Urban green wave band optimization method, device, storage medium and program product

Abstract

Embodiments of the present application relate to the technical field of intelligent transportation, and disclose a city green wave band optimization method, device, storage medium and program product. The method comprises the following steps: finding at least one path from a starting point to a terminal point; calculating a green light synchronization rate of each path according to a vehicle departure time, a passing time between each signal light and a prescribed green light time of each signal light; and determining an optimal path according to the green light synchronization rate of each path, thereby solving the problem of overall traffic being not smooth in the prior art.

Description

Technical Field

[0001] This application relates to the field of intelligent transportation technology, and in particular to a method, device, storage medium and program product for optimizing urban green wave traffic. Background Technology

[0002] Urban green wave optimization technology aims to improve road traffic efficiency by coordinating the green light periods of multiple traffic lights, allowing vehicles to pass through multiple traffic lights smoothly without stopping.

[0003] Currently, a common optimization method is the dynamic adjustment method based on real-time data. The principle is to collect traffic flow data in real time and dynamically adjust the timing of traffic lights to ensure that the green wave can adapt to the current traffic conditions.

[0004] However, the drawback of this method is that although it can adjust traffic lights based on real-time data, it usually does not take into account the global coordination between traffic lights. This may result in some road sections having well optimized green waves, while other road sections have poor performance, thus affecting the overall traffic flow. Summary of the Invention

[0005] One objective of this application is to provide a method, device, storage medium, and program product for optimizing urban green wave traffic, at least to address the problem of overall traffic congestion in the prior art.

[0006] To achieve the above objectives, some embodiments of this application provide the following aspects:

[0007] In a first aspect, some embodiments of this application also provide a method for optimizing urban green wave zones, the method comprising:

[0008] Find at least one path from the starting point to the ending point;

[0009] Calculate the green light synchronization rate for each route based on the vehicle departure time, the passage time between each traffic light, and the prescribed green light time for each traffic light.

[0010] The optimal path is determined based on the green light synchronization rate of each path.

[0011] Furthermore, the calculation of the green light synchronization rate for each route based on the vehicle departure time, the passage time between each traffic light, and the prescribed green light time for each traffic light includes:

[0012] Calculate the estimated arrival time of the vehicle at each traffic light along the route based on the passage time between each traffic light and the vehicle's departure time.

[0013] Based on the estimated arrival time of each traffic light in the route and the prescribed green light time of each traffic light, determine the effective green light time of each traffic light in the route;

[0014] Calculate the green light synchronization rate for each route based on the effective green light time of each traffic light along the route.

[0015] Furthermore, based on the effective green light time of each traffic light along the route, the green light synchronization rate of each route is calculated using the following formula.

[0016]

[0017] in, The valid green light time of the traffic light is represented by n, the number of traffic lights in the path is represented by j, and the number of different traffic lights is represented by j.

[0018] Furthermore, determining the optimal path based on the green light synchronization rate of each path includes:

[0019] The optimal route is determined based on the passage time between each traffic light and the green light synchronization rate of each path.

[0020] Furthermore, determining the optimal path based on the passage time between each traffic light and the green light synchronization rate of each path includes:

[0021] The weight of each path is calculated according to the following formula, based on the passage time between each traffic light and the green light synchronization rate of each path.

[0022]

[0023] in, Indicates the passage time between traffic lights. Indicates the green light synchronization rate. These are the weighting coefficients;

[0024] The optimal path is determined based on the path weight.

[0025] Furthermore, determining the optimal path based on path weights includes:

[0026] The optimal path is obtained through a priority matrix, which sequentially stores the weights of each path.

[0027] Furthermore, the Trie tree stores connectivity data between road segments, and finding at least one path from the starting point to the ending point includes:

[0028] Starting from the root node of the Trie tree, traverse each branch level by level to find all paths from the starting point to the ending point;

[0029] Before calculating the green light synchronization rate of each route based on vehicle departure time, transit time between traffic lights, and the prescribed green light time of each traffic light, the process also includes:

[0030] The prescribed green time for each traffic light is queried using a B+ tree, where the keys and values ​​of the B+ tree are used to store the traffic light number and the prescribed green time interval of the traffic light, respectively.

[0031] The passage time between each traffic light can be queried using an adjacency matrix, where the matrix elements represent the passage time of different traffic lights.

