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Method for predicting multi-period travel time distribution based on floating vehicle data

A technology of floating car data and travel time, applied in traffic flow detection and other directions, can solve problems such as travel time distribution prediction

Active Publication Date: 2014-07-16
WUHAN UNIV
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  • Abstract
  • Description
  • Claims
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Problems solved by technology

[0005] In order to solve the problem of predicting travel time distribution in complex urban networks, the present invention proposes a method for predicting multi-period travel time distribution based on low sampling frequency floating car data

Method used

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  • Method for predicting multi-period travel time distribution based on floating vehicle data
  • Method for predicting multi-period travel time distribution based on floating vehicle data
  • Method for predicting multi-period travel time distribution based on floating vehicle data

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Embodiment Construction

[0029] The present invention aims to solve the problem of prediction of travel time distribution in complex urban networks. Aiming at low sampling frequency GPS data of floating cars, an improved KNN multi-period travel time distribution prediction method with learning ability is proposed.

[0030] In order to facilitate those of ordinary skill in the art to understand and implement the present invention, the present invention will be described in further detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the implementation examples described here are only used to illustrate and explain the present invention, and are not intended to limit this invention.

[0031] The input data of the present invention is the GPS trajectory point and the road network data of the floating car after map matching, and the GPS trajectory data is composed of a series of GPS points sorted in chronological order, and each GPS point is composed of s...

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Abstract

The invention discloses a method for predicting multi-period travel time distribution based on floating vehicle data. According to the method, on the basis of a traditional KNN algorithm and through the adoption of the learning ability of a Bayesian model, the robustness and accuracy of travel time predicting are improved; through the effective fusion of history data and real-time data, the travel time average and variance prediction of the continuous multiple time ranges are achieved, and the confidence interval of the travel time floating is built. Compared with a traditional KNN model, the travel time distribution information for half an hour can be predicted effectively in various complex city networks, the result is more reliable and precise.

Description

technical field [0001] The invention belongs to the field of traffic travel time estimation and prediction, and relates to a method for predicting multi-period travel time distribution based on floating car data, in particular to a method for predicting multi-period travel time distribution based on low sampling frequency floating car data, belonging to a Improved KNN multi-period travel time distribution prediction method with learning ability. Background technique [0002] With the acceleration of my country's urbanization process and the increase of car ownership, urban traffic congestion is increasing day by day, and advanced traffic information service system (ATIS) is an effective way to alleviate traffic congestion. As an essential factor in the application of ATIS, accurate and reliable travel time information has attracted great attention. As an important indicator to measure traffic conditions, travel time information provides traffic managers with an effective qu...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G08G1/01
Inventor 陈碧宇时朝阳李清泉
Owner WUHAN UNIV
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