Research On Data Analysis Of Public Transit Passenger Flow

The public transit is the important constituent of city p-traffic. The main character of public transit passenger flow is its distribution along space and time, which affects the dispatching and constructing plan of the public transit. Therefore the research on data analysis in the public transit passenger flow is valuable. Along with the application of advanced gathering technology in the public transit,lots of data is collected. The traditional man-power data analysis method is not good at processing these data. We need new method to deal with the public transit passenger flow.In this article, the research on data analysis in public transit passenger flow mainly concentrates in two aspects:Firstly, we research the application of clustering data analysis in public transit passenger flow. We studied the application of clustering analysis in simplifying the metewand of the public transit route, and the class of public transit route. We also use clustering analysis to divide a day to several intervals for public transit dispatching. And then we carried on the examination with some data to these methods, the result indicated that these methods are feasible and effective.Secondly, we research the optimization of public transit static dispatching. The key of public transit static dispatching is the formulation of driving timetable. The formulation of driving timetable is based on distributing rule of passenger flow. This article considered the public transit passenger flow distribution both in time and in the spatial, and establishes the public transit static dispatching model according to the establishment step and the method for modeling. This model takes smallest crowding complaint, slightly waiting complaint and the least number of buses as the goal. And then we solves the model with non_dominated sorting generic algorithm (NSGA).Finally, we using some data to examine this model, the result indicated that this model is effective.