EP3991161A1

EP3991161A1 - Method for controlling a traffic system, device, computer program, and computer-readable storage medium

Info

Publication number: EP3991161A1
Application number: EP20797740.6A
Authority: EP
Inventors: Fritz Schinkel; Markus Kirsch
Original assignee: Fujitsu Technology Solutions Intellectual Property GmbH
Current assignee: Fujitsu Technology Solutions GmbH
Priority date: 2019-11-06
Filing date: 2020-10-27
Publication date: 2022-05-04
Also published as: JP7394219B2; US20220301426A1; JP2023500351A; DE102020116669A1; WO2021089367A1; US11948456B2

Abstract

The invention relates to a method (100) for controlling a traffic system (1) with a plurality of intersections (3) with switchable traffic light systems (5) and road sections (4) lying between the intersections (3). The method (100) has the following steps: detecting (101) traffic loads for multiple relevant road sections (4); determining (102) a local stress function for each relevant road section (4) on the basis of the detected traffic loads for each relevant road section (4); determining (103) a global stress function for the entire traffic system (1) on the basis of the local stress functions; determining (104) optimized switching times for the traffic light systems (5) of the intersections (3) lying on the relevant road sections (4) using a quantum concept processor (11), wherein the optimized switching times are determined such that the global stress function assumes the smallest discoverable value; and switching (105) the traffic light systems (5) according to a switching model which is based on the optimized switching times. The invention additionally relates to a corresponding device (7), a computer program, and a computer-readable storage medium.

Description

description

Method for controlling a traffic system, device, computer program and computer-readable storage medium

The present invention relates to a method for controlling a traffic system with a plurality of intersections with switchable traffic lights and road sections lying between the intersections. The invention also relates to a corresponding device, a computer program and a computer-readable storage medium.

Traffic on roads is increasing worldwide, especially in cities and metropolitan areas. Traffic jams, congested streets and creeping traffic are not only a significant loss of time for road users, but also increasingly contribute to air pollution and health problems for residents of the congested streets. The longer a vehicle is stuck in traffic, the more exhaust gases are released into the environment. Consequently, it would be desirable to avoid congested roads and traffic jams as much as possible.

The problem, however, is that traffic systems are becoming more and more complex and so simple control of traffic in a traffic system is becoming more and more difficult.

The object of the present invention is to describe a method, a device, a computer program and a computer-readable storage medium which solve or reduce the above-mentioned problem. The object is achieved by the features of the independent patent claims. Advantageous refinements are characterized in the subclaims.

According to a first aspect, a method for controlling a traffic system with a plurality of intersections with switchable traffic lights and road sections between the intersections comprises the following steps:

Recording of traffic loads of several relevant road sections,

Determination of a local stress function for each relevant road section as a function of the recorded traffic load of the relevant relevant road section,

Determining a global stress function for the entire transport system based on the local stress functions, determining, using a

Quantum concept processor, optimized switching times for the traffic light systems of the intersections adjacent to the relevant road sections, the optimized switching times being determined in such a way that the global stress function assumes the smallest value that can be found, and switching of the traffic light systems adjacent to the relevant road sections according to a switching model based on the optimized Switching times based.

It is advantageous here that, by minimizing the global stress function, switching times for the traffic light systems are determined, which enable the flow of traffic as smoothly and without congestion as possible. With the help of

With the quantum concept processor, the switching times for a large number of traffic light systems are simultaneously modulated in such a way that the global traffic system Stress function is as low as possible. With the method described here, using a quantum concept processor, the determination of the optimized switching times can be determined particularly quickly, so that a quick reaction to increased traffic volume is possible, and thus congestion and slow traffic are avoided or at least reduced.

For the traffic load, for example, vehicle densities in the relevant road sections are considered here. Furthermore, it is also possible, for example, to record vehicle types, vehicle sizes and other recordable data in terms of traffic load, which can have an influence on the traffic stress on the road sections.

Here, traffic stress and stress functions describe variables that are, for example, a measure of the congestion of a road section. For example, the stress function is calculated using a difference between currently available vehicles on a road section and a maximum number of vehicles that can be tolerated stress-free for the road section. Alternatively or additionally, values that are a measure of environmental pollution, such as exhaust gas values, can also be used to determine the stress function. The global stress function can be determined from the sum of all specific local stress functions.

