TWI615814B - Mobile device location grouping, time system grouping and method of generating traffic time planning using big data - Google Patents
Mobile device location grouping, time system grouping and method of generating traffic time planning using big data Download PDFInfo
- Publication number
- TWI615814B TWI615814B TW106111485A TW106111485A TWI615814B TW I615814 B TWI615814 B TW I615814B TW 106111485 A TW106111485 A TW 106111485A TW 106111485 A TW106111485 A TW 106111485A TW I615814 B TWI615814 B TW I615814B
- Authority
- TW
- Taiwan
- Prior art keywords
- big data
- block
- location information
- location
- traffic
- Prior art date
Links
Landscapes
- Traffic Control Systems (AREA)
Abstract
本發明係一種利用大數據之行動裝置位置分群、時制分群及產生交通時制計劃之方法，首先接收ㄧ區域內所有行動裝置大量的複數位置資訊，並據其移動變化量篩選出區域中的起點與終點，再從起點至終點的複數路徑中，根據位置資訊的數量篩選出至少ㄧ主要路徑；接著將主要路徑區分為複數區塊，並根據位置資訊的數量在複數區塊中篩選出至少ㄧ壅塞區塊，以取其內所有具有紅綠燈號誌的每一路口，並擷取每一路口的車流量產生的紅綠燈時制計劃，組成壅塞區塊的群組定時時制計劃。本發明可快速判斷出交通壅塞的區域後，使用較精確的方式產生定時時制計劃，以彌補使用大數據時較模糊的部分。The invention relates to a mobile device using big data, a location grouping, a time system grouping and a traffic time planning method, which firstly receives a large number of plural position information of all mobile devices in the area, and selects a starting point in the area according to the moving variation. End point, and then from the starting point to the end point of the complex path, according to the number of location information to filter out at least the main path; then the main path is divided into multiple blocks, and according to the number of location information in the complex block to filter out at least congestion Block, in order to take all the intersections with traffic lights in each of them, and take the traffic light schedule generated by the traffic flow of each intersection to form a group timing plan for the congestion block. The invention can quickly determine the area of traffic congestion and use a more accurate method to generate a timed schedule to compensate for the more blurred part when using big data.
Description
本發明係有關一種數據處理之技術，特別是指一種利用大數據之行動裝置位置分群、時制分群及產生交通時制計劃之方法。The present invention relates to a technique for data processing, and more particularly to a method for location grouping, time grouping, and traffic planning for a mobile device using big data.
大數據是由巨型數據集組而成，這些數據集的大小常常超出人類在可接受時間下收集、應用、管理和處理的能力。大數據的定義是容量(Volume)、速度(Velocity)和多樣性(Variety)，雖然也有人另外加上真實性(Veracity)和價值(Value)，但不論如何，大數據的資料特質和傳統資料最大的不同是，資料來源多元、種類繁多，大多是非結構化資料，而且更新速度非常快，以至於資料量相當龐大。Big data is made up of giant data sets that are often larger than humans' ability to collect, apply, manage, and process at acceptable times. The definition of big data is Volume, Velocity, and Variety. Although some people add Veracity and Value, in any case, the data traits and traditional data of big data. The biggest difference is that the sources of data are diverse and diverse, mostly unstructured, and the update rate is so fast that the amount of data is quite large.
目前大數據可被應用在商業、醫學、交通等數據的收集，以交通來說，Google地圖中所顯示車流量的壅塞程度也是屬於大數據的一種應用，車主攜帶的手機可隨著車主在道路上行駛，不斷將全球定位系統(Global Positioning System，GPS)訊號以匿名方式傳回，隨時回報自己的位置和速度，使Google的伺服器能蒐集到即時交通資訊。At present, big data can be applied to the collection of data such as business, medicine, transportation, etc. In terms of transportation, the congestion of traffic displayed in Google maps is also an application of big data. The mobile phone carried by the owner can follow the road of the owner. Drive on, continuously transmit the Global Positioning System (GPS) signal anonymously, and return your position and speed at any time, so that Google's server can collect instant traffic information.
