Study On Recognition And Optimization Of Improvement Area In Public Transport Network Based On Mobile Signaling

With the rapid development of big data and Internet technology,mobile phone signaling data has become a new type of data source,playing an important role in the field of traffic planning and management.Compared with traditional traffic surveys,mobile phone signaling data has the characteristics of large sample size and wide coverage.It can accurately obtain real-time urban population spatial distribution rules,and at the same time,it can further explore the traffic problems of existing cities for guidance and improvement.There is an important basis for urban traffic control and management.This paper uses the mobile phone signaling data of Guiyang residents to study the temporal and spatial characteristics of the urban residents’ travel activities in Guiyang,and combines the existing urban public transportation system in Guiyang to optimize the conventional public transportation network.The main work is reflected in the following aspects :In terms of data processing,based on the positioning principles and characteristics of mobile phone signaling data,the redundancy and "noise data" in the signaling data in Guiyang City in 2017 were cleaned,and the density-based spatiotemporal clustering algorithm was used to analyze the data in the signaling data.Recognition of staying points and moving points,extracting the trajectory of residents out of one trip,and analyzing the trajectory,screening effective travel,and obtaining peak hour OD.In terms of travel mode identification,the use of statistical analysis methods to extract the characteristics of residents’ travel,select travel distance,travel time,and 95% travel speed as feature indicators,construct a travel mode weighted cluster recognition model,and identify Guiyang City in 2017 Common urban travel modes such as walking,bicycle,car,and public transportation,and according to the urban planning of Guiyang City,current land use attributes and road network to divide the traffic districts,to match the residents’ travel OD,and use the residents’ travel structure between the traffic districts to identify public transportation.The line network improvement area,the results show that most of the improvement area is distributed between the old city and the newly built city.In terms of bus network optimization,for the local improvement area of the bus network,the trip-end bus station optimization model is constructed based on the Thiessen polygon principle to improve the accessibility of the bus station.At the same time,the trajectory points of the bus travel are used for road condition extraction,and the construction is based on The bus route optimization model of road conditions uses the K shortest path algorithm to solve the model,and the candidate routes obtained by the solution are reasonably combined to obtain the final optimized layout of the bus route network.