基于軌跡和POI數(shù)據(jù)的熱點區(qū)域?qū)崟r預(yù)測
發(fā)布時間：2018-09-08 06:51
【摘要】：目前,智能移動設(shè)備通常都帶有利用了全球定位技術(shù)的位置傳感器,可以準確的捕捉到移動設(shè)備的具體位置信息。隨著位置獲取和移動計算方面的技術(shù)的進步,產(chǎn)生了大量的數(shù)據(jù),這些數(shù)據(jù)代表了各種不同種類的移動的物體如人,動物,車輛等的軌跡記錄。并且這些位置信息可以通過眾包的方式利用無線傳輸技術(shù)上傳到服務(wù)器當中,形成大量的GPS軌跡數(shù)據(jù)。GPS軌跡大數(shù)據(jù)中包含了史無前例的信息,可以讓我們更好的理解移動對象和地理位置,伴隨產(chǎn)生了大量的基于位置的社交網(wǎng)絡(luò)、智能交通系統(tǒng)和城市計算等應(yīng)用。這些應(yīng)用的流行,反過來又不斷的促進新的系統(tǒng)性的軌跡大數(shù)據(jù)挖掘的研究。在這個良性的發(fā)展循環(huán)當中,GPS軌跡大數(shù)據(jù)的挖掘成為了一個熱點的研究問題,吸引著包括計算機領(lǐng)域、社會學領(lǐng)域和地理信息領(lǐng)域的學者對此不懈研究。但是單純從軌跡數(shù)據(jù)中挖掘人類活動語義信息是困難的。從更抽象的層次看,軌跡數(shù)據(jù)只是時空數(shù)據(jù)的一種,時空數(shù)據(jù)是帶有空間坐標和時間戳的數(shù)據(jù),具有時間屬性和空間屬性。能夠準確預(yù)測時空數(shù)據(jù)變化情況對于地理位置推薦等城市計算相關(guān)應(yīng)用具有重要意義。另一方面,城市中的興趣點(Point of Interests,POI)等靜態(tài)的地理空間信息也會對人類活動產(chǎn)生重要甚至是根本性的影響,因為人類是生活在一定地理環(huán)境中的,POI往往成為人類日常社會活動的參與場所。本文試圖融合使用軌跡時空數(shù)據(jù)和地理靜態(tài)信息兩種數(shù)據(jù),在復雜的城市環(huán)境中預(yù)測出不同區(qū)域的停留點數(shù)量變化情況。停留點也是一種時空數(shù)據(jù),指出租車低速巡游或靜止等待乘客的地點。停留點預(yù)測對于出租車載客,乘客乘車,地理位置的熱度變化,乃至交通疏導,城市安全,城市建設(shè)規(guī)劃等方面都具有重要參考作用。對停留點的預(yù)測往往是地理位置推薦系統(tǒng)中的一部分,但是現(xiàn)有的熱點區(qū)域推薦或出租車載客點推薦等研究中往往在這些方面有所欠缺,一是使用數(shù)據(jù)源不夠豐富,導致推薦準確率不夠高,二是直接對于歷史軌跡數(shù)據(jù)進行挖掘分析,計算復雜度高,難以拓展到更大地理范圍,而且實時性難以保證。本文融合了交通軌跡數(shù)據(jù)和基于微博簽到的POI數(shù)據(jù),提出了自己的預(yù)測框架,框架首先根據(jù)基于POI的空間相似性找到相似區(qū)域,并根據(jù)相似區(qū)域預(yù)測出下個時間段目標區(qū)域的停留點變化情況。本文還基于Apache Storm構(gòu)建了一個實時處理系統(tǒng),模擬了整個實時分析與預(yù)測過程。實驗結(jié)果表明,預(yù)測的地點的情況與實際情況相比具有較高的準確率,并且整個流處理系統(tǒng)也具有實時處理大數(shù)據(jù)的低延遲,高吞吐量的特性。
[Abstract]:At present, intelligent mobile devices are usually equipped with a position sensor using global positioning technology, which can accurately capture the specific location information of mobile devices. With the development of the technology of position acquisition and mobile computing, a lot of data are produced, which represent the track records of various kinds of moving objects such as human, animal, vehicle and so on. And these location information can be uploaded to the server by means of wireless transmission technology by crowdsourcing, forming a large amount of GPS trajectory data. Big data contains unprecedented information. It can give us a better understanding of mobile objects and geographical location, with a large number of location-based social networks, intelligent transportation systems and urban computing applications. The popularity of these applications, in turn, continues to promote the new systematic trajectory of big data mining research. In this benign cycle of development, the excavation of GPS trajectory big data has become a hot research issue, attracting scholars including computer field, sociology field and geographical information field to unremitting research on it. But it is difficult to mine the semantic information of human activities from track data. From a more abstract level, track data is only one kind of spatio-temporal data, and space-time data is data with spatial coordinates and timestamp, which has temporal and spatial attributes. It is very important to predict the change of spatiotemporal data accurately for the application of city calculation such as geographical location recommendation. On the other hand, static geospatial information such as (Point of Interests,POI) can have an important or even fundamental impact on human activities. Because people are living in a certain geographical environment, the POI often becomes the place of human daily social activities. This paper attempts to predict the change of the number of residence points in different regions in complex urban environment by using two kinds of data of locus spatiotemporal data and geographic static information. Stop points are also temporal and spatial data, indicating where taxis travel at low speeds or wait for passengers at rest. The prediction of stopping point plays an important reference role in the aspects of taxi passenger, passenger ride, heat change of geographical location, even traffic diversion, urban safety, urban construction planning and so on. The prediction of stopping point is often a part of the geographic location recommendation system, but the existing research on the recommendation of hot spot or taxi passenger spot is often lacking in these aspects. First, the use of data sources is not rich enough. As a result, the recommendation accuracy is not high enough, and the second is to mine and analyze the historical track data directly, which has high computational complexity and is difficult to extend to a larger geographical range, and the real-time performance is difficult to guarantee. This paper combines the traffic trajectory data with the POI data signed by Weibo, and proposes its own prediction framework. Firstly, the framework finds the similar region according to the spatial similarity based on POI. According to the similar region, the change of the residence point of the target area in the next time period is predicted. A real-time processing system based on Apache Storm is constructed, and the whole real-time analysis and prediction process is simulated. The experimental results show that the predicted location has a high accuracy compared with the actual situation, and the whole flow processing system also has the characteristics of real-time processing big data's low delay and high throughput.
