Travel time real-time prediction method facing license plate data identification and device

A travel time, license plate recognition technology, applied in the field of intelligent transportation and information, can solve the problem of low efficiency and accuracy of real-time prediction of travel time, and achieve the effect of low delay

Active Publication Date: 2015-12-09
NORTH CHINA UNIVERSITY OF TECHNOLOGY
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0009] The purpose of the present invention is to overcome the above-mentioned technical defects, thereby solving t

Method used

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  • Travel time real-time prediction method facing license plate data identification and device
  • Travel time real-time prediction method facing license plate data identification and device
  • Travel time real-time prediction method facing license plate data identification and device

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Embodiment 1

[0063] The invention provides a real-time travel time prediction device for license plate recognition data, which mainly includes four components: a data storage module, an online calculation module, an offline calculation module, and a human-computer interaction module. Refer to the attached figure 1 Describe each module in detail.

[0064] Data storage module: This module is based on the storage realized by the distributed file system; this module stores the travel time measured result set, the time interval attribute prior rule base, the link attribute prior rule base and the travel time prediction result set; this module is compatible with the offline The calculation module is connected to provide the travel time measurement result set as input and the time interval attribute prior rule base and road section attribute prior rule base as output for the offline calculation module; this module is connected with the online calculation module to provide online calculation The ...

Embodiment 2

[0084] combine figure 2 The basic process describes the calculation process of the prior rule mining of road segment attributes. In a specific embodiment, the prior rule mining process of road segment attributes can be described as the following two MapReduce job steps:

[0085] (1) The first job, with a given calculation frequency and time interval unit, divides the measured result set according to the attributes of the road section, classifies the time interval under the corresponding road section, and the travel time value under the time interval; among them, Map stage (i.e., the mapping stage) loads the travel time measurement result set, divides the travel time measurement result set according to the road segment attributes, and obtains the travel time measurement value set sorted in chronological order with the road segment as the primary key; The intervals are integrated sequentially to obtain a set of travel time measured values ​​sorted by time intervals with road s...

Embodiment 3

[0088] combine image 3 The basic flow describes the calculation process of prior rule mining for time interval attributes. In a specific embodiment, the prior rule mining process of the time interval attribute can be described as the following two MapReduce job steps:

[0089] (1) The first job, with a given calculation frequency and time interval unit, divides the measured result set according to the attributes of the road section, classifies the time interval under the corresponding road section, and the travel time value under the time interval; among them, Map stage (i.e., the mapping stage) loads the travel time measurement result set, divides the travel time measurement result set according to the road segment attributes, and obtains the travel time measurement value set sorted in chronological order with the road segment as the primary key; The intervals are integrated sequentially to obtain a set of travel time measured values ​​sorted by time intervals with road sec...

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Abstract

The invention discloses a travel time real-time prediction method facing license plate data identification and a device. The travel time real-time prediction method comprises three steps of exploration of prior rules, practical measurement and calculation of travel time and prediction calculation of travel time. The invention overcomes the problem that the data scale and effectiveness are limited, the prediction calculation respond is delayed and the accuracy is not high. The method based on the license plate identification data and the device reduce the calculation respond time , improve the prediction accuracy, can be realized in an Apache Storm and Hadoop MapReduce cluster environment, finish travel time prediction in the real-time data environment, and can be used for the road state monitoring and travel service publication in the traffic field. The invention greatly improves the instantaneity and reliability of smart traffic application in the big data environment, provides the traffic big data information to the user for real-time inquiry and prediction, and improves the user experience.

Description

technical field [0001] The present invention relates to the field of information technology and the field of intelligent transportation, in particular, relates to a real-time prediction technology for completing travel time prediction using the method in ApacheStorm and HadoopMapReduce cluster environments, and a method and device for real-time prediction of road section travel time. Background technique [0002] Road segment travel time is the most widely concerned traffic operation status information in the field of transportation. Compared with other traffic parameters such as location, it can better evaluate the smoothness of the road, reflect the transportation efficiency of the road, and reflect the state of road traffic congestion. With the expansion of city scale, traffic congestion at peak hours has become a norm. How to release travel services for road traffic congestion ahead, predict the changing trend of road conditions to travelers in the future, and guide trave...

Claims

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Application Information

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IPC IPC(8): G06Q50/30G08G1/00
Inventor 丁维龙赵卓峰韩燕波
Owner NORTH CHINA UNIVERSITY OF TECHNOLOGY
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