發(fā)布時間：2018-11-28 17:47

【摘要】：隨著機動車數(shù)量的飛速增加,城市交通問題日益凸顯,對人們的日常生活產(chǎn)生了巨大的影響。據(jù)統(tǒng)計,我國機動車保有量在2016年上半年已達到2.58億,大量的機動車數(shù)量帶來了嚴(yán)重的城市擁堵和停車?yán)щy等交通問題。為了解決交通問題,各個國家大力發(fā)展智能交通系統(tǒng),通過實時監(jiān)測交通系統(tǒng),采集交通數(shù)據(jù)來分析、研究并制定交通措施。各國學(xué)者也利用采集的交通數(shù)據(jù)對交通系統(tǒng)展開了廣泛的研究。但是,現(xiàn)有的研究主要關(guān)注于路網(wǎng)本身,對路段的運行狀態(tài)進行研究。對于路段外,即停車問題的研究則較少。并且,已有的關(guān)于停車問題的研究也受到停車數(shù)據(jù)的限制,無法對沒有數(shù)據(jù)的情況進行研究。而區(qū)域內(nèi)的機動車積累量包含了路網(wǎng)中運行和區(qū)域中停靠的兩部分機動車數(shù)量,涵蓋了更加全面的交通信息。通過機動車積累量,我們既可以分析路網(wǎng)的運行狀態(tài),也可以推斷區(qū)域的停車需求。因此,本文使用區(qū)域的機動車積累量進行研究。使用杭州市路段微波流量數(shù)據(jù),提出并實現(xiàn)了基于微波數(shù)據(jù)的機動車積累量估計方法,并基于估計的機動車積累量對區(qū)域的交通狀態(tài)進行分析,推算區(qū)域的停車需求。同時,也搭建了基于WEB端的可視化平臺,對本文的交通數(shù)據(jù)和計算結(jié)果進行展示。首先,針對交通流量數(shù)據(jù)量大,傳統(tǒng)工具效率低的問題,本文使用Spark分布式計算平臺對流量數(shù)據(jù)進行預(yù)處理,極大地提高了處理效率。其次,通過對微波流量數(shù)據(jù)的分析,提出灰色模型和歷史均值的加權(quán)方法來解決流量數(shù)據(jù)中記錄缺失的問題,并使用實際微波數(shù)據(jù)與其他方法對比說明方法的性能。然后,根據(jù)路網(wǎng)數(shù)據(jù)對城市進行區(qū)域劃分。針對區(qū)域機動車積累量的估計問題,提出利用進出區(qū)域的路段流量數(shù)據(jù)進行有向累加的基本方法來估計初步的機動車積累量結(jié)果。并進一步改進,提出基于區(qū)域劃分的偏差分配補全方法來解決基本方法存在偏差的問題。并利用實際杭州市微波數(shù)據(jù)進行實例機動車積累量的估算,以此推斷區(qū)域的停車需求,并對比實際真值驗證本文方法的效果。最后,本文設(shè)計了可視化的方案并搭建了基于WEB端的可視化平臺實現(xiàn)了交通數(shù)據(jù)的展示、分析功能。通過可視化平臺來直觀展示本文的微波數(shù)據(jù),呈現(xiàn)區(qū)域機動車積累量的變化規(guī)律,分析不同數(shù)據(jù)之間的關(guān)系。
[Abstract]:With the rapid increase of the number of motor vehicles, urban traffic problems become increasingly prominent, which has a great impact on people's daily life. According to statistics, the number of motor vehicles in China has reached 258 million in the first half of 2016, a large number of motor vehicles have brought serious urban congestion and parking difficulties and other traffic problems. In order to solve the traffic problem, every country develops the intelligent transportation system vigorously, through monitoring the traffic system in real time, collecting the traffic data to analyze, studying and formulating the traffic measure. Scholars from all over the world also make use of the collected traffic data to carry out extensive research on the traffic system. However, the existing research mainly focuses on the road network itself. There are few studies on the parking problem outside the section. Moreover, the existing research on parking problem is limited by parking data. The amount of motor vehicle accumulation in the region includes the number of two parts of motor vehicles in the road network and in the region, which covers more comprehensive traffic information. Through the accumulation of motor vehicles, we can not only analyze the running state of the road network, but also infer the parking demand of the area. Therefore, this paper uses the area of motor vehicle accumulation to study. Based on the microwave flow data of Hangzhou section, a method of vehicle accumulation estimation based on microwave data is proposed and realized. The traffic state of the area is analyzed based on the estimated amount of vehicle accumulation, and the parking demand of the area is calculated. At the same time, the visualization platform based on WEB is also built to display the traffic data and calculation results. Firstly, aiming at the problem of large amount of traffic flow data and low efficiency of traditional tools, this paper uses the Spark distributed computing platform to preprocess the traffic data, which greatly improves the processing efficiency. Secondly, based on the analysis of microwave flow data, the grey model and the weighted method of historical mean are proposed to solve the problem of missing records in the flow data, and the performance of the method is illustrated by comparing the actual microwave data with other methods. Then, according to the road network data, the city is divided into regions. In view of the problem of estimating regional motor vehicle accumulation, a basic method of directional accumulation based on the flow data of road sections in and out of the region is proposed to estimate the preliminary results of motor vehicle accumulation. Furthermore, a new method based on region partition is proposed to solve the problem of deviation in the basic method. The actual microwave data of Hangzhou are used to estimate the accumulative amount of motor vehicle in order to infer the parking demand of the area and to verify the effectiveness of the method compared with the actual value. Finally, the visualization scheme is designed and the visualization platform based on WEB is built to display and analyze traffic data. The microwave data of this paper are displayed intuitively by visual platform, and the variation law of regional motor vehicle accumulation is presented, and the relationship between different data is analyzed.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：U491.1

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