Saturation flow rate determination method and device, equipment, storage medium and program product

Abstract

The invention provides a saturation flow rate determination method and device, equipment, a storage medium and a program product, and relates to the technical field of computers, in particular to the fields of traffic control, traffic management, traffic design and the like. The method comprises the steps that in a signal lamp period of a target intersection, the number of queuing vehicles on a target lane and the number of parking times of vehicles passing through the target intersection through the target lane are counted; determining whether a time period corresponding to the signal lamp period is a supersaturation time period or not based on the number of queuing vehicles and the number of parking times; in response to the fact that the time period corresponding to the signal lamp period is a supersaturation time period, determining the traffic flow and the green time ratio of the target lane in the supersaturation time period; and determining the saturation flow rate of the target lane in the supersaturation time period based on the duration of the supersaturation time period, the traffic flow and the green time ratio. The accuracy of the saturation flow rate can be improved.

Description

Technical Field

[0001] The present disclosure relates to the field of intelligent transportation technology, specifically to the fields of traffic control, traffic management, traffic design, and more particularly to a saturation flow rate determination method, a saturation flow rate determination device, an electronic device, a computer-readable storage medium, and a computer program product. Background Art

[0002] Saturation flow rate refers to the maximum number of vehicles passing through a unit of effective green light time. The most basic unit of saturation flow rate is the saturation flow rate of a lane. The saturation flow rate of a lane is related to road conditions, traffic conditions, channelization conditions, signal conditions, environmental conditions, etc. In traffic engineering, saturation flow rate is usually used to represent the saturation capacity of a lane. The saturation capacity of a lane plays an important role in traffic signal design and optimization, traffic capacity analysis, traffic management and control, and road design and planning. Summary of the invention

[0003] The embodiments of the present disclosure provide a saturation flow rate determination method, a saturation flow rate determination device, an electronic device, a computer-readable storage medium, and a computer program product, which can improve the accuracy of the saturation flow rate.

[0004] In a first aspect, an embodiment of the present disclosure proposes a method for determining a saturation flow rate, comprising: within a traffic light cycle at a target intersection, counting the number of queued vehicles on a target lane and the number of stops of vehicles passing through the target intersection via the target lane; determining whether a time period corresponding to the traffic light cycle is an oversaturated time period based on the number of queued vehicles and the number of stops; in response to the time period corresponding to the traffic light cycle being an oversaturated time period, determining a vehicle flow rate and a green-to-signal ratio of the target lane in the oversaturated time period; determining a saturated flow rate of the target lane in the oversaturated time period based on the length of the oversaturated time period, the vehicle flow rate and the green-to-signal ratio.

[0005] In a second aspect, an embodiment of the present disclosure proposes a saturation flow rate determination device, comprising: a statistical module, configured to count the number of queuing vehicles on a target lane and the number of stops of vehicles passing through the target intersection via the target lane within a traffic light cycle at the target intersection; a judgment module, configured to determine whether the time period corresponding to the traffic light cycle is an oversaturated time period based on the number of queuing vehicles and the number of stops; a processing module, configured to determine the vehicle flow and green-to-signal ratio of the target lane in the oversaturated time period in response to the time period corresponding to the traffic light cycle being an oversaturated time period; a calculation module, configured to determine the saturation flow rate of the target lane in the oversaturated time period based on the length of the oversaturated time period, the vehicle flow and the green-to-signal ratio.

[0006] In a third aspect, an embodiment of the present disclosure provides an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the saturation flow rate determination method described in any implementation manner in the first aspect.

[0007] In a fourth aspect, an embodiment of the present disclosure provides a non-transitory computer-readable storage medium storing computer instructions, which are used to enable a computer to implement the saturation flow rate determination method described in any implementation of the first aspect or the second aspect when executed.

[0008] In a fifth aspect, an embodiment of the present disclosure provides a computer program product comprising a computer program, which, when executed by a processor, can implement the saturation flow rate determination method as described in any implementation of the first aspect or the second aspect.

[0009] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Other features, objects and advantages of the present disclosure will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0011] Figure 1 is an exemplary system architecture diagram to which the present disclosure may be applied;

[0012] Figure 2 A flow chart of a method for determining a saturation flow rate provided for an embodiment of the present disclosure;

[0013] Figure 3 A flowchart of another saturation flow rate determination method provided for an embodiment of the present disclosure;

[0014] Figure 4 A flowchart of an application scenario of the saturation flow rate determination method provided in an embodiment of the present disclosure;

[0015] Figure 5 A schematic diagram of dividing the working day from 0:00 to 12:00 into multiple initial time intervals;

[0016] Figure 6 A structural block diagram of a saturation flow rate determination device provided in an embodiment of the present disclosure;

[0017] Figure 7 A schematic diagram of the structure of an electronic device suitable for executing a saturation flow rate determination method provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0018] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description. It should be noted that the embodiments in the present disclosure and the features in the embodiments may be combined with each other without conflict.

[0019] In the technical solution of the present disclosure, the collection, storage, use, processing, transmission, provision and disclosure of user personal information involved are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0020] Figure 1 An exemplary system architecture 100 is shown to which embodiments of the saturation flow rate determination method, apparatus, electronic device, computer-readable storage medium, and computer program product of the present disclosure may be applied.

[0021] like Figure 1 As shown, the system architecture 100 may include terminal devices 101, 102, 103, a network 104 and a server 105. The network 104 is used to provide a medium for communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0022] Users can use terminal devices 101, 102, 103 to interact with server 105 through network 104 to receive or send messages, etc. Various applications for realizing information communication between the terminal devices 101, 102, 103 and server 105 can be installed, such as traffic design applications, map applications, instant messaging applications, etc.

[0023] Terminal devices 101, 102, 103 and server 105 can be hardware or software. When terminal devices 101, 102, 103 are hardware, they can be various electronic devices with display screens, including but not limited to smart phones, tablet computers, laptop computers, desktop computers, etc.; when terminal devices 101, 102, 103 are software, they can be installed in the electronic devices listed above, which can be implemented as multiple software or software modules, or as a single software or software module, and no specific limitation is made here. When server 105 is hardware, it can be implemented as a distributed server cluster composed of multiple servers, or as a single server; when the server is software, it can be implemented as multiple software or software modules, or as a single software or software module, and no specific limitation is made here.

