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

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

[0001] The present invention relates to a method for controlling a traffic system with a plurality of intersections with switchable traffic lights and road sections located between the intersections. The invention further relates to a corresponding device, a computer program, and a computer-readable storage medium.

[0002] Road traffic is increasing worldwide, especially in cities and metropolitan areas. Congestion, gridlock, and slow-moving traffic not only represent a significant loss of time for road users but also increasingly contribute to air pollution and health problems for residents living along congested streets. The longer a vehicle is stuck in traffic, the more exhaust fumes are released into the environment. Consequently, it would be desirable to avoid congested roads and traffic jams in transportation systems as much as possible.

[0003] The problem, however, is that traffic systems are becoming increasingly complex, making simple traffic control within a traffic system increasingly difficult.

[0004] WO 2018 / 224872 A1 concerns a traffic control procedure and system. It expands the scope and capabilities of real-time monitoring of traffic characteristics and uses artificial intelligence to predict and / or detect traffic disruptions, as well as to determine corrective actions to be implemented by the relevant available mechanisms. These corrective actions are then initiated by providing suitable data and instructions to the selected mechanisms and systems using compatible interfaces provided for this purpose.

[0005] US Patent 2019 / 0164418 A1 concerns a method and system for maximizing traffic flow. To control traffic flow, a combination of classical machine learning is used to predict traffic flow minimization before it occurs, along with quantum annealing to optimize future vehicle positions. Vehicles are rerouted to minimize travel time for each vehicle, taking into account other vehicles on the road network.

[0006] DE 102008050822 relates to a traffic control system with online optimization of the control parameters of signal plans for traffic signal systems, a method for its implementation and a computer program product.

[0007] The object of the present invention is to describe a method, a device, a computer program, and a computer-readable storage medium that solve or mitigate the above-mentioned problem.

[0008] The problem is solved by the features of the independent patent claims. Advantageous embodiments are characterized in the dependent claims.

[0009] According to a first aspect, a method for controlling a traffic system with a plurality of intersections with switchable traffic light systems and road sections located between the intersections comprises claim 1.

[0010] The advantage here is that, by minimizing the global stress function, switching times for the traffic lights can be determined that enable the smoothest possible and most congestion-free traffic flow. Using the quantum concept processor, the switching times for a large number of traffic lights are modulated simultaneously in such a way that the global stress function is minimized across the entire traffic system under consideration. With the method described here, using a quantum concept processor, the optimized switching times can be determined particularly quickly, allowing for a rapid response to increased traffic volumes and thus preventing or at least reducing congestion and stop-and-go traffic.

[0011] For traffic capacity analysis, factors such as vehicle density on relevant road sections are considered. Furthermore, it is also possible to record vehicle types, vehicle sizes, and other measurable data related to traffic capacity that may influence traffic stress on these road sections.

[0012] Traffic stress and stress functions describe quantities that, for example, measure the congestion of a road segment. For instance, the stress function is calculated as the difference between the number of vehicles currently on a road segment and the maximum number of vehicles that can be tolerated without causing stress on that segment. Alternatively or additionally, values ​​that measure environmental pollution, such as exhaust emissions, can also be used to determine the stress function. The global stress function can be determined by summing all the determined local stress functions.

[0013] In this context, a quantum concept processor is defined as a processor based on quantum algorithms for the accelerated execution of optimization tasks. For example, this is a processor designed to solve an optimization problem using quantum annealing simulation. Such a processor can be based on conventional hardware technology, such as 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 processor, including those based on true qubit technologies, can be used for the method described here.In other words, a quantum concept processor is a processor that implements the concept of minimizing a QUBO (Quadratic Unconstrained Binary Optimization) function, either on a special processor using classical technology or on a quantum annealer.

[0014] The switching times for the traffic light systems are determined, for example, by the ratios of red to green phases of the respective traffic light systems.

[0015] The smallest detectable value of the global stress function is either a local or an absolute minimum of a corresponding stress function.

[0016] The relevant road segments can be all road segments of the traffic system. Alternatively, the relevant road segments can also be only a subset of the road segments of the traffic system, particularly if only the control of specific traffic light systems is of interest or possible.

