本文選題：多層級(jí)常規(guī)公交 + 區(qū)域協(xié)調(diào)調(diào)度��； 參考：《昆明理工大學(xué)》2015年碩士論文

【摘要】：在多層級(jí)常規(guī)公交線網(wǎng)優(yōu)化背景下,為了使公交運(yùn)營(yíng)能更好地滿足多樣化公交出行需求,依據(jù)客流時(shí)空分布特征,確定合理的調(diào)度形式,建立公交時(shí)刻表優(yōu)化模型。層層深入編制出各層級(jí)基于客流需求預(yù)測(cè)的公交時(shí)刻表和適用于多層級(jí)公交線網(wǎng)優(yōu)化的區(qū)域協(xié)調(diào)時(shí)刻表。首先,分析了影響多層級(jí)常規(guī)公交區(qū)域協(xié)調(diào)時(shí)刻表編制的主要因素,研究了公交客流的時(shí)空分布特征和公交線網(wǎng)結(jié)構(gòu),確定了多層級(jí)常規(guī)公交區(qū)域協(xié)調(diào)調(diào)度的目標(biāo)和運(yùn)營(yíng)調(diào)度形式。其次,分析了公交客流數(shù)據(jù)的采集和審核方法,分別采用BP神經(jīng)網(wǎng)絡(luò)和RBF神經(jīng)網(wǎng)絡(luò)算法預(yù)測(cè)并計(jì)算得到公交斷面客流需求。設(shè)計(jì)了層級(jí)內(nèi)基于客流預(yù)測(cè)的單線公交時(shí)刻表優(yōu)化流程和約束條件,并構(gòu)建了時(shí)刻表方案評(píng)價(jià)模型。結(jié)合實(shí)例得出,基于BP神經(jīng)網(wǎng)絡(luò)和RBF神經(jīng)網(wǎng)絡(luò)預(yù)測(cè)算法得到的斷面客流量,編制得到的時(shí)刻表方案,相對(duì)于優(yōu)化前分別節(jié)省了2.44%和4.80%的運(yùn)營(yíng)總成本。第三,依據(jù)多層級(jí)常規(guī)公交線網(wǎng)優(yōu)化銜接模式,以各層級(jí)公交線路的發(fā)車間隔和車輛調(diào)度形式作為決策變量,從乘客出行時(shí)間成本與公交企業(yè)運(yùn)營(yíng)收益的角度,考慮乘客舒適性、協(xié)同發(fā)車間隔和企業(yè)運(yùn)能等方面的約束,建立了多目標(biāo)優(yōu)化模型。綜合分析各種優(yōu)化算法的特點(diǎn)后,采用遺傳算法、粒子群優(yōu)化算法和遺傳粒子群優(yōu)化算法求解模型。依據(jù)實(shí)際問(wèn)題在MATLAB軟件中設(shè)計(jì)求解模型的算法步驟。最后,通過(guò)實(shí)例分析得到,在求解本文構(gòu)建的公交時(shí)刻表編制模型過(guò)程中,模型目標(biāo)都能夠有效收斂,遺傳粒子群優(yōu)化算法的精度和收斂效率都明顯高于遺傳算法和粒子群優(yōu)化算法。求解結(jié)果方面：遺傳粒子群算法求解得到的多層級(jí)常規(guī)公交區(qū)域協(xié)調(diào)時(shí)刻表方案,相對(duì)于基于RBF公交客流需求預(yù)測(cè)編制的時(shí)刻表,分別取三種權(quán)重值時(shí)的方案總成本分別節(jié)省了3.48%、5.47%和8.42%。驗(yàn)證了所建模型和優(yōu)化算法的可行性和適用性。實(shí)際中,需要根據(jù)給定的運(yùn)營(yíng)效益和乘客出行時(shí)間成本權(quán)重值選定時(shí)刻表優(yōu)化方案。
[Abstract]:Under the background of multi-level conventional bus network optimization, in order to make the bus operation better meet the needs of diversified public transport travel, according to the characteristics of space-time distribution of passenger flow, the reasonable dispatching form is determined, and the optimization model of bus timetable is established. Layer by layer, the bus timetable based on passenger flow demand prediction and the regional coordination schedule for multi-level bus network optimization are worked out. Firstly, the paper analyzes the main factors that affect the compilation of the regional coordination timetable of multi-level conventional public transport, and studies the space-time distribution characteristics of bus passenger flow and the structure of bus network. The objective and operation form of coordinated regional dispatching of multi-level conventional public transport are determined. Secondly, the methods of collecting and checking the bus passenger flow data are analyzed. BP neural network and RBF neural network algorithm are used to predict and calculate the passenger flow demand of public transport section. The optimization flow and constraint conditions of single line bus timetable based on passenger flow prediction are designed and the evaluation model of schedule scheme is constructed. Combined with an example, it is concluded that the total operating cost is 2.44% and 4.80% lower than that before optimization, respectively, based on BP neural network and RBF neural network prediction algorithm. Thirdly, according to the optimal connection mode of multi-level conventional bus network, taking the departure interval and vehicle dispatching form of each level of bus lines as decision variables, from the point of view of passenger travel time cost and public transport enterprise operating income. Considering the constraints of passenger comfort, cooperative departure interval and enterprise capacity, a multi-objective optimization model is established. After analyzing the characteristics of various optimization algorithms, genetic algorithm, particle swarm optimization algorithm and genetic particle swarm optimization algorithm are used to solve the model. The algorithm of solving the model is designed in MATLAB software according to the practical problem. Finally, through the analysis of an example, it is concluded that the model can converge effectively in the course of solving the model of the bus timetable constructed in this paper. The precision and convergence efficiency of genetic particle swarm optimization are obviously higher than those of genetic algorithm and particle swarm optimization. The solution results are as follows: genetic Particle Swarm Optimization algorithm (GPSO) is used to solve the multi-level bus coordination schedule, which is relative to the schedule based on RBF bus passenger demand prediction. The total cost of the scheme was saved by 3.48% 5.47% and 8.42% respectively. The feasibility and applicability of the proposed model and optimization algorithm are verified. In practice, it is necessary to select the timetable optimization scheme according to the given operation benefit and the weight value of passenger travel time cost.
【學(xué)位授予單位】：昆明理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：U491.17

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本文編號(hào)：1918239