WO2011079707A1 - Procédé et système de chargement d'information de conditions de route de circulation - Google Patents

Procédé et système de chargement d'information de conditions de route de circulation Download PDF

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Publication number
WO2011079707A1
WO2011079707A1 PCT/CN2010/079732 CN2010079732W WO2011079707A1 WO 2011079707 A1 WO2011079707 A1 WO 2011079707A1 CN 2010079732 W CN2010079732 W CN 2010079732W WO 2011079707 A1 WO2011079707 A1 WO 2011079707A1
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WIPO (PCT)
Prior art keywords
road
data
traffic
trend
road condition
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PCT/CN2010/079732
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English (en)
Chinese (zh)
Inventor
贾学力
李建军
梅生
申小次
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北京世纪高通科技有限公司
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Publication of WO2011079707A1 publication Critical patent/WO2011079707A1/fr

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    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions

Definitions

  • the present invention relates to the field of traffic information processing, and in particular, to a method and system for filling traffic condition information. Background technique
  • the Advanced Traffic Information System is based on a well-established information network and is equipped with sensors and transmission equipment on the road, on the bus, on the transfer station, on the parking lot, and in the weather center. All kinds of real-time traffic information are comprehensively processed to provide comprehensive, accurate and real-time road traffic information to the society in real time.
  • Traffic travellers can use this information to determine the mode of travel and route selection, and the driver can dynamically select the route of travel through the automatic positioning and navigation system.
  • the advanced traffic information service system not only provides timely and accurate traffic information for traffic management personnel, but also enables the traffic management control system to effectively adapt to various traffic conditions, provide decision support for the planning and transformation of traffic network capacity, and help road users effectively. Avoid traffic congestion, relieve congestion anxiety, thereby reducing frequent traffic accidents and improving the effective capacity of road network systems.
  • the floating car technology is one of the advanced technical means for obtaining real-time road conditions in the international advanced intelligent transportation system in recent years. It can obtain the average speed of the vehicle on the road segment and the travel time between the two points in the road segment in real time.
  • many roads are not covered by floating cars during the same processing cycle, especially when there are fewer floating cars in the early morning. Therefore, if the road traffic congestion that the floating car does not cover is not calculated, there will be a lot of road vacancies when the information is released, which reduces the integrity and availability of the information. Therefore, how to fill the vacant road congestion information in a processing cycle is an urgent problem in traffic information processing.
  • the vacancy road congestion information filling is to use the local correlation of road traffic congestion status in time to fill the vacancy data.
  • the prior art provides a method for filling traffic condition information, which is empty The missing road congestion information is used in the historical data of the floating road road congestion information, and the same road is filled with the average value of all the traveling speeds at the same time.
  • the prior art road condition information filling method has at least the following problems in implementing the technical solution of the present invention: Although the daily speed change trend is substantially similar, the daily road traffic congestion has a certain delay in the morning and evening peaks, if the usage history The mean method will result in large errors and the filled data is not accurate enough. Summary of the invention
  • the technical problem to be solved by the present invention is to provide a method and system for filling traffic condition information, which can improve the accuracy of road condition information filling.
  • a method for filling traffic condition information comprising:
  • the road condition trend of the road during the time period t is matched with the road condition change trend of the road in the road traffic road trend mode library, and the time point t is used as the driving speed for filling the road condition.
  • the data for completing the lack of traffic condition data includes: reading historical road condition data of at least three months;
  • the road condition data at a time point before the time point when the road condition data is vacant is filled to the time point when the road condition data is vacant:
  • the time period is divided according to the time point of the road condition.
  • the road condition change trend obtained by clustering all the road condition data of the same road in the same time period, and establishing a road traffic road trend trend pattern library includes:
  • the road condition trend of the road in the time period of the time point t is matched with the road condition change trend of the road in the road traffic road trend mode library, and the time point t is calculated as the speed of the road to fill the road condition:
  • All road trend polynomial curves of the roads with vacant road conditions at time point t are read; the vacancy road conditions on the road are estimated by pattern matching.
  • a traffic condition information filling system comprising:
  • the historical data pre-processing unit is configured to complete the data lacking in the traffic condition data according to the historical road condition data