[0017] Traffic light systems are, for example, visual signaling systems that use corresponding color signals (red / green) to indicate to a driver whether they must stop at an intersection or can proceed. Alternatively, traffic light systems can also be other types of systems used to control traffic flow. For example, they can be specialized traffic light systems that regulate traffic flow using non-visual signals, particularly in traffic systems where autonomous vehicles are predominantly or exclusively used.

[0018] The switching model can, for example, be based directly on the specified switching times, meaning each traffic light is switched immediately according to the switching times that have been determined as optimized. Alternatively, it is also possible to use a switching model that is based on these switching times but additionally takes into account other functions, such as offsets of individual switching times or similar.

[0019] According to this invention, the global stress function is defined as a term for quadratic optimization, in this case as a Quadratic Unconstrained Binary Optimization (QUBO) term.

[0020] The advantage here is that such terms are particularly suitable for solving the optimization problem using a quantum concept processor.

[0021] 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 located on the respective relevant road section.

[0022] The various possible green phases for the traffic lights each describe the red-to-green ratio of the respective traffic lights. Examples of possible green phases are: 40% green to 60% red; 50% green to 50% red; 70% green to 30% red – as well as any other distribution of red and green times. Adjacent traffic lights are those located directly on the relevant road segment, but can also include all traffic lights at intersections adjacent to that segment.

[0023] In at least one embodiment, the procedure also includes the following step: Reading in historical data of the transport system, Furthermore, the determination of the local stress functions is carried out taking into account the historical data.

[0024] An advantage of this approach is that the traffic system can also be controlled based on historical data. For example, more precise local stress functions can be determined. Historical data can be used, for instance, to define a maximum traffic flow at each intersection or to determine a value for each switching cycle that corresponds to the number of vehicles choosing a specific route. Furthermore, historical data can be used to determine the traffic load more accurately for each switching cycle or to specify the boundary conditions of the traffic system, such as how many vehicles appear on each peripheral road segment per switching cycle. Alternatively or additionally, periodic boundary conditions can be chosen for such conditions.The assumption is made that the same number of vehicles leave the traffic system as new ones enter the traffic system.

[0025] 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 lights are repeated periodically, and the optimized switching times are always determined for the next switching period. In at least one further embodiment, the recording of traffic loads is also repeated periodically, either alternatively or additionally. The advantage here is that continuously optimized switching times can be determined, thus enabling a response to changes in the traffic system. For example, these values ​​are recalculated every 90 seconds. Alternatively, shorter or longer time intervals can be chosen, for example, adapted to traffic times—such as rush hour or holiday traffic.

[0026] According to a second aspect, a device for controlling a traffic system with a plurality of intersections with switchable traffic light systems and road sections located between the intersections comprises claim 9.

[0027] Suitable sensors for this purpose include those designed to continuously and in real time record the traffic volumes of the relevant road segments. In at least one configuration, the processing unit is further configured to determine the local stress function for each relevant road segment based on the traffic volumes predicted for different switching times.

[0028] According to a third aspect, the invention is characterized by a computer program according to claim 11, wherein the computer program comprises 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.

[0029] According to a fourth aspect, the invention is characterized by a computer-readable storage medium according to claim 12, comprising a computer program according to the third aspect.

[0030] Embodiments of the first aspect can also be present and exhibit corresponding effects of the second, third, and fourth aspects, and vice versa. Exemplary embodiments of the invention are explained in more detail below with reference to the schematic drawings. They show:

[0031] Figure 1 is a schematic representation of a traffic system, Figure 2 is a flowchart of a procedure for controlling the traffic system according to Figure 1 , and Figure 3 a schematic representation of a device for controlling the traffic system according to Figure 1 .

[0032] Figure 1 Figure 1 shows a schematic representation of a traffic system 1. For the sake of simplicity in describing the invention disclosed herein, traffic system 1 is shown in a highly simplified form. However, this highly simplified representation is not intended to limit the invention.

