Railway Passenger Volume Forecasting On Support Vector Machine Model Optimized By Genetic Algorithm

Railway is the most convenient transportation means that has been one of the mainway of travelling.With the high-speed continually accelerating,growth in passengerchoosing to take the railway has helped the railway passenger flow to increaserapidly.As the railway passenger flow grows,it is urged to speed up the railwayconstruction.So,passenger volume forecast has been playing a great role in railwayconstruction.Choosing the right entry point can help improve prediction accuracy.In this paper,amethod based on Support Vector Genetic Algorithm is proposed to forecast passengertraffic volume.The goal is to discover the way to discuss the prediction method ofGA-SVM algorithm.Firstly,the paper study the importance of railway passenger traffic volumeprediction and introduces the general ways,such as time series techniques、RegressionAnalysis model、Grey Prediction and so on.Through the methods compare,the papershows its necessity to explores new methods on prediction theories.Secondly,the paper analyzes the impact of railway passengervolume-related.According to the Correlation Analysis,main factors that influence therailway passenger volume are established.Then,by introducing Genetic Algorithm and Support Vestor Machine,the paperchooses the GASVM model as main prediction model and estlabishes the GASVMmodel.A comparison of simulation results between these commom models and GASVMmodels is made to verify the efficiency.Finally,summarize the job in this paper and the shortages along with directions ofimprovement for further studies are discussed...