[0032] Furthermore, determining the optimal path based on the green light synchronization rate of each path includes:

[0033] If a change in the status of a traffic light is detected in the path, the green light synchronization rate of each path is recalculated.

[0034] The optimal path is determined based on the recalculated green light synchronization rate of each path.

[0035] Secondly, embodiments of this application also provide an urban green wave optimization device, the device comprising:

[0036] The search module is used to find at least one path from the starting point to the ending point;

[0037] The synchronization calculation module is used to calculate the green light synchronization rate of each route based on the vehicle departure time, the passage time between each traffic light, and the prescribed green light time of each traffic light.

[0038] The optimal path module is used to determine the optimal path based on the green light synchronization rate of each path.

[0039] Thirdly, embodiments of this application also provide an electronic device, the electronic device comprising:

[0040] One or more processors; and

[0041] A memory storing computer program instructions, which, when executed, cause the processor to perform the steps of the method as described in any one of the embodiments of this application.

[0042] Fourthly, embodiments of this application also provide a computer-readable medium having a computer program / instructions stored thereon, which, when executed by a processor, implement the steps of any of the methods described in the embodiments of this application.

[0043] Fifthly, embodiments of this application also provide a computer program product, including a computer program / instructions, which, when executed by a processor, implement the steps of any of the methods described in the embodiments of this application.

[0044] Compared with related technologies, the solution provided in this application calculates the green light synchronization rate for each path based on the vehicle departure time, the passage time between each traffic light, and the prescribed green light time of each traffic light. The green light synchronization rate reflects the green wave synchronicity, and the optimal path is determined based on this synchronicity. Because the green wave synchronicity of the entire path is considered, global coordination between traffic lights is taken into account, avoiding the problem of some road sections having well-optimized green wave bands while others are less effective, thus ensuring smooth overall traffic flow along the path.

[0045] By efficiently combining multiple data structures such as Trie trees, B+ trees, adjacency matrices, and priority queues, the retrieval efficiency of paths, specified green light times, passage times between traffic lights, and minimum weight paths is improved.

[0046] If a change in the status of a traffic light is detected in the path, the green light synchronization rate of each path is recalculated. Based on the recalculated green light synchronization rate of each path, the optimal path is determined, thereby achieving the effect of dynamic path planning that adjusts the path in real time according to changes in the status of traffic lights.

[0047] By using the green wave synchronization rate formula, the effective green light time of all traffic lights in the path is comprehensively evaluated, thus improving the globality of green wave synchronization calculation.

[0048] By balancing travel time with green wave synchronization, the optimal route is recommended, thereby determining the optimal route. Attached Figure Description

[0049] One or more embodiments are illustrated by way of example with reference numerals in the accompanying drawings. These illustrations do not constitute a limitation on the embodiments. Elements with the same reference numerals in the drawings are denoted as similar elements. Unless otherwise stated, the figures in the drawings are not to be limited by scale.

[0050] Figure 1 This is a flowchart of an urban green wave optimization method provided according to some embodiments of this application;

[0051] Figure 2 This is a schematic diagram of a Trie tree data structure provided according to some embodiments of this application;

[0052] Figure 3 This is a schematic diagram of a data structure for an adjacency matrix according to some embodiments of this application;

[0053] Figure 4 This is a schematic diagram of the structure of an urban green wave optimization device according to some embodiments of this application;

[0054] Figure 5 This is a schematic diagram of the structure of an electronic device according to some embodiments of this application. Detailed Implementation

[0055] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0056] First Embodiment

[0057] The first embodiment of this application relates to an urban green wave optimization method, which is applied to systems with path planning requirements, such as navigation systems. Figure 1 The flowchart below shows a method for optimizing urban green wave zones according to some embodiments of this application. Figure 1 As shown, the method may include the following steps:

[0058] S110, Find at least one path from the starting point to the ending point.

[0059] The starting point is the origin of the journey, and the ending point is the destination. The connectivity between road segments is pre-stored using one or more data structures. Optionally, each road segment can be identified by a road segment number or the traffic light number on it.

[0060] By analyzing the stored data structure and the connectivity between road segments, different combinations of connected road segments from the starting point to the end point are found, and these different combinations of connected road segments constitute at least one path.

[0061] Optionally, the connectivity relationships between road segments can be stored in a Trie tree, which is a multi-branch tree structure. The nodes of the Trie tree are used to store road segment identifiers, which can be either road segment numbers or traffic light numbers. Each path from the root node to a leaf node represents a possible travel route.