但目前只能由大數據的資料中知道交通壅塞的位置，並無法針對壅塞情形進行改善，要改變壅塞情形只能依靠交通號誌之功能，如將每日劃分為不同時段，並根據先前所紀錄的每一時段的交通型態，即車流量來採用特定時制計劃，週而復始的來搭配每個時段而有不同的時制計劃。其中用來計算定時時制計劃的地點、車流量、時間完全是使用人為判斷，或使用人力勘查與運算等，導致過去定時時制計劃所耗費的人力成本相當高。因此，如何有效取得路況，並藉由取得路況來改善交通壅塞情形，一直是交通管制所努力的目標。However, at present, only the location of traffic congestion can be known from the data of big data, and it is impossible to improve the congestion situation. To change the congestion situation, it can only rely on the function of traffic signs, such as dividing the daily time into different time periods, and according to the previous The traffic pattern at each time of the record, that is, the traffic flow, is based on a specific time plan, and is repeated to match each time period with different time schedules. The location, traffic flow, and time used to calculate the timing plan are entirely based on human judgment, or use of human exploration and calculations, resulting in a relatively high labor cost for the past time-based plan. Therefore, how to effectively obtain road conditions and improve traffic congestion by obtaining road conditions has always been the goal of traffic control.
有鑑於此，本發明遂針對上述習知技術之缺失，提出一種利用大數據之行動裝置位置分群、時制分群及產生交通時制計劃之方法，以有效克服上述之該等問題。In view of the above, the present invention has been directed to the lack of the above-mentioned prior art, and proposes a method for location grouping, time-based grouping, and traffic-time scheduling of mobile devices using big data to effectively overcome the above problems.
本發明之主要目的係提供一種利用大數據之行動裝置位置分群、時制分群及產生交通時制計劃之方法，其可利用大數據的行動裝置位置資訊來判斷出交通壅塞的區域，再以較精確的方式判定壅塞區域中每一路口的交通流量，以產生壅塞區域中所有路口紅綠燈的定時時制計劃，來彌補使用大數據時，流量較模糊的部分。The main object of the present invention is to provide a mobile device location grouping, a time system grouping, and a traffic time planning method using big data, which can utilize the mobile device location information of big data to determine the traffic congestion area, and then more accurately The method determines the traffic flow at each intersection in the sluice area to generate a timing plan for all traffic lights in the occlusion area to compensate for the blurring of the traffic when using big data.
本發明之另一目的係提供一種利用大數據之行動裝置位置分群、時制分群及產生交通時制計劃之方法，其可即時接收大數據的行動裝置位置資訊計算出交通流量的變化，使本發明能自動擷取對應交通流量的紅綠燈時制計劃，以自動化達到紓解交通等目的。Another object of the present invention is to provide a method for location grouping, time-based grouping, and traffic-time scheduling of mobile devices using big data, which can instantaneously receive mobile device location information of big data to calculate changes in traffic flow, so that the present invention can Automatically capture the traffic light schedule corresponding to traffic flow to automate the purpose of mitigating traffic.
本發明之又一目的係提供一種利用大數據之行動裝置位置分群、時制分群及產生交通時制計劃之方法，其可將每ㄧ時間區間的交通流量變化進行分類，以利後續時制計劃的重整。Another object of the present invention is to provide a method for location grouping, time grouping, and traffic time planning of mobile devices using big data, which can classify traffic flow changes in each time interval to facilitate reorganization of subsequent time plans. .
為達上述之目的，本發明係提供一種利用大數據之行動裝置位置產生交通時制計劃之方法，包括下列步驟，首先接收ㄧ區域內所有行動裝置大量的複數位置資訊；根據複數位置資訊的移動變化量篩選出區域中的至少ㄧ起點與至少ㄧ終點；接著根據複數位置資訊的數量，在起點至終點的複數路徑中，篩選出至少ㄧ主要路徑；並將主要路徑區分為複數區塊，以根據複數位置資訊的數量在主要路徑上的複數區塊中篩選出至少ㄧ壅塞區塊；接下來，擷取壅塞區塊中所有具有紅綠燈號誌的每一路口，並擷取每一路口的車流量；最後，根據每ㄧ路口的車流量產生紅綠燈時制計劃，並將每一路口的紅綠燈時制計劃組成壅塞區塊的至少ㄧ群組定時時制計劃。To achieve the above object, the present invention provides a method for generating a traffic schedule by using a mobile device location of big data, comprising the steps of first receiving a plurality of plural location information of all mobile devices in a region; and changing the movement according to the plurality of location information The quantity is filtered out at least the starting point of the 与 and the at least ㄧ end point; then, according to the quantity of the plural position information, at least the main path is selected in the complex path from the starting point to the ending point; and the main path is divided into a plurality of blocks, according to The number of plural position information is filtered out of at least the blocking block in the plurality of blocks on the main path; next, each intersection having the traffic light number in the blocking block is captured, and the traffic volume of each intersection is taken. Finally, a traffic light schedule is generated based on the traffic volume at each intersection, and the traffic light schedule at each intersection constitutes at least the group timing plan of the congestion block.
