本文選題：智能交通 + 路徑選擇算法�。� 參考：《沈陽航空航天大學(xué)》2014年碩士論文

【摘要】：隨著經(jīng)濟的穩(wěn)步增長，城市的快速發(fā)展帶來了日益嚴(yán)重的交通擁堵和頻發(fā)的交通事故，且交通污染等問題已在影響著人們的生活和社會的和諧安定。面對如此嚴(yán)酷的交通問題，運用先進的管理技術(shù)為出行者提供多種服務(wù)的智能交通應(yīng)運而生。動態(tài)路徑誘導(dǎo)系統(tǒng)作為其核心，成為當(dāng)前的研究熱點。本文就對存在駕駛員偏好的最優(yōu)路徑選擇問題進行了研究。 駕駛員選擇路徑通常是感知路段的各種屬性的綜合評價的結(jié)果。本文將駕駛員的個人偏好應(yīng)用在路徑誘導(dǎo)過程中，提出一個路徑選擇指標(biāo)體系，，來體現(xiàn)路徑誘導(dǎo)系統(tǒng)的個性化特點。而層次分析法把定性方法與定量方法有機地結(jié)合起來，將人們的思維過程數(shù)學(xué)化、系統(tǒng)化，因此運用層次分析法來確定駕駛員的偏好。 往往路徑誘導(dǎo)系統(tǒng)中收集到的信息是不完全的，且其中駕駛員有不好描述的偏好信息。路徑選擇是一個復(fù)雜的系統(tǒng)問題，為此本文運用灰色系統(tǒng)理論來對系統(tǒng)中的灰色信息進行研究。在提出的路徑選擇指標(biāo)體系下，由層次分析法確定駕駛員的偏好后，運用基于灰色決策方法來進行路徑選擇。由誘導(dǎo)信息得到的可行路徑集，本文運用基于灰色關(guān)聯(lián)決策的方法和基于多目標(biāo)智能加權(quán)灰靶決策的方法在其中搜索滿足駕駛員個人偏好的最優(yōu)路徑。 通過仿真實驗，對所提出的算法進行驗證。運用VISSIM在一個實際路網(wǎng)上進行路徑選擇仿真，Dijkstra算法得到最短路徑，基于灰色關(guān)聯(lián)決策和基于多目標(biāo)智能加權(quán)灰靶決策的路徑選擇算法均得到能夠反映駕駛員個人偏好的最優(yōu)路徑。改變仿真路網(wǎng)上行駛車輛使用算法的比例，研究不同算法使用比例變化對整個路網(wǎng)交通的影響。對于有突發(fā)事件造成交通擁堵時，運用Dijkstra算法的車輛行駛速度大幅降低，而基于多目標(biāo)智能加權(quán)灰靶決策的算法能立即改變路徑選擇，使出行者繞過擁堵路段，行駛速度沒有受到太大影響。仿真結(jié)果表明：所提出的路徑選擇算法具有很好的可行性和適用性。
[Abstract]:With the steady growth of economy, the rapid development of the city has brought more and more serious traffic congestion and frequent traffic accidents, and traffic pollution and other problems have affected people's lives and social harmony and stability. In the face of such severe traffic problems, intelligent transportation (its), which uses advanced management technology to provide multiple services for travelers, emerges as the times require. As the core of dynamic path guidance system, dynamic path guidance system has become a hot research topic. In this paper, the problem of optimal path selection with driver preference is studied. The driver's choice of path is usually the result of comprehensive evaluation of the various attributes of the perceived road section. In this paper, the driver's personal preference is applied to the path guidance process, and a path selection index system is proposed to reflect the personalized characteristics of the path guidance system. The Analytic hierarchy process (AHP) combines qualitative and quantitative methods organically, and makes people's thinking process mathematical and systematic. Therefore, AHP is used to determine drivers' preferences. The information collected in the path guidance system is often incomplete, and the driver has bad preference information. Path selection is a complex system problem. In this paper, grey system theory is used to study the grey information in the system. Under the proposed path selection index system, after determining the driver's preference by AHP, the path selection is based on grey decision method. Based on the set of feasible paths derived from the induced information, this paper uses the method based on grey relational decision and the method based on multi-objective intelligent weighted grey target decision to search for the optimal path satisfying the individual preference of the driver. The proposed algorithm is verified by simulation experiments. The shortest path can be obtained by using VISSIM to simulate the path selection in a real road network by using Dijkstra algorithm. The path selection algorithm based on grey relational decision and multi-objective intelligent weighted grey target decision can obtain the optimal path which can reflect the individual preference of driver. By changing the proportion of the vehicle usage algorithm on the road network, the influence of different algorithms on the whole road network traffic is studied. For traffic jams caused by unexpected events, the speed of vehicles using Dijkstra algorithm is greatly reduced, while the algorithm based on multi-objective intelligent weighted grey target decision can change the path choice immediately, and make travelers bypass the congested section. The speed of driving was not greatly affected. Simulation results show that the proposed path selection algorithm is feasible and applicable.
【學(xué)位授予單位】：沈陽航空航天大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：U495

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