  • a road condition trend analysis unit is configured to establish a road traffic road trend mode library according to a road condition change trend obtained by clustering all road condition data of the same road in the same time period; a road condition filling processing unit, configured to use the time point The road condition trend of the road during the time period is matched with the road condition change trend of the road in the road traffic road trend pattern library, and the road speed at the time point t is calculated as the driving speed for filling the road condition.
  • the historical data preprocessing unit includes:
  • a first reading module configured to read historical road condition data of at least three months
  • a filling module configured to fill road condition data at a time point before the time point when the road condition data is vacant to a time point at which the road condition data is vacant;
  • a marking module used to mark a time period in which the time point is located
  • the save module is used to save the traffic condition data after completion.
  • the road condition trend analysis unit includes:
  • a second reading module configured to read the completed traffic condition data
  • the clustering processing module is configured to perform clustering processing on traffic road data of the same road, different dates and the same time period, and obtain cluster data of different dates and the same time period under the same classification; the average calculation module is used for the The clustering data is used for mean calculation;
  • a curve fitting module for performing curve fitting on the result of the mean calculation
  • the pattern library building module is configured to establish a road traffic trend trend pattern library according to the curve fitting results of different classifications under different time periods of each road.
  • the road condition filling processing unit includes:
  • a third reading module configured to read real-time traffic condition data
  • a fourth reading module configured to read all road trend polynomial curves of the roads that are vacant at the time point t ;
  • a speculation module for estimating the vacant road conditions on the road by pattern matching is provided.
  • the method and system for filling traffic condition information provided by the embodiment of the present invention firstly supplements the vacancy information of the floating vehicle historical data, and then performs cluster analysis on all the road condition data of the same road in the same certain period of time, respectively, and establishes each road.
  • the different speed trends in the time period constitute the trend mode library of road traffic conditions.
  • the road traffic information trend in the road traffic trend mode library is matched with the road traffic trend in the current time period, and the driving speed of the road at the current time is estimated as the filling road condition, so that the estimated traffic information of the vacancy is more accurate and improved.
  • the accuracy of road condition information filling is DRAWINGS
  • FIG. 1 is a flowchart of a method for filling traffic condition information according to an embodiment of the present invention
  • FIG. 2 is a schematic structural diagram of a traffic condition information filling system according to an embodiment of the present invention.
  • FIG. 3 is a flowchart of a method for filling traffic condition information according to Embodiment 2 of the present invention.
  • FIG. 4 is a schematic structural diagram of a traffic road condition information filling system according to Embodiment 2 of the present invention. Detailed ways
  • Embodiments of the present invention provide a method and system for filling traffic condition information, which can improve the accuracy of filling road condition information.
  • Embodiment 1 An embodiment of the present invention provides a method for filling traffic condition information. As shown in FIG. 1, the method includes:
  • Step S101 Complement data that is lacking in traffic condition data according to historical road condition data
  • Step S102 Establish a trend of road traffic conditions according to a trend of road conditions obtained by clustering all road condition data of the same road in the same time period Pattern library
  • Step S103 Matching the road condition trend of the road in the time period of the time point t with the road condition change trend of the road in the road traffic road trend mode library, and calculating the traveling speed of the road at the time point t as the filling road condition.
  • the embodiment of the present invention further provides a traffic road condition information filling system. As shown in FIG. 2, the system includes: a historical data pre-processing unit 1, a road condition trend analysis unit 2, and a road condition filling processing unit 3.
  • the historical data preprocessing unit 1 is configured to complement the data of the traffic condition data that is lacking according to the historical road condition data.
  • the unit takes the accumulated historical road condition information as input, firstly completes the information of the historical floating vehicle road condition data, and then divides the statistical time period according to the time point of the road condition.
  • the choice of roads includes the main roads of the city, such as the collection of all road units in the main line of Beijing, such as: Zhichun Road, College Road, etc.
  • the value of the time point includes the demarcation from 00:00-23:59 every 5 minutes. Time periods, such as: 08:00, 08:05, 08: 10, 08:15, etc., there are 288 time points in a day.
  • time points in the day are divided into 70 time periods, and each 8 time points (ie 40 minutes) is a time period, and the time point is The interleaving exists in two consecutive time periods, for example: time point 0-7 is time period 0, time point 4-11 is time period 1, time point 8-15 is time period 2, ... time point 280- 287 is time period 70.