其中在篩選出至少ㄧ壅塞區塊之步驟後，更可紀錄壅塞區塊之複數時間區間的複數位置資訊之數量變化量，並將複數時間區間進行分類，將具有相似複數位置資訊之數量變化量的複數時間區間歸類為同ㄧ類別後，分別計算出壅塞區塊之每ㄧ類別的群組定時時制計劃。除此之外，更可在至少ㄧ時間區間中偵測壅塞區塊的位置資訊之數量變化量，並判斷複數位置資訊之數量變化量的類別，以套用對應該類別的群組定時時制計劃。After the step of filtering at least the blocking block, the quantity change of the complex position information of the complex time interval of the blocking block can be recorded, and the complex time interval is classified, and the quantity change with similar complex position information is recorded. After the complex time interval is classified as the same category, the group timing plan for each category of the blocked block is calculated separately. In addition, the amount of change in the position information of the blocking block may be detected in at least the time interval, and the category of the quantity change amount of the plurality of position information may be determined to apply the group timing plan corresponding to the category.
本發明亦提供ㄧ種利用大數據之行動裝置位置分群之方法，包括下列步驟，首先指定至少ㄧ主要路徑，並擷取主要路徑內所有行動裝置大量的複數位置資訊；接著，將主要路徑區分為複數區塊，並根據複數位置資訊的數量在主要路徑上的複數區塊中篩選出至少ㄧ壅塞區塊。The present invention also provides a method for location grouping of mobile devices using big data, comprising the steps of first specifying at least a primary path and extracting a plurality of complex location information of all mobile devices in the primary path; and then dividing the primary path into The plurality of blocks are filtered out of at least the blocking block in the plurality of blocks on the main path according to the number of complex position information.
另外，本發明亦提供ㄧ種利用大數據之行動裝置位置時制分群之方法，包括下列步驟，首先指定至少ㄧ壅塞區塊，並擷取壅塞區塊中所有行動裝置大量的複數位置資訊；接著，紀錄壅塞區塊之複數時間區間的複數位置資訊之數量變化量，並將複數時間區間進行分類，將具有相似的位置資訊之數量變化量的時間區間歸類為同ㄧ類別。In addition, the present invention also provides a method for time-division grouping of mobile devices using big data, comprising the steps of first specifying at least a blocking block and extracting a plurality of complex position information of all mobile devices in the blocking block; The quantity change of the complex position information of the complex time interval of the blocking block is recorded, and the complex time intervals are classified, and the time interval of the quantity change amount with similar position information is classified into the same category.
底下藉由具體實施例詳加說明，當更容易瞭解本發明之目的、技術內容、特點及其所達成之功效。The purpose, technical content, features and effects achieved by the present invention will be more readily understood by the detailed description of the embodiments.
本實施例係可利用移動的行動裝置之位置資訊來作為路上車輛的車流，藉此判斷出車流壅塞的區塊，並對區塊中每ㄧ時間區間的流量來分群，同時針對區塊中路口的紅綠燈號誌產生群組定時時制計劃，以針對不同狀況的交通流量來使用不同的群組定時時制計劃。In this embodiment, the location information of the mobile mobile device can be used as the traffic flow of the vehicle on the road, thereby judging the traffic congestion block, and grouping the traffic in each time interval in the block, and simultaneously targeting the intersection in the block. The traffic lights generate a group timed schedule to use different group timing plans for traffic flows of different conditions.