In the present case, a quantum concept processor is a processor based on quantum algorithms for the accelerated implementation of optimization tasks. For example, this is a processor that is set up to do this is to solve an optimization problem using quantum annealing simulation. Such a processor can, for example, be based on conventional hardware technology, for example complementary metal-oxide-semiconductor (CMOS) technology. An example of such a quantum concept processor is the "Digital Annealer" from the company "FUJITSU". Alternatively, any other quantum processors can also be used for the method described here, in the future also those based on real quantum bit technologies. In other words, a quantum concept processor is a processor that uses the concept of minimizing a QUBO (Quadratic Unconstrained Binary Optimization) function realized either on a special processor in classic technology or on a quantum annealer.

The switching times for the traffic light systems are, for example, determined here between the red and green phases of the respective traffic light systems.

The smallest value of the global stress function that can be found is either a local or an absolute minimum of a corresponding stress function.

Anyone can do the relevant road sections

Be road sections of the transport system. Alternatively, however, the relevant road sections can also be only a part of the road sections of the traffic system, in particular if only a control of special traffic light systems is interesting or possible.

The traffic light systems are, for example, visual light signal systems with corresponding color signals (red / green) signal to a driver of a vehicle whether he has to stop at an associated intersection or whether he can pass it. Alternatively, however, the traffic light systems can also be other traffic light systems that are used to control a traffic flow. For example, it can be special traffic light systems that regulate a flow of traffic, for example with non-visual signals, in particular when predominantly or exclusively autonomous vehicles are used in the traffic system.

The switching model can, for example, be based directly on the specific switching times, i.e. each traffic light system is switched immediately according to the switching times that have been determined as optimized switching times. Alternatively, it is also possible to use a switching model that is based on these switching times, but also takes into account other functions, such as offsets of individual switching times or the like.

In at least one embodiment, the global stress function is defined as a term for a quadratic optimization, in particular as a quadratic Unconstrained Binary Optimization (QUBO) term.

It is advantageous here that such terms are particularly suitable for solving the optimization problem by means of a quantum concept processor.

In at least one embodiment, the determination of the local stress function is additionally carried out based on selection values of various possible green phases for traffic light systems adjacent to the respective relevant road section. Different possible green phases for the traffic light systems each describe the red-to-green ratio of the respective traffic light systems. Various possible green phases can accordingly be, for example: 40% green to 60% red; 50% green to 50% red; 70% green to 30% red - as well as any other division of the red and green times to one another. Adjacent traffic light systems are the traffic light systems located directly on the relevant road section, but can also include all traffic light systems that exist at the intersections adjacent to the relevant road section.

In at least one embodiment, the method further comprises the step:

- Reading in historical data of the traffic system, furthermore determining the local stress functions under

Taking into account the historical data is carried out.

It is advantageous here that the traffic system can also be controlled via the traffic system based on empirical values. For example, more precise local stress functions can be determined in this way. For example, the historical data is used to define a maximum traffic flow at each intersection, or to determine a value for each switching period that corresponds to the number of vehicles choosing a particular route. Furthermore, the historical data can be used to determine the traffic load for each switching period more precisely, or to concretize boundary conditions of the traffic system, for example how many vehicles appear on each road section located at the edge per switching period. Alternatively or additionally Periodic boundary conditions can be selected for such boundary conditions, ie it is assumed that the same number of vehicles leave the traffic system as new ones appear in the traffic system.

In at least one embodiment, the determination of the local stress functions, the determination of the global stress function, the determination of the optimized switching times, and the switching of the traffic light systems are repeated periodically and the optimized switching times are always determined for the next switching period. In at least one further embodiment, the acquisition of the traffic load is also repeated periodically as an alternative or in addition.

It is advantageous here that continuously optimized switching times can be determined and thus it is possible to react to changes in the traffic system. For example, these values are redetermined every 90 seconds. Alternatively, shorter or longer time intervals can also be selected, for example adapted to traffic times - such as, for example, rush hour traffic or holiday and public holiday traffic.

