本文關(guān)鍵詞：基于移動(dòng)設(shè)備的路況估計(jì)算法研究　出處：《北京郵電大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 手機(jī)GPS數(shù)據(jù) 路況估計(jì) 插值算法 RBF神經(jīng)網(wǎng)絡(luò)

【摘要】：道路交通擁堵是燃油利用率低,資源浪費(fèi)的主要原因。準(zhǔn)確估計(jì)道路交通狀況、定位交通擁堵是減少人們出行時(shí)間的重要一步,也是智能交通系統(tǒng)中的重要課題之一。目前已有的路況估計(jì)方法主要有三種,分別是基于路邊固定單元,基于無線探測與定位和基于車載GPS定位與通信。感應(yīng)線圈,RFID射頻卡等路邊固定單元,車載定位與通信模塊安裝和維護(hù)費(fèi)用高,覆蓋率低,利用基站的無線探測與定位受移動(dòng)網(wǎng)絡(luò)影響嚴(yán)重,都不能很好地實(shí)現(xiàn)路況估計(jì)。隨著智能手機(jī)的普及,利用手機(jī)集成的GPS定位作為交通探測器得到廣泛關(guān)注,使用從司機(jī)的手機(jī)中獲取的GPS信息監(jiān)控交通,開辟了交通狀況估計(jì)的新途徑。 論文將構(gòu)建基于智能手機(jī)GPS定位功能的路況估計(jì)系統(tǒng)架構(gòu)。首先,對單車LATT (Link Average Travel Time,路段行程時(shí)間)的估計(jì)算法進(jìn)行探索性研究,使用三次埃米爾特插值算法,提高車輛通過路段端點(diǎn)時(shí)刻的估算準(zhǔn)確性。其次,由于同類型車輛具有相似的運(yùn)動(dòng)特征,充分考慮單車對路段平均速度的貢獻(xiàn),采用權(quán)重法對單車路段平均速度進(jìn)行融合得到同類車輛的路段平均速度。再次,不同狀況下,路段平均速度與車輛的平均速度映射關(guān)系復(fù)雜,鑒于此,引入RBF (Radial Basis Function,徑向基函數(shù))網(wǎng)絡(luò),以同類型車輛路段平均速度和不同類型車輛數(shù)目的比值作為輸入計(jì)算路段平均速度。最后,介紹了表征道路交通狀況的重要參數(shù),針對手機(jī)GPS數(shù)據(jù)獲取量較小的情況,引入速度密度互推導(dǎo)算法構(gòu)建新的路況估計(jì)模型。 實(shí)驗(yàn)結(jié)果表明,三次埃米爾特插值算法、同類車輛平均行程速度融合算法、RBF神經(jīng)網(wǎng)絡(luò)法，，都降低了相應(yīng)參數(shù)的估計(jì)誤差,提高了系統(tǒng)對路段平均速度的估計(jì)準(zhǔn)確性。此外,在低滲透率下,基于速度密度互推導(dǎo)算法構(gòu)建的補(bǔ)充方案,對于降低路段平均速度和車輛密度的估計(jì)誤差也有明顯效果。
[Abstract]:Traffic congestion is the low utilization rate of fuel, mainly due to the waste of resources. The accurate estimation of road traffic conditions, location of traffic congestion is an important step in reducing people to travel time, but also in the intelligent transportation system is one of the important topics. The existing traffic estimation method mainly has three kinds, respectively is fixed roadside unit based on wireless detection with the positioning and GPS positioning based on vehicle and communication. Based on the induction coil, RFID radio frequency card fixed roadside unit, vehicle positioning and communication module installation and high maintenance cost, low coverage, using a wireless base station probe measurement and positioning by the mobile network influence is serious, can well realize the traffic estimation. With the popularity of smart mobile phone the use of mobile phone GPS positioning integrated as traffic detector to get attention, GPS information obtained from monitoring traffic drivers in the mobile phone, open up traffic. A new way to plan.
The construction of intelligent mobile phone GPS positioning function based on the traffic estimation system architecture. Firstly, the LATT (Link Average Travel Time bike, travel time) of an exploratory study of the estimation algorithm using three Hermite interpolation algorithm, improve the vehicle by estimating the accuracy of Lu Duanduan point. Secondly, due to the same type of vehicle with motion characteristics similarly, fully consider the bike section average velocity contribution was obtained by fusion of similar vehicles on the bike section average velocity of section average velocity using the weight method. Thirdly, under different conditions, the average velocity mapping section average velocity and vehicle complex relationship, in view of this, the introduction of RBF (Radial Basis Function, the radial basis function network) the ratio of the number of vehicles of the same type, average road speeds and different types of vehicle as the input calculation of section average velocity. Finally, the table In view of the important parameters of the road traffic conditions, in view of the small amount of GPS data acquisition of mobile phone, a new estimation model of road condition is constructed by introducing the speed density mutual deduction algorithm.
The experimental results show that the three Hermite interpolation algorithm, fusion algorithm of average travel speed of similar vehicles, RBF neural network method can reduce the estimation error of the corresponding parameters, improve the estimation accuracy of section average velocity. In addition, in low permeability, supplementing the construction speed of the algorithm is derived based on density, also has the obvious the effect for reducing the error estimates of section average velocity and density of vehicles.

【學(xué)位授予單位】：北京郵電大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：U495

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本文編號：1384617