: Grid
S210~S270:方法步驟 S210~S270: Method steps
圖1為根據本發明一實施例之一種路線車輛軌跡分析系統的方塊示意圖。 FIG. 1 is a block diagram of a route vehicle trajectory analysis system according to an embodiment of the present invention.
圖2為根據本發明一實施例之一種路線車輛軌跡分析方法的流程圖。 Figure 2 is a flow chart of a route vehicle trajectory analysis method according to an embodiment of the present invention.
圖3為根據本發明一實施例之一種路線車輛軌跡分析系統所接收的靜態資料示意圖。 Figure 3 is a schematic diagram of static data received by a route vehicle trajectory analysis system according to an embodiment of the present invention.
圖4為根據本發明一實施例之一種路線車輛軌跡分析系統所接收的動態資料示意圖。 Figure 4 is a schematic diagram of dynamic data received by a route vehicle trajectory analysis system according to an embodiment of the present invention.
圖5及圖6為根據本發明一實施例之一種路線車輛軌跡分析系統所建構的標準網格模型之示意圖。 5 and 6 are schematic diagrams of a standard grid model constructed by a route vehicle trajectory analysis system according to an embodiment of the present invention.
圖7及圖8為根據本發明一實施例之一種路線車輛軌跡分析系統判斷車輛是否偏離路線之示意圖。 7 and 8 are schematic diagrams of a route vehicle trajectory analysis system for determining whether a vehicle deviates from the route according to an embodiment of the present invention.
圖9為根據本發明一實施例之一種路線車輛軌跡分析系統所計算的融合統計資料之示意圖。 Figure 9 is a schematic diagram of fusion statistical data calculated by a route vehicle trajectory analysis system according to an embodiment of the present invention.
以下藉由特定的具體實施例說明本發明之實施方式,在本技術領域具有通常知識者可由本說明書所揭示之內容輕易地瞭解本發明之其他優點及功效。 The following describes the implementation of the present invention through specific embodiments. Those with ordinary knowledge in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification.
圖1為根據本發明一實施例之一種路線車輛軌跡分析系統100的方塊示意圖。路線車輛軌跡分析系統100包括資料收集模組110、網格建構模組120、資料融合模組130、資料儲存模組140、以及資料分析模組150。
FIG. 1 is a block diagram of a route vehicle
上述每一模組均可為軟體、硬體或韌體;若為硬體,則可為具有資料處理與運算能力之處理單元、處理器、電腦或伺服器;若為軟體或韌體,則可包括處理單元、處理器、電腦或伺服器可執行之指令,且可安裝於同一硬體裝置或分布於不同的複數硬體裝置。 Each of the above modules can be software, hardware or firmware; if it is hardware, it can be a processing unit, processor, computer or server with data processing and computing capabilities; if it is software or firmware, then It may include instructions executable by a processing unit, processor, computer or server, and may be installed on the same hardware device or distributed across multiple different hardware devices.
路線車輛軌跡分析系統100係透過無線網路或行動通訊網路接收裝設於車輛170之行動裝置180所產生之動態資料,該動態資料與車輛170行駛之軌跡相關,且路線車輛軌跡分析系統100係對該動態資料進行計算、儲存與分析。
The route vehicle
在一實施例中,車輛170為依循固定路線行駛之車輛,例如公車、校車或接駁車。該路線設有複數站點,車輛170在行駛途中會在該路線的每一站點停留。
In one embodiment, the
圖2為根據本發明一實施例之一種路線車輛軌跡分析方法的流程圖,該方法係由路線車輛軌跡分析系統100執行。
Figure 2 is a flow chart of a route vehicle trajectory analysis method according to an embodiment of the present invention. The method is executed by the route vehicle
在一實施例中,該方法可分為兩階段,其中,第一階段為在步驟S210~S220建構該路線之標準網格模型,第二階段為在步驟S230~S270利用該標準網格模型進行該路線之各車輛班次行駛時的軌跡資料之融合儲存與分析。以下詳述該方法之各階段與各步驟之細節。 In one embodiment, the method can be divided into two stages, in which the first stage is to construct a standard grid model of the route in steps S210~S220, and the second stage is to use the standard grid model in steps S230~S270. Fusion, storage and analysis of trajectory data of each vehicle on the route. Details of each stage and step of the method are described below.
