Expressway vehicle OD point determination method based on checkpoint data

Abstract

The embodiment of the invention discloses a rapid vehicle OD point determination method based on checkpoint data. According to the rapid vehicle OD point determination method based on the checkpoint data provided by the embodiment of the invention, OD extraction of a single travel track of a vehicle is carried out by defining a vehicle travel chain and combining the preprocessed original data; andthen, the integrity and classification of the single travel trajectory of the vehicle are analyzed, classification is carried out, and different OD point determination methods are adopted for the single travel trajectories of different types of vehicles, so that the method has the pertinence of urban expressways, and the OD point determination process is relatively simple.

Description

technical field [0001] The invention relates to the technical field of intelligent transportation, in particular to a fast vehicle OD point determination method based on bayonet data. Background technique [0002] With the rapid advancement of urbanization and motorization, the density of urban expressway network is getting higher and higher. The urban expressway is the main skeleton of the urban road system that is exclusively for automobiles, fully enclosed, and controlled in and out. It connects the main transportation hubs, main highway entrances and exits, and main scenic spots in the city, and is the lifeblood of urban transportation. Extracting OD points of vehicles traveling on urban expressways is one of the key contents of traffic demand analysis, which can completely reproduce the characteristics of urban traffic with both regularity and randomness, and provide a scientific and reliable basis for the formulation of traffic demand management policies. [0003] At...

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Problems solved by technology

[0003] At present, there are many studies on the method of determining the OD point of expressway vehicles. For example, Wang Meihong uses the license plate data obtained by the monitoring equipment at the intersection checkpoint in Tianjin, and combines the FCM fuzzy clustering algorithm and the K-means++ algorithm to identify the traffic state. If the initial clustering center of the FCM algorithm is not properly selected, it will easily lead to local optimal problems; Zhu Yaokun used the license plate data obtained from the roadside checkpoints of a provincial capital city in China to propose a method based on deep belief networks that combines road network structure and spatio-temporal characteristics. Short-term vehicle trajectory prediction method, and complete the missing nodes of each trajectory; Wang Yinpu used license plate data to reconstruct vehicle trajectory with LSTM neural network, and then used CNN combined with LSTM to determine the dynamic OD of road network; Zhou Tao et al. Use the checkpoint data obtained from the checkpoint with incomplete express road network, combined with other multi-source data for OD calculation

Although there are many researches on the recognition of license plate data, most of the researches focus on the vehicle travel trajectory in the city, and there are few researches on the urban expressway, and the existing research methods are too complicated and the calculation level is relatively large. Big

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