According to a second aspect, a device for controlling a traffic system with a plurality of intersections with switchable traffic lights and road sections located between the intersections comprises: at least one sensor that is set up to detect traffic loads of several relevant road sections, a computing unit that is set up to generate a local Stress function for each relevant road section depending on the recorded traffic loads of the to determine the relevant relevant road section and a global stress function for the entire traffic system based on the local stress functions, a quantum concept processor which is set up to determine optimized switching times for the traffic lights at the intersections adjacent to the relevant road sections, the optimized switching times being determined in such a way that the global stress function assumes a smallest value that can be found, and a switching device which is set up to switch the traffic light systems in accordance with a switching model, the switching model being based on the optimized switching times.

Suitable sensors here are, for example, sensors that are set up to record the traffic load on the relevant road sections continuously and in real time. In at least one embodiment, the computing unit is also set up to determine the local stress function for each relevant road section additionally as a function of the traffic load of the relevant relevant road section predicted for different switching times.

According to a third aspect, the invention is characterized by a computer program, the computer program comprising instructions which, when the program is executed by a computer arrangement, cause the computer arrangement to carry out the method according to the first aspect. According to a fourth aspect, the invention is characterized by a computer-readable storage medium comprising a computer program according to the third aspect.

Refinements of the first aspect can also be present in accordance with the second, third and fourth aspects and have corresponding effects, and vice versa.

Embodiments of the invention are explained in more detail below with reference to the schematic drawings.

Show it:

Figure 1 is a schematic representation of a traffic system,

FIG. 2 shows a flow diagram of a method for controlling the traffic system according to FIG. 1, and

FIG. 3 shows a schematic representation of a device for controlling the traffic system according to FIG. 1.

FIG. 1 shows a schematic representation of a traffic system 1. The traffic system 1 is shown in a greatly simplified manner in order to simplify the description of the invention disclosed here. However, this greatly simplified representation is not intended to represent a limitation of the invention.

The traffic system 1 comprises several streets 2. The streets 2 run both in an east-west direction and in a north-south direction. Every meeting of two streets 2 represents an intersection 3. The intersections 3 are numbered for the purpose of mathematical description, "n" denotes the intersections 3 in the west-east direction, "m" the intersections 3 in the south-north direction. The west-east direction corresponds to the x-direction of the coordinate system shown in FIG. 1, the south-north direction corresponds to the y-direction of the coordinate system, "n" runs from 0 to N - 1, where N represents the total number of streets 2 running in south-north direction y, "m" runs from 0 to M - 1, where M represents the total number of streets 2 running in west-east direction x.

The roads 2 are formed by road sections 4 between the intersections 3. At each intersection 3, four incoming road sections 4 arrive and four outgoing road sections 4 exit. The incoming and outgoing road sections 4 are numbered according to their orientation:

The road sections 4 can now each be described as an incoming or outgoing road section 4 of an intersection 3 or as an outgoing or incoming road section 4 of a correspondingly adjacent intersection 3. "ModN" and "modM" are used here to denote periodic boundary conditions.

For the description of the method according to the invention and the device according to the invention, the outgoing road sections 4 are each used below. Alternatively, an equivalent consideration of the incoming road sections 4 would of course also be possible.

For the purpose of a simpler description, a switchable traffic light system 5, which communicates with road users by means of light signals, is located at each intersection 3 in the exemplary embodiment shown. On the road sections 4 there are vehicles 6 that drive on the roads 2 and pass the traffic lights 5 or stop at them.

In the _{exemplary embodiment shown here, a traffic load l n} , _m , _d (t) denotes a number of vehicles 6 on an outgoing road section 4 “from _n , _m , _d ” at a point in time t.