[0033] The traffic system 1 comprises several roads 2. The roads 2 run both in an east-west direction and in a north-south direction. Every meeting of two roads 2 constitutes an intersection 3.

[0034] The 3 intersections are numbered for mathematical description purposes. "n" denotes the 3 intersections in a west-east direction, "m" the 3 intersections in a south-north direction. The west-east direction corresponds to the x-direction of the Figure 1 In the coordinate system shown, the south-north direction of the y-direction of the coordinate system. "n" runs from 0 to N - 1, where N represents the total number of roads running in the south-north direction y. "m" runs from 0 to M - 1, where M represents the total number of roads running in the west-east direction x.

[0035] Between the intersections 3, the roads 2 are formed by road segments 4. At each intersection 3, four incoming road segments 4 arrive and four outgoing road segments 4 depart. The incoming and outgoing road segments 4 are numbered according to their orientation: Eastbound (positive x-direction): 0 North direction (positive y-direction): 1 Westbound (negative x-direction): 2 South-facing (negative y-direction): 3

[0036] 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. aus n , m , 0 = ein n + 1 modN , m , 2 aus n , m , 1 = ein n , m + 1 modM , 3 aus n , m , 2 = ein n − 1 modN , m , 0 aus n , m , 3 = ein n , m − 1 modM , m , 1

[0037] Here, "modN" and "modM" denote periodic boundary conditions.

[0038] In the following description of the method and the device according to the invention, reference will be made to the outgoing road sections 4. An equivalent consideration of the incoming road sections 4 would, of course, also be possible.

[0039] For the sake of simplicity, in the illustrated embodiment, each intersection 3 is equipped with a switchable traffic light system 5, which communicates with road users by means of light signals. Vehicles 6 are located on the road sections 4, traveling on the roads 2 and passing the traffic light systems 5 or stopping at them.

[0040] In the embodiment shown here, a traffic load ln,m,d (t) denotes a number of vehicles 6 on an outgoing road section 4 "from n,m,d" at a time t.

[0041] The following auxiliary functions are defined for discrete steps along the roads 2 in the 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}->{-1,0,1}; yd(0)=0, yd(1)=1, yd(2)=0, yd(3)=-1 xd(0) represents, for example, a step in the east direction, yd(3) represents a step in the south direction, i.e. in the negative y-direction, etc.

[0042] A global stress function S, which provides a value for the overload of traffic system 1, is the sum of the local stress functions f of the individual road segments 4. The global stress function S can be defined as: S t = ∑ n = 0 N − 1 ∑ m = 0 M − 1 ∑ d = 0 3 f n , m , d l n , m , d t Here, ln,m,d(t) represents the traffic load, and fn,m,d are the local stress functions of road segments 4, whose position is characterized by the indices n and m, and whose direction by the index d. The dependence of the respective local stress function is chosen here for the sake of simplicity. The method can consider several different parameters assignable to the respective roads, e.g., the current drivable speed and / or the current CO₂ emissions, to determine a stress function.

[0043] For the sake of simplicity, in the present example the local stress functions f are defined as: f n , m , d l n , m , d : = max 0 , l n , m , d − V R 2

[0044] Here, VR is a constant representing the maximum number of vehicles 6 that can be on a specific road segment 4 without causing excessive traffic congestion that could lead to a traffic jam or slow-moving traffic. In other words, VR is the maximum number of vehicles 6 that can be on a particular road segment 4 without causing stress. For simplicity, in the illustrated embodiment, a constant VR is assumed for all road segments 4.

[0045] The local stress functions f can be designed to be arbitrarily complex and can be formulated for traffic system 1 according to the needs and requirements of a desired traffic optimization (e.g., reducing congestion, reducing exhaust concentrations, etc.). In addition to traffic load, the stress function can also depend on many other influencing factors, such as traffic throughput, exhaust emissions, noise levels, etc. For example, the local stress functions f can be adapted to real-world conditions in a real traffic system, for instance, by using road-specific thresholds and progressive functions.

[0046] The definition used here for the local stress functions f yields a value of 0 as long as the number of vehicles 6 on road segment 4 is less than the constant VR. If the number of vehicles 6 on road segment 4 is greater than the constant VR, the local stress increases with an increasing number of vehicles 6.

