Design Implementation And Application Of Data Collection System For Path Choice Experiment

With the acceleration of China’s urbanization process,traffic congestion is becoming more and more severe.Nowadays,more researchers are conducting path choice experiments to find out the path choice of the traveler or the dynamic evolution of traffic flow,to understand the causes of traffic congestion and to find a solution to the traffic congestion situation.Collecting valid experimental data is a prerequisite for conducting the above research.In the past,people often used questionnaires to collect data,and the experimental period was long and the data was difficult to organize.The currently known software that can collect data for path choice experiments is complicated to use and has poor versatility.Therefore,this thesis designs and implements a data collection system for path choice experiment for the above problems.The main work of this thesis includes:1.System requirements analysis and overall functional design.According to the daily path choice experiment ideas and the data information that needs to be collected,the system has functional and non-functional requirements.The main functional modules of the system include: login module,Path choice module,experiment management module and data statistics module.2.System design and implementation.This thesis uses the Spring Boot framework,microservice architecture and My Batis,Bootstrap,Echarts technology to design and implement the system.The overall architecture of the system,the database tables,the relationships between the tables,and the core functional modules of the system are designed and implemented in detail.3.Analysis of experiments and data.The daily paths of four different scale road networks choice experiment is performed in this thesis.The influence law of the road network scale on the arrival of user network equilibrium state and road network change traffic is established by analyzing the experimental collected data,and the road network flow is predicted by using BP neural network,and a relatively effective prediction method is foundIn summary,this thesis designs and implements an experimental path choice data collection system,which has automaticity,versatility,and data visualization features,and can perform the daily path choice experiments simply and quickly,also collects accurate experimental data.In this thesis,the system is applied to the experiment to collect data.The statistical analysis data is used to find out the influence of the road network scale on the path cost and the change flow.And using BP neural network prediction,the conclusion that the whole road network traffic is better than the predicted single path traffic is obtained,which provides reference for the actual traffic flow prediction and allocation work.