[0024] The server 105 can provide various services through various built-in applications. Taking traffic design applications as an example, the server 105 can achieve the following effects when running traffic design applications: within a traffic light cycle at the target intersection, count the number of queued vehicles on the target lane and the number of stops of vehicles passing through the target intersection via the target lane; based on the number of queued vehicles and the number of stops, determine whether the time period corresponding to the traffic light cycle is an oversaturated time period; in response to the time period corresponding to the traffic light cycle being an oversaturated time period, determine the traffic flow and green-to-signal ratio of the target lane in the oversaturated time period; based on the length of the oversaturated time period, the traffic flow and the green-to-signal ratio, determine the saturated flow rate of the target lane in the oversaturated time period.

[0025] It should be noted that the data for counting the number of queued vehicles on the target lane and the number of stops of vehicles passing through the target intersection via the target lane can be obtained from the terminal devices 101, 102, and 103 through the network 104, or can be pre-stored in the server 105 in various ways. Therefore, when the server 105 detects that these data have been stored locally, it can choose to directly obtain these data locally. In this case, the exemplary system architecture 100 may also not include the terminal devices 101, 102, 103 and the network 104.

[0026] Since the calculation of the saturated flow rate requires more computing resources and stronger computing power, the saturated flow rate determination method provided in the subsequent embodiments of the present disclosure is generally performed by the server 105 with stronger computing power and more computing resources. Accordingly, the saturated flow rate determination device is generally also set in the server 105. However, it should also be pointed out that when the terminal devices 101, 102, and 103 also have computing power and computing resources that meet the requirements, the terminal devices 101, 102, and 103 can also complete the above-mentioned operations that are handed over to the server 105 through the traffic design application installed thereon, and then output the same result as the server 105. Especially in the case where there are multiple terminal devices with different computing powers at the same time, but when the traffic design application determines that the terminal device has stronger computing power and more remaining computing resources, the terminal device can be allowed to perform the above-mentioned operations, thereby appropriately reducing the computing pressure of the server 105. Accordingly, the saturated flow rate determination device can also be set in the terminal devices 101, 102, and 103. In this case, the exemplary system architecture 100 may also not include the server 105 and the network 104 .

[0027] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0028] Please refer to Figure 2 , Figure 2 A flowchart of a saturation flow rate determination method provided by an embodiment of the present disclosure, wherein process 200 may include the following steps:

[0029] Step 201: within a traffic light cycle at a target intersection, counting the number of vehicles queuing on a target lane and the number of stops of vehicles passing through the target intersection via the target lane;

[0030] This step is intended to be performed by the execution entity of the data migration method (e.g. Figure 1 The server 105 shown counts the number of queued vehicles on the target lane and the number of stops of vehicles passing through the target intersection via the target lane within a traffic light cycle at the target intersection.

[0031] In the embodiments of the present disclosure, an intersection generally refers to a place where two or more roads intersect. An intersection is a node of a road network and bears the functions of traffic flow collection and direction conversion in the road network. The target intersection may be an intersection in a road network, such as a crossroads, etc. The embodiments of the present disclosure do not limit the type of the target intersection. An intersection generally has multiple directions of travel, and each direction of travel may also have multiple lanes. For example, an intersection has four directions of travel, namely, direction a from west to east, direction b from east to west, direction c from north to south, and direction d from south to north. The target lane may be a lane in a direction of travel of the target intersection, such as a lane in direction a of the intersection. A signal light cycle generally refers to a complete process in which various light colors of a signal light are displayed in turn. For example, when all signal lights in the four directions of travel of an intersection have completed a color change of "red light-green light-yellow light-red light", a complete signal light cycle is formed.

[0032] In the embodiments of the present disclosure, within a traffic light cycle, the number of queued vehicles on the target lane of the target intersection and the number of stops of vehicles passing through the target intersection from the target lane can be counted in a variety of ways. For example, data statistics can be performed by setting up equipment on site, or data statistics can be performed based on data of the intelligent transportation system, or data statistics can be performed based on data of an electronic map, and the embodiments of the present disclosure are not limited to this. The number of queued vehicles on the target lane within a predetermined distance range from the target intersection can be counted within the traffic light cycle, or the number of vehicles parked on the target lane within a predetermined distance range from the target intersection can be counted within the green light duration of the traffic light cycle as the number of queued vehicles, and the embodiments of the present disclosure are not limited to this. The vehicles passing through the target intersection via the target lane within the green light duration of the traffic light cycle can be determined as passing vehicles, and the number of stops of each passing vehicle within a predetermined distance range from the target intersection can be counted, or after the number of stops of each passing vehicle within a predetermined distance range from the target intersection is obtained, the average number of stops of each passing vehicle is calculated as the number of stops of each passing vehicle, and the embodiments of the present disclosure are not limited to this. In some optional embodiments of the present disclosure, step 201 may include: within a traffic light cycle, counting the number of queued vehicles on the target lane within a predetermined distance range from the target intersection; determining vehicles that pass through the target intersection via the target lane within the green light duration of the traffic light cycle as passing vehicles, and counting the number of stops of each passing vehicle within the predetermined distance range from the target intersection.

[0033] Step 202: Based on the number of queued vehicles and the number of parking times, determine whether the time period corresponding to the traffic light cycle is an oversaturated time period;

[0034] On the basis of step 201, this step aims to determine whether the time period corresponding to the traffic light cycle is an oversaturated time period based on the number of queued vehicles and the number of stops by the above-mentioned execution subject.