在步驟S210,資料收集模組110接收車輛170所行駛之路線的靜態資料及車輛170之行動裝置180所產生的第一動態資料。如圖3所示,在本實施例中,路線300為車輛170所行駛之路線,路線300設有三個站點301~303。另外,如下列之表1所示,該靜態資料包括路線300之每一站點301~303的路線編號、序號、站名及座標,其中,座標包括經度及緯度。本實施例之路線300設有三個網站,但本發明不限於此,在其他實施例中,車輛所行駛之路線可設有任意數量之站點。
In step S210 , the
該第一動態資料則如圖4與下列之表2所示。該第一動態資料包括車輛170行駛於路線300時之複數軌跡資料,其中,每一項軌跡資料包括該軌跡資料之序號、車輛170之車牌號碼及所行駛之路線編號、行動裝置180產生該軌跡資料之時間、車輛170於該時間之所在座標的經度及緯度、車輛170於該時間之行車狀態、以及車輛170於該時間之即時速度。
The first dynamic data is shown in Figure 4 and Table 2 below. The first dynamic data includes multiple trajectory data when the
車輛170上之行動裝置180可通過GPS等定位系統週期性擷取車輛170之座標經緯度,據以計算車輛170之即時速度。此外,行動裝置180可預先儲存車輛170之車牌號碼及路線300之靜態資料,並比對車輛170之座標與該靜態資料中各站點之座標,以產生車輛170之行車狀態,再將以上軌跡資料傳送至路線車輛軌跡分析系統100。行動裝置180可每隔一段預設時間產生並傳送一項軌跡資料,例如,該預設時間可為30秒,但本發明不限於此。
The
圖4中之軌跡資料1~10分別位於各該軌跡資料1~10之座標所對應之位置。在本實施例中,該第一動態資料包括10項軌跡資料,但本發明不限於此,在其他實施例中,該第一動態資料可包括任意項數之軌跡資料。 The trajectory data 1 to 10 in Figure 4 are respectively located at the positions corresponding to the coordinates of the trajectory data 1 to 10. In this embodiment, the first dynamic data includes 10 items of trajectory data, but the invention is not limited thereto. In other embodiments, the first dynamic data may include any number of items of trajectory data.
接著,在步驟S220,網格建構模組120根據該靜態資料及該第一動態資料建構路線300之標準網格模型500,如圖5及圖6所示。標準網格模型500包括基準點550及切割該第一動態資料中車輛170之座標的分布範圍所得之複數網格S11~S13、S21~S23以及S31~S33,其中,該等網格排列成矩陣,且每一網格均有相同之寬度與高度,即具有相同之尺寸及面積。基準點
550位於該矩陣之左上角,但本發明不限於此,在其他實施例中,基準點550可位於該矩陣之其他角落或位於該矩陣之中心。
Next, in step S220, the
此外,標準網格模型500復包括複數參數,例如下列之表3所示的基準點550之座標(例如GPS座標或其他定位系統之座標)、該矩陣之橫向網格個數與縱向網格個數、以及各該網格之寬度與高度。在本實施例中,該矩陣之橫向網格個數m與縱向網格個數n均為3,而在其他實施例中,m與n可為其他整數,且m與n可相等或不相等。
In addition, the
再者,標準網格模型500亦包括路線300所行經之網格編號及所行經之網格順序,例如下列之表4所示,路線300所行經之網格依序為S11、S12、S22、S23及S33。
Furthermore, the
接著,在步驟S230,資料收集模組110接收第二動態資料。在本實施例中,該第二動態資料即為該第一動態資料,而在其他實施例中,該第二動態資料可為車輛170之行動裝置180於另一班次所產生之另一動態資料,或另一車輛行駛於路線300時,該車輛之行動裝置所產生之另一動態資料。若該第二動態資料為另一動態資料,則同樣可包括複數軌跡資料,其格式亦如表2所示。
Next, in step S230, the
接著,在步驟S240,資料分析模組150根據該第二動態資料之軌跡資料其中之車輛座標,判斷產生該第二動態資料之車輛(車輛170或另一車輛)是否偏離路線300,即判斷該車輛是否即將進入或已進入標準網格模型500之網格中路線300未行經之網格。例如圖7所示,該車輛所行經之網格依序為S11、S12、S22、S23及S33,與路線300所行經之網格相同(在圖7中以網點標示),因此,資料分析模組150判斷該車輛未偏離路線300。
Next, in step S240, the
在另一實施例中,該車輛之行駛軌跡為圖8中之軌跡800,該車輛所傳送之第二動態資料包括軌跡資料1~5及6’~10’,其中,軌跡資料1~5和該第一動態資料之軌跡資料1~5相同,且圖8中之軌跡資料6’~10’的所在位置分別為各該軌跡資料6’~10’其中之座標位置。軌跡800之前半段符合路線300之前半段,因此,當該車輛已傳送軌跡資料1~5時,資料分析模組150判斷該車輛未偏離路線300。當該車輛傳送軌跡資料6’時,資料分析模組150判斷該車輛仍未偏離路線300,因為軌跡資料6’其中之座標仍位於路線300行經之網格S22中。
In another embodiment, the driving trajectory of the vehicle is
稍後,當該車輛傳送軌跡資料7’時,資料分析模組150判斷該車輛即將進入路線300未行經之網格S32。換言之,該車輛可能會偏離路線300,因此,資料分析模組150於路線300之管理中心的監控畫面發出告警,以提醒管理人員監控該車輛,或採取其他應變措施。當該車輛傳送軌跡資料8’時,該車輛已進入路線300未行經之網格S32,資料分析模組150判斷該車輛已偏離路線300,因此,資料分析模組150可繼續發出告警,且記錄此異常狀況,並持續追蹤該車輛。
Later, when the vehicle transmits the trajectory data 7', the
接著,在步驟S250,資料融合模組130根據該車輛之第二動態資料中的軌跡資料之座標,判斷該車輛於標準網格模型500之網格中所行經的每一網格,且計算該車輛所行經之各該網格中的軌跡資料之融合統計資料。
Next, in step S250, the