請參照第一圖，在說明本發明之方法流程前，首先說明本發明用以實施的硬體架構，如圖所示，ㄧ資料庫10可為能儲存資料的硬碟，資料庫10可接收大量行動裝置50的位置資訊，其中行動裝置50信號連接資料庫10，且行動裝置50係為具有通訊功能的裝置，如手機等裝置。ㄧ控制器12可為計算機，控制器12信號連接資料庫10，可接收資料庫10中大量的位置資訊，並根據大量的位置資訊計算產生各項資訊，如紅綠燈的定時時制計劃。複數紅綠燈號誌14，設置在道路的路口，並信號連接控制器12，以接收控制器12的控制。Referring to the first figure, before describing the flow of the method of the present invention, the hardware architecture used in the present invention is first described. As shown, the database 10 can be a hard disk capable of storing data, and the database 10 can receive A plurality of location information of the mobile device 50, wherein the mobile device 50 is connected to the database 10, and the mobile device 50 is a device having a communication function, such as a mobile phone. The controller 12 can be a computer, and the controller 12 is connected to the database 10, can receive a large amount of location information in the database 10, and generate various information according to a large amount of location information, such as a scheduled time schedule of traffic lights. A plurality of traffic lights 12 are placed at the intersection of the road and signaled to the controller 12 to receive control of the controller 12.
接下來請配合第一圖與第二圖，以說明本發明實施例之方法流程。首先，進入步驟S10，控制器12先定義出ㄧ大範圍的區域，本實施例的區域以ㄧ個包括有住宅區、以及工業區等的都會區域為例，控制器12定義出ㄧ都會區域後，即可從資料庫10中挑選出該區域內的所有行動裝置大量的複數位置資訊，並接收複數位置資訊進行處理；本實施例是將位置資訊當作都會區的車流量，因此為了使位置資訊的數據更加貼近車流量，本實施例的控制器12會將持續ㄧ預定時間沒有產生移動變化量的位置資訊濾除掉，如五分鐘不動的位置資訊，可能代表這個位置資訊是停住不動的，並非在行走的車輛上的行動裝置所發出的訊號，因此無法作為車流量的資訊，可將這個位置資訊濾除。Next, please cooperate with the first figure and the second figure to illustrate the method flow of the embodiment of the present invention. First, the process proceeds to step S10, and the controller 12 first defines a wide area. The area of the embodiment is exemplified by a metropolitan area including a residential area and an industrial area. , a plurality of multiple location information of all the mobile devices in the area can be selected from the database 10, and the plurality of location information is received for processing; in this embodiment, the location information is regarded as the traffic volume of the metropolitan area, so in order to make the location The information of the information is closer to the traffic flow. The controller 12 of this embodiment filters out the location information that does not generate the amount of movement change for a predetermined period of time. For example, the location information that does not move for five minutes may indicate that the location information is not stopped. It is not a signal from a mobile device on a moving vehicle, so it cannot be used as information on traffic flow. This location information can be filtered out.
接著進入步驟S12，控制器12根據留下來的複數位置資訊的移動變化量篩選出區域中的至少ㄧ起點與至少ㄧ終點，其中篩選出起點與終點的方法可根據位置資訊的移動變化量進行判斷，控制器12可以從移動變化量知道在特定時間中，這些位置資訊可由某幾個地點即起點，往另一個地點集中，這個集中的地點就可以做為終點。詳細來說，請參照第三圖，以都會區域為例，都會區域包括有多個住宅區A40、住宅區B42以及住宅區C44以及工業區46等，第三圖中的圓形點代表位置資訊，我們可以由位置資訊的移動變化量觀察到起點與終點，如在上班時間我們看到位置資訊可由住宅區A40、住宅區B42以及住宅區C44往工業區46移動，此時就可以判斷出住宅區A40、住宅區B42以及住宅區C44係為起點，工業區46係為終點。Next, proceeding to step S12, the controller 12 filters at least the starting point and the at least the ending point in the region according to the movement change amount of the remaining complex position information, wherein the method of filtering the starting point and the ending point can be judged according to the movement variation of the position information. The controller 12 can know from the amount of movement change that the location information can be concentrated from a certain location, that is, the starting point, to another location, and the concentrated location can be used as the destination. In detail, please refer to the third figure. Take the metropolitan area as an example. The metropolitan area includes multiple residential areas A40, residential area B42, residential area C44, and industrial area 46. The circular points in the third figure represent location information. We can observe the starting point and the ending point from the movement change of the location information. For example, during the working hours, we can see that the location information can be moved from the residential area A40, the residential area B42, and the residential area C44 to the industrial area 46. The area A40, the residential area B42, and the residential area C44 are the starting points, and the industrial area 46 is the ending point.