[0062] The path finding method based on Trie trees is as follows: starting from the root node, traverse each branch level by level to find all complete paths starting from the root node; input a path prefix and output all paths starting from that prefix.

[0063] For example, see Figure 2 If the input path prefix is ​​L100, then the output paths are: Path 1: L100→L101→L102; Path 2: L100→L102. Where L100, L101, and L102 are road segment identifiers.

[0064] Trie-based pathfinding methods have the following advantages:

[0065] 1. Efficient path matching:

[0066] Trie trees utilize prefix matching to quickly find all paths that begin with a specific traffic light number. The search time complexity is O(k), where k is the prefix length.

[0067] 2. Reduce redundant calculations:

[0068] Compared to directly storing a list of paths, a Trie tree avoids repeatedly storing common prefixes, thus improving storage efficiency.

[0069] 3. Good scalability:

[0070] When adding a new path, simply add the new node to the Trie tree; there is no need to rebuild the entire structure.

[0071] 4. Supports dynamic updates:

[0072] When traffic lights or route information change, the corresponding nodes and routes can be quickly added or deleted.

[0073] S120 calculates the green light synchronization rate for each route based on the vehicle departure time, the passage time between each traffic light, and the prescribed green light time for each traffic light.

[0074] The specified green light time is the green light time interval for each traffic light, for example, [08:05, 08:35].

[0075] Green light synchronization rate describes the probability that all traffic lights on a path are green when a vehicle passes through; it can also be described as green light synchronicity.

[0076] To improve data retrieval efficiency, before calculating the green light synchronization rate of each path based on vehicle departure time, passage time between traffic lights, and the prescribed green light time of each traffic light, the following steps are also included:

[0077] The prescribed green time for each traffic light is queried using a B+ tree, where the keys and values ​​of the B+ tree are used to store the traffic light number and the prescribed green time interval of the traffic light, respectively.

[0078] The passage time between each traffic light can be queried using an adjacency matrix, where the matrix elements represent the passage time of different traffic lights.

[0079] B+ trees are balanced trees that can find the green light time interval of a traffic light in O(log N) time complexity, ensuring efficient query speed. Assuming the traffic light number is L100 and its green light time is 08:00-08:30, its storage in a B+ tree can be: key: traffic light number, such as L100; value: a range containing the start time, end time, and duration of the green light, such as [08:00, 08:30].

[0080] For multiple traffic lights with green light times, a B+ tree sorts them by traffic light number, and the green light time interval for each traffic light is used as a data item in the tree node. For example, suppose we have the following traffic lights and their green light times:

[0081] L100: [08:00, 08:30]

[0082] L101: [08:10, 08:40]

[0083] L102: [08:20, 08:50]

[0084] Suppose we want to check whether a certain time (e.g., 08:05) is within the green light period of a certain traffic light. The search process is as follows: search for the value corresponding to L100 [08:00, 08:30]. When querying, compare whether the current time (e.g., 08:05) is within this time interval. If the current time is within this interval, it means that the green light status of the traffic light is valid.

[0085] The application of B+ trees has the following advantages:

[0086] 1. Efficient range queries: B+ trees can efficiently determine whether a specific time point falls within the green light period of a traffic light. Their sorting structure ensures efficient searching, making them suitable for scenarios requiring frequent queries of time ranges.

[0087] 2. Adapting to Dynamic Data: Green light times can change dynamically, and B+ trees can easily perform add, delete, and modify operations to maintain data order. In practical applications, green light times may be adjusted based on traffic flow, road conditions, etc., and B+ trees can efficiently handle these dynamic changes.

[0088] 3. Fast range query: In addition to single-time queries, B+ trees also support range queries, which can query the green time of all traffic lights within a certain time period, thereby realizing the coordinated control of multiple traffic lights.

[0089] For example, the adjacency matrix is ​​as follows: Figure 3As shown, the matrix element W{ij} represents the travel time from traffic light i to traffic light j. For example, if i represents traffic light L100 and j represents traffic light L101, from matrix W{12} = 5, we can deduce that the travel time from L100 to L101 is 5. Similarly, according to W{13} = 10, we can deduce that the travel time from L100 to L102 is 10.

[0090] The adjacency matrix-based search method is as follows: Input a path, such as L100→L101→L102; traverse each segment of the path, query W{ij} to obtain the travel time of each segment, and sum the travel times of all segments to obtain the total travel time.