舉例而言,若該車輛行經網格S11、S12、S22、S23及S33,則上述每一網格均對應一項融合統計資料,且各項融合統計資料均由該車輛於對應之網格內所傳送之軌跡資料融合統計而產生。如圖9及下列之表5所示,每一項融合統計資料均包括該車輛進入對應網格之時間、該車輛離開對應網格之時間、該車輛於對應網格內停留之站點及停留時間長度、以及該車輛於對應網格內之平均速度。 For example, if the vehicle travels through grids S11, S12, S22, S23 and S33, each of the above grids corresponds to a fused statistical data, and each fused statistical data is generated by the vehicle in the corresponding grid. The transmitted trajectory data is generated by integrating statistics. As shown in Figure 9 and Table 5 below, each fusion statistical data includes the time when the vehicle entered the corresponding grid, the time when the vehicle left the corresponding grid, the station where the vehicle stayed in the corresponding grid, and the time it stayed. The length of time, and the average speed of the vehicle in the corresponding grid.
接著,在步驟S260,資料儲存模組140儲存上述之融合統計資料。例如,資料儲存模組140可以對應之網格編號為索引,將該等融合統計資料存入資料庫。該資料庫可為資料儲存模組140其中一部分,亦可為獨立於路線車輛軌跡分析系統100以外之外部資料庫。
Next, in step S260, the
行駛於路線300之車輛均可產生上述之第二動態資料,這些第二動態資料均可根據同一個標準網格模型500,用上述方式計算產生如圖9及表5所示之融合統計資料,並儲存於資料庫中。
Vehicles traveling on the
接著,在步驟S270,資料分析模組150可根據各網格之融合統計資料即時判斷目前行駛於路線300之車輛是否發生異常狀況。若發生異常狀況,則資料分析模組150發出告警,以提醒管理人員監控該車輛,或採取其他應變措施。
Next, in step S270, the
例如,資料分析模組150可將該車輛行經之每一網格的融合統計資料和同一網格中先前班次之融合統計資料的平均值相比較,以判斷是否發生異常狀況。
For example, the
例如,若該車輛在某一網格的進入時間和同一網格中先前班次的平均進入時間之差值大於預設值,則資料分析模組150判斷發生異常狀況。
For example, if the difference between the entry time of the vehicle in a certain grid and the average entry time of previous shifts in the same grid is greater than a preset value, the
同理,若該車輛在某一網格的離開時間和同一網格中先前班次的平均離開時間之差值大於預設值,則資料分析模組150判斷發生異常狀況。
Similarly, if the difference between the departure time of the vehicle in a certain grid and the average departure time of previous shifts in the same grid is greater than the preset value, the
或者,若該車輛於某站點的停留時間長度和先前班次於同一站點的平均停留時間長度之差值大於預設值,則資料分析模組150判斷發生異常狀況。
Alternatively, if the difference between the length of stay of the vehicle at a certain station and the average length of stay of previous flights at the same station is greater than a preset value, the
或者,若該車輛在某一網格的平均速度和先前班次在同一網格的平均速度之差值大於預設值,則資料分析模組150判斷發生異常狀況。
Alternatively, if the difference between the vehicle's average speed in a certain grid and the average speed of the previous shift in the same grid is greater than a preset value, the
又例如,資料分析模組150可以不根據先前班次之融合統計資料,而是將該車輛行經之每一網格的融合統計資料和預設值或預設範圍做比對,以判斷是否發生異常狀況。
For another example, the
例如,若該車輛在某一網格的進入時間或離開時間超出預設範圍,則資料分析模組150判斷發生異常狀況。
For example, if the entry time or exit time of the vehicle in a certain grid exceeds a preset range, the
或者,若該車輛於某站點的停留時間長度大於預設值,則資料分析模組150判斷發生異常狀況。
Or, if the length of stay of the vehicle at a certain station is greater than the preset value, the
或者,若該車輛在某一網格的平均速度超出預設範圍,則資料分析模組150判斷發生異常狀況。
Or, if the average speed of the vehicle in a certain grid exceeds the preset range, the
此外,本發明還揭示一種電腦可讀媒介,係應用於具有處理器(例如,CPU、GPU等)及/或記憶體的計算裝置或電腦中,且儲存有指令,並可利用此計算裝置或電腦透過處理器及/或記憶體執行此電腦可讀媒介,以於執行此電腦可讀媒介時執行上述之方法及各步驟。 In addition, the present invention also discloses a computer-readable medium, which is applied to a computing device or computer having a processor (eg, CPU, GPU, etc.) and/or a memory, and stores instructions, and can utilize the computing device or computer. The computer executes the computer-readable medium through the processor and/or memory to perform the above methods and steps when executing the computer-readable medium.
