EP1758065A2 - Procédé destiné à prévoir l'état du trafic dans un réseau routier et centrale de gestion du trafic - Google Patents

Procédé destiné à prévoir l'état du trafic dans un réseau routier et centrale de gestion du trafic Download PDF

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Publication number
EP1758065A2
EP1758065A2 EP06118087A EP06118087A EP1758065A2 EP 1758065 A2 EP1758065 A2 EP 1758065A2 EP 06118087 A EP06118087 A EP 06118087A EP 06118087 A EP06118087 A EP 06118087A EP 1758065 A2 EP1758065 A2 EP 1758065A2
Authority
EP
European Patent Office
Prior art keywords
traffic
road network
classes
records
network
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
EP06118087A
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German (de)
English (en)
Other versions
EP1758065A3 (fr
EP1758065B1 (fr
Inventor
Paul Mathias
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Siemens AG
Siemens Corp
Original Assignee
Siemens AG
Siemens Corp
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Publication date
Application filed by Siemens AG, Siemens Corp filed Critical Siemens AG
Publication of EP1758065A2 publication Critical patent/EP1758065A2/fr
Publication of EP1758065A3 publication Critical patent/EP1758065A3/fr
Application granted granted Critical
Publication of EP1758065B1 publication Critical patent/EP1758065B1/fr
Anticipated expiration legal-status Critical
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Classifications

