| With the continuous increase in the number of urban vehicles,the problem of megalopolis congestion has seriously affected the daily life of residents.In order to solve the congestion problem better,the analysis of the traffic operation pattern and the evaluation of traffic operations are the key.Although relevant research has been deeply explored in the past decades,but with the development of big data,analysis of traffic operation rules based on big data and evaluation of traffic operation status are still lacking.Based on the OD trip data,this paper proposes an analysis method of traffic operation regularity for major transportation hubs based on OD trip data and a method for evaluating the status of regional traffic operations.Take the Capital International Airport and the Beijing Municipality as examples.The traffic patterns of large cities and the evaluation of traffic conditions have conducted in-depth research.The main research work and conclusions of this paper include the following three aspects:(1)Based on the OD trip data,a grid model is established,and the grid state is divided at the same time.Then based on all the OD trip data from October 2014 to February 2017,the OD traffic parameters under ideal conditions are calculated.Based on a method of fuzzy estimation,the traffic parameters under ideal conditions which cannot be directly obtained from the source data were calculated.And their errors were analyzed.The error analysis results show that the fuzzy estimation method is effective.Finally,an OD trip database system is established.(2)Based on the data of OD trips to major transportation hubs,the method for analysis of traffic operation patterns for major transportation hubs is studied.The traffic operation patterns for arriving at major transportation hubs are studied from three aspects:travel demand,travel speed,and travel time reliability.Aiming at the spatial distribution of travel demand,this paper proposes a high-density traffic area detection method based on DBSCAN algorithm.Finally,taking the data arriving at the Capital International Airport as an example,the spatial distribution and time distribution of travel demand to the Capital International Airport are analyzed,and 10 high-density traffic areas that reached the Capital International Airport are detected based on the spatial distribution of travel demand;The spatial distribution and time distribution of the speed of the journey to the Capital International Airport are analyzed;and we also analyze the spatial distribution of travel time reliability at the Capital International Airport.(3)Based on the OD trip data and the grid model,a traffic community clustering method based on modular indicators is used to cluster into 20 clusters within the study area through the connectivity relationship between different node pairs.The traffic relations inside and outside the traffic community are analyzed,and the results of the analysis prove that the traffic community method works well.Based on the traffic parameter data between the well-divided traffic community and the ideal OD pair,a regional traffic operation status evaluation method based on OD trip data was established.Finally,using the floating car OD trip data for November 2016,the overall and local traffic conditions within the study area were evaluated.For the entire urban area,a two-dimensional map of hours and dates reflecting the evaluation results is designed;for urban areas,a time-space distribution map reflecting the evaluation results is designed.The evaluation results show that the traffic operation status assessment method can be specifically identified for the city's overall early morning peaks,evening peaks,midday break peaks,and working days and non-working traffic conditions.Different areas of traffic can be identified for part areas of the city.The change of operating status proves that this method can well reflect the traffic status of different time ranges and spatial scopes. |
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