The following auxiliary functions are defined for discrete steps along roads 2 in traffic system 1 in west-east direction x or south-north direction y: xd: {0,1,2,3} -> {- 1,0,1}; xd (0) = 1, xd (1) = 0, xd (2) = - 1, xd (3) = 0 yd: {0, 1, 2, 3} -> {- !, 0, 1}; yd (0) = 0, yd (l) = l, yd (2) = 0, yd (3) = - l xd (0) represents a step in the east direction, yd (3) represents a step in the south direction , i.e. in the negative y-direction, etc. A global stress function S, which supplies a value for an overload of the traffic system 1, is the sum of local stress functions f of the individual road sections 4. The global stress function S can be defined as: Here, l _{n, m, d} (t) is the traffic load and f _n , _m , _d are the local stress functions of the road sections 4, the position of which is characterized by the indices n and m, and the direction of which is characterized by the index d. The dependency of the respective local stress function is chosen here to simplify the representation. The method can take into account several different parameters that can be assigned to the respective roads, for example the currently drivable speed and / or the current CO ₂ emissions, in order to determine a stress function.

For the sake of simplicity, in the present example the local stress functions f are defined as:

Here, V _{R is} a constant that corresponds to a maximum number of vehicles 6 that can be on a specific road section 4 without excessive traffic on the respective road section 4, which could lead to traffic jams or slow-moving traffic. In other words: V _R is the maximum number of vehicles 6 that can be located on a specific road section 4 without stress. For the sake of simplicity, the _{A constant V R is} assumed for all road sections 4 shown in the exemplary embodiment shown.

The local stress functions f can be designed as complex as required and can be set up for the traffic system 1 depending on the needs and requirements of a desired traffic optimization (e.g. reduction of traffic jams, reduction of exhaust gas concentrations, etc.). In addition to traffic load, the stress function can also depend on many other influencing variables, such as traffic throughput, exhaust gas emissions, noise development, etc. For example, the local stress functions f can be adapted to real conditions in a real traffic system, for example by using road-specific threshold values and progressive functions.

The definition used here for the local stress functions f supplies a value 0 as long as the number of vehicles 6 on the road section 4 is below the constant V _R. If the number of vehicles 6 on the road section 4 is above the constant V _R , the local stress increases as the number of vehicles 6 increases.

In the present case, the global stress function is then defined as:

Here, only the outgoing road sections 4 at each intersection 3 are taken into account, since otherwise, due to the summation over all intersections 3 and all directions d, all road sections 4 would be evaluated twice. For the purpose of an easily understandable description, it is also assumed here that all traffic light systems 5 have a common clock cycle and that the influence of phase shifts between the traffic light systems 5 is neglected. Alternatively, phase shifts between the clock cycles of the traffic light systems 5 and / or different clock cycles can of course also be taken into account.

A portion of a green phase λ _{n, m} in a cycle time T _{P of} a special traffic light system 5 is modeled, for example, in R steps r, in this case r is a natural number from 0 to Rl, where R is the total number of steps r. The cycle time T _{P of} a traffic light system 5 is, for example, the time, in seconds, from the start of a red phase to the start of the next red phase of the traffic light system 5. In the embodiment shown here, a fixed cycle time T _{P is} assumed, which is also used for the purpose of a simple Description, for all traffic lights 5 is clocked together. Alternatively, the cycle time T _P can also vary for the individual traffic light systems 5 or can additionally be optimized using the method shown here. For this purpose, the cycle time T _{P could} also be taken into account via the local stress functions f _{n, m, d.}

The green phase λ _{n, m} is defined as λ _nm = - and gives the

Proportion of the cycle time T _P for which the traffic light system 5 of a certain intersection 3 in the west-east direction x is switched to green. Furthermore, T _{c is} a so-called clearance time, which specifies in seconds how much time elapses between switching over the traffic light system 5 and clearing the associated intersection 3. T _T is a traffic time that indicates the time in seconds during which vehicles 3 actually drive Junction 3 can pass. The traffic time T _{T is} calculated from: T _T - = T _P - 2T _c . Furthermore, a traffic flow F indicates how many vehicles 6 can pass an intersection 3 in a direction d during a green phase per second.

The traffic load 1 of a special road section 4 in west-east direction x for a next point in time t + 1 then results from the current traffic load 1 on this road section 4 at time t, that is to say at the next round trip time T _P plus incoming traffic from an adjacent road section 4, and minus an outgoing traffic to another neighboring road section 4:

The incoming and outgoing traffic is defined here as a minimum function, whereby either the total incoming or outgoing traffic load 1 is taken into account if this is less than the maximum possible incoming or outgoing traffic via the respective traffic light system 5, or otherwise the maximum possible incoming or outgoing traffic outbound traffic.

