EP3714445A1 - Procédé et dispositif de commande dynamique d'un système de signal lumineux - Google Patents

Procédé et dispositif de commande dynamique d'un système de signal lumineux

Info

Publication number
EP3714445A1
EP3714445A1 EP18807049.4A EP18807049A EP3714445A1 EP 3714445 A1 EP3714445 A1 EP 3714445A1 EP 18807049 A EP18807049 A EP 18807049A EP 3714445 A1 EP3714445 A1 EP 3714445A1
Authority
EP
European Patent Office
Prior art keywords
time
loss time
estimated
phase
vehicles
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.)
Withdrawn
Application number
EP18807049.4A
Other languages
German (de)
English (en)
Inventor
Robert Markowski
Robert Oertel
Jan Trumpold
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.)
Deutsches Zentrum fuer Luft und Raumfahrt eV
Original Assignee
Deutsches Zentrum fuer Luft und Raumfahrt eV
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Deutsches Zentrum fuer Luft und Raumfahrt eV filed Critical Deutsches Zentrum fuer Luft und Raumfahrt eV
Publication of EP3714445A1 publication Critical patent/EP3714445A1/fr
Withdrawn legal-status Critical Current

Links

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
    • G08G1/0112—Measuring and analyzing of parameters relative to traffic conditions based on the source of data from the vehicle, e.g. floating car data [FCD]
    • 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
    • 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
    • G08G1/04—Detecting movement of traffic to be counted or controlled using optical or ultrasonic detectors

