Study On Multi-mode Traffic Travel Path Optimization Of Urban Public Transport

The development of urban public transport is an effective way to alleviate urban traffic congestion and build a smooth and low-carbon city.However,in the face of diversified public transport modes,large and complicated urban public transport system and random and time-varying urban transport network,it is difficult for travelers to find the best or better travel path,which makes it difficult to improve the attractiveness of urban public transport.Therefore,it is urgent to study the travel path decision-making in multi-mode public transport network.However,most of the existing studies on urban travel path decision-making are based on static single mode traffic network,without considering the time-varying and random characteristics of urban road traffic and the multi-mode characteristics of urban public transport network.In addition,the schedule of urban public transport will affect the travel path decision-making of travelers,but the existing research is rarely involved.Therefore,the research on multi-mode travel path decision-making in random and time-varying public transport networks has high academic value and application value.First of all,taking the travel time as the objective function,the transfer times and the travel generalized cost as the constraints,this paper establishes the travel path decisionmaking model of multi-mode public transport network under time-varying deterministic network.According to the model,two algorithms,variable length coding genetic algorithm(V-GA)and fully arranged integer coding genetic algorithm(F-GA),are designed.The numerical analysis of the two algorithms is carried out by using the Solomon example,and the experimental results show that the two algorithms are effective and the V-GA algorithm is better than the F-GA algorithm.Secondly,some urban residents pay more attention to the travel time,but some pay more attention to the actual cost of travel,followed by the travel time.Therefore,this paper takes the actual money payment cost as the objective function,and establishes the mathematical model with the transfer times,walking distance and travel time as the constraint conditions.The simulated annealing algorithm is added to the V-GA algorithm.The effective combination of genetic algorithm and simulated annealing algorithm can improve the quality of the global optimal solution to solve the multi-mode traffic travel path optimization problem.Therefore,four algorithms are designed: single population strategy algorithm with fixed mutation probability(SGA-1);single population strategy algorithm(SGA-2)for adjusting mutation probability with the number of iterations;multiple population strategy algorithm with fixed mutation probability(MGA-1);multiple population strategy algorithm(MGA-2)in which mutation probability is adjusted with the number of iterations,and make a comparative analysis of several cases.In addition,randomness is also one of the important characteristics of urban traffic network,at the same time,it is noted that different lines will have a great impact on the state of the node(stop or transfer),and the travel time will change greatly.Therefore,under the condition of uncertain and random network,this paper considers the influence of different lines of different vehicles on urban travel path,and establishes a multi-mode traffic stochastic network travel path model with line set.The Monte Carlo simulation method is used to deal with the random problem,and a mathematical model is constructed with the travel time as the objective function and the actual cost and transfer times as the constraints,a single population management strategy(MCGA-I)and a multi-population management strategy(MCGA-II)with inconsistent population size are proposed to compare and analyze several cases.Finally,the research content of this paper is summarized,and the main innovation of this paper and the direction of further research are put forward.