【摘要】：隨著物質(zhì)生活水平的提高,越來越多的家庭開始擁有私家車。我國車輛數(shù)量的快速增長,伴隨而來的是交通擁堵、環(huán)境污染等一系列問題。同時,雖然道路上車輛眾多,但各大城市卻仍然存在著不同程度的打車難問題。一味地拓寬道路和增加出租車數(shù)量都是無法很好解決這個問題的。為此,車輛合乘開始走入人們生活,將車上空余的座位作為乘坐資源利用起來。目前,歐美國家車輛合乘已經(jīng)常態(tài)化了,而我國尚處于起步階段。早期,一些提供車輛合乘信息的網(wǎng)站只提供基本的出行時間和起終點信息,采用文字匹配的技術(shù)使得乘客與車主之間難以得到有效配對,沒有被大眾廣泛接受。因而,一個有效的車輛合乘匹配算法是車輛合乘問題研究的關(guān)鍵。當前,不少學者對車輛合乘匹配問題的研究大多只是研究確定性靜態(tài)車輛匹配問題,研究中缺乏考慮外界因素對問題的影響,如道路通行情況、車主與乘客自身因素等。尤其是確定性靜態(tài)車輛匹配問題在車輛出發(fā)前就已經(jīng)規(guī)定了乘客上下車時間和位置,實際生活中車主與乘客很難能夠嚴格按照時間窗規(guī)定到達乘車點,只要有一個環(huán)節(jié)出錯,整個合乘很可能就是一個失敗的合乘匹配。本文著眼于研究車輛動態(tài)合乘匹配問題,只有實時位置在乘客附近的車輛才有可能與之匹配,以此解決車輛到達時間不確定性的問題。借助于Dijkstra算法和遺傳算法,使得隨著乘客的加入,車輛總能得到當前新的最短路徑。并在此基礎(chǔ)上,檢驗時間窗,計算乘客與車輛之間的匹配程度,確定匹配關(guān)系。論文最后在仿真系統(tǒng)中構(gòu)建了包含昆明市主要干道的路網(wǎng),在此之上試驗乘客與車輛的匹配過程,證明本文的算法能夠有效解決車輛動態(tài)合乘問題。
[Abstract]:With the improvement of material standard of living, more and more families begin to own private cars. The rapid growth of the number of vehicles in China is accompanied by a series of problems, such as traffic congestion, environmental pollution and so on. At the same time, although there are a lot of traffic on the road, the major cities still have varying degrees of difficulty in taxi problems. Simply widening roads and increasing the number of taxis will not solve this problem very well. To this end, car-sharing began to walk into people's lives, using the spare seats as a ride resource. At present, the European and American countries have regular vehicle ride, and China is still in its infancy. In the early days, some websites that provided information on vehicle rides provided only basic travel time and terminal information, and the use of text matching technology made it difficult for passengers and car owners to match effectively and were not widely accepted by the public. Therefore, an effective vehicle matching algorithm is the key to the research of vehicle ridership problem. At present, many scholars only study the static vehicle matching problem, and lack of considering the influence of external factors, such as road traffic situation, vehicle owner and passenger factors, etc. In particular, the deterministic static vehicle matching problem has already stipulated the time and location of passengers getting on and off before the departure of the vehicle. In actual life, it is very difficult for the owner and passenger to reach the point of the ride strictly according to the time window, as long as there is a link error. The whole combination is likely to be a failed multiplicative match. In this paper, we focus on the dynamic matching problem of vehicles, which can only be matched by vehicles located near passengers in real time, so as to solve the problem of vehicle arrival time uncertainty. With the help of Dijkstra algorithm and genetic algorithm, with the addition of passengers, the vehicle can always get the new shortest path. On this basis, the time window is checked, the matching degree between passenger and vehicle is calculated, and the matching relationship is determined. Finally, the paper constructs a road network including the main trunk roads in Kunming, and tests the matching process of passenger and vehicle on the simulation system, which proves that the algorithm in this paper can effectively solve the problem of vehicle dynamic matching.
【學位授予單位】：昆明理工大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：U491

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