[0047] In this case, the global stress function is defined as: S t = ∑ n = 0 N − 1 ∑ m = 0 M − 1 ∑ d = 0 3 max 0 , l n , m , d t − V R 2

[0048] Only the outgoing road sections 4 at each intersection 3 are taken into account, because otherwise, due to the summation over all intersections 3 and all directions d, all road sections 4 would be counted twice.

[0049] For the sake of clarity, it is further assumed here that all traffic light systems 5 have a common cycle time and that the influence of phase shifts between the traffic light systems 5 is neglected. Alternatively, phase shifts between the cycle times of the traffic light systems 5 and / or different cycle times can of course also be taken into account.

[0050] The proportion of a green phase λ n,m to the cycle time TP of a specific traffic light system 5 is modeled, for example, in R steps r. Here, r is a natural number from 0 to R-1, where R is the total number of steps r. The cycle time TP of a traffic light system 5 is, for example, the time, in seconds, from the beginning of one red phase to the beginning of the next red phase of the traffic light system 5. In the embodiment shown here, a fixed cycle time TP is assumed, which, for the sake of simplicity, is clocked for all traffic light systems 5. Alternatively, the cycle time TP can also vary for the individual traffic light systems 5 or be further optimized using the method shown here. For this purpose, the cycle time TP could also be considered via the local stress functions fn,m,d.

[0051] The green phase λ n,m is defined as λ n , m = r R − 1 and indicates the fraction of the cycle time TP for which the traffic light 5 at a specific intersection 3 in the west-east direction x is green. Furthermore, Tc is a so-called clearance time, which indicates in seconds how much time elapses between the traffic light 5 changing and the corresponding intersection 3 becoming clear. TT is a traffic time, which indicates in seconds the time during which vehicles 3 can actually pass through the intersection 3. The traffic time TT is calculated from: TT :=TP -2T C . Furthermore, a traffic flow F indicates how many vehicles 6 can pass an intersection 3 in one direction d per second during a green phase.

[0052] The traffic load l of a specific road section 4 in west-east direction x for a next time t+1 is then calculated from the current traffic load l on this road section 4 at time t, i.e. at the next cycle time TP, plus any incoming traffic from a neighboring road section 4, and minus any outgoing traffic to another neighboring road section 4: l n , m , 0 t + 1 = l n , m , 0 t + min l n − 1 modN , m , 0 t , λ n , m FT T − min l n , m , 0 t , λ n + 1 modN , m FT T

[0053] The incoming and outgoing traffic is defined here as a minimum function, whereby either the total incoming or outgoing traffic load l is taken into account if it is smaller than the maximum possible incoming or outgoing traffic via the respective traffic light system 5, or otherwise the maximum possible incoming or outgoing traffic.

[0054] The traffic load l and consequently the local stress function f of a road section 4 therefore depends on which values ​​are chosen for the green phase λ n,m of an intersection n,m adjacent to the road section 4 and which values ​​are chosen for the green phase λ (n+1)modN,m of a neighboring intersection n+1,m adjacent to the road section 4.

[0055] If rc is the value for r of a green phase λ n,m with respect to a central intersection and ro is the value for r of a green phase λ (n+1)modN,m with respect to an intersection adjacent to the central intersection, then the following results: λ n , m λ n + 1 modN , m = r n , m R − 1 r n + 1 modN , m R − 1 = r C R − 1 r O R − 1 ∈ r R − 1 r = 0 , 1 , … , R − 1 2

[0056] With ro and rc, the traffic load l on the road section 4 extending eastward from the intersection n,m in direction x at time t+1 is as follows: l n , m , 0 r C , r O t + 1 = l n , m , 0 t + min l n − 1 modN , m , 0 t , r C R − 1 FT T − min l n , m , 0 t , r O R − 1 FT T

[0057] In general, for all directions d, this term can be written as follows: l n , m , d r C , r O t + 1 = l n , m , d t + min l n − xd d modN , m − yd d modM , d t , R − 1 yd d 2 + − 1 yd d 2 r C R − 1 FT T − min l n , m , d t , R − 1 yd d 2 + − 1 yd d 2 r O R − 1 FT T