[0091] The adjacency matrix-based search method has the following advantages:

[0092] 1. Low time complexity:

[0093] The adjacency matrix is ​​a two-dimensional array. Finding the travel time of any path only requires a simple indexing operation, with a time complexity of O(1).

[0094] 2. Global View:

[0095] The adjacency matrix stores the passage information between all traffic lights, and a single query can obtain the passage time of multiple paths at the same time.

[0096] 3. Facilitates dynamic updates:

[0097] If the travel time for a certain road segment changes due to traffic conditions, the corresponding matrix values ​​can be updated quickly.

[0098] 4. Supports complex algorithms:

[0099] Adjacency matrices can be directly used in path planning algorithms to quickly calculate the shortest path or other optimization metrics.

[0100] S130, determine the optimal path based on the green light synchronization rate of each path.

[0101] Alternatively, the path with the highest green light synchronization rate can be directly selected as the optimal path.

[0102] The solution provided in this application calculates the green wave synchronicity of each path, where the green light synchronization rate reflects the green wave synchronicity, and determines the optimal path based on the green wave synchronicity. Because the green wave synchronicity of the entire path is considered, global coordination between traffic lights is taken into account, avoiding the problem that some road sections have well-optimized green wave bands while other road sections have poor performance, thus ensuring smooth overall traffic flow along the path.

[0103] Optionally, calculating the green light synchronization rate for each path based on the vehicle departure time, the passage time between each traffic light, and the prescribed green light time for each traffic light includes:

[0104] Calculate the estimated arrival time of the vehicle at each traffic light along the route based on the passage time between each traffic light and the vehicle's departure time.

[0105] Based on the estimated arrival time of each traffic light in the route and the prescribed green light time of each traffic light, determine the effective green light time of each traffic light in the route;

[0106] Calculate the green light synchronization rate for each route based on the effective green light time of each traffic light along the route.

[0107] The effective green light time is the time from when a vehicle arrives at the traffic light until the traffic light remains green.

[0108] Specifically, the green light synchronization rate of each route is calculated according to the effective green light time of each traffic light in the route, based on the following formula.

[0109]

[0110] in, It's the green light synchronization rate. The valid green light time of the traffic light is represented by n, the number of traffic lights in the path is represented by j, and the number of different traffic lights is represented by j.

[0111] To comprehensively consider the impact of travel time factors, the determination of the optimal route based on the green light synchronization rate of each route includes:

[0112] The optimal route is determined based on the passage time between each traffic light and the green light synchronization rate of each path.

[0113] Specifically, determining the optimal route based on the passage time between each traffic light and the green light synchronization rate of each path includes:

[0114] The weight of each path is calculated according to the following formula, based on the passage time between each traffic light and the green light synchronization rate of each path.

[0115]

[0116] in, It is the weight of each path. Indicates the passage time between traffic lights. Indicates the green light synchronization rate. These are the weighting coefficients;

[0117] The optimal path is determined based on the path weight.

[0118] The weighting coefficient is used to control the balance between travel time and green wave synchronicity. By adjusting the weighting coefficient, priority can be given to less waiting time (i.e., lower travel time) or higher green wave synchronicity (i.e., reducing the number of times stopping at red lights).

[0119] Path weights are used to describe the probability that a path is the optimal path; the larger the path weight, the lower the probability that it is the optimal path.

[0120] Optionally, the weights of each path can be stored using any data structure.

[0121] To improve the efficiency of finding the minimum path weight, the step of determining the optimal path based on the path weight includes:

[0122] The optimal path is obtained through a priority matrix, which sequentially stores the weights of each path.

[0123] To achieve dynamic route planning that adjusts routes in real time based on traffic light status changes, the process of determining the optimal route based on the green light synchronization rate of each route includes:

[0124] If a change in the status of a traffic light is detected in the path, the green light synchronization rate of each path is recalculated.

[0125] The optimal path is determined based on the recalculated green light synchronization rate of each path.

[0126] The change in traffic light status can be from red to green, or from green to yellow, etc.

[0127] Optionally, the triggering condition for recalculating the green light synchronization rate of each path can also be that the traffic light to be passed is about to change from green to another light, or it can be triggered by a set time. The specific setting depends on actual needs, and this application embodiment does not limit this.

[0128] For example, suppose there are three traffic lights in a green wave optimization project in city A, namely L100, L101 and L102.

