Traffic distribution model parameter quick checking method based on multi-source data fusion

Abstract

The invention discloses a traffic distribution model parameter rapid checking method based on multi-source data fusion. A bottom-to-top parameter verification method and a top-to-bottom parameter verification method are respectively adopted, parameter verification calibration is carried out in two stages, parameters obtained in the first stage are a1, b1 and c1, parameters obtained in the second stage are a3, b3 and c3, and finally obtained model parameters adopt average numbers a4, b4 and c4 of the two methods. The combined utilization of multi-source data and survey data is realized; automatic iteration and calibration of parameters can be realized by applying a heuristic algorithm; and the model precision is greatly improved, so that the parameter value better conforms to the reality, and the reality can be better simulated.

Description

technical field [0001] The invention belongs to the field of rail transit, and in particular relates to a method for quickly checking traffic distribution model parameters based on multi-source data fusion. Background technique [0002] Passenger flow forecasting is a key link in rail transit network planning, which directly affects the necessity and layout of rail transit network planning, and the traffic distribution model directly affects the accuracy of passenger flow forecast results. As a classic traffic distribution model, the gravity model has been widely used in various transportation modes such as high-speed railways, intercity railways, urban railways, urban rail transit, and roads. Considering that the three parameters in the gravity model are affected by multidimensional factors such as urban heterogeneity, distribution of functional areas, and job-housing balance, model parameter calibration must be carried out for specific research objects. [0003] The curre...

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Problems solved by technology

Judging from the survey data of more than 20 cities completed by our institute, such a low sampling rate leads to the lack of data in many communities, and the expansion of sampling will amplify this error, but we still use this data for model prediction

[0007] (2) In the actual comparison of TLD and OTLD data, the value of the distance segment is relatively large, and the data usually has a high degree of fitting. However, once the distance is short and the value is small, the data fitting error increases, which is mainly due to the lack of data in some traffic areas caused by

[0008] (3) At present, the development of big data is in full swing, especially mobile phone signaling data. Ma

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