EP3866135A1 - Procédé de commande d'une installation de signalisation lumineuse - Google Patents

Procédé de commande d'une installation de signalisation lumineuse Download PDF

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Publication number
EP3866135A1
EP3866135A1 EP21151471.6A EP21151471A EP3866135A1 EP 3866135 A1 EP3866135 A1 EP 3866135A1 EP 21151471 A EP21151471 A EP 21151471A EP 3866135 A1 EP3866135 A1 EP 3866135A1
Authority
EP
European Patent Office
Prior art keywords
traffic
control signals
signal
schedule
current
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
EP21151471.6A
Other languages
German (de)
English (en)
Other versions
EP3866135B1 (fr
EP3866135C0 (fr
Inventor
David Borst
Florian Fanderl
Markus Mauder
Evren Pamir
Konrad Vowinckel
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.)
Yunex GmbH
Original Assignee
Siemens Mobility GmbH
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Publication date
Application filed by Siemens Mobility GmbH filed Critical Siemens Mobility GmbH
Publication of EP3866135A1 publication Critical patent/EP3866135A1/fr
Application granted granted Critical
Publication of EP3866135B1 publication Critical patent/EP3866135B1/fr
Publication of EP3866135C0 publication Critical patent/EP3866135C0/fr
Active legal-status Critical Current
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
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
    • G08G1/0116—Measuring and analyzing of parameters relative to traffic conditions based on the source of data from roadside infrastructure, e.g. beacons
    • 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
    • G08G1/0125—Traffic data processing
    • G08G1/0129—Traffic data processing for creating historical data or processing based on historical data
    • 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
    • G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
    • G08G1/0145—Measuring and analyzing of parameters relative to traffic conditions for specific applications for active traffic flow control
    • 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