[0035] In the embodiment of the present disclosure, if the flow rate actually arriving in a green light time period is greater than the flow rate actually passing, the time period corresponding to the signal light cycle including the green light time period is called the oversaturation time period. Among them, the flow rate actually passing is relatively easy to measure directly, and the flow rate actually arriving is relatively difficult to measure directly, and can be estimated by other indicators. It is more in line with objective traffic common sense to estimate the flow rate actually arriving by two indicators, the number of queued vehicles and the number of parking times. For example, in a green light time period, the number of queued vehicles on the target lane of the target intersection is greater than the number of passing vehicles passing through the target intersection via the target lane, and the number of parking times of vehicles passing through the target intersection via the target lane is greater than once, it can be considered that the flow rate actually arriving is greater than the flow rate actually passing, and the time period corresponding to the signal light cycle including the green light time period is the oversaturation time period. Therefore, it can be judged whether the time period corresponding to the signal light cycle is the oversaturation time period based on the number of queued vehicles and the number of parking times. Among them, the number of passing vehicles passing through the target intersection via the target lane is the vehicle flow.

[0036] In the embodiments of the present disclosure, it is possible to determine whether the time period corresponding to the signal light cycle is an oversaturated time period by judging whether the number of queued vehicles is greater than the traffic flow, and whether the number of stops of each passing vehicle within a predetermined distance range from the target intersection is greater than once; in response to the number of queued vehicles being greater than the traffic flow, and whether the number of stops of each passing vehicle within a predetermined distance range from the target intersection is greater than once, the time period corresponding to the signal light cycle is determined to be an oversaturated time period. It is also possible to determine whether the time period corresponding to the signal light cycle is an oversaturated time period by judging whether the number of queued vehicles is greater than the traffic flow, and whether the average number of stops of each passing vehicle within a predetermined distance range from the target intersection is greater than once; in response to the number of queued vehicles being greater than the traffic flow, and the average number of stops of each passing vehicle within a predetermined distance range from the target intersection is greater than once, the time period corresponding to the signal light cycle is determined to be an oversaturated time period. The embodiments of the present disclosure are not limited to this. In some optional embodiments of the present disclosure, process 200 may further include: within the traffic light cycle, counting the traffic flow passing through the target intersection via the target lane; step 202 may include: in response to the number of queued vehicles being greater than the traffic flow, and the number of stops of each passing vehicle within a predetermined distance range from the target intersection being greater than once, determining that the time period corresponding to the traffic light cycle is an oversaturated time period.

[0037] Step 203: In response to the time period corresponding to the signal light cycle being an oversaturated time period, determining the vehicle flow and green-signal ratio of the target lane in the oversaturated time period;

[0038] On the basis of step 202, this step aims to determine the traffic volume and green-signal ratio of the target lane in the oversaturated time period in response to the time period corresponding to the traffic light cycle being an oversaturated time period by the above-mentioned execution subject.

[0039] In the embodiment of the present disclosure, the traffic flow of the target lane of the target intersection in the oversaturation time period has been obtained through step 201. The traffic flow of the target lane of the target intersection in the oversaturation time period can usually be called saturation flow, which can be expressed as Q t Indicates saturated flow. The green-to-signal ratio usually refers to the proportion of time available for vehicle traffic within a traffic light cycle, that is, the ratio of the effective green light time within the traffic light cycle to the traffic light cycle time. For example, the effective green light time in the east-west direction of the intersection is 2 minutes, and the traffic light cycle time is 5 minutes, then the green-to-signal ratio is 40%. The green-to-signal ratio of a traffic light cycle can be determined in a variety of ways. For example, the green-to-signal ratio can be measured by setting up equipment on site, or according to the data of the intelligent transportation system, or according to the data of the electronic map. The embodiments of the present disclosure are not limited to this. Usually, G t Indicates the effective green light time, t0 is used to indicate the duration of the oversaturation period, and g t0 Represents the green-signal ratio. Formula 1 can be used to calculate the green-signal ratio of the target lane at the target intersection during the oversaturated time period. Formula 1 is as follows:

[0040]

[0041] Step 204: Based on the duration of the oversaturated time period, the vehicle flow rate, and the green-signal ratio, determine the saturated flow rate of the target lane in the oversaturated time period.

[0042] On the basis of step 203, this step aims to determine the saturation flow rate of the target lane in the oversaturated time period based on the traffic light cycle, vehicle flow and green-to-signal ratio by the above-mentioned execution subject.

[0043] In the embodiments of the present disclosure, the saturation flow rate refers to the maximum number of vehicles that pass through the stop line within a unit of effective green light time. The unit of the saturation flow rate is vehicles / (hour·lane) (pcu / (h·ln)). That is, the saturation flow rate can represent the number of vehicles that can pass through the stop line if the green light time of the vehicle flow continuously passes through the stop line at a normal speed is accumulated to 1 hour. Usually, S t0 Represents the saturated flow rate. Formula 2 can be used to calculate the saturated flow rate of the target lane at the target intersection during the oversaturated time period. Formula 2 is as follows:

[0044]

[0045] According to the relationship between the green-light ratio and the effective green light time in formula 1, the green-light ratio can also be used to calculate the saturated flow rate. Therefore, the saturated flow rate of the target lane at the target intersection during the oversaturated time period can also be calculated using formula 3, which is as follows:

[0046]

[0047] The saturated flow rate determination method provided by the embodiments of the present invention can quickly and accurately find the time period when the traffic flow reaches saturation by counting the number of queued vehicles and the number of stops to determine the oversaturated time period. After determining the oversaturated time period, the saturated flow rate is determined according to the traffic flow and the green-to-signal ratio in the oversaturated time period. Since the traffic flow and the green-to-signal ratio are relatively easy to count and calculate, and accurate results can be obtained, compared with converting the saturated flow rate by saturated headway, the cost of calculating the saturated flow rate can be reduced, and the calculation efficiency and accuracy of the saturated flow rate can be improved.

[0048] Please refer to Figure 3 , Figure 3 A flowchart of another saturation flow rate determination method provided by an embodiment of the present disclosure, wherein process 300 may include the following steps:

[0049] Step 301: according to the characteristics of the total traffic volume of the road network in one day, the time of one day is divided into a plurality of initial time intervals, wherein the duration of each initial time interval is negatively correlated with the total traffic volume of the road network in each initial time interval, and each initial time interval includes a plurality of signal light cycles;

[0050] This step is intended to be performed by the execution entity of the data migration method (e.g. Figure 1 The server 105 shown divides a day into multiple initial time intervals according to the characteristics of the total amount of road network traffic in a day, wherein the duration of each initial time interval is negatively correlated with the total amount of road network traffic within each initial time interval, and each initial time interval includes multiple traffic light cycles.

