| With the rapid development of urbanization,the urban population is increasing dramatically,and accordingly the demand for urban transportation is increasing,causing a series of problems such as urban pollution,traffic congestion,and energy crisis.To solve these series of problems,the government began to vigorously develop public transportation.Urban railway has become the first choice of residents for its high efficiency,convenience,safety and energy saving,while conventional public transport has become the second for its flexibility,versatility and high cost performance.Urban railway transportation alone cannot meet the demand for terminal transportation of passengers,and conventional public transportation alone cannot meet the need of fast and safe travel either.By coordinating the operation of the two modes of travel,the advantages of both can be effectively brought into play and the efficiency of travel experience can be greatly improved.On the premise of satisfying the travel demand,it is of great significance to study the issue of synergistic optimization of urban railway and conventional public transportation.Based on the analysis of the travel process and transportation network,the paper developed the urban railway transit and conventional public transport cooperative optimization model(Model 1)and the urban railway transit and conventional public transport cooperative optimization model(Model 2)under uncertainty scenario.The genetic algorithm and the genetic-simulated annealing hybrid algorithm are also designed respectively to find the solution to greatly improve the travel efficiency by minimizing the travel time.The main tasks of this paper are as follows:(1)A cooperative optimization model of urban railway and conventional public transportation is established.After analyzing the travel process,the travel time calculation formulae are given,including the walking time,waiting time,in-train time and transfer time.Taking into account the operational safety constraints and transfer control constraints of railway trains and buses,a cooperative optimization model with the minimum travel time as the goal is established,and a genetic algorithm is designed to find a solution to realize the cooperative optimization of urban railway transit and the conventional public transport by optimizing the rail train departure interval,rail train stopping time,conventional bus departure interval and conventional bus stopping time.(2)A cooperative optimization model for urban railway and conventional public transport under uncertainty scenario is established.On the basis of Model 1,by analyzing the impact of the uncertainty of the running time between conventional public transport stations on the transport network,the uncertainty theory is introduced,the uncertainty variables are used to represent the uncertainty of the running time between public transport stations,and the chance constraint is taken into account to establish a cooperative optimization model for urban railway and conventional public transport under uncertainty scenario.And the general equivalent form of the uncertainty model is given according to the uncertainty theory.Finally,a genetic-simulated annealing hybrid algorithm is designed to solve the model efficiently.(3)An example analysis of Models 1 and 2 is carried out taking the Beijing subway and bus network as an example.Firstly,model 1 is solved using a genetic algorithm with elitism strategy.The results show that model 1 can greatly improve the travel efficiency and reduce the total travel time by 14.2% compared with the unoptimized model.Furthermore,model 2 is solved applying the genetic-simulated annealing hybrid algorithm.After the optimization of model 2,the total travel time is reduced by 16.6% compared with that before the optimization,and it is concluded that the uncertainty theory is applicable to the optimization of urban railway and conventional public transport,and can achieve excellent optimization results. |
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