  • the road condition trend analysis unit 2 is configured to establish a road traffic trend trend mode library according to a road condition change trend obtained by clustering all the road condition data of the same road in the same time period.
  • the unit clusters all the road condition data of the road in the same period of time, calculates the road condition change trend on the road during this period of time, and finally establishes the road traffic information trend according to different speed change trends in all time periods of all roads. Pattern library.
  • the road condition filling processing unit 3 is configured to match the road condition trend of the road in the time period of the time point t with the road condition change trend of the road in the road traffic road trend mode library, and calculate the time point t as the road to fill the road condition. speed.
  • the unit reads the real-time floating car traffic information, scans the road vacancy road condition data, matches the road traffic information trend in the road traffic information trend mode library with the road condition trend of the current time period, and estimates the current speed of the road at the current time. As a way to fill the road.
  • the historical road condition data of the floating vehicle is supplemented with the vacancy information, and then all the road condition data of the same road are clustered and analyzed in the same period of time, and similar road condition information units are combined to distinguish Dissimilar road condition information units, and then the speed change trend of these road condition unit responses is fitted to a polynomial curve, and different speed change trends in all time periods of all roads constitute a road traffic road trend pattern library.
  • the road traffic trend in the road traffic trend mode library is matched with the road traffic trend in the current time period, and the driving speed of the road at the current time is estimated as the filling road condition, so that the filling road condition is more accurate.
  • the filling method uses the pipeline method, and the statistical analysis of all roads is completed ahead of time, and the filling of the road conditions of the empty roads with real-time road information is carried out one by one.
  • the method and system for filling traffic condition information provided by the embodiment of the present invention firstly supplements the vacancy information of the floating vehicle historical data, and then performs cluster analysis on all the road condition data of the same road in the same certain period of time, respectively, and establishes each road.
  • the different speed trends in the time period constitute the trend mode library of road traffic conditions.
  • the road traffic information trend in the road traffic trend mode library is matched with the road traffic trend in the current time period, and the current speed of the road is estimated as the filling road condition, so that the estimated traffic information of the vacancy is more accurate.
  • the embodiment of the present invention provides a method for filling traffic condition information. As shown in FIG. 3, the method includes:
  • the historical data preprocessing module is based on the accumulated historical road data of more than 3 months, and carries out the filling calculation of the vacant road conditions at the time points on each road, that is, reading the historical road conditions of the floating car city of n (n ⁇ 3) months. data.
  • the statistical time period is divided according to the time point of the road condition.
  • the choice of roads includes the main roads of the city, such as the collection of all road units in the main line of Beijing, such as: Zhichun Road, Xueyuan Road, etc.
  • the value of the time point includes the demarcation from 00:00-23:59 every 5 minutes. Time period, such as: 08:00, 08:05, 08: 10, 08: 15, etc., a total of 288 a day Time point.
  • time points in the day are divided into 70 time periods, and each 8 time points (ie 40 minutes) is a time period, and the time point is The interleaving exists in two consecutive time periods, for example: time point 0-7 is time period 0, time point 4-11 is time period 1, time point 8-15 is time period 2, ... time point 280- 287 is time period 70.
  • S202 Scanning the road condition data of each road from time point 0 in time, and finding a time point when the road condition is empty;
  • time point 6 Located in the back of time period 0 (00: 00 to 00: 40) and at the same time as time period 1 (00: 20 to 00: 55).
  • Each time period contains 8 time points, and a road corresponds to a speed value at each time point of the day, so the road has 8 speed values in each time period of the day, and these 8
  • the velocity value is treated as a vector (array) of length 8, so a road will have m vectors (array) for each time period on m dates.
  • the vector (array) of length 8 corresponding to the road speed at each time point in the time period reflects the trend of the speed of the road during this period.
  • the present invention distinguishes dissimilar vectors (array) and merges similar vectors (array) by cluster analysis in data mining, and the obtained result data (or mode) reflects the road. All speed trends during this time period.
  • the present invention uses the maximum and minimum clustering algorithm for cluster analysis, and selects a new initial class center in the vector set with the maximum Euclidean distance between the speed change trend curves, and performs model classification by the minimum clustering principle.