[0058] The local stress function for an outgoing road segment 4 from the intersection 3 with the indices "n,m" in the direction d at time t+1 is then: f n , m , d r C , r O t + 1 = max 0 , l n , m , d r C , r O t + 1 − V R 2

[0059] For the sake of clarity, the local stress functions f shown here are based on relatively simple assumptions regarding traffic system 1. However, these functions can be extended and made arbitrarily complex, particularly to improve their adaptation to real traffic systems. For this purpose, historical data can be incorporated into the local stress functions f, such as data obtained through statistical analyses of traffic system 1 or using artificial intelligence methods. Furthermore, continuous adjustment of the local stress functions f, for example based on such historical data at runtime, is possible.

[0060] All possible values ​​for the green phases λ can then be represented in a bit model. Is a specific value for r R − 1 If a specific traffic light system 5 is selected, then the corresponding bit xn,m,r = 1. If a different value is selected for traffic light system 5, then xn,m,r = 0.

[0061] For each traffic light system 5, exactly one value must be selected for the green phase λ, i.e., exactly one of the bits xn,m,r (r = 0, 1, ..., R - 1) must be equal to 1, while the others are 0. This is the case when the following H 0 is minimized: H 0 = ∑ n = 0 N − 1 ∑ m = 0 M − 1 1 − ∑ r = 0 R − 1 x n , m , r 2

[0062] In this case, H 0 is minimal at H 0 = 0.

[0063] To minimize the global stress on transport system 1, such measures are then necessary. r R − 1 to be chosen for which the following H 1 or H becomes minimal: H 1 = ∑ n = 0 N − 1 ∑ m = 0 M − 1 ∑ d = 0 3 ∑ r C = 0 R − 1 ∑ r O = 0 R − 1 f n , m , d r C , r O t + 1 x n , m , r C x n + xd d modN , n + yd d modM , r O H = AH 0 + BH 1 = A ∑ n = 0 N − 1 ∑ m = 0 M − 1 1 − ∑ r = 0 R − 1 x n , m , r 2 + B ∑ n = 0 N − 1 ∑ m = 0 M − 1 ∑ d = 0 3 ∑ r C = 0 R − 1 ∑ r O = 0 R − 1 f n , m , d r C , r O t + 1 x n , m , r C x n + xd d modN , m + yd d modM , r O

[0064] Based on the Figures 2 and 3The following describes how this optimization problem can be solved according to the invention.

[0065] Figure 2 shows a flowchart of a procedure 100 for controlling the traffic system 1 according to Figure 1 .

[0066] In a first step, traffic loads l of road segments 4 are recorded. In the embodiment shown here, traffic loads for all road segments 4 of the traffic system 1 are recorded. In an alternative embodiment, only traffic loads of relevant road segments 4, i.e., those road segments 4 for which an optimization of the traffic system 1 is to be carried out, can be recorded or taken into account.

[0067] Traffic volumes are recorded, for example, using road sensors, Floating Phone Data (FPD), or Floating Car Data (FCD). Additionally or alternatively, historical data from traffic system 1, i.e., empirical values ​​from previous measurements or other available data relating to the traffic of the traffic system, can also be used to record traffic volumes.

[0068] In a second step, a local stress function f is determined for each road segment 4 as a function of the recorded traffic loads l of the respective road segment 4. For determining the local stress function f of a road segment 4, current switching times of traffic lights 5 at intersections 3 adjacent to this road segment 4 can also be taken into account. In other words, it can be considered how many vehicles 6 will enter the road segment 4 under consideration in the next switching cycle, and how many vehicles 6 will leave it.

[0069] In a third step 103, a global stress function S for the entire traffic system 1 is calculated based on the local stress functions. f n , m , d r C , r O t + 1 The global stress function S is determined for all possible proportions of the green phases at the entering and exiting intersections. Here, the global stress function S is a measure of congestion or overload in traffic system 1. Congestion in fewer road segments 4 results in a higher overall global stress value than a distribution of vehicles 6 where the maximum possible stress-free number of vehicles 6 in the road segments 4 of traffic system 1 is not exceeded, even if, in the second case, more vehicles 6 are traveling in traffic system 1 overall.

