本文選題：交通預(yù)測(cè) + 路徑選取�。� 參考：《北京交通大學(xué)》2014年碩士論文

【摘要】：摘要：智能交通系統(tǒng)(Intelligent Transportation Systems,ITS)是目前國(guó)際公認(rèn)的緩解城市交通擁堵、減少交通事故和降低汽車尾氣排放污染等交通問(wèn)題的最佳途徑。道路交通信息是ITS不可或缺的前提和主要內(nèi)容。在短時(shí)間得到準(zhǔn)確的交通信息,并根據(jù)這些信息快速準(zhǔn)確地為駕駛員提供最佳行駛路徑,是ITS領(lǐng)域的一個(gè)重要研究方向。 目前,隨著智能手機(jī)的普及,它在給人們帶來(lái)通信便利的同時(shí)還形成了大量的交通數(shù)據(jù)。通過(guò)對(duì)這些手機(jī)交通數(shù)據(jù)的采集和處理,用于交通路網(wǎng)的短時(shí)預(yù)測(cè)及路徑誘導(dǎo)已成為ITS的一個(gè)前沿課題。 本文根據(jù)現(xiàn)有條件,主要研究利用手機(jī)數(shù)據(jù)進(jìn)行交通路網(wǎng)預(yù)測(cè)和路徑選擇的建模和算法實(shí)現(xiàn)問(wèn)題,主要內(nèi)容如下： 第一,根據(jù)交通路網(wǎng)特性和手機(jī)交通數(shù)據(jù)特點(diǎn),引入虛擬路徑(VTLs)的思想,設(shè)計(jì)了基于交通路網(wǎng)的時(shí)空自回歸移動(dòng)平均模型(STARMA)和邏輯回歸算法(LR),分別用于預(yù)測(cè)交通路網(wǎng)的旅行時(shí)間和擁堵狀況。 第二,根據(jù)時(shí)空自回歸移動(dòng)平均模型和邏輯回歸算法,提出一個(gè)基于連通可靠性的路徑選取算法。通過(guò)邏輯回歸算法得到的擁堵概率數(shù)據(jù)作為誘導(dǎo)路徑中的旅行時(shí)間權(quán)重值,使得在路徑選取過(guò)程中更傾向于那些沒(méi)有或很少擁堵的路徑。從而解決了一般算法中一味地追求最少的時(shí)間花費(fèi)反而導(dǎo)致局部地區(qū)車輛的激增,進(jìn)而影響交通路網(wǎng)可靠性的問(wèn)題。 第三,依托Paramics仿真軟件通過(guò)編程,實(shí)現(xiàn)了對(duì)利用手機(jī)采集交通數(shù)據(jù)過(guò)程的模擬。利用Paramics仿真軟件對(duì)路網(wǎng)進(jìn)行建模及仿真,并將STARMA和LR的仿真結(jié)果與真實(shí)數(shù)據(jù)進(jìn)行對(duì)比分析；另外,將上述路徑選取算法的仿真結(jié)果與其他組合算法進(jìn)行對(duì)比分析。
[Abstract]:Abstract: intelligent Transportation (Intelligent Transportation) is the best way to alleviate traffic congestion, reduce traffic accidents and reduce vehicle exhaust pollution. Road traffic information is an indispensable prerequisite and main content of ITS. It is an important research direction in the field of ITS to get accurate traffic information in a short time and provide drivers with the best driving path quickly and accurately according to these information. At present, with the popularity of smart phones, it not only brings convenience to people, but also forms a lot of traffic data. Through the collection and processing of these mobile phone traffic data, short-term prediction and route guidance of traffic network has become a frontier topic of ITS. According to the existing conditions, this paper mainly studies the modeling and algorithm implementation of traffic network prediction and path selection using mobile phone data. The main contents are as follows: First, according to the characteristics of traffic network and mobile phone traffic data, the idea of virtual path (VTLs) is introduced. A spatio-temporal autoregressive moving average model based on traffic network (STARMA) and a logical regression algorithm (LRN) are designed to predict the travel time and congestion of traffic network respectively. Secondly, a path selection algorithm based on connected reliability is proposed based on the moving average model and logical regression algorithm. The congestion probability data obtained by the logical regression algorithm is used as the travel time weight value in the induced path, which makes the path selection more prone to those paths that have no or little congestion. Thus solving the problem that the pursuit of the least amount of time in the general algorithm leads to the proliferation of vehicles in local areas and thus affects the reliability of the traffic network. Thirdly, the simulation of traffic data acquisition using mobile phone is realized by programming with Paramics simulation software. The simulation results of STARMA and LR are compared with real data by using Paramics simulation software. In addition, the simulation results of the above path selection algorithm are compared and analyzed with other combined algorithms.
【學(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：U495

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