Definitions

  • the invention relates to a method and a device for dynamically controlling a traffic signal system.
  • Traffic lights are often used to control road traffic nodes. These have as a primary task to keep the waiting and loss times of the road users by a skillful release time distribution low. Often for this purpose in Germany established control procedures such as fixed-time control or
  • Traffic lights without a fixed orbital period are usually controlled by rules.
  • the overall traffic situation at the node is at most on request loops for other phases in combination with a maximum waiting time or requirements by public transport with a.
  • Freewheelers thus achieve greater flexibility than systems with fixed-time control or a master plan, but almost no longer account for interactions at the network level. So they are only for isolated
  • framework plans can be used instead of fixed schedules. These are characterized by certain core release times, which are matched to each other as in the fixed-time control, that an unobstructed passage of the network section should be possible.
  • stretch areas are provided where the release can generally be stretched based on rule-based approaches. In this way, a limited traffic dependence can be established.
  • this approach has the disadvantage that the phases are generally stretched maximum and thus adjusts the behavior of a fixed-time control again.
  • a disadvantage compared to the fixed-time control is additionally in the poor coordination of the nodes with each other, since the sole vote of the core release times for an ideal coordination is not sufficient.
  • Independent-decentralized control methods try to detect interactions at the network level on the basis of local measured values.
  • the advantage here is that no communication infrastructure must be set up to neighboring plants or a central processing unit. This is especially important where many traffic lights are not integrated into a communication network and the construction of such a network would be possible only with great effort.
  • the disadvantage of the independent-decentralized control method is that only the local data is available. This can, for example, impair the detection of vehicle sparks and cause the traffic light system to react too late. With an extension of the detection radius, however, this problem could be counteracted.
  • the second method is based on self-organized decentralized systems.
  • adjacent traffic signal systems can communicate with each other and thus, for example, expand their virtual forecast horizon, exchange requirements or otherwise coordinate with each other and thus further optimize their local control. For this, however, a corresponding communication infrastructure must be present or created.
  • the third method is based on centrally organized systems. Here are the
  • the central processing unit usually carries out an optimization on the basis of the aggregated measured values of the individual nodes, which as a result delivers, for example, direct control commands or master plans for the individual nodes.
  • the problem here is the increased complexity of the system. This can lead to long delays during communication, which means that the system can only respond to local changes with a delay.
  • such a system is also difficult to introduce and expand, since individual components are virtually without function in themselves and new components must be incorporated into an existing, complex overall system.
  • the invention is based on the technical problem, a method and a
  • a method for dynamically controlling a traffic signal system wherein phases of the traffic signal system are controlled by means of a control on the basis of a loss time, wherein the loss time a
  • an apparatus for dynamically controlling a traffic signal system comprising a controller, wherein the controller is designed to control phases of the traffic signal system by means of a control based on a loss time, the control comprising a prediction device, wherein the loss time is a total loss time of all a detection radius is located, and wherein the predicting means is adapted to estimate the total loss time based on current and estimated future vehicle positions in the detection radius.
  • the basic idea of the invention is to dynamically control a traffic light system at a node on the basis of the total loss time of all vehicles detected in a detection radius.
  • current, but also future loss times arising in the current circulation of the phases of the individual vehicles are taken into account.
  • the respective loss time is estimated based on the current and future vehicle positions in the detection radius.
  • the advantage of the invention is that an efficient control of nodes, taking into account interactions at the network level, can be realized without static approaches.
  • the real-time-capable, model-based approach estimates the resulting total loss time at the node, and in this way one
  • the device is not by a fixed orbital period
  • a number of vehicles in a queue and a queue length are estimated. This allows a queue length to be included in the dynamic control. The queue length is always updated when another vehicle is added to the corresponding inflow to the hub.
  • a remaining release time of a phase and a release start of a phase following this phase are estimated on the basis of the number of vehicles in the queue. This estimation then forms the basis for the estimation of the respective loss times of the vehicles in the phases following the current phase. The longer the current phase lasts, the longer vehicles that are not allowed to drive, wait, etc.
  • a loss time for each vehicle located in the detection radius is estimated on the basis of the remaining release duration, wherein for the loss time of a vehicle a respective loss of waiting time, a
  • Waiting Loss Time refers to the time that a vehicle stops at a standstill
  • the reaction loss time is the time that the vehicle needs to react after starting a preceding vehicle.
  • Acceleration Loss Time is the time it takes for the vehicle to be brought to its final speed from a standstill.
  • a current phase is terminated when a total loss time estimated for a complete phase revolution with immediate termination of the current phase is less than one estimated at any other possible remaining release time for the complete phase revolution
  • Total loss of time This allows a flexible response to a changed state at the node and dynamic control of the traffic signal. If, for example, the number of vehicles changes in a currently not released inflow to the node, then the estimated total loss time can change depending on the considered time horizon. For example, was originally one