接著進入步驟S14並請持續參照第三圖，控制器12可從起點通往終點的複數路徑中，根據位置資訊的分布篩選出起點至終點的至少ㄧ主要路徑，其中主要路徑的篩選亦根據複數位置資訊所篩選出，當某個路徑上的複數位置資訊的數量多於其餘路徑位置資訊數量的一半以上即可表示為主要路徑。以住宅區C44至工業區46為例，若住宅區C44到工業區46有路徑20、路徑22以及路徑24三條路徑，本實施例係以圓形點代表位置資訊，就可以知道路徑24中的位置資訊的數量明顯多於其餘路徑20以及路徑22的一半以上，控制器12可判定路徑24是起點住宅區C44通往終點工業區46的主要路徑。Next, proceeding to step S14 and continuing to refer to the third figure, the controller 12 may filter at least the primary path from the start point to the end point according to the distribution of the location information from the complex path from the start point to the end point, wherein the primary path is also filtered according to the plural number The location information filters out that when the number of complex location information on a certain path is more than half of the number of remaining path location information, it can be expressed as the main path. Taking the residential area C44 to the industrial area 46 as an example, if the residential area C44 to the industrial area 46 has three paths of the path 20, the path 22, and the path 24, in this embodiment, the circular point represents the position information, and the path 24 can be known. The number of location information is significantly more than the remaining path 20 and more than half of the path 22, and the controller 12 can determine that the path 24 is the primary path from the starting residential area C44 to the destination industrial area 46.
接下來進入步驟S16，控制器12將主要路徑區分為複數區塊，其中每ㄧ區塊範圍為至少50公尺，控制器12再根據複數位置資訊的數量在主要路徑24上的複數區塊中篩選出至少ㄧ壅塞區塊，其中由主要路徑的複數區塊中，篩選出壅塞區塊的方法是根據區塊中的複數位置資訊的數量進行判斷的，當區塊中的位置資訊的數量多餘其餘複數區塊內的複數位置資訊數量的一半以上即可判定為位置資訊。舉例來說，請同時參照第四圖，本實施例以路徑24為主要路徑為例，將路徑24區分成複數區塊30、32後，可明顯看出區塊30的位置資訊明顯多於其餘區塊32位置資訊的數量，因此控制器12即將區塊30定義為壅塞區塊。Next, proceeding to step S16, the controller 12 divides the main path into a plurality of blocks, wherein each block ranges from at least 50 meters, and the controller 12 further uses the number of complex position information in the plurality of blocks on the main path 24. Screening at least the blocking block, wherein the method of filtering out the blocking block in the complex block of the main path is determined according to the quantity of the complex position information in the block, when the number of position information in the block is redundant More than half of the number of complex location information in the remaining complex blocks can be determined as location information. For example, please refer to the fourth figure at the same time. In this embodiment, the path 24 is taken as the main path. After the path 24 is divided into the plurality of blocks 30 and 32, it is obvious that the location information of the block 30 is significantly more than the rest. Block 32 sets the amount of location information, so controller 12 defines block 30 as a blocked block.
如步驟S18所示，控制器12在判定出壅塞區塊之後，同時紀錄壅塞區塊中多個時間區間的複數位置資訊之數量變化量。詳細來說，請參照第五圖，其係為壅塞區塊凌晨4點至晚上10點的車流量示意圖，時間區間可為小時、天，禮拜或月為單位，本實施例則舉例ㄧ個時間區間係以兩個小時為單位。擷取壅塞區塊凌晨4點至晚上10點中每ㄧ個時間區間的位置資訊數量之後，將相似位置資訊數量的時間區間分類成同一個類別；其中8點至10點、12點至14點與18點至20點之間的位置資訊的數量變化量係相似，因此歸類為計劃I，而4點至6點、6點至8點、10點至12點、16點至18點與20點至22點的位置資訊的數量變化量，則歸類為計劃II，14點至16點的位置資訊的數量變化量與計劃I、計劃II都不同，因此定義成計劃III。同時，擷取壅塞區塊中所有具有紅綠燈號誌的路口，以擷取壅塞區塊中每ㄧ個路口的的實際車流量，但為了方便說明，本實施例舉例壅塞區塊中只有ㄧ個路口。As shown in step S18, after determining the blocked block, the controller 12 simultaneously records the amount of change in the plurality of time intervals of the plurality of time intervals in the blocked block. For details, please refer to the fifth figure, which is a schematic diagram of the traffic flow from 4 am to 10 pm in the block. The time interval can be hour, day, week or month. In this example, the time is taken as an example. The interval is in two hours. After collecting the position information of each time interval from 4 am to 10 pm in the blocking block, the time intervals of the similar position information quantity are classified into the same category; 8:00 to 10:00, 12:00 to 14:00 The amount of change in position information between 18 and 20 is similar, so it is classified as Plan I, and 4 to 6, 6 to 8, 10 to 12, and 16 to 18 The amount of change in position information from 20:00 to 22:00 is classified as Plan II, and the amount of change in position information from 14 to 16 is different from Plan I and Plan II, and is therefore defined as Plan III. At the same time, all the intersections with the traffic lights in the block are taken to capture the actual traffic flow of each intersection in the block. However, for the sake of convenience, there are only one intersection in the block. .