    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/07—Controlling traffic signals
    • G08G1/08—Controlling traffic signals according to detected number or speed of vehicles
    • 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 invention relates to a method for forecasting a future traffic condition characterizing a road network with a plurality of network nodes and links and / or for detecting an accident in the road network.
  • the invention also relates to a traffic management center.
  • a method for controlling traffic light signaling at a node of a road traffic network in which traffic conditions already acquired at an earlier point in time at the node are clustered into classes, a characteristic traffic state being determined as a representative for each class of traffic states.
  • a defined metric determines the characteristic traffic state closest to the current traffic state and a signal program for the traffic signal transmitter assigned to this characteristic traffic state is executed.
  • the clustering limits the variety of traffic conditions occurring at the node to a reasonably limited number of typically occurring traffic conditions, the characteristic traffic conditions. This in turn restricts the number of signal programs to be stored, which are adapted to the characteristic traffic conditions. If the current traffic status is also included in the clustering for future evaluations, this results in a method for controlling a light signal transmitter at a node, which learns dynamically from traffic state to traffic state in order always to select the best possible signal program in future traffic states.
  • hydrographs which are time series of traffic loads one day, usually in equidistant sections of 15 min.
  • the invention has for its object to provide a method for forecasting future traffic conditions and / or for detecting an accident, with which the precision or quality of the prognosis is improved compared to known methods.
  • clustering of traffic conditions deposited in the data sets takes place not only locally at a single intersection but also at interpolation points distributed over the road network.
  • the clustering also takes place based on data sets, in particular vectors, which are based on many nodes distributed over the road network.
  • the data sets or vectors in the second variant do not only consist of individual values of the interpolation points, but of local traffic patterns, for example hydrographs, which concern the individual interpolation points.
  • An accident detection can in both cases be done in particular by comparing the current traffic volume or a current traffic flow with classes or their representatives, and concluding that an accident is the result of the absence of a similar accident-free class.
  • the data sets or vectors used for clustering contain not only time profiles of the traffic volume or - possibly in amplitude representation interpolated - hydrographs of a single measuring point in the road network, but it is the hydrographs or time courses of all or many measuring points in the road network in a vector or Record included.
  • the data set or vector for each link or street contains exactly one hydrograph.
  • many data sets or vectors are formed over several days, which are then used for clustering.
  • the traffic courses or hydrographs are, in particular, daily diurnal lines encompassing one day interval.
  • a link is, for example, a road, a tunnel, a bridge or the like, that is to say a trafficable connection for motor vehicles between two network nodes, which is understood to mean, in particular, an intersection, a junction, a driveway or the like.
  • the traffic courses before being combined into the data records are interpolated by a plurality of curve courses, in particular by Gaussian bell curves.
  • This method is for a single measuring point in the beginning described technical article.
  • the amplitudes, and preferably only these, of the Gaussian bell curves are used to form the data sets.
  • the following preferred embodiments relate to both variants of the invention:
  • the values of the traffic volume flowing into the data sets are measured in particular by a plurality of roadside detectors.
  • Essential to the invention is that a plurality of nodes distributed over the road network are used to construct the datasets or the vector.
  • the support points are in particular distributed over the road network in such a way that the traffic volume at a plurality of links and / or network nodes in the road network is determined.
  • the interpolation points have a distance of at least 1 km, in particular of at least 3 km, relative to one another.
  • a characteristic class representative is used, which is used for the prediction.
  • the class representatives or prototypes are the center of all the amplitude vectors aggregated in the cluster or class.
  • a good cluster ring is given if each amplitude vector is as close as possible to the prototype of the cluster to which it is assigned.
  • the distance between any amplitude vector and the prototype can be defined by a weighted maximum norm as a metric.
  • the class or cluster division that assigns each amplitude vector to the nearest prototype is optimal.
  • a cluster division can be calculated, in which alternately the Cluster centers and cluster divisions are recalculated. Once a local optimum is reached, class scheduling no longer changes in successive iterations so that the procedure can be terminated. Details of this type of cluster method are described in the above-mentioned technical article.
  • a current traffic situation is compared with the characteristic class representatives or prototypes. For example, the forecasting process selects from a larger set of class representatives a class representative well suited to the current situation and bases the prediction on them.
  • a metric is defined for comparing the data records with one another and / or for comparing the data records with a current traffic situation, in particular a standard.
  • the metric is a measure of the mutual location or spacing of the dataset vectors.
  • the predicted traffic condition can be displayed on a display device in graphical representation.
  • disjoint classes are preferably used.
  • a prognosis of a future traffic condition is determined and using the predicted traffic condition driving a roadside signaling device - by an operator or automatically - is made.
  • the method according to the invention is preferably implemented in a traffic management center.
  • a traffic management center 3 For monitoring and generating traffic forecasts for the traffic in the road network 1, a traffic management center 3 is provided, in which a display device 5 for displaying current and predicted traffic conditions of the entire road network 1 in graphic or topological form is formed.
  • the traffic management center 3 is prepared for remotely influencing signaling devices 7 present at the nodes K1, K2, K3, of which only one is indicated schematically.
  • several measuring points or detectors M1, M2, M3 are installed, each of which measures a local traffic flow.
  • the schematically indicated measured value extrapolation 11 expresses that the measuring points do not have to be identical to the supporting points S1, S2, S3. Rather, in the measured value extrapolation 11, a Extrapolation take place at locations where there are no detectors.
  • corresponding methods are known, for example a so-called current route determination with a subsequent allocation-based dynamic measured value propagation.
  • the Gaussian bell curves 23, 24, 25 used for interpolation are shown for illustrative reasons only for one of the traffic courses 12 (day d 3 ).
  • the hydrographs 12 to 20 each represent a plot of the traffic volume or traffic flow V over the time t.
  • the result of the interpolation are the amplitudes a 1 , a 2 , a 3 ,... Of the Gaussian bell curves 23 to 25 used with the same width:
  • the double indices of the amplitudes a then stand for a certain day and a specific bell curve; b and c are used in the example for the amplitudes of the other links.
  • a ⁇ d ⁇ 2 . a ⁇ d ⁇ 3 becomes the resulting feature vector or record a ⁇ d ⁇ 1 . which describes exactly one traffic situation of the road network 1, processed in a cluster procedure.
  • a cluster method for example, the method shown below with the vectors shown in FIG 2 a ⁇ d ⁇ 1 . a ⁇ d ⁇ 2 . a ⁇ d ⁇ 3 . ... be applied.
  • the clustering or class formation is indicated schematically in FIG. The vectors are symbolized there with their endpoints, which lie real in a N x n-dimensional space.
  • a ⁇ d ⁇ 2 . a ⁇ d ⁇ 3 . ... are grouped into clusters or classes C1, C2, C3 which describe similar traffic situations in the road network 1.
  • a characteristic class representative R1, R2, R3, ... is determined which is typical or meaningful for the respective class.
  • a prototype is defined as the class representative is usable for later long-term traffic forecasts.
  • the metric D is a weighted maximum standard.
  • the exemplary clustering method can be formulated mathematically as follows:
  • Good clustering is given when each amplitude vector is as close as possible to the prototype of the cluster to which it is assigned.
  • the distance D between the d-th amplitude vector and the i-th prototype R i is defined, for example, by the weighted maximum norm ⁇ id . In the simplest case, the weights are all the same.
  • a (locally) optimized cluster division can now be calculated by recalculating the cluster centers using G1.8 and the cluster divisions using G1.9, starting from any initial cluster division. The process is aborted as soon as the cluster division barely changes in successive iterations.
  • the traffic volume V ie in each case a single value, at the individual support points S1, S2, S3 determined. Every record or vector a ⁇ t ⁇ 1 . a ⁇ t ⁇ 2 . a ⁇ t ⁇ 3 . ... contains therefore no traffic course, but in each case a single measured value of the traffic flow V to the n support points (n-dimensional space).

