Design And Implementation Of Bus Station Location Selection Visualization System Based On Multi-modal Analysis

Today,in the context of the era of big data,intelligent transportation systems will provide effective help in solving urban traffic problems.Vehicle-mounted GPS systems such as buses,taxis,and Internet-based taxis can provide a relatively complete set of raw vehicle data sets.These data sets include information such as license plate number,starting time,starting latitude and longitude,passenger status,vehicle speed,arrival time,destination latitude and longitude,and price.Among them,a taxi pushes a GPS record to the server every 15 seconds.A city has about 12 million vehicle records per day,and the data set size is about 500 GB.Internet taxis and buses also have millions of data sets every day.Therefore,this thesis aims to analyze the city behind the data by analyzing the original GPS data of the vehicle on-board for the problems that the location of urban bus stops does not match the actual needs of residents ’outings,the low rate of online car and taxi load factor,and traffic congestion Citizen travel patterns and vehicle driving laws,explore potential urban bus station location locations,maximize geographic coverage while taking into account the crowd,and then solve the uneven distribution of residents’ riding resources,improve the efficiency of bus transportation,and improve the city Citizen travel experience.This article takes Hangzhou as an example,and based on multi-modal traffic big data including shared bicycles,network rides,subways,and buses,the following work is performed:(1)Clear and store all kinds of vehicle GPS data: cleanup and reconstruction of original traffic data.The original GPS record of the vehicle contains multi-dimensional attributes,such as start time,license plate,start latitude and longitude,speed,price,vehicle status,end point latitude and longitude,arrival time,etc.At the same time,there are problems such as time errors,missing key information,and duplication in the original data.Means and other methods need to be used to repair the missing data,eliminating time information errors and duplicate data.(2)Division and optimization of traffic district based on quadtree: based on the GPS data record of the vehicle,according to the structure of the quadtree,the geographical space of Hangzhou is divided by setting the upper limit of the traffic volume of the traffic area to divide the entire Hangzhou city For multiple different blocks,a traffic zone is formed.For some areas,the excessive number of traffic cells greatly increases the grid storage difficulty and the system response time.This paper restricts the traffic cell flow value and the area area in two dimensions,designs an optimization algorithm,and performs a preliminary division of traffic cells.Optimize the merger and improve the efficiency of user query exploration.(3)Original vehicle data OD extraction and flow analysis: extract the GPS data with multi-dimensional attributes as the OD starting point data,and cluster the data on the map with the traffic area for clustering and statistical analysis to mine Analysis of road traffic flow and travel distance in traffic districts,using big data prediction models to predict road traffic flow,and explore potential areas for bus stop site selection.(4)Visualization system design of bus station location based on the above data: visual analysis of the above data processing results,display data from different aspects with the help of various visual coding methods and graphics,find the traffic flow of each traffic district,and discover the travel mode of urban residents,Design interactive visualization system to enable users to explore data interactively and discover potential bus stop locations.