[0129] The traffic lights specify the green light times as follows: L100: [08:00, 08:30], L101: [08:05, 08:35], L102: [08:10, 08:40].

[0130] The passage times between traffic lights were obtained by querying the adjacency matrix as follows:

[0131] L100→L101: 5 minutes

[0132] L101→L102: 6 minutes

[0133] L100→L102: 10 minutes

[0134] Vehicle departure time: 08:00, departing from L100.

[0135] Step 1: Use a Trie tree to find all paths starting from L100: L100→L101→L102 and L100→L102.

[0136] Step 2: Calculate the estimated arrival time for each path:

[0137] Path 1: L100→L101→L102

[0138] L100 arrival time: 08:00.

[0139] L101 arrival time: 08:00 + 5 minutes = 08:05.

[0140] L102 arrival time: 08:05 + 6 minutes = 08:11.

[0141] Path 2: L100 → L102

[0142] L100 arrival time: 08:00.

[0143] L102 arrival time: 08:00 + 10 minutes = 08:10.

[0144] Step 3: Calculate the effective green time for each traffic light:

[0145] Path 1:

[0146] L100: Arrived at 08:00, green light lasts until 08:30, effective green light time: 30 minutes.

[0147] L101: Arrived at 08:05, green light lasts until 08:35, effective green light time: 30 minutes.

[0148] L102 arrived at 08:11, the green light lasted until 08:40, the effective green light time was 29 minutes.

[0149] Path 2:

[0150] L100: Arrived at 08:00, green light lasts until 08:30, effective green light time: 30 minutes.

[0151] L102: Arrived at 08:10, green light lasts until 08:40, effective green light time: 30 minutes.

[0152] Step 4: Calculate the path synchronization score:

[0153] Path 1: S sync =

[0154] Path 2: S sync =

[0155] Step 5: Calculate based on path weights:

[0156] The total travel time for route 1 is: 5 minutes + 6 minutes = 11 minutes, with a weighting coefficient α = 0.7. The final weight of route 1 is:

[0157] W path1 =0.7×11+0.3×(1-29.66 / 30)=7.7+0.0034=7.7034

[0158] The total travel time for route 2 is 10 minutes, with a weighting coefficient α = 0.7. The final weight of route 2 is:

[0159] W path1 =0.7×10+0.3×(1-30 / 30)=7+0=7

[0160] Final result:

[0161] Based on the priority queue, path 2 with the minimum weight is found to be the optimal path.

[0162] Second Embodiment

[0163] The second embodiment of this application relates to an urban green wave optimization device. Figure 4 This is a schematic diagram of the structure of an urban green wave optimization device according to some embodiments of this application, such as... Figure 4 As shown, the device includes:

[0164] The search module 10 is used to find at least one path from the starting point to the ending point;

[0165] The synchronization calculation module 20 is used to calculate the green light synchronization rate of each path based on the vehicle departure time, the passage time between each traffic light, and the prescribed green light time of each traffic light.

[0166] The optimal path module 30 is used to determine the optimal path based on the green light synchronization rate of each path.

[0167] The solution provided in this application calculates the green wave synchronicity of each path, where the green light synchronization rate reflects the green wave synchronicity, and determines the optimal path based on the green wave synchronicity. Because the green wave synchronicity of the entire path is considered, global coordination between traffic lights is taken into account, avoiding the problem that some road sections have well-optimized green wave bands while other road sections have poor performance, thus ensuring smooth overall traffic flow along the path.

[0168] The synchronous computing module is specifically used for:

[0169] Calculate the estimated arrival time of the vehicle at each traffic light along the route based on the passage time between each traffic light and the vehicle's departure time.

[0170] Based on the estimated arrival time of each traffic light in the route and the prescribed green light time of each traffic light, determine the effective green light time of each traffic light in the route;

[0171] Calculate the green light synchronization rate for each route based on the effective green light time of each traffic light along the route.

[0172] Furthermore, based on the effective green light time of each traffic light along the route, the green light synchronization rate of each route is calculated using the following formula.

[0173]

[0174] in, The valid green light time of the traffic light is represented by n, the number of traffic lights in the path is represented by j, and the number of different traffic lights is represented by j.

[0175] The optimal path module is specifically used for:

[0176] The optimal route is determined based on the passage time between each traffic light and the green light synchronization rate of each path.

