Fusion data-based resident trip OD distribution extraction method

Abstract

The invention discloses a fusion data-based resident trip OD distribution extraction method, and the method is realized through fusing mobile phone signalling data and traditional resident investigation data. The method comprises the following steps of: setting a related threshold value through resident trip feature information obtained through traditional resident investigation data on the basisof acquiring and preprocessing original mobile phone signalling data, and converting trip track information in the mobile phone signalling data into a preliminary trip OD distribution matrix; and obtaining a final resident trip OD distribution condition through gross sample expansion. According to the method, the targets of full-sample sampling and sample distribution uniformity are realized underthe condition that resident trip distribution grosses re highly consistent with real trip features, obtained resident trip distribution results are greatly enhanced when being compared with traditional methods, and important theoretical basis and support are provided for the construction of urban traffic facilities and the construction of transit systems.

Description

technical field [0001] The invention relates to the technical field of traffic planning, in particular to a method for extracting OD distribution of residents' trips based on fusion data. Background technique [0002] The distribution of residents' trips has varying degrees of impact on urban space utilization, industrial layout, environmental protection, energy supply, and transportation development. An accurate OD distribution matrix of residents' trips is an important support for the construction of urban transportation facilities and public transport systems. [0003] In the existing technology, the OD distribution of residents' travel is constructed according to the data source, which can be divided into two categories: [0004] One is based on the traditional traffic survey data, which is an important part of the four-stage model; this method is based on regression analysis to count the survey data as traffic generation, and then distribute it to the traffic area with ...

AI Technical Summary

Problems solved by technology

[0004] One is based on the traditional traffic survey data, which is an important part of the four-stage model; this method is based on regression analysis to count the survey data as traffic generation, and then distribute it to the traffic area with a mathematical model to convert the travel volume into traffic volume; This method can collect the socioeconomic attributes of travelers, and then predict the traffic demand of the planning area in the next few years, but its shortcomings are also obvious: heavy survey workload, time-consuming data collection and processing, low sampling uniformity, and large sample size. Small size, long data update cycle, difficult to grasp the dynamic changes of travel distribution

[0005] The other is to use the existing traffic detector to obtain the observed traffic volume of the road section, and calculate the OD matrix through the inverse process of traffic allocation; although this method skips the generation, distribution and division of traffic trips, it directly uses the traffic volume of the road section to estimate the OD matrix , but it is difficult to obtain the traffic volume of the entire road network section, and it is easy to underestimate the traffic volume. Compared with the traditional traffic survey method, the socioeconomic attributes of travelers cannot be obtained, and it is difficult to apply in future traffic planning.

Images