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Travel Time Estimation Method Based on Auxiliary Supervised Learning

A technology of travel time and supervised learning, applied in the field of intelligent transportation, can solve the problems of not making full use of trajectory data and losing useful information

Active Publication Date: 2021-06-04
FUDAN UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

In this splitting process, a lot of useful information is lost
Moreover, they do not make full use of the unique intermediate supervision labels of trajectory data, that is, the timestamp information of each intermediate GPS sampling point

Method used

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  • Travel Time Estimation Method Based on Auxiliary Supervised Learning
  • Travel Time Estimation Method Based on Auxiliary Supervised Learning
  • Travel Time Estimation Method Based on Auxiliary Supervised Learning

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Embodiment Construction

[0070] The specific implementation process of the present invention will be described below in conjunction with specific examples:

[0071] Such as figure 1 The historical trajectories in are used for training, and the estimated figure 2 travel time in .

[0072] 1. The preprocessing stage, the feature extraction and representation stage, preprocesses the trajectory data and extracts its various features. by figure 1 For example, the specific steps are:

[0073] (1) In the urban area, fine-grained grid division is carried out, and it is divided into adjacent small areas. Such as figure 1 , divide the map into 5×6 grids. Map each coordinate point in the trajectory sequence to the corresponding small area to form a grid sequence, that is, g={g 1 , g 2 ,..., g 10}.

[0074] (2) For each grid, mining its characteristics in different aspects. For example, for g 1 , using a random vector and to represent spatiotemporal semantic information. which is:

[0075]

[...

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Abstract

The invention belongs to the technical field of intelligent transportation, in particular to a method for estimating travel time based on auxiliary supervised learning. It looks for statistical laws from massive historical trajectory data, and estimates the time of the entire trip through an end-to-end deep learning model; the steps include: feature extraction and representation stages, preprocessing the trajectory data, extracting its time and Spatial features, driving state features, short-term and long-term traffic condition features; in the training and prediction phase, these extracted features are trained and predicted with a unified two-way cyclic neural network; each step of the cyclic neural network outputs through the current small area The time cost of these small areas is the sum of the time cost of the total path. At the same time, a bidirectional interval loss function is also introduced to constrain the intermediate time overhead. This method can efficiently and accurately estimate the vehicle travel time in the city, and has a good effect in the actual environment.

Description

technical field [0001] The invention belongs to the technical field of intelligent transportation, and in particular relates to a method for estimating travel time based on auxiliary supervised learning. Background technique [0002] Travel time estimation is an essential and important technology in the field of urban transportation, which can provide assistance for people's travel and commuting, and can also provide support for government planning decisions. But this is not a simple small problem, but will be affected by various dynamic factors, such as traffic dynamics, intersection conditions, changes in driver's driving behavior and historical periodic data evolution, etc. These factors lead to uncertainty and difficulty in estimating travel time. With the development and popularization of GPS-enabled mobile devices, a large amount of trajectory data has been continuously generated and covers every corner of the city. With these massive historical trajectory data, we c...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G08G1/01G06N3/04
CPCG08G1/0129G08G1/0137G06N3/045
Inventor 孙未未章瀚元吴昊
Owner FUDAN UNIV