[0051] Usually, the saturation flow rates of different time periods and different lanes vary greatly, and refined calculations are required in order to use the saturation flow rate to more accurately perform traffic design, traffic control, and traffic management. In the embodiments of the present disclosure, a day can be divided into multiple small initial time intervals based on the characteristics of the total amount of road network traffic in a day, so that the length of each initial time interval is negatively correlated with the total amount of road network traffic in each initial time interval, and each initial time interval can include multiple signal light cycles. For example, for the peak time period of a day, since the total amount of road network traffic is large, every 30 minutes can be divided into an initial time interval; for the off-peak time period of a day except the peak time period, since the total amount of road network traffic is small, the initial time interval can be divided into any number of hours from 1 hour to 6 hours.

[0052] Because in a period of time, for the same lane at the same intersection, the characteristics of the total traffic volume of the road network on each working day are usually similar, the characteristics of the total traffic volume of the road network on each non-working day are usually similar, and the characteristics of the total traffic volume of the road network on each working day and non-working day are usually quite different. Therefore, the initial time intervals can be divided for working days and non-working days respectively. The characteristics of the total traffic volume of the road network on each working day can be comprehensively considered, and the initial time intervals of each working day can be divided, so that the initial time intervals divided for each working day are consistent. The characteristics of the total traffic volume of the road network on each non-working day can be comprehensively considered, and the initial time intervals of each non-working day can be divided, so that the initial time intervals divided for each non-working day are consistent.

[0053] For example, Figure 5 As shown, each working day can divide the off-peak time period from 0:00 to 6:00 into the initial time interval P1, and the off-peak time period from 6:00 to 7:00 into the initial time interval P2. For the peak time period from 7:00 to 9:00, each working day can divide 7:00 to 7:30 into the initial time interval P3, 7:30 to 8:00 into the initial time interval P4, 8:00 to 8:30 into the initial time interval P5, and 8:30 to 9:00 into the initial time interval P6. Afterwards, each working day can divide the off-peak time period from 9:00 to 10:00 into the initial time interval P7, and the off-peak time period from 10:00 to 12:00 into the initial time interval P8. Each working day can be deduced in this way until the division of the initial time interval of a day is completed.

[0054] Non-working days can be Figure 5 The working days shown are similar, except that the initial time intervals are different. For example, for each non-working day, 10 a.m. to 12 p.m. is the peak time period, and each non-working day can divide the off-peak time period from 6 a.m. to 8 p.m. into the initial time interval P2, the off-peak time period from 8 a.m. to 9 p.m. into the initial time interval P3, and the off-peak time period from 9 a.m. to 10 p.m. into the initial time interval P4. For the peak time period from 10 a.m. to 12 p.m., each non-working day can divide 10 a.m. to 10:30 a.m. into the initial time interval P5, 10:30 a.m. to 11 a.m. into the initial time interval P6, 11 a.m. to 11:30 a.m. into the initial time interval P7, and 11:30 a.m. to 12 p.m. into the initial time interval P8. Each non-working day can be deduced in this way until the division of the initial time interval of a day is completed.

[0055] Step 302: During a plurality of signal light cycles in the initial time interval, the number of queued vehicles on the target lane of the target intersection and the number of stops of vehicles passing through the target intersection via the target lane are counted respectively;

[0056] Based on step 301, this step aims to count the number of queued vehicles on the target lane of the target intersection and the number of stops of vehicles passing through the target intersection via the target lane by the above-mentioned execution subject within multiple traffic light cycles of the initial time interval.

[0057] In the embodiment of the present disclosure, after a day is finely divided into initial time intervals, each initial time interval can be used as an object for determining the saturation flow rate. An initial time interval can include multiple signal light cycles, and the saturation flow rate can be determined only based on the signal light cycle determined as an oversaturation time period. Therefore, the number of queued vehicles on the target lane of the target intersection and the number of stops of vehicles passing through the target intersection via the target lane can be counted in multiple signal light cycles of each initial time interval. Among them, the content of counting the number of queued vehicles and the number of stops in each signal light cycle in step 302 is the same as that in the above Figure 2 The same steps as shown in step 201 are shown in FIG. Figure 2 The corresponding parts in the illustrated embodiment will not be described in detail here.

[0058] Step 303: Based on the number of queued vehicles and the number of stops in each signal light cycle, determine whether there is an oversaturated time period from the time periods corresponding to the multiple signal light cycles;

[0059] On the basis of step 302, this step aims to determine whether there is an oversaturated time period from the time periods corresponding to the multiple traffic light cycles based on the number of queued vehicles and the number of stops in each traffic light cycle by the above-mentioned execution subject.

[0060] In the embodiment of the present disclosure, for the multiple signal light cycles in each initial time interval, it is possible to determine whether there is an oversaturated time period from the time periods corresponding to the multiple signal light cycles in each initial time interval according to the number of queued vehicles and the number of stops in each signal light cycle. In particular, step 303 determines whether the time period corresponding to the signal light cycle is an oversaturated time period according to the number of queued vehicles and the number of stops in each signal light cycle. Figure 2 The same steps as shown in step 202 are shown in FIG. Figure 2 The corresponding parts in the embodiment shown are not described in detail here.

[0061] Step 304: In response to the existence of an oversaturated time period in the initial time interval, determining the initial time interval as a target time interval;

[0062] On the basis of step 303, this step aims at determining the initial time interval as the target time interval by the execution subject in response to the existence of an oversaturated time period in the initial time interval.

