| The continuous expansion of the urban population and spatial area has driven the growth of residents’ travel demand,which cause the increasing traffic pressure about the subsequent imbalanced traffic structure and congestion problems.In order to optimize travel structure and alleviate traffic pressure,ground public transit has gradually become one of the main daily-travel methods of residents under the vigorous advocacy of relevant policies.As a large-capacity,environmentally friendly,and cost-effective travel method,ground public transit account for a large proportion of urban residents’ daily travel.Therefore mining and summarizing the travel characteristics of urban residents based on the conventional ground bus travel data has a long-term significance for improving the travel service,relieving congestion and optimizing urban travel structure.In the past,relative analysis of residents’ travel characteristics focuses on the summary and prediction of the public travel.Based on the existing research,this paper analyzes residents’ travel characteristics from the temporal and spatial perspective based on the data from Advanced Public Transportation System(APTS).The initial process is data processing and fusion based on multi-source data.Firstly,this paper screens and corrects the problems,such as data redundancy,missing and format error,in the raw data of APTS in Huangdao District.Then these data were modified and corrected by Oracle database to ensure data accuracy and calculation efficiency.Secondly,based on the temporal correlation of residents’ activity and passenger consumption data,set the appropriate temporal threshold to identify the boarding station.Then residents’ daily bus travel is divided into one-way travel and return travel based on the trip chain theory.By analyzing the spatial relationship of the boarding station between the current data and historical data,the residents’ exiting station of one-way travel can be identified.And by comparing the line ID,travel direction of current data with historical data,the residents’ exiting station of return travel can be identified.This method can improve the accuracy of the determination of the exiting station.In terms of the spatial-temporal analysis of residents’ travel,this article adopts a combination of quantitative and qualitative methods.As for temporal characteristics,the periodicity,similarity and concentration of residents’ travel time were analyzed based on different temporal scales;and the travel data of residents on weekday/weekend,peak hour/off-peak hour were extracted to form four temporal combinations,thus Obtain the change trend of the residents’ travel volume with the change of temporal scale;then the predictability of the residents’ travel in different time periods is evaluated according to the analysis conclusion,so as to realize the quantitative analysis of the residents’ travel characteristics that the temporal characteristics are better in the peak period,and on weekdays are better than weekends.In the analysis of spatial characteristics,the study area is divided into multiple traffic zones by using the grid division method,then the travel hotspots of resident at different times are extracted based on the spatial clustering algorithm,so as to summarize the concentrated area and distribution range of daily travel of the residents.Besides,according to the results of traffic district division,overlay analysis method is used to superimpose bus travel OD to each traffic district,and the trip interaction relationship and trip volume spatial distribution of each traffic area can be analyzed,so as to realize the qualitative analysis of residents’ trip characteristics.The results indicate that the travel range of residents in early peak hour is larger and more scattered than evening peak hour,the travel spatial characteristics of weekend travel are opposite to those of working days,and the OD distribution has obvious inclination characteristics of entertainment community on weekend. |
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