Definitions

  • the invention relates to a method for controlling a traffic light system.
  • the invention relates to a device, a computer program and a machine-readable storage medium.
  • Traffic light systems are usually operated or controlled based on a signal time schedule.
  • a signal schedule is fixed and not changed while the light signal system is being operated.
  • the signal schedule is changed during operation. This, for example, based on a traffic condition in the vicinity of the traffic light system.
  • algorithms from machine learning can be used to change a signal schedule based on a traffic condition in the vicinity of the traffic light system.
  • Such algorithms are usually trained using training data.
  • different traffic conditions in the vicinity of the traffic light system can be simulated. These simulated traffic conditions can be used as training data for machine learning.
  • the object on which the invention is based is to be seen in providing a concept for the efficient control of a light signal system, which makes it possible to efficiently determine training data for machine learning with regard to a prediction of a behavior of the light signal system.
  • a device which is set up to carry out all steps of the method according to the first aspect.
  • a computer program which comprises instructions which, when the computer program is executed by a computer, for example by the device according to the second aspect, this cause a method according to the first aspect to be carried out.
  • a machine-readable storage medium is provided on which the computer program according to the third aspect is stored.
  • the invention is based on and includes the knowledge that the above object can be achieved in that a current signal time schedule is changed or replaced while the traffic light system is in operation.
  • this has the technical advantage that the light signal system can or will be controlled based on the changed signal time schedule or based on the replaced signal time schedule.
  • the current traffic situation in the vicinity of the traffic signal system can advantageously be influenced.
  • This recorded traffic condition can advantageously be used as training data for machine learning, for example to predict or predict the behavior of the traffic signal system, for example to predict a signal time of the traffic signal system.
  • the current traffic condition is compared with a historical traffic condition in order to determine a deviation of the current traffic condition from the historical traffic condition.
  • the control signals are generated based on the determined deviation.
  • the determined deviation is too great, it can be assumed that there is a problem in the vicinity of the traffic signal system, for example an accident or a traffic jam. In such a situation, the already critical traffic condition should not be potentially negatively influenced by experiments with regard to changed or respectively replaced signal schedules.
  • the historical traffic condition also has the advantage of recognizing particularly suitable traffic conditions with regard to changing or replacing the current signal time schedule in order to obtain or determine training data for different real traffic conditions.
  • the technical advantage is thus achieved in particular that a concept for efficient control of a traffic light system is provided, which makes it possible to efficiently determine training data for machine learning, for example based on a suitably trained algorithm during ongoing operation of the Traffic light system to predict or predict a behavior of a traffic in response to a change or a replacement of the signal time schedule.
  • signal schedule can also be used for the term “signal schedule”.
  • deviation threshold value signals are received which represent a deviation threshold value, the determined deviation being compared with the deviation threshold value, the control signals being generated depending on the comparison of the determined deviation with the deviation threshold value.
  • control signals are not generated if the determined deviation is greater than or greater than or equal to the deviation threshold value.
  • the determined deviation is a percentage.
  • the deviation threshold value is a percentage.
  • the percentage can refer, for example, to the historical traffic situation.
  • control signals are only generated when the determined deviation is less than or less than or equal to the deviation threshold value.
  • the deviation can be zero, for example.
  • control signals are such that the changed signal time schedule or the replaced signal time schedule can cause a deterioration or an improvement of a current traffic flow through the traffic light system.
  • traffic condition threshold value signals are received which represent at least one traffic condition threshold value, wherein the control signals are generated based on the at least one traffic condition threshold value.
  • the at least one traffic condition threshold is in each case an element from the following group of traffic condition thresholds: a minimum and / or maximum number of vehicles per green second and / or a minimum and / or maximum number of vehicles per cycle time unit and / or a minimum and / or maximum detector occupancy per cycle time unit.
  • the current traffic status with the at least one Traffic condition threshold value is compared, the control signals being generated based on the comparison of the current traffic condition with the at least one traffic condition threshold value.
  • control signals are such that the changed signal schedule or the replaced signal schedule correspond to a signal time base schedule.
  • this has the technical advantage that if the current traffic condition is outside predefined thresholds (traffic condition thresholds), a return is made to a signal time base plan in order to ensure that the current traffic condition is again within the predefined thresholds.
  • a check is carried out to determine whether the current traffic condition would exceed the traffic condition threshold value by changing or replacing the signal schedule, the control signals being output depending on a result of the checking.
  • control signals are not output.
  • control signals are only output if the result of the checking indicates that the current traffic status remains within the predefined threshold values.
  • the current and the historical traffic status each include a number of vehicles per green second and / or number of vehicles per cycle time unit and / or a detector occupancy per cycle time unit.
  • the cycle time unit is a cycle second or a value which is smaller or greater than a cycle second.
  • a detector occupancy represents a number of vehicles that are detected by means of a detector in the vicinity of the light signal system.
  • a detector includes, for example, a video camera and / or an induction coil, which is embedded or arranged, for example, within a roadway.
  • a detector includes, for example, an environment sensor or several environment sensors.
  • An environment sensor is, for example, one of the following environment sensors: radar sensor, ultrasound sensor, magnetic field sensor, lidar sensor, magnetic field sensor, infrared sensor, video sensor, in particular video sensor of a video camera.
  • training data signals are generated and output which represent training data for machine learning, the training data comprising one or more of the following data: Traffic status in the vicinity of the traffic signal system, while the traffic signal system is based on the changed signal schedule or on the replaced signal schedule is operated, at least one parameter of the changed signal schedule or the replaced signal schedule, the currently applied change to the signal schedule, the resulting signal schedule, detection times of vehicles, derived vehicle numbers, static information about the traffic light system, in particular the topology of a junction, in particular an intersection, whose traffic is regulated by the traffic light system, and / or static information about a system assigned to the traffic light system, occupancy data, the occupancy data in particular being an or several elements of the following group of occupancy data include: position of a detection of a vehicle, trajectory of the detected vehicle, occupancy values of a detector per unit of revolution, number of vehicles per detector per unit of revolution, duration.
  • the above exemplary data for training data relate to the traffic signal system.
  • one or more corresponding data related to an immediate or include indirect neighboring light signaling the light signals in addition to or instead of the training data, one or more corresponding data related to an immediate or include indirect neighboring light signaling the light signals.
  • a traffic condition within the meaning of the description includes, for example, a lane-specific or lane-dependent traffic condition.
  • the traffic condition or the traffic condition threshold is or are defined in relation to a lane.
  • the historical traffic condition comprises an averaged historical traffic condition.