[0063] In an embodiment of the present disclosure, for a plurality of signal light cycles in each initial time interval, in response to a time period corresponding to at least one signal light cycle in an initial time interval being an oversaturated time period, the initial time interval has an oversaturated time period, and the initial time interval can be determined as a target time interval. An initial time interval may or may not have an oversaturated time period. When the time periods corresponding to the signal light cycles in an initial time interval are not oversaturated time periods, the initial time interval does not have an oversaturated time period, and the initial time interval is not a target time interval.

[0064] Step 305: Determine the saturation flow rate of the target lane in the target time interval based on the duration of the oversaturation time period, the vehicle flow rate, and the green-signal ratio of the target time interval.

[0065] On the basis of step 304, this step aims to determine the saturation flow rate of the target lane in the target time interval based on the duration of the supersaturated time period of the target time interval, the vehicle flow and the green-to-signal ratio by the above-mentioned execution subject.

[0066] In the embodiment of the present disclosure, a target time interval may have one oversaturation time period or multiple oversaturation time periods. When a target time interval has one oversaturation time period, step 305 determines the content of the saturation flow rate of the target lane in the target time interval and the content of the saturation flow rate of the target lane in the target time interval. Figure 2 Step 203 is consistent with step 204, and the same content can be found in Figure 2 The corresponding parts in the embodiment shown are not described here. When a target time interval has multiple supersaturated time periods, the vehicle flow and green-to-signal ratio of the target lane in each supersaturated time period can be determined according to the multiple supersaturated time periods of the target time interval, and the saturated flow rate of each supersaturated time period in the target time interval can be determined according to the length, vehicle flow and green-to-signal ratio of each supersaturated time period. The saturated flow rate of the target lane in the target time interval is obtained by calculating the average value of the saturated flow rate of each supersaturated time period in the target time interval.

[0067] Therefore, step 305 may include: determining the saturated flow rate of the target lane in each oversaturated time period of the target time interval based on the duration, vehicle flow, and green-to-signal ratio of the oversaturated time periods corresponding to the multiple oversaturated time periods of the target time interval; calculating the average of the saturated flow rates of the target lane in each oversaturated time period of the target time interval to obtain the saturated flow rate of the target lane in the target time interval. Among them, step 305 determines the vehicle flow and green-to-signal ratio of the target lane in each oversaturated time period of the target time interval, and determines the content of the saturated flow rate of the target lane in each oversaturated time period of the target time interval according to the duration, vehicle flow, and green-to-signal ratio of each oversaturated time period of the target time interval. Figure 2 Step 203 is consistent with step 204, and the same content can be found in Figure 2 The corresponding parts in the embodiment shown are not described in detail here.

[0068] In some optional implementations of the present disclosure, after step 305, the execution entity may also calculate the average of the saturation flow rates of the target lanes in the target time interval over multiple days to obtain the target saturation flow rate of the target lanes in the target time interval. The multiple days may have a consistent initial time interval division, for example, the multiple days may all be working days or non-working days. The implementations of the present disclosure do not limit the number of days in the multiple days, for example, the multiple days may be 10 days, 20 days, 30 days, etc. Calculating the average of the saturation flow rates of the target lanes in the target time interval over multiple days is helpful in reducing errors and improving the accuracy of the saturation flow rate. The saturation flow rate of the target lanes of the target intersection in the oversaturated time period within the target time interval over multiple days can be used as n samples, and S t0_i The saturation flow rate of the i-th sample can be expressed as S pi Represents the target time interval P i The target saturation flow rate of the target lane at the target intersection within the target time interval can be calculated using Formula 4, which is as follows:

[0069]

[0070] In some optional embodiments of the present disclosure, calculating the average saturation flow rate of the target lane in the target time interval for multiple days to obtain the target saturation flow rate of the target lane in the target time interval may include: calculating the average saturation flow rate of the target lane in the target time interval for a preset number of days before the current day; taking a weighted sum of the saturation flow rate of the target lane in the target time interval for the current day and the average saturation flow rate of the target lane in the target time interval for the preset number of days to obtain the target saturation flow rate of the target lane in the target time interval.

[0071] Please refer to Figure 4 , Figure 4A flowchart of an application scenario of a saturation flow rate determination method provided in an embodiment of the present disclosure, wherein process 400 may include the following steps:

[0072] Step 401: in response to receiving an optimization instruction for a traffic light cycle at a target intersection, obtaining a target saturation flow rate of a target lane within a target time interval one day before the current moment;

[0073] This step is intended to be performed by the execution entity of the data migration method (e.g. Figure 1 The server 105 shown in the figure obtains the target saturation flow rate of the target lane within the target time interval one day before the current moment in response to receiving the optimization instruction for the traffic light cycle of the target intersection.

[0074] In an embodiment of the present disclosure, the instruction to optimize the traffic light cycle of the target intersection may be from, for example, Figure 1 The terminal devices 101, 102 or 103 shown are received via the network 104. Since traffic conditions and signal light conditions etc. may change over time, the target saturated flow rate of the target lane of the target intersection one day before the current moment in the target time interval may be used as the latest saturated traffic capacity of the target lane in the target time interval to optimize the signal light cycle of the target intersection. The target saturated flow rate of the target lane of the previous day in the target time interval in step 401 may be determined based on the saturated flow rate of the target lane of the previous day in the target time interval and the saturated flow rate of the target lane of the preset number of days before the previous day in the target time interval. For example, the average of the saturated flow rate of the target lane of the previous day in the target time interval and the saturated flow rate of the target lane of the preset number of days before the previous day may be calculated to obtain the target saturated flow rate of the target lane of the previous day in the target time interval, and the embodiments of the present disclosure are not limited to this.

[0075] Since the saturated capacity of the same lane at the same intersection in the same time interval usually does not change too drastically within a certain period of time, in order to avoid too drastic changes in the saturated capacity of the target lane in the target time interval of the previous day and to ensure the performance of signal light cycle optimization at the target intersection, a smooth update method can be used to determine the target saturated flow rate of the target lane in the target time interval of the previous day. In this way, the saturated capacity of the target lane in the target time interval that includes both the latest and smoothly changing saturated capacity of the target lane in the target time interval can be obtained. In the embodiments of the present disclosure, a variety of smooth update methods can be used to determine the target saturated flow rate of the target lane in the target time interval of the previous day, and the embodiments of the present disclosure are not limited to this. For example, smooth update can be performed by weighted summation.