  • the remaining release time would be reduced to 9 seconds in the next second, etc.
  • this may result in a change in the estimated future loss time, since the newly added Vehicles with their lost time contribute to the total lost time. It may thus occur the situation that the originally estimated residual release duration of 10 seconds after another 5 seconds is not reduced to the remaining 5 seconds, but only to 1 second and after this second on
  • the vehicle positions are detected within the detection radius by means of suitable detectors.
  • suitable detectors all known methods can be used.
  • the traffic signal system may include suitable detectors for this purpose.
  • the vehicles transmit their respective vehicle position to the traffic light system within the detection radius. For this purpose, the
  • Traffic light system corresponding communication devices include, which receives the transmitted vehicle positions and supplies them to the controller.
  • the vehicle positions within the detection radius are detected and / or determined by means of floating car data and / or vehicle-to-X communication and / or camera data. This includes the
  • Traffic signal corresponding means for detecting and / or receiving the corresponding data.
  • Parts of the device may be formed individually or in a group as a combination of hardware and software, for example as program code executed on a microcontroller or microprocessor.
  • 1 shows a schematic representation of an embodiment of the device for the dynamic control of a traffic signal system
  • 2 shows a schematic representation of a time sequence of a prognosis of a phase revolution with three phases PO, P1, P2 for clarification of the method
  • FIG. 3 shows a schematic sequence for an arbitrary number of phases P1, P2, P, of a phase revolution to clarify the method
  • Fig. 1 is a schematic representation of an embodiment of the device 1 for dynamically controlling a traffic signal system 2 is shown at a node.
  • the device 1 comprises a controller 3 and a prediction device 4.
  • the controller 3 controls phases of the traffic signal system based on a loss time of the vehicles at the node.
  • the loss time is in this case a total loss time 21 of all vehicles located in a detection radius.
  • the device 1 current vehicle positions 10-x within a detection radius to the node or the traffic signal 2 are supplied.
  • the detection of the vehicle positions 10-x can take place, for example, by means of floating-car data and / or vehicle-to-X communication and / or camera data.
  • the device 1 may include suitable interfaces 5 for this purpose.
  • the prediction device 4 estimates current and future loss times of all vehicles located in the detection radius and supplies a current and estimated future total loss time 21 derived from these loss times to the controller 3. Based on the estimated current and future total loss time 21, the controller 3 controls the phases of the traffic signal system 2.
  • the controller 3 can, for example, a
  • Remaining release duration 22 of the current phase of the traffic signal system 2 control.
  • the dynamic control is based on three models. These models are a traffic model, a model of traffic signal 2 and a Loss of time model. For example, these models may be partially or completely implemented in the forecasting device 4 and / or the controller 3.
  • the traffic model models a behavior of the vehicles and different states of a vehicle. It uses a microscopic traffic model that looks at each vehicle individually. Each vehicle can assume only one of two states: “driving at maximum speed” (d), with the maximum
  • Traffic lights is limited to 70 km / h and should therefore be accessible to most vehicles. With this assumption, only the vehicle positions need to be known, not the (real) vehicle speeds.
  • Queues can only be established in front of a stop line of a traffic signal system. There is also the origin of a respective coordinate system of the tributaries. When a vehicle reaches the end of a queue, it is added to the queue. Queues are resolved at the beginning of the share belonging to the queue.
  • the length L q of a queue results from a number of vehicles in the queue N q , the vehicle length l ve h and the size of the gap l gap between the vehicles (as length specification):
  • An end of a queue is considered reached when a vehicle position S ve h, r is closer to the stop line in the model at the prediction time t than the queue end U: Syeh, t Lq (3)
  • Traffic signal system plays a decisive role.
  • Phase transitions here no release times. On the assumption that t rg is known, a beginning of the subsequent phase of the phase to be measured can be determined very precisely.
  • T g, veh corresponds to the average release time per vehicle, ie
  • T g , ve h 2 s (ie in each two seconds release time, a vehicle is degraded in a queue). It is limited downwards by the minimum release time T g , min and up by the maximum release time T g , m ax. N q corresponds to the queue of the corresponding phase i.
  • the background to this consideration is that in most cases it makes sense to at least reduce the existing queue.
  • the release time can be extended during the optimization of this phase, so that the release time with the given equation is estimated down to a certain extent. This yields the estimated start of release of a phase i + 1: + l - ti ⁇ " tg, i T ⁇ 0 [ ⁇ : ( ⁇ + 1)] (®)
  • FIG. 2 shows a schematic representation of a chronological sequence of a prognosis of a phase revolution with three phases PO, P1, P2 in order to illustrate the invention.
  • the individual phases PO, P1, P2 are interconnected by means of the transition times tu.
  • the transition periods take account of safety requirements, in particular that it must be ensured that pedestrians and vehicles have sufficient time to clear the lane or the junction after a change of phase.
  • the loss-time model is described below.
  • the loss time model is based on the states of the traffic model described above. Vehicles that travel at maximum speed (state “d") do not accumulate a loss time, ie in state "d" no loss time is incurred. In contrast, for every second that a vehicle lingers in state "w", one second of loss time is incurred. This results in the waiting time loss of a vehicle tv.ve h , which has a distance S veh, o , i to the stop line at which a queue with length L q and a remaining time t, until
  • t r indicates the reaction time of the vehicles to the respective predecessor in the queue.
  • a vehicle is in state "d" at the time of detection. If it is already in a queue at the given time, this is expressed by a remaining travel time of 0 s and an immediate transition to the state "w". However, this formula would give negative results and thus negative loss times when vehicles queue up to