  • the mean value of the road conditions for different date and time periods under the same classification is calculated. After clustering, calculate the mean value of the road speed at the same time point in different arrays in each class. Finally, multiple arrays in each class are combined into one array, which reflects the trend of the road speed in a certain period of time. The trend of road speed is called a mode.
  • the present invention performs a m-degree polynomial curve fitting according to a least squares criterion by a velocity variation trend corresponding to a series of consecutive time points corresponding to a road, and m+1 parameters of the m-th order polynomial function are used as a final curve. The result of the combination.
  • the curve fitting results of different classifications under different time periods of each road are saved as a historical trend model of road traffic conditions.
  • the road traffic trend mode library describes the m-degree polynomial curve of all speed trends of all roads in the road network in each time period in the historical data of the floating car.
  • This model library is based on roads, that is, a pattern library for each road, which contains polynomial curves of all reaction road speed change patterns at various time periods.
  • the curve in S213 is matched ( Suppose the number of curves is k). Since the curves in the trend pattern library are all m-degree polynomial curves, the curve equation is:
  • the smallest curve is the curve that matches the success.
  • the velocity value Vi ⁇ ⁇ + a - u _1 + - + ⁇ u t + a o, at the time point t can be calculated from the equation of the curve. That is, the road speed at the time point t is calculated as the traveling speed for filling the road condition.
  • the real-time road condition data of the complete road network is output.
  • the embodiment of the present invention further provides a traffic road condition information filling system.
  • the system includes: a historical data preprocessing unit 1, a road condition analysis unit 2, and a road condition filling processing.
  • Unit 3 the system includes: a historical data preprocessing unit 1, a road condition analysis unit 2, and a road condition filling processing.
  • the historical data pre-processing unit 1 includes: a first reading module 11, a padding module 12, a marking module 13, and a saving module 14.
  • the first reading module 11 is configured to read the historical road condition data of at least three months; the filling module 12 is configured to fill the road condition data at a time point before the time point when the road condition data is vacant to the time when the road condition data is vacant.
  • the marking module 13 is used to mark the time period in which the time point is located; the saving module 14 is configured to save the completed traffic condition data.
  • the road condition trend analysis unit 2 includes: a second reading module 21, a clustering processing module 22, an average calculation module 23, a curve fitting module 24, and a pattern library establishing module 25.
  • the second reading module 21 is configured to read the completed traffic condition data;
  • the cluster processing module 22 is configured to perform clustering processing on the same road, different dates, and the same time period, to obtain the same classification. Different date, cluster data of the same time period; mean calculation module 23 for performing mean calculation on the cluster data; curve fitting module 24 for curve fitting the result of the mean calculation;
  • Module 25 is configured to establish a road traffic trend trend mode library according to a curve fitting result of different classifications under different time segments of each road.
  • the road condition filling processing unit 3 includes: a third reading module 31, a fourth reading module 32, and a speculative module 33.
  • a third reading module 31, configured to read real-time traffic condition data
  • a fourth reading module 32 configured to read all road condition trend polynomial curves of the road that is vacant at the T time point; Predict the vacant road conditions on the road by pattern matching.
  • the method and system for filling traffic condition information provided by the embodiment of the present invention firstly supplements the vacancy information of the floating vehicle historical data, and then performs cluster analysis on all the road condition data of the same road in the same certain period of time, and merges similar road condition information.
  • the unit distinguishes the dissimilar road condition information units, and then fits the speed change trend of the reaction of the road condition units to the polynomial curve, and the different speed change trends in each time period of all the roads constitute the road traffic road trend pattern library.
  • Use the road traffic information trend in the road traffic trend mode library to match the road traffic trend in the current time period, and estimate the current speed of the road as the filling road condition.
  • the embodiment of the present invention takes all the speed change trends of the road in the historical data of the floating vehicle as an optional mode, and overcomes the historical mean method by treating the speed change in a certain period of time as a whole.
  • the defects when actually filling the vacant traffic information, make the estimated traffic information of the vacancies more accurate by matching the optional modes.
  • the steps of a method or algorithm described in connection with the embodiments disclosed herein can be implemented in hardware, a software module executed by a processor, or a combination of both.
  • the software module can be placed in random access memory (RAM), memory, read only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or technical field. Any other form of storage medium known.
  • RAM random access memory
  • ROM read only memory
  • electrically programmable ROM electrically erasable programmable ROM
  • registers hard disk, removable disk, CD-ROM, or technical field. Any other form of storage medium known.