  • the method comprises controlling the traffic signal system based on the output control signals.
  • the method according to the first aspect is a computer-implemented method.
  • the surroundings of the light signal system particularly denotes an area around the light signal system up to a maximum distance of, for example, 1 km, in particular 500 m, in particular 200 m, in particular 100 m, in particular 50 m, in particular 20 m.
  • the method according to the first aspect is carried out or carried out by means of the device according to the second aspect.
  • Machine learning includes, in particular, a neural network and / or reinforcement learning or reinforcement learning (in English: “reinforcement learning”)
  • changing the current signal time plan comprises changing at least one parameter of the current signal time plan.
  • the at least one parameter of the signal plan is an element selected from the following group of parameters: start time of a signal state, duration of a signal state, start point, switch-on time, cycle time.
  • FIG 1 shows a flowchart of a method for controlling a traffic light system, comprising the following steps: receiving 101 of first traffic status data which represent a current traffic status in the vicinity of the traffic light system, Receiving 103 second traffic status data which represent a historical traffic status in the vicinity of the traffic light system, Determining 105 a deviation of the current traffic condition from the historical traffic condition, Generating 107 control signals for controlling the traffic light system based on the determined deviation in such a way that a current signal schedule is changed or replaced when the traffic signal system is controlled based on the control signals, Output 109 the generated control signals.
  • FIG 2 shows a device 201.
  • the device 201 is set up to carry out all steps of the method according to the first aspect.
  • the device 201 comprises an input 203 which is set up to receive the first traffic status data.
  • the input 203 is further set up to receive the second traffic status data.
  • the device 201 comprises a processor 205 which is set up to determine a deviation of the current traffic condition from the historical traffic condition.
  • the processor 205 is further set up to generate control signals for controlling the light signal system based on the determined deviation such that a current signal time schedule is changed or replaced when the light signal system is controlled based on the control signals.
  • the device 201 comprises an output 207 which is set up to output the control signals generated.
  • the device 201 comprises a plurality of processors instead of the one processor 205.
  • FIG 3 shows a machine-readable storage medium 301.
  • a computer program 303 is stored on the machine-readable storage medium 301.
  • the computer program 303 comprises commands which, when the computer program 303 is executed by a computer, for example by the device 201 according to FIG FIG 2 to cause this to carry out a method according to the first aspect.
  • changing the current signal time plan comprises changing at least one parameter of the current signal time plan.
  • the concept described here is based, among other things, on researching traffic system behavior in real time by changing or replacing a current signal schedule of a traffic light system.
  • this has the technical advantage that the resulting traffic conditions can be examined or recorded for different signal time schedules.
  • training data for machine learning can advantageously be determined efficiently without complex and expensive traffic simulations having to be carried out for this purpose.
  • traffic detectors also referred to simply as detectors
  • Such traffic detectors include, for example, video cameras and / or induction loops that are embedded or embedded in a roadway.
  • the current traffic status can include, for example, a number of vehicles per green second and / or number of vehicles per cycle time unit and / or a detector occupancy per cycle time unit.
  • the current traffic condition is compared in particular with a historical traffic condition in the vicinity of the traffic light system.
  • the historical traffic status can include, for example, a number of vehicles per green second and / or number of vehicles per cycle time unit and / or a detector occupancy per cycle time unit.
  • the historical traffic status is or the historical traffic status comprises an averaged historical traffic status.
  • the historical traffic status includes an averaged number of vehicles per green second and / or an averaged number of vehicles per cycle time unit and / or an averaged detector occupancy per cycle time unit.
  • a current date is assigned to the current traffic status.
  • a historical date corresponding to the current date is assigned to the historical traffic condition.
  • a date includes in particular the specification of a weekday and / or the specification of a time and / or the specification of a year.
  • a deviation of the current traffic condition from the historical traffic condition is determined. For example, a deviation of the current number of vehicles per green second and / or number of vehicles per cycle time unit and / or a detector occupancy per cycle time unit with a historical number of vehicles per green second and / or number of vehicles per cycle time unit and / or a detector occupancy per cycle time unit , in particular an averaged historical number of vehicles per green second and / or averaged historical number of vehicles per cycle time unit and / or an averaged historical detector occupancy per cycle time unit.
  • a decision is made, in particular, as to whether the current signal schedule of the traffic light system should be changed or replaced.
  • the deviation can be a percentage, for example.
  • a deviation threshold value is specified, the control signals being generated as a function of a comparison of the determined deviation with the deviation threshold value.
  • the determined deviation is less than or less than or equal to the specified deviation threshold value, provision is made in particular to change or replace the current signal time schedule.
  • the current signal time schedule is not changed or replaced, for example.
  • the background to such a procedure is, in particular, that in the event of a deviation greater than or greater than or equal to the specified deviation threshold value, it can be assumed that an atypical traffic situation, i.e. an atypical traffic situation, is present in the vicinity of the traffic light system.
  • an atypical traffic condition can occur, for example, due to an accident or a traffic jam.
  • the characteristic numbers of the traffic are compared with predetermined traffic condition threshold values.
  • the limits (traffic state threshold values) which must not be exceeded by changing or maintaining a signal plan are in particular defined by, for example, a minimum and / or maximum number of Vehicles per green second and / or a minimum and / or maximum number of vehicles per cycle time unit and / or a minimum and / or maximum detector occupancy per cycle time unit.
  • the control signals are generated in particular as a function of a result of this comparison.
  • the concept described here offers a multitude of advantages when controlling the traffic light system and its effects on the traffic condition, primarily by improving efficiency, lowering costs and expanding the possibilities of adding traffic light systems based on a large number of changed or replaced signal schedules control or operate.
  • the limiting factor of known concepts is the amount of time it takes an engineer to simulate a traffic system or to monitor the state of the traffic system in order to avoid negative influences on the traffic system. Neglecting negative influences on the traffic system would restrict the determination of training data to traffic situations in which only limited negative effects on traffic would be expected. In doing so, the obligation of local governments to optimally control their networks is taken into account.
  • an AI Artificial Intelligence
  • the proposed solution represents a compromise between the need for large, diverse training data sets and the one to be expected negative impact on the traffic situation through automatic monitoring of the traffic system and generation of control signals based on the comparison of the historical with the current traffic condition.
  • the current traffic volume and the historical traffic volume are advantageously taken into account, in particular based on different lanes of a junction, in particular a road intersection, so that changing or replacing the current signal schedule is automatically reduced or deactivated if the current control of the traffic light system is based on the current signal schedule could have a significant negative impact on the transport system.
  • the concept advantageously enables a machine learning algorithm, for example, to optimize the traffic system.
  • the concept also allows the application of machine learning algorithms to be transferred to a large number of road junctions.