綜上所述,本發明具有下列優點: To sum up, the present invention has the following advantages:
第一,建立路線的標準網格模型,係依據網格範圍將各網格內之眾多軌跡資料融合為少數幾項統計資料,進而大幅減少資料量並簡化分析運算,以提高後續之資料分析及應用之效率。 First, to establish a standard grid model of the route, the numerous trajectory data in each grid are integrated into a few statistical data based on the grid range, thereby significantly reducing the amount of data and simplifying the analysis operations, so as to improve subsequent data analysis and Application efficiency.
第二,網格內之融合統計資料可避免因單點GPS座標誤差而影響系統後續判斷,有助於提升運輸服務之資訊預估準確度,並提高智慧交通服務技術產品面的競爭力。 Second, the integrated statistical data within the grid can avoid single-point GPS coordinate errors from affecting subsequent system judgments, help improve the accuracy of information predictions for transportation services, and improve the competitiveness of smart transportation service technology products.
第三,有別於過去採用站點間行駛軌跡之GPS座標做分析,可能忽略站點間距離、站點間平均速度、以及行駛時間等數據而無法反應實際狀況,透過本發明之標準網格模型,能將路線行駛軌跡做有效率的切分,且能同時記錄網格內進出時間、平均速度及站點停留時間等細節資料,可以更詳細且更有效率地記錄及儲存軌跡資料。 Third, unlike the past analysis using GPS coordinates of driving trajectories between stations, data such as distance between stations, average speed between stations, and driving time may be ignored and cannot reflect the actual situation. Through the standard grid of the present invention, The model can efficiently segment route driving trajectories, and can simultaneously record detailed data such as entry and exit time, average speed, and stop time within the grid. It can record and store trajectory data in more detail and more efficiently.
第四,標準網格模型建構完成後,係透過資料分析模組依據網格分析先前之歷史資料及目前之即時資料,以迅速判斷車輛是否偏離既定路線,並可透過平均速度及站點停留時間等參數,判斷當日班次之行駛軌跡是否有異常狀況發生,以作為管理人員處理之依據。 Fourth, after the construction of the standard grid model is completed, the data analysis module is used to analyze previous historical data and current real-time data based on the grid to quickly determine whether the vehicle deviates from the scheduled route, and can use the average speed and stop time and other parameters to determine whether there are any abnormal conditions in the driving trajectory of the day's shift, which can be used as a basis for management personnel to handle.
上述實施形態僅例示性說明本發明之原理及其功效,而非用於限制本發明。任何在本技術領域具有通常知識者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。因此,本發明之權利保護範圍,應如後述之申請專利範圍所列。 The above embodiments are only illustrative to illustrate the principles and effects of the present invention, but are not intended to limit the present invention. Anyone with ordinary knowledge in this technical field can modify and change the above embodiments without departing from the spirit and scope of the present invention. Therefore, the protection scope of the present invention should be as listed in the patent application scope described below.
100:路線車輛軌跡分析系統 100:Route vehicle trajectory analysis system
110:資料收集模組 110:Data collection module
120:網格建構模組 120:Grid construction module
130:資料融合模組 130:Data fusion module
140:資料儲存模組 140:Data storage module
150:資料分析模組 150:Data analysis module
170:車輛 170:Vehicle
180:行動裝置 180:Mobile device
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TW200420168A (en) * | 2003-03-21 | 2004-10-01 | Benq Corp | Method and apparatus for avoiding route deviation |
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TW200420168A (en) * | 2003-03-21 | 2004-10-01 | Benq Corp | Method and apparatus for avoiding route deviation |
US20070268155A1 (en) * | 2006-05-22 | 2007-11-22 | Phelps Dodge Corporation | Position tracking and proximity warning system |
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