Planning Bike Lanes Based On Sharing-Bikes’ Data

Under the dual drive of capital and scientific strength,shared bicycles suddenly break into our city and develop rapidly,becoming a part of the urban landscape and an indispensable way of transportation for citizens.However,in the same time,it also brings severe pressure and challenge to the urban traffic operation management and planning development.Therefore,it is necessary to explore a people-oriented statistical method based on the objective analysis and scientific conclusions to extract the hot spots of the urban non motorized lanes for rational planning and management.In this paper,the location data of ofo bicycles in September 20,2017 was obtained by means of Python program.Then,using the ArcGIS traditional analysis tools,the spatial-temporal feature of riding behavior for Xi’an users were analyzed,the hot spots of urban riding were extracted,and the user travel OD model was developed.The results indicated that riding lines had different spatial characteristics under the influence of urban rail transportation,urban industrial distribution,hospital and school distribution and time dimension.In the technology method,the statistical method based on grid is replaced by the universal thermal expression,the static position data is pushed through the model,and the greedy algorithm is put forward on the basis of network analysis,and the hot section of the vehicle is extracted.The conclusion can not only solve the single vehicle scheduling problem at the different entrance of a subway station at the micro level,but also provide an improved way for the macro urban traffic problems.Finally,the experiment was carried out in Xi’an to share first and second sections of bicycle riding hot spots,and the planning principles and design schemes for sharing single vehicle parking facilities and non motorized lanes were proposed for two different sections.The hot spots derived by Big Data analysis collaborated with the results of comprehensive analysis of visual expression data sets as well as related city data sets makes a new way of thinking for the centralized planning of non-motorvehicle facilities and the optimization for urban spatial layout.