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Historical data-based missing path recovery method

A path recovery and historical data technology, applied in the field of trajectory calculation, can solve the problems of increased path diversity, low specificity of popular paths, unsatisfactory recovery results, etc., and achieve the effect of maintaining reliability and accuracy

Active Publication Date: 2016-12-21
FUDAN UNIV
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AI Technical Summary

Problems solved by technology

When the missing distance of the path becomes longer, the number of trajectories between the beginning and the end of the missing path in the historical data will also decrease, and the diversity of paths will also increase, resulting in less specificity of popular paths, and eventually leading to Recovery results are not ideal

Method used

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  • Historical data-based missing path recovery method

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Embodiment Construction

[0038] The specific implementation process of the present invention will be described below in conjunction with specific examples:

[0039] 1. Train model parameters based on historical data

[0040] (1) Define MDP model, state set S={s 1 ,s 2 ,...,s 17}, an action is defined as a transition between two adjacent states, such as s 1 →s 2 ,s13 →s 17 , define the attenuation coefficient γ, such as γ=0.95.

[0041] (2) The initial reward function of each state is defined as its road length,

[0042] That is, R(s 1 )=s 1 .len, R(s 2 )=s 2 .len,...,R(s 17 )=s 17 .len,

[0043] Among them, s.len represents the length of road segment s. Total return function set R={R(s 1 ), R(s 2 ),...,R(s 17 )}.

[0044] (3) For the dotted historical track tr 1 Do Posterior Probability Calculations

[0045] (a) will terminate state s 17 The return function of is set to 0;

[0046] (b) Use the value iteration method for the current MDP to obtain the optimal value function V for e...

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Abstract

The invention belongs to the technical field of trajectory calculation and particularly relates to a historical data-based missing path recovery method. The method comprises the steps of in a training stage, performing modeling on a trajectory by utilizing a Markov decision process model, and training model parameters according to historical trajectory data; and in an online query stage, calculating a transfer probability between roads according to a trained model, constructing a graph, assigning a weight of the edge by using a negative logarithm of the transfer probability, obtaining a path with a highest probability by using shortest path search in the graph, and recovering a missing part by using the path. According to the method, the reliability and accuracy of an algorithm also can be kept under a long-distance missing condition.

Description

technical field [0001] The invention belongs to the technical field of trajectory calculation, and in particular relates to a method for recovering missing paths based on historical data. Background technique [0002] The popularity of mobile GPS devices has promoted the development of location-based services, and trajectory calculation has also emerged as the times require. The accuracy of trajectory data directly affects the quality of service. However, due to various factors in real life, such as equipment power, storage space, and online transmission costs, the sampling frequency of GPS data is not high, that is, there are a large number of trajectory data samples. The interval is more than 1 minute. The low sampling rate of GPS trajectory data directly leads to the lack of user driving paths, which will greatly affect the quality of location-based services, such as route recommendation, road condition prediction, trajectory prediction, frequent pattern mining, etc. Ap...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30
CPCG06F16/29
Inventor 孙未未吴昊
Owner FUDAN UNIV
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