本文選題：道路行程時間 + 固定檢測器　； 參考：《大連海事大學》2014年碩士論文

【摘要】：道路行程時間是反映道路交通狀況的重要指標。一方面,在現(xiàn)實情境中,由于交通需求在一天當中變化很大,使得道路網(wǎng)絡交通流的時空分布規(guī)律具有時變特性,從而導致道路路段行駛時間很大程度上依賴于交通負荷的變化。因此,準確的路段行程時間動態(tài)預測,是交通誘導系統(tǒng)、交通信息服務系統(tǒng)以及交通協(xié)調(diào)控制系統(tǒng)的重要基礎；另一方面,由于道路交通流運行的高度復雜性、隨機性和不確定性,傳統(tǒng)的基于檢測線圈的路段行程時間預測方法、基于GPS浮動車的路段行程時間預測方法等單一方法一直未能取得令人滿意的預測效果,這在一定程度上影響了道路交通控制以及道路交通誘導的效果。鑒于以上預測方法的優(yōu)缺點及其互補性,本文在多源數(shù)據(jù)融合方法基礎上,采用改進的BP神經(jīng)網(wǎng)絡圍繞基于數(shù)據(jù)融合的城市道路行程時間預測方法開展了相關探索性研究工作,以期進一步提高道路行程時間預測的準確性。 首先,針對固定檢測器和GPS浮動車獲取的道路交通參數(shù)中存在數(shù)據(jù)異常和丟失等問題,本文提出了基于相鄰時段數(shù)據(jù)平均值法的故障數(shù)據(jù)修復改進方法。改進后的方法能夠較好地修復故障數(shù)據(jù),從而提高數(shù)據(jù)的質(zhì)量。 其次,針對多輛GPS浮動車數(shù)據(jù)進行道路行程時間預測過程中存在浮動車在道路上駛過的距離長度不等問題,本文提出了自適應權(quán)重系數(shù)線性加權(quán)融合方法,用于多輛GPS浮動車數(shù)據(jù)的道路行程時間預測。 最后,針對單類型檢測器數(shù)據(jù)存在道路行程時間預測不準確問題,本文提出采用改進的BP神經(jīng)網(wǎng)路對兩種檢測器獲取的道路行程時間數(shù)據(jù)進行融合,從而建立基于數(shù)據(jù)融合的道路行程時間預測模型。 通過對上述研究成果進行經(jīng)過仿真實驗,初步達到了預期的研究目標。 城市道路行程時間預測問題相對復雜,鑒于作者研究能力有限,論文研究還有許多需要進一步完善的地方,將在后續(xù)工作學習中不斷改進。
[Abstract]:Road travel time is an important indicator to reflect road traffic situation. On the one hand, in the real situation, because of the great change of traffic demand in one day, the space-time distribution of traffic flow in road network is time-varying. As a result, the driving time of road section depends on the change of traffic load to a great extent. Therefore, accurate road travel time dynamic prediction is the important foundation of traffic guidance system, traffic information service system and traffic coordination control system. On the other hand, because of the high complexity of road traffic flow, Randomness and uncertainty, the traditional method based on detection coil to predict the travel time of road section, and the method based on GPS floating vehicle to predict the travel time of road section have not been able to achieve satisfactory results. To a certain extent, this affects the effect of road traffic control and road traffic guidance. In view of the advantages and disadvantages of the above prediction methods and their complementarities, based on the multi-source data fusion method, the improved BP neural network is used to carry out the related exploratory research work around the urban road travel time prediction method based on the data fusion. In order to further improve the accuracy of road travel time prediction. First of all, there are some problems such as data anomaly and loss in the road traffic parameters obtained by fixed detector and GPS floating vehicle. In this paper, an improved method for repairing fault data based on the mean value method of adjacent interval data is proposed. The improved method can repair the fault data and improve the quality of the data. Secondly, in the course of road travel time prediction based on the data of multiple GPS floating vehicles, the distance length of the floating vehicle passing on the road is not equal. In this paper, an adaptive weighting coefficient linear weighted fusion method is proposed to predict the road travel time of multiple GPS floating vehicle data. In this paper, an improved BP neural network is proposed to fuse the road travel time data obtained by two detectors, and a road travel time prediction model based on data fusion is established. The prediction problem of urban road travel time is relatively complex. In view of the author's limited research ability, there are still many areas that need to be further improved, which will be continuously improved in the follow-up study.
【學位授予單位】：大連海事大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：U491;U495;TP202

【參考文獻】

相關期刊論文 前10條

1 高紅;;BP神經(jīng)網(wǎng)絡學習率的優(yōu)化方法[J];長春師范學院學報(自然科學版);2010年04期

2 彭春華;劉建業(yè);劉岳峰;晏磊;鄭江華;;車輛檢測傳感器綜述[J];傳感器與微系統(tǒng);2007年06期

3 王力,王川久,沈曉蓉,范躍祖;智能交通系統(tǒng)中實時交通信息采集處理的新方法[J];系統(tǒng)工程;2005年02期

4 姜桂艷;常安德;張瑋;唐永勇;;基于GPS浮動車的自然路段行程時間估計方法[J];公路;2009年11期

5 楊少輝;王殿海;王英平;董斌;;最小二乘擬合法確定行程時間[J];公路交通科技;2006年09期

6 張和生;張毅;溫慧敏;胡東成;;利用GPS數(shù)據(jù)估計路段的平均行程時間[J];吉林大學學報(工學版);2007年03期

7 孫娓娓;劉瓊蓀;;一種基于放大誤差信號的自適應BP算法[J];計算機應用;2008年08期

8 張存保;楊曉光;嚴新平;;基于浮動車的交通信息采集系統(tǒng)研究[J];交通與計算機;2006年05期

9 張存保;嚴新平;;固定檢測器和移動檢測器的交通信息融合方法[J];交通與計算機;2007年03期

10 姜桂艷,Q，

本文編號：2008644