[0070] In a fourth step, optimized switching times, i.e., optimized lengths of green phases λ, for the traffic lights 5 at the intersections 3 are determined using a quantum concept processor. This is done by minimizing the function H, which in part H1 represents the global stress under the respective decision for the green phases at all traffic lights in the network. The optimized switching times are determined such that the global stress function S assumes the smallest findable value. In other words, an optimization problem for the global stress function S is solved, whereby the solution of the optimization problem considers the traffic system 1 in its entirety and does not merely regulate switching times for traffic lights 5 at individual intersections 3 independently of one another.The method 100 shown here determines optimized switching times for all (or all relevant) traffic light systems 5 simultaneously, thus determining the best possible system state for the entire traffic system 1, i.e. a system state with the smallest possible global stress.

[0071] In the traffic system 1 shown here, only one type of traffic light is present at each intersection 3. However, the method 100 shown here allows for different types of traffic lights to be used at each intersection 3. For example, in addition to the traffic lights 5, turning signals or similar devices can be present at all or some intersections 3. To optimize the green phases λ for different traffic light types at an intersection 3, different values ​​for r can be selected. These different values ​​of r then depend on each other, for example.

[0072] In a fifth step 105, the traffic light systems 5 are switched according to a switching model based on the optimized switching times. The switching model can, for example, be based directly on the optimized switching times, meaning 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 additionally takes into account, for example, offsets, intermediate states such as yellow phases, additional traffic flows such as crossing trams or turning lanes, or similar factors. Such additions can also be optimized using the method 100 shown here.

[0073] The procedure 100 shown here is carried out periodically, for example, in parallel with the ongoing operation of traffic system 1. This allows switching times for the traffic lights 5 to be determined that are always adapted to the current traffic volume. For example, procedure 100 is carried out after a specific time interval, such as every 90 seconds, or at each cycle time TP for the next cycle time. This cycle time TP can be fixed for all traffic lights 5, or individual cycle times TP can exist for different traffic lights 5. Alternatively or additionally, procedure 100 can also be carried out dynamically, for example, depending on the traffic volume or a global stress value in traffic system 1.

[0074] The cycle time TP can also be optimized, either additionally or as an alternative to the green phases λ. In this case, bits for the cycle times TP must be added to the functions to be optimized for each relevant intersection 3, or the bits described above must be replaced with these. The cycle times TP can also be considered for the local stress functions f and thus, in particular, included in the future local stress f(t+1).

[0075] Furthermore, switching phases, i.e., offsets between cycle times TP of different traffic light systems 5, can be optimized in addition to or as an alternative to the green phases λ and the cycle times TP. In this case, bits for the switching phases must be added to the functions to be optimized for each relevant intersection 3, or the bits described above must be replaced with these. The switching phases can also be taken into account for the local stress functions f and thus, in particular, be included in the future local stress f(t+1).

[0076] Figure 3 shows a schematic representation of a device 7 for controlling the traffic system 1 according to Figure 1 .

[0077] The device 7 comprises sensors 8 with which traffic loads l 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).

[0078] The device 7 further comprises a computing unit 9, with which local stress functions f can be determined for each road segment 4 as a function of the recorded traffic loads l of the respective road segment 4. 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, for example, is used as the computing unit 9. The computing unit 9 is connected to a network 10, for example, the Internet.

[0079] The device 7 further comprises a quantum concept processor 11, which is configured to determine optimized switching times for the traffic light systems 5, wherein the optimized switching times are determined such that the global stress function S assumes a smallest findable value.

[0080] In this case, for example, a quantum concept processor 11 is used that is configured to solve an optimization problem using quantum annealing simulation. Such a quantum concept processor 11 can, for example, be based on conventional technology, such as complementary metal-oxide-semiconductor (CMOS) technology. Alternatively, any other quantum concept processor 11, including those based on actual qubit technologies in the future, can be used for the device 7.