The traffic load 1 and consequently the local stress function f of a road section 4 thus depends on which values for the green phase λ _{nm of} an intersection n, m adjacent to the road section 4 and which values for the green phase λ _{(n + 1) modN, m} a adjacent intersection n + 1, m adjacent to the road section 4 can be selected. If r _{c is} the value for r of a green phase A _nm with respect to a central intersection and r _{0 is} the value for r one

Green phase λ _{(-n + 1) modN, m} with respect to an intersection adjacent to the central intersection, we get:

With r ₀ and r _c , the traffic load 1 on road section 4 starting at intersection n, m in east direction x at time t + 1 results as follows:

In general, for all directions d, this term can be written as follows:

The local stress function for an outgoing road section 4 from the intersection 3 with the indices "n, m" in direction d at time t + 1 is then: The local stress functions f shown here are based, for the purpose of an easily understandable description, on relatively simple assumptions with regard to the traffic system 1. However, the local stress functions f can be expanded as desired and represented as complex as desired, in particular to improve adaptation to real traffic systems. For this purpose, historical data, for example, can also be taken into account for the local stress functions f, which are collected, for example, via statistical evaluations with regard to the traffic system 1 or by means of artificial intelligence methods. A constant adaptation of the local stress functions f, for example based on such historical data at runtime, is also possible.

All possible values for the green phases 1 can then be represented in a bit model. Is a specific value for selected for a special traffic light system 5, a corresponding bit x _n , _m , _r = 1. If another value is selected for the traffic light system 5, then x _n , _m , _r = 0.

For each traffic light system 5, however, exactly one value must be selected for the green phase l, ie exactly one of the bits x _n , _m , _r (r = 0, 1, ..., R - 1) must be equal to 1, while the others are 0. This is the case when the following Ho becomes minimal:

In this case, Ho is minimal at Ho = 0. In order to minimize the global stress of the traffic system 1, those must then be selected for which the following Hi or H becomes minimal:

Using FIGS. 2 and 3, the following describes how this optimization problem can be solved according to the invention.

FIG. 2 shows a flow chart of a method 100 for controlling the traffic system 1 according to FIG. 1.

In a first step 101, traffic loads 1 of road sections 4 are recorded. In the exemplary embodiment shown here, traffic loads are recorded for all road sections 4 of the traffic system 1. In an alternative exemplary embodiment, only traffic loads of relevant road sections 4, ie those road sections 4 for which an optimization of the traffic system 1 is to be carried out, can also be recorded or taken into account. The traffic loads 1 are recorded for example by means of road sensors, floating phone data (FPD) or floating car data (FCD). Additionally or alternatively, historical data of the traffic system 1, ie empirical values from previous measurements or other values available with regard to the traffic of the traffic system, can also be used to record the traffic loads 1.

In a second step 102, a local stress function f is determined for each road section 4 as a function of the recorded traffic loads 1 of the respective road section 4. For the determination of the local stress function f of a road section 4, current switching times of traffic light systems 5 at intersections 3 adjacent to this road section 4 can also be taken into account. In other words, it can be taken into account how many vehicles 6 will enter the road section 4 under consideration in a next switching cycle and how many vehicles 6 will leave it.

In a third step 103, a global stress function S is created for the entire traffic system 1 based on the local stress functions + 1) for all possible proportions of the

Green phases determined at the entering and exiting intersections. The global stress function S is a measure of a congestion or overloading of the traffic system 1. A congestion of fewer road sections 4 provides a higher overall global stress value than a distribution of vehicles 6 with the maximum possible stress-free number of vehicles 6 in the road sections 4 of the transport system 1 does not are exceeded, even if, in the second case, a total of more vehicles 6 are traveling in the traffic system 1.