[0177] Furthermore, determining the optimal path based on the passage time between each traffic light and the green light synchronization rate of each path includes:

[0178] The weight of each path is calculated according to the following formula, based on the passage time between each traffic light and the green light synchronization rate of each path.

[0179]

[0180] in, Indicates the passage time between traffic lights. Indicates the green light synchronization rate. These are the weighting coefficients;

[0181] The optimal path is determined based on the path weight.

[0182] Furthermore, determining the optimal path based on path weights includes:

[0183] The optimal path is obtained through a priority matrix, which sequentially stores the weights of each path.

[0184] The search module includes:

[0185] Starting from the root node of the Trie tree, traverse each branch level by level to find all paths from the starting point to the ending point;

[0186] The device further includes:

[0187] The data lookup module is used to query the specified green light time of each traffic light before calculating the green light synchronization rate of each path based on the vehicle departure time, the passage time between each traffic light, and the specified green light time of each traffic light. The key and value of the B+ tree are used to store the traffic light number and the specified green light time interval of the traffic light, respectively.

[0188] The passage time between each traffic light can be queried using an adjacency matrix, where the matrix elements represent the passage time of different traffic lights.

[0189] The optimal path module is specifically used for determining the optimal path based on the green light synchronization rate of each path, including:

[0190] If a change in the status of a traffic light is detected in the path, the green light synchronization rate of each path is recalculated.

[0191] The optimal path is determined based on the recalculated green light synchronization rate of each path.

[0192] The steps of the various methods described above are only for clarity. In practice, they can be combined into one step or some steps can be split into multiple steps. As long as they include the same logical relationship, they are all within the scope of protection of this patent. Adding insignificant modifications or introducing insignificant designs to the algorithm or process, but without changing the core design of the algorithm and process, are also within the scope of protection of this patent.

[0193] Furthermore, some embodiments of this application also provide an electronic device. The electronic device can be various forms of digital computer, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, etc. The electronic device can also be various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices.

[0194] The electronic device includes: one or more processors; and a memory storing computer program instructions that, when executed, cause the processor to perform the steps of the methods provided in any one or more of the above embodiments. Figure 5 An exemplary structural diagram of the electronic device is disclosed. For example... Figure 5As shown, the electronic device includes one or more processors 1101, a memory 1102, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components are interconnected via different buses and can be mounted on a common motherboard or otherwise as required. The processors can process instructions executed within the electronic device, including instructions stored in or on memory to display graphical information of a GUI on an external input / output device (such as a display device coupled to the interface). In some other embodiments, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple electronic devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). The components, their connections and relationships, and their functions shown herein are merely examples and are not intended to limit the implementation of the present application described and / or claimed herein.

[0195] The electronic device may further include an input device 1103 and an output device 1104. The processor 1101, memory 1102, input device 1103, and output device 1104 may be connected via a bus or other means. Figure 5 Taking the example of a connection between China and Israel via a bus.

[0196] Input device 1103 can receive input numerical or character information, and generate key signal inputs related to user settings and function control of the electronic device, such as a touch screen, keypad, mouse, trackpad, touchpad, joystick, one or more mouse buttons, trackball, joystick, etc. Output device 1104 may include a display device, auxiliary lighting device (e.g., LED), and haptic feedback device (e.g., vibration motor). The display device may include, but is not limited to, a liquid crystal display (LCD), a light-emitting diode (LED) display, and a plasma display. In some embodiments, the display device may be a touch screen.

[0197] To provide interaction with the user, the electronic device can be a computer. The computer has: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides 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 sound input, voice input, or tactile input).

[0198] In this embodiment, a computer-readable medium stores a computer program / instructions that, when executed by a processor, implement the steps of the methods provided in any one or more of the above embodiments. This computer-readable medium may be included in the electronic device described in the above embodiments; or it may exist independently and not assembled into that device. The aforementioned computer-readable medium carries one or more computer-readable instructions.

[0199] The memory 1102 can serve as a non-transitory computer-readable storage medium, used to store non-transitory software programs, non-transitory computer-executable programs, and modules. The processor 1101 executes various functional applications and data processing of the server by running the non-transitory software programs, instructions, and modules stored in the memory 1102, thereby implementing the program instructions / modules corresponding to the methods provided in any one or more of the embodiments described above in this application.