接著進入步驟S20，根據該路口的車流量計算出紅綠燈時制計劃，由於先前已將壅塞區塊30的時間區間進行分類，因此在計算路口的紅綠燈時制計劃時，只要擷取計劃I中的其中ㄧ個時間區間、計劃II中的其中ㄧ個時間區間以及計劃III中的其中ㄧ個時間區間，來進行計算，即可取得計劃I、計劃II以及計劃III的三個紅綠燈時制計劃，以將這三個紅綠燈時制計劃成為這個壅塞區域的三群組定時時制計劃。當然壅塞區域中的路口可能為複數個路口，而每ㄧ個路口所產生的紅綠燈時制計劃方法皆相同，故不重複敘述，當每ㄧ個路口的紅綠燈時制計劃產生後，則可將每個紅綠燈時制計劃組合成ㄧ群組定時時制計劃。當然每ㄧ個類別中的複數個路口的紅綠燈時制計劃可組成ㄧ群組定時時制計劃，因此當有複數個類別時就會有複數個群組定時時制計劃。Next, proceeding to step S20, the traffic light schedule is calculated based on the traffic meter of the intersection. Since the time interval of the blocking block 30 has been previously classified, when calculating the traffic light schedule at the intersection, only the plan I is selected. Three time intervals, one of the time intervals in Plan II, and one of the time intervals in Plan III, to calculate, you can get the three traffic light schedules of Plan I, Plan II and Plan III to A traffic light schedule is planned to become a three-group time-based plan for this congestion area. Of course, the intersections in the congestion area may be a plurality of intersections, and the traffic light planning methods generated at each intersection are the same, so the description is not repeated. When the traffic light planning for each intersection is generated, each traffic light can be The time plan is combined into a group timed schedule. Of course, the traffic light schedules of multiple intersections in each category can be grouped into a group timing schedule, so when there are multiple categories, there will be multiple group timing plans.
除此之外，請參照第六圖，以說明本發明產生群組定時時制計劃之後，除了可直接將群組定時時制計劃套用在每ㄧ個時間區間之外，本實施例之行動裝置50更可直接將位置資訊傳遞至控制器12中，使控制器12即時接收到位置資訊，並擷取壅塞區塊中的所有位置資訊，以紀錄壅塞區塊中位置資訊的數量變化量後，來判斷目前區間的數量變化量是符合哪ㄧ個計劃，以直接套用對應的群組定時時制計劃。舉例來說，請參照第五圖與第七圖，如第七圖所示，當量測到ㄧ時間區間的位置資訊之數量變化量後，比對第五圖發現第七圖的位置資訊之數量變化量與計劃II相同，因此即可套用先前計劃II所產生的群組定時時制計劃，以利即時配合目前的交通狀態。In addition, please refer to the sixth figure to illustrate that after the group timing plan is generated by the present invention, in addition to directly applying the group timing plan to each time interval, the mobile device 50 of this embodiment is further The location information can be directly transmitted to the controller 12, so that the controller 12 can immediately receive the location information, and capture all the location information in the blocking block to record the amount of position information in the blocking block, and then judge The current amount of change in the interval is in accordance with which plan to directly apply the corresponding group timing plan. For example, please refer to the fifth and seventh figures. As shown in the seventh figure, after the amount of change in the position information of the 测 time interval is measured, the position information of the seventh figure is found in the fifth figure. The amount of change is the same as that of Plan II, so the group timed plan generated by the previous Plan II can be applied to facilitate immediate coordination with the current traffic status.