[0076] Therefore, in some optional embodiments of the present disclosure, the target saturated flow rate of the target lane in the target time interval of the previous day in step 401 can be obtained by calculating the average saturated flow rate of the target lane in the target time interval of the preset number of days before the previous day; weighted summing the saturated flow rate of the target lane in the target time interval of the previous day and the average saturated flow rate of the target lane in the target time interval of the preset number of days, to obtain the target saturated flow rate of the target lane in the target time interval of the previous day. pi_last Indicates that the target lane on the previous day was in the target time interval P i The saturation flow rate is S pi The target saturation flow rate of the target lane in the target time interval Pi for the preset number of days before the previous day is represented by γ, and the weight of the average value of the saturation flow rate is represented by γ. The target saturation flow rate of the target lane in the target time interval of the previous day can be calculated using Formula 5, which is as follows:

[0077]

[0078] Among them, the weight of the weighted summation can be set based on experience, and the embodiments of the present disclosure do not limit this. Usually, the weight of the average value of the saturation flow rate of the target lane in the target time interval on the preset number of days can be greater than the weight of the saturation flow rate of the target lane in the target time interval on the previous day, for example, γ can be 0.8. The preset number of days can be selected based on experience, and the embodiments of the present disclosure do not limit this, for example, the preset number of days can be 10 days.

[0079] Step 402: Based on the target saturation flow rate of the previous day, the green light duration, signal light cycle duration and signal light timing of the target intersection within the target time interval are adjusted.

[0080] On the basis of step 401, this step aims to adjust the green light duration, signal light cycle duration and signal light timing of the target intersection within the target time interval based on the target saturation flow rate of the previous day by the above-mentioned execution entity.

[0081] In an embodiment of the present disclosure, the target saturation flow rate of the target lane in the target time interval of the previous day can be determined based on the saturation flow rate of the target lane in the target time interval of the previous day and the saturation flow rate of the target lane in the target time interval of a preset number of days before the previous day. This can be used as the latest saturated capacity of the target lane in the target time interval, and the green light duration, signal light cycle duration and signal light timing at the target intersection in the target time interval can be adjusted to optimize traffic flow and reduce delays and queue lengths.

[0082] In addition to being used in the design and optimization of traffic signals, the use of saturated flow rate to represent the saturated capacity of lanes can also be used in traffic capacity analysis, traffic management and control, road design and planning, etc. For example, in traffic capacity analysis, the traffic capacity of roads or intersections can be evaluated based on the saturated capacity of lanes to help urban planners and traffic engineers determine whether a facility needs to be expanded or renovated; in traffic management and control, the saturated capacity of lanes can be used to adjust traffic signals and traffic flows in real time to optimize the overall performance of the traffic network; in road design and planning, the saturated capacity of lanes can be used to guide the design of roads and intersections to ensure that they can handle the expected traffic flow, thereby improving the efficiency and safety of traffic flow.

[0083] Further references Figure 6 As an implementation of the methods shown in the above figures, the present disclosure provides a saturation flow rate determination device, the implementation of the saturation flow rate determination device is similar to Figure 2 The embodiments of the method shown correspond to those described above.

[0084] like Figure 6 As shown, the saturation flow rate determination device 600 of this embodiment may include: a statistical module 601, a judgment module 602, a processing module 603 and a calculation module 604.

[0085] In the embodiment of the saturation flow rate determination device 600 and Figure 2 The case corresponds to the implementation method shown.

[0086] The statistical module 601 is configured to count the number of queued vehicles on the target lane and the number of stops of vehicles passing through the target intersection via the target lane within a traffic light cycle of the target intersection. The judgment module 602 is configured to determine whether the time period corresponding to the traffic light cycle is an oversaturated time period based on the number of queued vehicles and the number of stops. The processing module 603 is configured to determine the traffic volume and green-to-signal ratio of the target lane in the oversaturated time period in response to the time period corresponding to the traffic light cycle being an oversaturated time period; the calculation module 604 is configured to determine the saturated flow rate of the target lane in the oversaturated time period based on the duration of the oversaturated time period, the traffic volume and the green-to-signal ratio.

[0087] In this embodiment, the specific processing of the statistical module 601, the judgment module 602, the processing module 603 and the calculation module 604 in the saturation flow rate determination device 600 and the technical effects thereof can be referred to in Figure 2 The relevant descriptions of steps 201 - 204 in the corresponding implementation manner are omitted here.

[0088] In some optional implementations of this embodiment, the statistical module 601 is further configured to: count the number of queued vehicles on the target lane within a predetermined distance range from the target intersection during the signal light cycle;

[0089] Vehicles passing through the target intersection via the target lane during the green light duration of the signal light cycle are determined as passing vehicles, and the number of stops of each passing vehicle within a predetermined distance range from the target intersection is counted.

[0090] In some optional implementations of this embodiment, the statistics module 601 is further configured to count the traffic volume passing through the target intersection via the target lane during the signal light cycle;

[0091] The judgment module 602 is further configured to determine that the time period corresponding to the traffic light cycle is an oversaturated time period in response to the number of queuing vehicles being greater than the traffic flow and the number of stops of each passing vehicle within a predetermined distance range from the target intersection being greater than once.

[0092] In some optional implementations of this embodiment, the saturation flow rate determining device 600 further includes:

[0093] The interval division module is configured to divide a day into a plurality of initial time intervals according to the characteristics of the total amount of road network traffic in a day, wherein the duration of each initial time interval is negatively correlated with the total amount of road network traffic in each initial time interval, and each initial time interval includes a plurality of signal light cycles;

[0094] The statistics module 601 is further configured to count the number of queued vehicles on the target lane of the target intersection and the number of stops of vehicles passing through the target intersection via the target lane, respectively, within a plurality of signal light cycles of the initial time interval;

[0095] The determination module 602 is further configured to determine whether there is an oversaturated time period from the time periods respectively corresponding to the plurality of signal light cycles based on the number of queued vehicles and the number of parking times in each signal light cycle;

[0096] The processing module 603 is further configured to, in response to the existence of an oversaturated time period in the initial time interval, determine the initial time interval as a target time interval;

[0097] The calculation module 604 is further configured to determine the saturation flow rate of the target lane in the target time interval based on the duration of the supersaturation time period of the target time interval, the vehicle flow rate and the green-signal ratio.