  • the resulting loss time is derived as follows: The loss time tv , a or a time difference between a drive with maximum speed and an acceleration from standstill in this
  • Speed is given by the following equation: s a corresponds to the distance traveled during the acceleration process from standstill to v max : t a indicates the time until v max is reached: vmax
  • the total loss time for a given residual release time of a current phase then results as follows.
  • the process is shown schematically in FIG. 3 for the phases P1, P2, P.
  • a total loss time of all vehicles in the detection radius can be estimated using the models described in the previous section.
  • a schematic sequence of the method is shown in FIG.
  • each of the diagonal lines shown for the individual phases corresponds to a trajectory of a vehicle position 10-1,..., 10-7 with respect to a vertical time axis and a horizontal position axis.
  • the procedure now proceeds as follows: First, the start of release ti of the phase following the current phase is estimated according to equation (4). On the basis of the estimated start of release, it is possible to use the iteration above all in the
  • Detection radius located vehicles (step 101) of the considered phase with equations (1), (2) and (3) the number of vehicles in the queue and the queue length are estimated.
  • a resulting loss time tv. veh is calculated (step 102). Fall for the full queue
  • Approval start of the next phase are estimated (step 100), concomitantly again the queue length, the loss times and the release period. The process continues until a phase revolution is completed and estimated by equation (7), the re-release start of the current phase.
  • the estimated total loss time tv (t rg ) as a function of the remaining release time t rg then corresponds to the sum of all loss times calculated during the process described here.
  • the controller controls the timing of each phase of the traffic signal.
  • the control approach consists in comparing the total loss times resulting from the different residual release times and so on to determine an optimal residual release time. Since this is an expansion criterion, it is only interesting if the current phase should be aborted or not.
  • control receives the estimation of the forecasting device the
  • the biggest advantage of the device and the method is that the loss times of the vehicles are used as a direct decision variable. As a result, the loss time is not only used for quality assessment, but also directly for control.
  • the method and the device have the advantage that they have an often applied time-dependent, but fixed phase influence are designed traffic-dependent. This can be reacted directly to changing traffic conditions at the hub, which favors the flow of traffic.
  • the control is based on the current traffic situation and leads to a more effective release time allocation in the context of a phase revolution, since the weighting of a phase is directly related to the traffic volume. Phases with large traffic streams accumulate faster loss times and are considered by means of the described method and the device described rather than weak demand traffic streams, which then receive correspondingly low release times.
  • FIG. 4 shows results for the mean velocity of a simulation carried out by means of the method in comparison to the results of conventional methods.
  • the route includes five consecutive nodes.
  • the access roads or tributaries are each 500 m long.
  • the wait-time-optimal fixed-time control is calculated, for example, according to the manual for the design of road traffic facilities (Research Association for Roads and Transportation, FGSV Verlag, Cologne, 2015) as a standard method.
  • the nodes are evenly distributed once and unevenly. Accordingly, the fixed time and frame plan control coordinates once in both and once in only one direction.
  • the orbital period is prescribed for these two procedures with 60 s; in the regular distribution, the nodes are at the sub-point distance.
  • the networks are burdened with the utilization of 60%, 85% and 100%.
  • phase sequence is fixed and unchanging. It is the same for all tested approaches.
  • test vehicles only passenger cars are used.
  • the minimum permissible release time is 5 s, the maximum release time 90 s.
  • the constant, permissible maximum speed is 50 km / h.
  • the critical time gap for the time gap control is defined as 2 s. It is detected at detectors 20 m before the stop line of the corresponding access road.
  • Traffic intensity of the planning traffic strength simulate and check the flexibility of the procedures.
  • the simulation time is 1 h for each simulation run, whereby the measured values of the first hour are discarded because the first hour should only be used for grid filling.
  • Figure 4 shows the mean velocities in the network in the different scenarios compared to the conventional approaches.
  • the y-axis corresponds to the mean velocities in the network, the x-axis indicates the respective scenario. Shown are the individual quartiles, where the upper quartile is so small that it is in this
  • N1 denotes the network with regular node spacings without intersecting currents
  • N2 the network with irregular inter-node distances without intersecting currents
  • N3 and N4 are each those with intersecting currents.
  • the fixed-time control (FZS) works particularly well with less complex nodes with low to medium utilization. At heavy load, traffic often collapses and in some cases only medium
  • the master plan control is so flexible that it can absorb fluctuations relatively well. By coordinating the core release times on the coordinated line, the overall result is even higher
  • the time gap control works basically according to the principle of
  • control unit based on the described method (VZP)
  • the advantages of the time gaps and master plan control It is not bound to fixed round-trip times, but still brings with it the network problem into the controller. In this way, in all scenarios better results can be achieved by the controller based on total time lost than with the other methods.
  • the average speed in the network is here increased on average by 5% to 25% compared to the usual methods (see Table 1).
  • the described method and the described device can be used in the field of control systems of traffic signal systems.
  • the field of traffic-dependent traffic signal control here increasingly importance is attached, since there are significant potential for saving loss times, fuel and pollutant emissions by the road users.
  • Loss times is of particular interest for light signal manufacturers and municipalities, in order to be able to process the increasing volume of traffic in the future in an appropriate manner.