Abstract

La présente invention concerne un procédé et un système de chargement d'information de conditions de route de circulation. Le procédé comprend: la complétion de données non attribuées dans des données de conditions de route de circulation selon les données historiques de conditions de route (S101); la réalisation de traitement de groupage sur toutes les données de conditions de route de la même route dans une même période de temps pour obtenir la tendance de changement de conditions de route, et l'établissement d'une bibliothèque de modes de tendance de conditions de route d'une route de circulation selon la tendance de changement de conditions de route (S102); l'appariement de la tendance de conditions de route de la route dans la période de temps contenant le point temporel t avec la tendance de changement de conditions de route dans la bibliothèque de modes de tendance de conditions de route de circulation, et le calcul de la vitesse de déplacement sur la route au point temporel t pour charger la condition de route (S103).
PCT/CN2010/079732 2009-12-30 2010-12-13 Procédé et système de chargement d'information de conditions de route de circulation WO2011079707A1 (fr)

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CN200910244141.7 2009-12-30
CN200910244141A CN101763730B (zh) 2009-12-30 2009-12-30 交通路况信息填补方法和系统

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CN105869404A (zh) * 2016-05-25 2016-08-17 成都联众智科技有限公司 交通拥堵预警系统
CN109118771A (zh) * 2018-09-19 2019-01-01 青岛海信网络科技股份有限公司 一种城市交通常发性拥堵特征确定的方法及装置
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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1637384A (zh) * 2003-12-26 2005-07-13 爱信艾达株式会社 交通信息数据的插补方法、插补装置及交通信息数据结构
CN1645402A (zh) * 2005-01-19 2005-07-27 北京交通大学 一种道路交通流数据质量控制方法与装置
CN101325004A (zh) * 2008-08-01 2008-12-17 北京航空航天大学 一种实时交通信息的数据补偿方法
JP2009087097A (ja) * 2007-09-28 2009-04-23 Panasonic Corp 交通情報生成装置及び交通情報提供システム
CN101763730A (zh) * 2009-12-30 2010-06-30 北京世纪高通科技有限公司 交通路况信息填补方法和系统

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1637384A (zh) * 2003-12-26 2005-07-13 爱信艾达株式会社 交通信息数据的插补方法、插补装置及交通信息数据结构
CN1645402A (zh) * 2005-01-19 2005-07-27 北京交通大学 一种道路交通流数据质量控制方法与装置
JP2009087097A (ja) * 2007-09-28 2009-04-23 Panasonic Corp 交通情報生成装置及び交通情報提供システム
CN101325004A (zh) * 2008-08-01 2008-12-17 北京航空航天大学 一种实时交通信息的数据补偿方法
CN101763730A (zh) * 2009-12-30 2010-06-30 北京世纪高通科技有限公司 交通路况信息填补方法和系统

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103854480A (zh) * 2012-12-05 2014-06-11 深圳先进技术研究院 交通监测数据矩阵补全算法
CN103854480B (zh) * 2012-12-05 2016-01-27 深圳先进技术研究院 交通监测数据矩阵补全方法
CN105869404A (zh) * 2016-05-25 2016-08-17 成都联众智科技有限公司 交通拥堵预警系统
CN110197583A (zh) * 2018-05-03 2019-09-03 腾讯科技（深圳）有限公司 一种道路路况的识别方法、装置及存储介质
CN109118771A (zh) * 2018-09-19 2019-01-01 青岛海信网络科技股份有限公司 一种城市交通常发性拥堵特征确定的方法及装置

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