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  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Chemical & Material Sciences (AREA)
  • Analytical Chemistry (AREA)
  • Traffic Control Systems (AREA)
  • Color Image Communication Systems (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
EP06118087A 2005-08-25 2006-07-28 Procédé destiné à prévoir l'état du trafic dans un réseau routier et centrale de gestion du trafic Not-in-force EP1758065B1 (fr)

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
DE102005040350A DE102005040350A1 (de) 2005-08-25 2005-08-25 Verfahren zur Prognose eines Verkehrszustandes in einem Straßennetz und Verkehrsmanagementzentrale

Publications (3)

Publication Number Publication Date
EP1758065A2 true EP1758065A2 (fr) 2007-02-28
EP1758065A3 EP1758065A3 (fr) 2007-03-14
EP1758065B1 EP1758065B1 (fr) 2008-09-10

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Application Number Title Priority Date Filing Date
EP06118087A Not-in-force EP1758065B1 (fr) 2005-08-25 2006-07-28 Procédé destiné à prévoir l'état du trafic dans un réseau routier et centrale de gestion du trafic

Country Status (4)

Country Link
EP (1) EP1758065B1 (fr)
AT (1) ATE408211T1 (fr)
DE (2) DE102005040350A1 (fr)
DK (1) DK1758065T3 (fr)

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP2040237A3 (fr) * 2007-09-11 2009-11-11 Hitachi, Ltd. Prédiction dynamique de congestion de trafic par le suivi de la trajectoire d'espace d'attributs des données rares Floating car data
CN103198711A (zh) * 2013-03-21 2013-07-10 东南大学 一种降低不同严重程度交通事故概率的车辆调控方法
CN113129585A (zh) * 2021-03-05 2021-07-16 浙江工业大学 基于重构交通网络的图聚合机制的道路交通流预测方法
CN119849312A (zh) * 2024-12-25 2025-04-18 中国公路工程咨询集团有限公司 基于bim的交通工程电子沙盘模拟与分析系统

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105679027B (zh) * 2016-02-23 2018-03-06 衡阳师范学院 一种交通道路变更情况统计方法
DE102018201787A1 (de) * 2018-02-06 2019-08-08 Siemens Aktiengesellschaft Verfahren und System zum Optimieren und Vorhersagen einer Verkehrssituation
CN112330962B (zh) * 2020-11-04 2022-03-08 杭州海康威视数字技术股份有限公司 交通信号灯控制方法、装置、电子设备及计算机存储介质

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE10146398A1 (de) 2001-09-20 2003-04-17 Siemens Ag System zum Steuern von Lichtsignalgebern an Kreuzungen

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE2911734C2 (de) * 1979-03-26 1982-06-24 Siemens AG, 1000 Berlin und 8000 München Verfahren zur Messung von Verkehrsstörungen
DE10025039C2 (de) * 2000-05-20 2003-09-04 Daimler Chrysler Ag Verfahren zur Ermittlung von Verkehrsregelungsphasendauern
DE10234367B3 (de) * 2002-07-27 2004-04-22 Daimlerchrysler Ag Verfahren zur Abbildung des Verkehrszustandes und System zur Verkehrsorganisation

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE10146398A1 (de) 2001-09-20 2003-04-17 Siemens Ag System zum Steuern von Lichtsignalgebern an Kreuzungen

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP2040237A3 (fr) * 2007-09-11 2009-11-11 Hitachi, Ltd. Prédiction dynamique de congestion de trafic par le suivi de la trajectoire d'espace d'attributs des données rares Floating car data
CN103198711A (zh) * 2013-03-21 2013-07-10 东南大学 一种降低不同严重程度交通事故概率的车辆调控方法
CN103198711B (zh) * 2013-03-21 2014-12-17 东南大学 一种降低不同严重程度交通事故概率的车辆调控方法
CN113129585A (zh) * 2021-03-05 2021-07-16 浙江工业大学 基于重构交通网络的图聚合机制的道路交通流预测方法
CN119849312A (zh) * 2024-12-25 2025-04-18 中国公路工程咨询集团有限公司 基于bim的交通工程电子沙盘模拟与分析系统

Also Published As

Publication number Publication date
DK1758065T3 (da) 2009-01-12
DE502006001532D1 (de) 2008-10-23
DE102005040350A1 (de) 2007-03-15
EP1758065A3 (fr) 2007-03-14
ATE408211T1 (de) 2008-09-15
EP1758065B1 (fr) 2008-09-10

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