In a fourth step 104, using a quantum concept processor, optimized switching times, that is to say optimized lengths of green phases 1 for the traffic lights 5 of the intersections 3, are determined. This is done by minimizing the function H, which in part Hi represents the global stress under the respective decision for the green portions at all traffic lights in the network. The optimized switching times are determined in such a way that the global stress function S assumes a smallest value that can be found. In other words: an optimization problem for the global stress function S is solved, the solution of the optimization problem taking into account the traffic system 1 in its entirety and not merely regulating switching times for traffic lights 5 of individual intersections 3 independently of one another. With the method 100 shown here, optimized switching times for all (or all relevant) traffic light systems 5 are determined simultaneously, and the best possible system state for the entire traffic system 1, i.e. a system state with the lowest possible global stress, is thus determined.

In the traffic system 1 shown here, there is only one type of traffic light at each intersection 3. With the method 100 shown here, however, different traffic light types at each intersection 3 can also be taken into account. For example, in addition to the traffic lights 5, turning lights or the like can also be present at all or some of the intersections 3. In order to optimize the green phases l, 3 different values for r can be set for different types of traffic lights at an intersection to get voted. These different values r then depend on one another, for example.

In a fifth step 105, the traffic light systems 5 are switched according to a switching model which is based on the optimized switching times. The switching model can, for example, be based directly on the optimized switching times, i.e. each traffic light system 5 is switched immediately according to the optimized switching times. Alternatively, it is also possible to use a switching model that is based on these optimized switching times, but also takes into account, for example, offsets, intermediate states such as yellow phases, additional traffic flows such as crossing trams or turning lanes, or the like. Such additions can also be optimized with the method 100 shown here.

The method 100 shown here is carried out periodically, for example, parallel to ongoing operation of the traffic system 1. In this way, switching times for the traffic light systems 5 that are always adapted to the current volume of traffic can be determined. For example, the method 100 is carried out after a certain time has elapsed, for example every 90 seconds, or at each cycle time T _P for a subsequent cycle time.

This cycle time T _P can be predetermined for all traffic light systems 5, or there can be individual cycle times T _P for different traffic light systems 5. Alternatively or additionally, the method 100 can also be carried out dynamically, for example as a function of a traffic volume or a global stress value in the traffic system 1. The cycle time T _P can also, in addition or as an alternative to the green phases I, be optimized. In this case, bits for the cycle times T _P for each relevant intersection 3 must be added to the functions to be optimized, or the bits described above must be replaced with these. The cycle times T _P can also be taken into account for the local stress functions f and thus, in particular, are included in the future local stress f (t + l).

In addition, circuit phases, ie offsets between cycle times T _{P of} different traffic light systems 5, can be optimized in addition or as an alternative to green phases 1 and cycle times T _P. In this case, bits for the circuit phases for each relevant intersection 3 must be added to the functions to be optimized, or the bits described above must be replaced with these. The circuit phases can also be taken into account for the local stress functions f and thus, in particular, are included in the future local stress f (t + l).

FIG. 3 shows a schematic representation of a device 7 for controlling the traffic system 1 according to FIG. 1.

The device 7 comprises sensors 8 with which traffic loads 1 of the road sections 5 can be detected. The sensors 8 are, for example, road sensors, sensors for collecting floating phone data (FPD) or sensors for collecting floating car data (FCD).

The device 7 further comprises a computing unit 9 with which the local stress functions f for each road section 4 as a function of the recorded traffic loads 1 des respective road section 4 can be determined. Furthermore, the computing unit 9 can determine a global stress function S for the entire traffic system 1 based on the local stress functions f. A conventional computer is used as the arithmetic unit 9, for example. The computing unit 9 is connected to a network 10, for example the Internet.

The device 7 further comprises a

Quantum concept processor 11, which is set up to determine optimized switching times for the traffic light systems 5, the optimized switching times being determined in such a way that the global stress function S assumes a smallest value that can be found.

In the present case, a processor is used as the quantum concept processor 11, for example, which is set up to solve an optimization problem by means of quantum annealing simulation. Such a quantum concept processor 11 can, for example, be based on conventional technology, for example complementary metal-oxide-semiconductor (CMOS) technology. As an alternative, any other can be used for the device 7

Quantum concept processors 11, in the future also those based on real quantum bit technologies, will be used.