除上述實施例之外，本發明更可忽略篩選出區域中的至少ㄧ起點與至少ㄧ終點之步驟，可直接指定主要路徑，不需使用位置資訊來篩選主要路徑。取得主要路徑後，擷取主要路徑內所有行動裝置大量的複數位置資訊，接下來產生群組定時時制計劃的步驟皆與上述步驟相同故不重複敘述。In addition to the above embodiments, the present invention can more neglect the steps of filtering out at least the starting point and at least the ending point in the area, and can directly specify the main path without using the location information to filter the main path. After the main path is obtained, a large number of complex position information of all the mobile devices in the main path is retrieved, and the steps of generating the group timing time plan are the same as the above steps, so the description is not repeated.
同理，本發明也可忽略選出區域中的至少ㄧ起點與至少ㄧ終點，以及篩選主要路徑之步驟，可直接指定壅塞區塊，不需使用位置資訊進行篩選。取得壅塞區塊後，擷取壅塞區塊中所有行動裝置大量的複數位置資訊，接下來產生群組定時時制計劃的步驟皆與上述步驟相同故不重複敘述。Similarly, the present invention can also neglect at least the starting point and at least the ending point in the selected area, and the step of screening the main path, and can directly specify the blocking block without using the position information for screening. After obtaining the congestion block, a large number of complex position information of all the mobile devices in the congestion block is retrieved, and the steps of generating the group timing time plan are the same as the above steps, so the description is not repeated.
綜上所述，本發明可利用大數據的行動裝置位置資訊來判斷出交通壅塞的區域，再使用較精確的方式判定壅塞區域中每一路口的交通流量，以產生壅塞區域中所有路口紅綠燈的定時時制計劃，來彌補使用大數據時，流量較模糊的部分，且本發明可即時接收到大數據的行動裝置位置資訊，即時計算出交通流量的變化，以自動擷取對應交通流量的紅綠燈時制計劃，以自動化達到紓解交通等目的，本發明更可將每ㄧ時間區間的交通流量變化進行分類，以利後續時制計劃的重整。In summary, the present invention can utilize the location information of the mobile device of the big data to determine the traffic congestion area, and then use a more accurate method to determine the traffic flow of each intersection in the congestion area to generate traffic lights of all intersections in the congestion area. Timing schedule to compensate for the blurring of traffic when using big data, and the present invention can instantly receive the location information of the mobile device of the big data, and instantly calculate the change of the traffic flow to automatically capture the traffic light corresponding to the traffic flow. In order to automate the purpose of mitigating traffic, the present invention can classify the traffic flow changes in each time interval to facilitate the reorganization of the follow-up time plan.
唯以上所述者，僅為本發明之較佳實施例而已，並非用來限定本發明實施之範圍。故即凡依本發明申請範圍所述之特徵及精神所為之均等變化或修飾，均應包括於本發明之申請專利範圍內。The above is only the preferred embodiment of the present invention and is not intended to limit the scope of the present invention. Therefore, any changes or modifications of the features and spirits of the present invention should be included in the scope of the present invention.
10‧‧‧資料庫
12‧‧‧控制器
14‧‧‧紅綠燈號誌
20‧‧‧路徑
22‧‧‧路徑
24‧‧‧路徑
30‧‧‧區塊
32‧‧‧區塊
40‧‧‧住宅區A
42‧‧‧住宅區B
44‧‧‧住宅區C
46‧‧‧工業區
50‧‧‧行動裝置10‧‧‧Database
12‧‧‧ Controller
14‧‧‧Traffic Lights
20‧‧‧ Path
22‧‧‧ Path
24‧‧‧ Path
30‧‧‧ Block
32‧‧‧ Block
40‧‧‧Residential Area A
42‧‧‧Residential Area B
44‧‧‧Residential Area C
46‧‧‧ industrial area
50‧‧‧ mobile devices
第一圖係為本發明實施例使用之系統方塊圖。 第二圖係為本發明實施例之步驟流程圖。 第三圖與第四圖係為本發明實施例表示位置資訊狀態示意圖。 第五圖係為本發明實施例之路口在多個時間區間的位置資訊數量變化量示意圖。 第六圖係為本發明實施例使用之另一實施例系統方塊圖。 第七圖係為本發明實施例之路口在ㄧ個時間區間的位置資訊數量變化量示意圖。The first figure is a system block diagram used in the embodiment of the present invention. The second figure is a flow chart of the steps of the embodiment of the present invention. The third and fourth figures are schematic diagrams showing the state of the location information according to an embodiment of the present invention. The fifth figure is a schematic diagram of the amount of change in the position information of the intersection in a plurality of time intervals according to the embodiment of the present invention. Figure 6 is a block diagram of another embodiment of the embodiment of the present invention. The seventh figure is a schematic diagram of the amount of change in the position information of the intersection in the time interval of the embodiment of the present invention.