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  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Chemical & Material Sciences (AREA)
  • Analytical Chemistry (AREA)
  • Traffic Control Systems (AREA)
EP21151471.6A 2020-02-14 2021-01-14 Procédé de commande d'une installation de signalisation lumineuse Active EP3866135B1 (fr)

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
DE102020201878.2A DE102020201878A1 (de) 2020-02-14 2020-02-14 Verfahren zum Steuern einer Lichtsignalanlage

Publications (3)

Publication Number Publication Date
EP3866135A1 true EP3866135A1 (fr) 2021-08-18
EP3866135B1 EP3866135B1 (fr) 2024-03-06
EP3866135C0 EP3866135C0 (fr) 2024-03-06

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EP (1) EP3866135B1 (fr)
DE (1) DE102020201878A1 (fr)
PL (1) PL3866135T3 (fr)

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP1628274A1 (fr) * 2004-08-17 2006-02-22 Siemens Aktiengesellschaft Procédé et système de fourniture des informations sur l'état du trafic routier et de regulation du trafic routier
EP3425608A1 (fr) * 2017-07-03 2019-01-09 Fujitsu Limited Commande de signal de circulation utilisant de multiples catégories de q-learning

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE1516616A1 (de) 1965-12-17 1969-06-12 Peat Marwick Mitchell & Co System zur Steuerung von Verkehrssignalen
DE10146398A1 (de) 2001-09-20 2003-04-17 Siemens Ag System zum Steuern von Lichtsignalgebern an Kreuzungen

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP1628274A1 (fr) * 2004-08-17 2006-02-22 Siemens Aktiengesellschaft Procédé et système de fourniture des informations sur l'état du trafic routier et de regulation du trafic routier
EP3425608A1 (fr) * 2017-07-03 2019-01-09 Fujitsu Limited Commande de signal de circulation utilisant de multiples catégories de q-learning

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Publication number Publication date
DE102020201878A1 (de) 2021-08-19
EP3866135B1 (fr) 2024-03-06
PL3866135T3 (pl) 2024-07-22
EP3866135C0 (fr) 2024-03-06

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