[0081] The quantum concept processor 11 is also connected to network 10. The computing unit 9 is configured to send the global stress function S to the quantum concept processor 11 via network 10. The quantum concept processor 11 then sends the determined optimized switching times back to the computing unit 9 via network 10.

[0082] The device 7 also includes a switching device 12, which is configured to switch the traffic light systems 5 according to a switching model, the switching model being based on the optimized switching times. The switching device 12 is connected to the computing unit 9, so that the computing unit controls the switching device 12 based on the optimized switching times. Reference symbol list

[0083] 1. Traffic system 2. Road 3. Intersection 4. Road section 5. Traffic light system 6. Vehicle 7. Device 8. Sensor 9. Processing unit 10. Network 11. Quantum concept processor 12. Switching device xWestEast direction ySouthNorth direction 100 procedures 101 - 105 steps

Claims

1. Method (100) for controlling a traffic system (1) with a plurality of intersections (3) with switchable traffic lights (5) and road sections (4) located between the intersections (3), the method (100) comprising the following steps: - detecting (101) traffic loads on several relevant road sections (4), - determining (102) a local stress function for each relevant road section (4) depending on the detected traffic load of the respective relevant road section (4), - determining (103) a global stress function for the entire traffic system (1) based on the local stress functions, wherein the global stress function is defined as a Quadratic Unconstrained Binary Optimization, QUBO, term, - determining (104), using a quantum concept processor (11), optimized switching times for the traffic lights (5) of the intersections (3) adjacent to the relevant road sections (4), wherein the optimized switching times are determined such that the global stress function reaches the smallest detectable value, and - switching (105) the traffic lights (5) according to a switching model based on the optimized switching times.

2. Method (100) according to claim 1, wherein the smallest detectable value of the global stress function is a local or absolute minimum of the global stress function.

3. Method (100) according to one of claims 1 or 2, wherein the determination (102) of the local stress function is additionally performed based on selected values of different possible green phases for traffic lights (5) adjacent to the respective relevant road section (4).

4. Method (100) according to one of claims 1 to 3, further comprising the step of: - loading historical data of the traffic system (1), wherein further the determination (102) of the local stress functions is performed taking into account the historical data.

5. Method (100) according to one of claims 1 to 4, 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 lights (5) is repeated periodically, and the optimized switching times are constantly determined for a next switching period.

6. Method (100) according to claim 5, wherein the detection (101) of traffic loads is also repeated periodically.

7. Method (100) according to one of claims 1 to 6, wherein for detecting (101) the traffic loads, a number of vehicles (6) on the respective relevant road section (4) is detected.

8. Method (100) according to one of claims 1 to 7, wherein, for determining (102) the local stress function, current switching times of the traffic lights (5) of intersections (3) adjacent to the respective relevant road section (4) are further taken into account.

9. Device (7) for controlling a traffic system (1) with a plurality of intersections (3) with switchable traffic lights (5) and road sections (4) located between the intersections (3), the device (7) comprising: - at least one sensor (8) configured to detect traffic loads on several relevant road sections (4), - a computing unit (9) configured to determine a local stress function for each relevant road section (4) depending on the detected traffic loads of the respective relevant road section (4), and to determine a global stress function for the entire traffic system (1) based on the local stress functions, wherein the global stress function is defined as a Quadratic Unconstrained Binary Optimization, QUBO, term, - a quantum concept processor (11) that is designed to determine optimized switching times for the traffic lights (5) at the intersections (3) adjacent to the relevant road sections (4), wherein the optimized switching times are determined such that the global stress function assumes a smallest detectable value, and - a switching device (12) that is configured to switch the traffic lights (5) according to a switching model, wherein the switching model is based on the optimized switching times.

10. Device (7) according to claim 9, wherein the quantum concept processor (11) is a processor that is designed to solve an optimization problem using quantum annealing simulation.

11. Computer program, wherein the computer program comprises instructions which, when executed by a computer device, cause the computer device to perform the method (100) according to one of claims 1 to 8.

12. Computer-readable storage medium on which the computer program according to claim 11 is stored.

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