[0200] The memory 1102 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the electronic device. Furthermore, the memory 1102 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory 1102 may optionally include memory remotely located relative to the processor 1101, and these remote memories can be connected to the electronic device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0201] It should be noted that the computer-readable medium described in this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0202] Computer-readable media include permanent and non-permanent, removable and non-removable media, which can store information by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, read-only optical disc (CD-ROM), digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device.

[0203] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0204] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. For example, it can be implemented using an application-specific integrated circuit (ASIC), a general-purpose computer, or any other similar hardware device. In some embodiments, the software program of this application can be executed by a processor to implement the steps or functions described above. Similarly, the software program of this application (including related data structures) can be stored in a computer-readable recording medium, such as RAM memory, magnetic or optical drives, floppy disks, or similar devices. Furthermore, some steps or functions of this application can be implemented in hardware, for example, as circuitry that works with a processor to perform the various steps or functions.

[0205] The computer program product provided in this application includes one or more computer programs / instructions. When executed by a processor, these computer programs / instructions generate, in whole or in part, the processes or functions described in this application. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0206] The flowcharts or block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of devices, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-specific system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0207] The scope of this application is defined by the appended claims rather than the foregoing description, and is therefore intended to encompass all variations falling within the meaning and scope of equivalents of the claims. No reference numerals in the claims should be construed as limiting the scope of the claims. Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices recited in a device claim may also be implemented by a single unit or device in software or hardware. Terms such as "first," "second," etc., are used only for distinguishing descriptions and do not indicate any particular order, nor should they be construed as indicating or implying relative importance.

[0208] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily made by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims, and the above embodiments should be regarded as exemplary and non-limiting.

Claims

1. A method for optimizing urban green wave zones, characterized in that, The method includes: Find at least one path from the starting point to the ending point; Calculate the green light synchronization rate for each route based on the vehicle departure time, the passage time between each traffic light, and the prescribed green light time for each traffic light. The optimal path is determined based on the green light synchronization rate of each path; The calculation of the green light synchronization rate for each route based on vehicle departure time, transit time between traffic lights, and the prescribed green light time for each traffic light includes: Calculate the estimated arrival time of the vehicle at each traffic light along the route based on the passage time between each traffic light and the vehicle's departure time. Based on the estimated arrival time of each traffic light in the route and the prescribed green light time of each traffic light, determine the effective green light time of each traffic light in the route; Calculate the green light synchronization rate of each route based on the effective green light time of each traffic light in the route; The process of determining the optimal path based on the green light synchronization rate of each path includes: The optimal route is determined based on the passage time between each traffic light and the green light synchronization rate of each route; The process of determining the optimal route based on the passage time between each traffic light and the green light synchronization rate of each path includes: The weight of each path is calculated according to the following formula, based on the passage time between each traffic light and the green light synchronization rate of each path. ； in, Indicates the passage time between traffic lights. Indicates the green light synchronization rate. These are the weighting coefficients; The optimal path is determined based on the path weight.

2. The method according to claim 1, characterized in that, Calculate the green light synchronization rate for each route based on the effective green light time of each traffic light in the route, using the following formula. ； in, The valid green light time of the traffic light is represented by n, the number of traffic lights in the path is represented by j, and the number of different traffic lights is represented by j.

3. The method according to claim 1, characterized in that, The process of determining the optimal path based on path weight includes: The optimal path is obtained through a priority matrix, which sequentially stores the weights of each path.

4. The method according to claim 1, characterized in that, The Trie tree stores connectivity data between road segments. Finding at least one path from the starting point to the ending point includes: Starting from the root node of the Trie tree, traverse each branch level by level to find all paths from the starting point to the ending point; Before calculating the green light synchronization rate of each route based on vehicle departure time, transit time between traffic lights, and the prescribed green light time of each traffic light, the process also includes: The prescribed green time for each traffic light is queried using a B+ tree, where the keys and values ​​of the B+ tree are used to store the traffic light number and the prescribed green time interval of the traffic light, respectively. The passage time between each traffic light can be queried using an adjacency matrix, where the matrix elements represent the passage time of different traffic lights.

5. An electronic device, characterized in that, The electronic device includes: One or more processors; and A memory storing computer program instructions, which, when executed, cause the processor to perform the steps of the method as described in any one of claims 1-4.

6. A computer-readable medium having a computer program / instructions stored thereon, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the method according to any one of claims 1-4.

7. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the method described in any one of claims 1-4.

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