【學位授予單位】：吉林大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP311.13
本文編號：2229701
[Abstract]:At present, intelligent mobile devices are usually equipped with a position sensor using global positioning technology, which can accurately capture the specific location information of mobile devices. With the development of the technology of position acquisition and mobile computing, a lot of data are produced, which represent the track records of various kinds of moving objects such as human, animal, vehicle and so on. And these location information can be uploaded to the server by means of wireless transmission technology by crowdsourcing, forming a large amount of GPS trajectory data. Big data contains unprecedented information. It can give us a better understanding of mobile objects and geographical location, with a large number of location-based social networks, intelligent transportation systems and urban computing applications. The popularity of these applications, in turn, continues to promote the new systematic trajectory of big data mining research. In this benign cycle of development, the excavation of GPS trajectory big data has become a hot research issue, attracting scholars including computer field, sociology field and geographical information field to unremitting research on it. But it is difficult to mine the semantic information of human activities from track data. From a more abstract level, track data is only one kind of spatio-temporal data, and space-time data is data with spatial coordinates and timestamp, which has temporal and spatial attributes. It is very important to predict the change of spatiotemporal data accurately for the application of city calculation such as geographical location recommendation. On the other hand, static geospatial information such as (Point of Interests,POI) can have an important or even fundamental impact on human activities. Because people are living in a certain geographical environment, the POI often becomes the place of human daily social activities. This paper attempts to predict the change of the number of residence points in different regions in complex urban environment by using two kinds of data of locus spatiotemporal data and geographic static information. Stop points are also temporal and spatial data, indicating where taxis travel at low speeds or wait for passengers at rest. The prediction of stopping point plays an important reference role in the aspects of taxi passenger, passenger ride, heat change of geographical location, even traffic diversion, urban safety, urban construction planning and so on. The prediction of stopping point is often a part of the geographic location recommendation system, but the existing research on the recommendation of hot spot or taxi passenger spot is often lacking in these aspects. First, the use of data sources is not rich enough. As a result, the recommendation accuracy is not high enough, and the second is to mine and analyze the historical track data directly, which has high computational complexity and is difficult to extend to a larger geographical range, and the real-time performance is difficult to guarantee. This paper combines the traffic trajectory data with the POI data signed by Weibo, and proposes its own prediction framework. Firstly, the framework finds the similar region according to the spatial similarity based on POI. According to the similar region, the change of the residence point of the target area in the next time period is predicted. A real-time processing system based on Apache Storm is constructed, and the whole real-time analysis and prediction process is simulated. The experimental results show that the predicted location has a high accuracy compared with the actual situation, and the whole flow processing system also has the characteristics of real-time processing big data's low delay and high throughput.
【學位授予單位】：吉林大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP311.13
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