[0098] In some optional implementations of this embodiment, the calculation module 604 is further configured to:

[0099] Determine the saturation flow rate of the target lane in each supersaturated time period of the target time interval based on the duration of the supersaturated time periods, the vehicle flow rate, and the green-signal ratio respectively corresponding to the multiple supersaturated time periods of the target time interval;

[0100] An average value of the saturation flow rates of the target lane in each supersaturation time period in the target time interval is calculated to obtain the saturation flow rate of the target lane in the target time interval.

[0101] In some optional implementations of the present embodiment, the calculation module 604 is further configured to calculate an average value of the saturation flow rate of the target lane in the target time interval over multiple days to obtain a target saturation flow rate of the target lane in the target time interval.

[0102] In some optional implementations of this embodiment, the saturation flow rate determination device 600 further includes: an optimization module configured to:

[0103] In response to receiving an optimization instruction for the traffic light cycle of the target intersection, obtaining a target saturation flow rate of the target lane within the target time interval one day before the current moment;

[0104] Based on the target saturation flow rate of the previous day, the green light duration, the signal light cycle duration and the signal light timing of the target intersection within the target time interval are adjusted.

[0105] In some optional implementations of this embodiment, the calculation module is further configured as follows:

[0106] Calculate an average value of the saturation flow rate of the target lane in the target time interval for a preset number of days before the current day;

[0107] The target saturated flow rate of the target lane in the target time interval is obtained by taking a weighted sum of the saturated flow rate of the target lane in the target time interval on the current day and the average of the saturated flow rates of the target lane in the target time interval on the preset days.

[0108] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can implement the saturation flow rate determination method described in any of the above-mentioned embodiments when executing.

[0109] According to an embodiment of the present disclosure, the present disclosure further provides a readable storage medium, which stores computer instructions, and the computer instructions are used to enable a computer to implement the saturation flow rate determination method described in any of the above embodiments when executed.

[0110] According to an embodiment of the present disclosure, the present disclosure further provides a computer program product, which, when executed by a processor, can implement the saturation flow rate determination method described in any of the above embodiments.

[0111] Figure 7 A schematic block diagram of an example electronic device 700 that can be used to implement an embodiment of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0112] like Figure 7 As shown, the device 700 includes a computing unit 701, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 702 or a computer program loaded from a storage unit 708 into a random access memory (RAM) 703. In the RAM 703, various programs and data required for the operation of the device 700 can also be stored. The computing unit 701, the ROM 702, and the RAM 703 are connected to each other via a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

[0113] A number of components in the device 700 are connected to the I / O interface 705, including: an input unit 706, such as a keyboard, a mouse, etc.; an output unit 707, such as various types of displays, speakers, etc.; a storage unit 708, such as a disk, an optical disk, etc.; and a communication unit 709, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 709 allows the device 700 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0114] The computing unit 701 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 701 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 701 performs the various methods and processes described above, such as the saturation flow rate determination method. For example, in some embodiments, the saturation flow rate determination method may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 708. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 700 via the ROM 702 and / or the communication unit 709. When the computer program is loaded into the RAM 703 and executed by the computing unit 701, one or more steps of the saturation flow rate determination method described above may be performed. Alternatively, in other embodiments, the computing unit 701 may be configured to perform the saturation flow rate determination method in any other appropriate manner (e.g., by means of firmware).

[0115] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0116] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0117] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0118] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0119] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0120] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship between the client and the server is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and virtual private servers (VPS) services.

[0121] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0122] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A method for determining a saturation flow rate, comprising: Within a traffic light cycle at a target intersection, counting the number of queued vehicles on a target lane and the number of stops of vehicles passing through the target intersection via the target lane; Based on the number of queued vehicles and the number of parking times, determining whether the time period corresponding to the signal light cycle is an oversaturated time period; In response to the time period corresponding to the signal light cycle being an oversaturated time period, determining the vehicle flow and green-signal ratio of the target lane in the oversaturated time period; Based on the duration of the oversaturation time period, the vehicle flow rate and the green-to-signal ratio, the saturation flow rate of the target lane in the oversaturation time period is determined.

2. The method according to claim 1, wherein: The method of counting the number of queued vehicles on the target lane and the number of stops of vehicles passing through the target intersection via the target lane within a traffic light cycle at the target intersection includes: During the signal light cycle, counting the number of queued vehicles on the target lane within a predetermined distance from the target intersection; Vehicles passing through the target intersection via the target lane during the green light duration of the signal light cycle are determined as passing vehicles, and the number of stops of each passing vehicle within a predetermined distance range from the target intersection is counted.

3. The method according to claim 2, wherein: Also includes: During the signal light cycle, counting the traffic volume passing through the target intersection via the target lane; The determining, based on the number of queuing vehicles and the number of parking times, whether the time period corresponding to the signal light cycle is an oversaturated time period includes: In response to the number of queuing vehicles being greater than the traffic flow, and the number of stops of each passing vehicle within a predetermined distance range from the target intersection being greater than once, it is determined that the time period corresponding to the traffic light cycle is an oversaturated time period.

4. The method according to claim 3, further comprising: According to the characteristics of the total traffic volume of the road network in one day, the time of one day is divided into multiple initial time intervals, wherein the duration of each initial time interval is negatively correlated with the total traffic volume of the road network in each initial time interval, and each initial time interval includes multiple signal light cycles; During a plurality of signal light cycles in the initial time interval, respectively counting the number of queued vehicles on a target lane of the target intersection and the number of stops of vehicles passing through the target intersection via the target lane; Based on the number of queued vehicles and the number of parking times in each signal light cycle, determining whether there is an oversaturated time period from the time periods corresponding to the multiple signal light cycles respectively; In response to the existence of an oversaturated time period in the initial time interval, determining the initial time interval as a target time interval; Based on the duration of the supersaturated time period of the target time interval, the vehicle flow rate and the green-to-signal ratio, the saturated flow rate of the target lane in the target time interval is determined.