The quantum concept processor 11 is also connected to the network 10. The computing unit 9 is set up to send the global stress function S to the quantum concept processor 11 via the network 10. The quantum concept processor 11 then sends the determined optimized switching times back to the computing unit 9 via the network 10. The device 7 also includes a switching device 12 which is set up to switch the traffic light systems 5 in accordance with a switching model, the switching model being based on the optimized switching times. The switching device 12 is connected here to the computing unit 9, so that the computing unit controls the switching device 12 based on the optimized switching times.

List of reference symbols

1 traffic system

2 street 3 intersection

4 section of road

5 traffic lights

6 vehicle 7 device 8 sensor

9 arithmetic unit

10 network 11 quantum concept processor 12 switching device x west-east direction y south-north direction

100 Procedure 101 105 Steps

Claims

1. A method (100) for controlling a traffic system (1) with a plurality of intersections (3) with switchable traffic lights (5) and road sections (4) lying between the intersections (3), the method (100) comprising the following steps:

Detection (101) of traffic loads of several relevant road sections (4),

Determining (102) a local stress function for each relevant road section (4) as a function of the recorded traffic load of the respective relevant road section (4),

Determining (103) a global stress function for the entire transport system (1) based on the local stress functions,

Determining (104), using a quantum concept processor (11), optimized switching times for the traffic light systems (5) of the intersections (3) adjacent to the relevant road sections (4), the optimized switching times being determined in such a way that the global stress function can find a smallest one Assumes value, and

Switching (105) the traffic light systems (5) according to a switching model which is based on the optimized switching times.

2. The method (100) according to claim 1, wherein the global stress function is defined as a term of a quadratic optimization, in particular as a QUBO (Quadratic Unconstrained Binary Optimization) term.

3. The method (100) according to any one of claims 1 or 2, wherein the smallest value that can be found is the global stress function is the local or absolute minimum of the global stress function.

4. The method (100) according to any one of claims 1 to 3, wherein the determination (102) of the local stress function is additionally carried out based on selection values of various possible green phases for traffic light systems (5) adjacent to the relevant road section (4).

5. The method (100) according to any one of claims 1 to 4, further comprising the step:

- Reading in historical data of the traffic system (1), the determination (102) of the local stress functions also being carried out taking into account the historical data.

6. The method (100) according to any one of claims 1 to 5, wherein the determination (102) of the local stress functions, the determination (103) of the global stress function, the determination (104) of the optimized switching times, and the switching (105) of the traffic light systems (5) is repeated periodically and the optimized switching times are always determined for the next switching period.

7. The method (100) according to claim 6, further comprising periodically repeating the detection (101) of the traffic loads.

8. The method (100) according to any one of claims 1 to 7, wherein a number of vehicles (6) on the relevant relevant road section (4) is recorded for the recording (101) of the traffic loads.

9. The method (100) according to any one of claims 1 to 8, wherein current switching times of the traffic light systems (5) of intersections (3) adjacent to the relevant road section (4) are also taken into account for determining (102) the local stress function.

10. Device (7) for controlling a traffic system (1) with a plurality of intersections (3) with switchable traffic lights (5) and road sections (4) lying between the intersections (3), the device (7) comprising: at least one sensor (8) which is set up to record traffic loads of several relevant road sections (4), a computing unit (9) which is set up to generate a local stress function for each relevant road section

(4) as a function of the recorded traffic loads of the relevant road section (4), and to determine a global stress function for the entire traffic system (1) based on the local stress functions, a quantum concept processor (11) which is set up to optimize switching times for the traffic light systems

(5) to determine the intersections (3) adjacent to the relevant road sections (4), the optimized switching times being determined in such a way that the global stress function assumes a smallest value that can be found, and a switching device (12) which is set up for the traffic light systems (5) to switch according to a switching model, the switching model being based on the optimized switching times.

11. The device (7) according to claim 10, wherein the quantum concept processor (11) is a processor for is set up to solve an optimization problem using quantum annealing simulation.

12. Computer program, the computer program comprising instructions which, when the program is executed by a computer arrangement, cause the computer arrangement to carry out the method (100) according to one of claims 1 to 9.

13. Computer-readable storage medium on which the

Computer program according to claim 12 is stored.