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A regional traffic mode division method based on travel distance

A traffic mode and distance technology, applied in the direction of instruments, data processing applications, prediction, etc., can solve problems such as rough prediction results, unrealistic engineering applications, complex model structures, etc., to achieve strong practicability, operability, and high accuracy and credibility, the simple effect of the aggregation model

Active Publication Date: 2019-04-02
SOUTHEAST UNIV
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  • Application Information

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Problems solved by technology

[0004] Generally speaking, the traffic mode division methods can be divided into two categories: one is the aggregate model, which is relatively simple to operate, but the limitation of the aggregate method is that in order to ensure the accuracy of the model, according to the theorem of large numbers, considerable At the same time, the assumption of the behavior principle of this method is not clear, the prediction results are too rough, and the general applicability of the model is poor; In the study, a single traveler was used as the analysis object, making full use of the internal connection of each group of survey sample data. The characteristics of the non-aggregated method are: the personal data of the survey can be fully mined, and the required sample is small, so the relative cost It will be much smaller, but its requirements for data are high, the model structure is complex, the estimation of the model is more difficult, and the application in engineering is not realistic

Method used

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  • A regional traffic mode division method based on travel distance
  • A regional traffic mode division method based on travel distance
  • A regional traffic mode division method based on travel distance

Examples

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Effect test

Embodiment 1

[0046] The regional traffic mode division method based on travel distance, such as figure 1 shown, including the following steps:

[0047] S1, regional traffic travel data collection: the data includes the distance between any two cities and the passenger traffic volume of various modes of transportation between the two cities. Acquisition can also be directly downloaded or captured from the Internet based on big data technology. The transportation methods include roads, railways, aviation and waterways. For example: collecting regional traffic travel data in my country, including the distance d between city i and city j i,j, the highway passenger traffic volume P between city i and city j r o ad i,j , the railway passenger traffic volume P between city i and city j rail i,j , the air passenger traffic volume P between city i and city j air i,j , the airway passenger traffic volume P between city i and city j wa t er i,j . Wherein, i and j represent the serial number...

Embodiment 2

[0079] The regional traffic mode division method based on travel distance, such as figure 2 Shown, present embodiment has selected my country's M=299 cities as the object of example, obtains the distance between 299 cities by Baidu map collection, and obtained the highway passenger traffic volume, railway passenger traffic volume between 299 cities by the traffic department volume, air passenger traffic volume, airway passenger traffic volume.

[0080] By adopting the method of step S2, the road passenger traffic sharing rate, the railway passenger transport sharing rate, the air passenger transport sharing rate, and the air passenger transport sharing rate among 299 cities are obtained.

[0081] According to the method described in step S3, establish and train based on the support vector machine to obtain the highway passenger transport distance-sharing rate model f road (d), the distance-sharing rate model f of railway passenger transport rail (d), the distance-sharing rat...

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Abstract

The invention discloses a regional traffic mode division method based on travel distance. The regional traffic mode division method comprises eight steps including regional traffic travel data collection, passenger transport sharing rate conversion and travel distance-sharing rate model training, travel distance matrix generation, transition sharing rate matrix generation mode connection matrix generation, sharing rate matrix calculation and regional traffic mode division result generation. The passenger distance-share ratio model is established and trained based on the support vector machineby collecting the passenger traffic of different modes of transportation between cities in the area and the distance data between the predicted cities, and finally an inter-city passenger transport mode sharing rate result is obtained. Inter-city travel mode prediction in the regional range is carried out, the whole process is clear in thinking, high practicability and operability are achieved, anew thought and method are provided for traffic travel prediction in the regional range, and the method is of great significance for traffic mode division prediction and traffic planning in the regional range.

Description

[0001] Field [0002] The invention belongs to the field of regional traffic planning, and in particular relates to a regional traffic mode division method based on travel distance. Background technique [0003] With the continuous development of social economy, my country's urbanization process continues to accelerate, and the coordinated development of regional integration based on urban agglomerations has become the trend of my country's economic development in the future. Transportation is the cornerstone of social and economic development, and regional development is closely related to the planning and construction of the integrated transportation system, which also puts forward higher requirements for the regional integrated transportation system. As an important part of the four stages of traffic demand forecasting, traffic mode division is essentially to predict the passenger flow shared by various traffic modes, so it is often called traffic mode share rate forecast. ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q50/26
CPCG06Q10/04G06Q50/26
Inventor 王炜华雪东苗迪项昀谢文杰于维杰
Owner SOUTHEAST UNIV