Claims (23)
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| TW106111485A TWI615814B (en) | 2017-04-06 | 2017-04-06 | Mobile device location grouping, time system grouping and method of generating traffic time planning using big data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| TW106111485A TWI615814B (en) | 2017-04-06 | 2017-04-06 | Mobile device location grouping, time system grouping and method of generating traffic time planning using big data |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| TWI615814B true TWI615814B (en) | 2018-02-21 |
| TW201837880A TW201837880A (en) | 2018-10-16 |
Family
ID=62014721
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| TW106111485A TWI615814B (en) | 2017-04-06 | 2017-04-06 | Mobile device location grouping, time system grouping and method of generating traffic time planning using big data |
Country Status (1)
| Country | Link |
|---|---|
| TW (1) | TWI615814B (en) |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040243298A1 (en) * | 2001-11-05 | 2004-12-02 | Oy Radiolinja Ab | Method and system for collecting traffic data |
| CN102496280A (en) * | 2011-12-13 | 2012-06-13 | 北京航空航天大学 | Method for obtaining road condition information in real time |
| TW201322201A (en) * | 2011-11-23 | 2013-06-01 | Chunghwa Telecom Co Ltd | Real-time vehicle speed information system and method based on mobile communication network signals |
-
2017
- 2017-04-06 TW TW106111485A patent/TWI615814B/en active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040243298A1 (en) * | 2001-11-05 | 2004-12-02 | Oy Radiolinja Ab | Method and system for collecting traffic data |
| TW201322201A (en) * | 2011-11-23 | 2013-06-01 | Chunghwa Telecom Co Ltd | Real-time vehicle speed information system and method based on mobile communication network signals |
| CN102496280A (en) * | 2011-12-13 | 2012-06-13 | 北京航空航天大学 | Method for obtaining road condition information in real time |
Non-Patent Citations (1)
| Title |
|---|
| 葉翰霖,"基於GPS之行動通訊網路訊號交通資訊估測方法之改善",國立交通大學, 上架日:2016年8月30日 * |
Also Published As
| Publication number | Publication date |
|---|---|
| TW201837880A (en) | 2018-10-16 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CA3027663C (en) | Systems and methods for displaying movement of vehicle on map | |
| CN100535957C (en) | Traffic information system | |
| Hans et al. | Real-time bus route state forecasting using particle filter and mesoscopic modeling | |
| KR101413505B1 (en) | Predicting method and device of expected road traffic conditions based on historical and current data | |
| US9778049B2 (en) | Calculating speed and travel times with travel delays | |
| US9280894B2 (en) | Filtering road traffic data from multiple data sources | |
| US9129449B2 (en) | Calculating speed and travel times with travel delays | |
| US8996287B2 (en) | Calculating speed and travel times with travel delays | |
| US20180350164A1 (en) | Systems and methods for assessing turns made by a vehicle | |
| CN102667404B (en) | The method of point of interest is analyzed with detection data | |
| US20150185031A1 (en) | Calculating speed and travel times with travel delays | |
| Chen et al. | Bus-arrival-time prediction models: Link-based and section-based | |
| US8713045B2 (en) | Location-type tagging using collected traveler data | |
| CA2811879A1 (en) | Driver profiling system and method | |
| CN102622880A (en) | Traffic information data recovery method and device | |
| WO2023088201A1 (en) | Method, apparatus and device for detecting abnormal vehicle | |
| TWI615814B (en) | Mobile device location grouping, time system grouping and method of generating traffic time planning using big data | |
| CA2917808A1 (en) | Calculating speed and travel times with travel delays | |
| Klunder et al. | The effect of inaccurate traffic data for ramp metering: Comparing loop detectors and cameras using information utility | |
| EP4352649A1 (en) | Multi-dimensional modeling of driver and environment characteristics | |
| CN114691760A (en) | OD track display method and device, computing equipment and storage medium | |
| EP3382342A1 (en) | Device, vehicle, system and method for monitoring acceleration | |
| US20240078725A1 (en) | Infrastructure diagnostic device, infrastructure diagnostic method, and recording medium | |
| CN118096082A (en) | Engineering construction wisdom building platform based on digital delivery | |
| Pan et al. | Evaluating ridesharing algorithms using the jargo real-time stochastic simulator |