5. The method according to claim 4, wherein: The determining the saturation flow rate of the target lane in the target time interval based on the duration of the supersaturated time period, the vehicle flow rate, and the green-signal ratio of the target time interval includes: Determine the saturation flow rate of the target lane in each supersaturated time period of the target time interval based on the duration of the supersaturated time periods, the vehicle flow rate, and the green-signal ratio respectively corresponding to the multiple supersaturated time periods of the target time interval; An average value of the saturation flow rates of the target lane in each supersaturation time period in the target time interval is calculated to obtain the saturation flow rate of the target lane in the target time interval.

6. The method according to claim 4 or 5, further comprising: An average value of the saturation flow rate of the target lane in the target time interval over multiple days is calculated to obtain a target saturation flow rate of the target lane in the target time interval.

7. The method according to claim 6, further comprising: In response to receiving an optimization instruction for the traffic light cycle of the target intersection, obtaining a target saturation flow rate of the target lane within the target time interval one day before the current moment; Based on the target saturation flow rate of the previous day, the green light duration, the signal light cycle duration and the signal light timing of the target intersection within the target time interval are adjusted.

8. The method according to claim 6, wherein: Calculating the average of the saturation flow rates of the target lane in the target time interval over multiple days to obtain the target saturation flow rate of the target lane in the target time interval includes: Calculate an average value of the saturation flow rate of the target lane in the target time interval for a preset number of days before the current day; The target saturated flow rate of the target lane in the target time interval is obtained by taking a weighted sum of the saturated flow rate of the target lane in the target time interval on the current day and the average of the saturated flow rates of the target lane in the target time interval on the preset days.

9. A saturation flow rate determination device, comprising: A counting module is configured to count the number of queued vehicles on a target lane and the number of stops of vehicles passing through the target intersection via the target lane within a traffic light cycle at the target intersection; A judgment module is configured to determine whether a time period corresponding to the signal light cycle is an oversaturated time period based on the number of queuing vehicles and the number of parking times; A processing module is configured to determine the vehicle flow and green signal ratio of the target lane in the oversaturated time period in response to the time period corresponding to the signal light cycle being an oversaturated time period; The calculation module is configured to determine the saturation flow rate of the target lane in the oversaturation time period based on the duration of the oversaturation time period, the vehicle flow rate and the green-to-signal ratio.

10. The device according to claim 9, wherein: The statistics module is further configured as follows: During the signal light cycle, counting the number of queued vehicles on the target lane within a predetermined distance from the target intersection; Vehicles passing through the target intersection via the target lane during the green light duration of the signal light cycle are determined as passing vehicles, and the number of stops of each passing vehicle within a predetermined distance range from the target intersection is counted.

11. The device according to claim 10, wherein: The statistical module is further configured to count the traffic volume passing through the target intersection via the target lane during the signal light cycle; The judgment module is further configured to determine that the time period corresponding to the traffic light cycle is an oversaturated time period in response to the number of queuing vehicles being greater than the traffic flow and the number of stops of each passing vehicle within a predetermined distance range from the target intersection being greater than once.

12. The apparatus according to claim 11, further comprising: The interval division module is configured to divide a day into a plurality of initial time intervals according to the characteristics of the total amount of road network traffic in a day, wherein the duration of each initial time interval is negatively correlated with the total amount of road network traffic in each initial time interval, and each initial time interval includes a plurality of signal light cycles; The statistical module is further configured to respectively count the number of queued vehicles on the target lane of the target intersection and the number of stops of vehicles passing through the target intersection via the target lane during a plurality of signal light cycles in the initial time interval; The determination module is further configured to determine whether there is an oversaturated time period from the time periods respectively corresponding to the plurality of signal light cycles based on the number of queued vehicles and the number of parking times in each signal light cycle; The processing module is further configured to, in response to the presence of an oversaturated time period in the initial time interval, determine the initial time interval as a target time interval; The calculation module is further configured to determine the saturation flow rate of the target lane within the target time interval based on the duration of the oversaturation time period of the target time interval, the vehicle flow rate and the green-to-signal ratio.

13. The device according to claim 12, wherein: The computing module is further configured to: Determine the saturation flow rate of the target lane in each supersaturated time period of the target time interval based on the duration of the supersaturated time periods, the vehicle flow rate, and the green-signal ratio respectively corresponding to the multiple supersaturated time periods of the target time interval; An average value of the saturation flow rates of the target lane in each supersaturation time period in the target time interval is calculated to obtain the saturation flow rate of the target lane in the target time interval.

14. The device according to claim 12 or 13, wherein: The calculation module is further configured to calculate an average value of the saturation flow rate of the target lane in the target time interval over multiple days to obtain a target saturation flow rate of the target lane in the target time interval.

15. The apparatus according to claim 14, further comprising: The optimization module is configured as follows: In response to receiving an optimization instruction for the traffic light cycle of the target intersection, obtaining a target saturation flow rate of the target lane within the target time interval one day before the current moment; Based on the target saturation flow rate of the previous day, the green light duration, the signal light cycle duration and the signal light timing of the target intersection within the target time interval are adjusted.

16. The device according to claim 14, wherein: The computing module is further configured to: Calculate an average value of the saturation flow rate of the target lane in the target time interval for a preset number of days before the current day; The target saturated flow rate of the target lane in the target time interval is obtained by taking a weighted sum of the saturated flow rate of the target lane in the target time interval on the current day and the average of the saturated flow rates of the target lane in the target time interval on the preset days.

17. An electronic device comprising: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the saturation flow rate determination method according to any one of claims 1 to 8.

18. A non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause the computer to execute the saturation flow rate determination method according to any one of claims 1 to 8.

19. A computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the method for determining the saturation flow rate according to any one of claims 1 to 8 is implemented.

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