Mining Bike Sharing Features Around Rail Transit Stations Based On GPS Data

As an important part of urban non-motorized travel,bike-sharing is favored by travelers for its flexible and convenient travel modes and free parking options,providing solutions for short-distance travel of 1-5 kilometers in the city.At the same time,as a connection type of transportation,bike-sharing play a significant role in expanding the scope of subway services and improving the accessibility of rail transit stations.The " bike-sharing + rail transit" travel mode is a diversified distance for urban residents Travel offers convenience.However,with the increase of the number of bike-sharing trips around the transit stations,the problems of vehicle access and unequal regional delivery appear one after another,which seriously affect the travel choice of residents.Based on this,this article analyzes the detailed travel of bike-sharing around rail transit stations,in order to drive the data to explore the characteristics of bike-sharing within the station's area of influence,and provide reference materials for the deployment of bike-sharing travel distribution points.Based on Xi'an bike-sharing trip data,according to the research purpose,Python is used to preprocess the data and match the starting and ending points of cycling.At the same time,it is determined to take Xi'an Rail Transit Line 1 phase I opening station as the research object to study and analyze the characteristics of bike-sharing travel around it.Based on the existing data and research objects,referring to the distance between stations and the actual travel choice of users,the boundary value of the influence range is defined,and according to the actual distribution of bike-sharing around stations,the standard ellipse difference is selected to fit the distribution of bike-sharing around stations,so as to get the actual influence range required by the research.Secondly,the study describes the spatio-temporal travel characteristics of bicycles based on the travel time and distance traveled by the vehicle.It focuses on the statistics of bicycle travel within 60 min and 5000 m,and uses the frequency and cumulative frequency to describe the overall space-time distribution.In addition,the study also supplements the other travel characteristics of the bicycle by statistically analyzing the vehicle's riding speed,vehicle turnover,and the activity of bicycles around the station.In addition,the research uses the Arc GIS nuclear density analysis tool to visualize the high-concentration hotspots of the start and end points of the bike-sharing trip,and combines with the surrounding road network to conduct in-depth mining of the land use properties of the hotspots concentration zone,so as to obtain the relationship between the hotspots concentration zone and the residents' travel destinations.Thirdly,from the micro perspective of influencing the size of the service scope of the site,the research uses the geographic detector model to detect the importance of the influence factors and the importance of the interaction factors within the service scope of the site.Finally,Beidajie Station was selected as the characteristic station,and Baidu map path planning was used to visually analyze the trajectories of bicycles with a high number of trips and bicycle trajectories with similar starting and ending points within the station's early peak radiation attraction range.