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  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Chemical & Material Sciences (AREA)
  • Analytical Chemistry (AREA)
  • Traffic Control Systems (AREA)

Abstract

L'invention concerne un procédé de commande dynamique d'un système de signal lumineux (2). Des phases (PO, P1, P2, Pi) du système de signal lumineux (2) sont commandées au moyen d'une commande (3) sur la base d'un temps de perte. Le temps de perte est un temps de perte total (21) de tous les véhicules se trouvant dans un rayon de détection. Le temps de perte total (21) est estimé par un moyen de prévision (4) sur la base des positions actuelles et futures estimées (10-x) des véhicules dans le rayon de détection. En outre, l'invention concerne un dispositif associé (1).
EP18807049.4A 2017-11-23 2018-11-19 Procédé et dispositif de commande dynamique d'un système de signal lumineux Withdrawn EP3714445A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DE102017221011.7A DE102017221011B4 (de) 2017-11-23 2017-11-23 Verfahren und Vorrichtung zum dynamischen Steuern einer Lichtsignalanlage
PCT/EP2018/081715 WO2019101676A1 (fr) 2017-11-23 2018-11-19 Procédé et dispositif de commande dynamique d'un système de signal lumineux

Publications (1)

Publication Number Publication Date
EP3714445A1 true EP3714445A1 (fr) 2020-09-30

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EP18807049.4A Withdrawn EP3714445A1 (fr) 2017-11-23 2018-11-19 Procédé et dispositif de commande dynamique d'un système de signal lumineux

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EP (1) EP3714445A1 (fr)
CN (1) CN111373454A (fr)
DE (1) DE102017221011B4 (fr)
WO (1) WO2019101676A1 (fr)

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CN112489456B (zh) * 2020-12-01 2022-01-28 山东交通学院 基于城市主干线车辆排队长度的信号灯调控方法及系统

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE10022812A1 (de) 2000-05-10 2001-11-22 Daimler Chrysler Ag Verfahren zur Verkehrslagebestimmung auf Basis von Meldefahrzeugdaten für ein Verkehrsnetz mit verkehrsgeregelten Netzknoten
DE102009033431B4 (de) 2009-07-15 2011-05-12 Deutsches Zentrum für Luft- und Raumfahrt e.V. Verfahren und Vorrichtung zur dynamischen Steuerung einer Signalanlage
DE102010027327B3 (de) 2010-07-15 2011-12-29 Deutsches Zentrum für Luft- und Raumfahrt e.V. Verfahren und Vorrichtung zur dynamischen Steuerung einer Signalanlage
DE102012214164B3 (de) 2012-08-09 2014-04-03 Deutsches Zentrum für Luft- und Raumfahrt e.V. Verfahren und Vorrichtung zur dynamischen Steuerung mindestens einer Lichtsignalanlage
US9830813B2 (en) * 2013-06-18 2017-11-28 Carnegie Mellon University, A Pennsylvania Non-Profit Corporation Smart and scalable urban signal networks: methods and systems for adaptive traffic signal control
DE102014206937A1 (de) 2014-04-10 2015-10-15 Deutsches Zentrum für Luft- und Raumfahrt e.V. Verfahren und Vorrichtung zur Steuerung von Verkehrsströmen an Knotenpunkten
US9349288B2 (en) * 2014-07-28 2016-05-24 Econolite Group, Inc. Self-configuring traffic signal controller
DE102014218848B4 (de) * 2014-09-19 2022-07-14 Deutsches Zentrum für Luft- und Raumfahrt e.V. Verfahren und Vorrichtung zur dynamischen Steuerung einer Signalanlage
US10297151B2 (en) * 2016-05-16 2019-05-21 Ford Global Technologies, Llc Traffic lights control for fuel efficiency

Also Published As

Publication number Publication date
DE102017221011B4 (de) 2022-11-03
CN111373454A (zh) 2020-07-03
WO2019101676A1 (fr) 2019-05-31
DE102017221011A1 (de) 2019-05-23

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