本文關(guān)鍵詞： 預(yù)約打車(chē) 合乘 路徑優(yōu)化 遺傳算法　出處：《哈爾濱工業(yè)大學(xué)》2015年碩士論文　論文類(lèi)型：學(xué)位論文

【摘要】：出租車(chē)合乘能夠提高出租車(chē)的利用率,從而實(shí)現(xiàn)在不增加出租車(chē)投放量的前提下,一定程度上緩解當(dāng)前中國(guó)大中城市打車(chē)難的問(wèn)題,然而目前尚未有較為科學(xué)的模式,能夠保證合乘路線(xiàn)的合理性并保障合乘中各方面的利益。另一方面,近年來(lái)出租車(chē)電召服務(wù)進(jìn)入人們視野,并逐漸培養(yǎng)著人們預(yù)約打車(chē)的習(xí)慣。預(yù)約打車(chē)模式在用戶(hù)需求收集方面具有先天優(yōu)勢(shì),為通過(guò)優(yōu)化方法實(shí)現(xiàn)車(chē)輛調(diào)度提供了平臺(tái)與支撐。因此,研究在預(yù)約打車(chē)的背景下,根據(jù)用戶(hù)出行需求,實(shí)現(xiàn)乘客合乘線(xiàn)路的優(yōu)化,從而提高出租車(chē)?yán)寐?緩解道路擁堵并減少環(huán)境污染具有一定的現(xiàn)實(shí)意義。本文根據(jù)出租車(chē)合乘系統(tǒng)的成本結(jié)構(gòu),建立了車(chē)輛合乘路徑優(yōu)化的數(shù)學(xué)模型,并設(shè)計(jì)了求解該模型的改進(jìn)遺傳算法,通過(guò)matlab對(duì)算法進(jìn)行實(shí)現(xiàn),分析了該模型在提高車(chē)輛利用率,降低系統(tǒng)成本方面的優(yōu)化效果。從研究出租車(chē)預(yù)約服務(wù)模式入手,分析出租車(chē)預(yù)約用戶(hù)和車(chē)輛的行為特點(diǎn),研究可行的出租車(chē)預(yù)約合乘模式。分析該模式下系統(tǒng)的成本結(jié)構(gòu),提出了單位里程成本與載客量的三種關(guān)系模型,并根據(jù)出租車(chē)運(yùn)營(yíng)的實(shí)際情況確定了各個(gè)參數(shù)的取值范圍,建立了帶有混合時(shí)間窗的車(chē)輛路徑模型用于優(yōu)化合乘系統(tǒng)中電召車(chē)的服務(wù)路徑。對(duì)比分析求解車(chē)輛路徑問(wèn)題的各類(lèi)相關(guān)算法,確定了采用遺傳算法求解該模型的思路。同時(shí)根據(jù)問(wèn)題特點(diǎn),設(shè)計(jì)了相應(yīng)的交叉、變異等遺傳操作,并將禁忌搜索算法與遺傳算法混合,改進(jìn)了算法的全局優(yōu)化特性。處理哈爾濱市出租車(chē)GPS實(shí)際數(shù)據(jù),通過(guò)出租車(chē)路徑的起訖點(diǎn)識(shí)別生成用戶(hù)出行需求點(diǎn)對(duì),建立了優(yōu)化效果測(cè)試集。通過(guò)matlab對(duì)算法進(jìn)行編程,分別對(duì)比了合乘與非合乘情況下的路徑優(yōu)化效果,以及三種不同里程成本結(jié)構(gòu)模型對(duì)優(yōu)化結(jié)果的影響,最后分析了模型的參數(shù)敏感性。
[Abstract]:Taxi co-riding can improve the utilization rate of taxis, so as not to increase the number of taxis in the premise, to some extent alleviate the current problem of large and medium-sized cities in China. However, there is no more scientific model, which can ensure the rationality of the line and the interests of all aspects. On the other hand, in recent years, the taxi call service has entered the field of vision. And gradually cultivate the habit of booking taxi booking. Booking taxi mode in the user demand collection has a congenital advantage, for the optimization method to achieve vehicle scheduling platform and support. In the background of booking taxi, according to the travel needs of users, the optimization of passenger co-rider line is studied, so as to improve the utilization rate of taxis. It is of practical significance to alleviate road congestion and reduce environmental pollution. According to the cost structure of taxi ride-sharing system, the mathematical model of vehicle co-rider path optimization is established in this paper. An improved genetic algorithm is designed to solve the model. The algorithm is implemented by matlab, and the model is used to improve the vehicle utilization ratio. The optimization effect of reducing the system cost. From the study of taxi booking service mode, the behavior characteristics of taxi reservation users and vehicles are analyzed. This paper studies the feasible taxi reservation and ride model, analyzes the cost structure of the system, and puts forward three models of the relationship between the unit mileage cost and the passenger capacity. According to the actual situation of taxi operation, the value range of each parameter is determined. A vehicle routing model with a hybrid time window is established to optimize the service path of the electric vehicle in a hybrid system, and the relative algorithms for solving the vehicle routing problem are compared and analyzed. The idea of using genetic algorithm to solve the model is determined. According to the characteristics of the problem, genetic operations such as crossover and mutation are designed, and Tabu search algorithm is mixed with genetic algorithm. The global optimization characteristics of the algorithm are improved. The actual data of Harbin taxi GPS is processed and the user travel demand point pair is generated by the identification of the starting and ending points of the taxi path. The optimization effect test set is established. The algorithm is programmed by matlab, and the path optimization effect in the case of combining and non-multiplying is compared respectively. And the influence of three different mileage cost structure models on the optimization results. Finally, the parameter sensitivity of the model is analyzed.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類(lèi)號(hào)】：U495

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本文編號(hào)：1491679