135 results about "Traffic optimization" patented technology

The invention provides an intelligent traffic data processing method and system based on edge computing, and the method comprises the steps: collecting multi-source traffic data through an edge computing node, carrying out the preprocessing of the data, and obtaining the preprocessing data; and performing space-time alignment and data association processing on the preprocessed data to generate an associated data set, and completing feature extraction and preliminary analysis at an edge computing node to obtain a local analysis result. The local analysis result is transmitted to the cloud server, deep data mining and mode recognition are carried out, and a global analysis result is generated. And based on the global analysis result, generating a local traffic control strategy at the edge computing node, and generating a global traffic optimization scheme at the cloud server. The local traffic control strategy is issued to traffic facilities for execution, and the global optimization scheme is distributed to each edge computing node to realize cooperative control. The advantages of edge computing and cloud processing are combined, and the real-time performance of traffic data processing and the overall optimization capability of an intelligent traffic system are effectively improved.

The invention discloses a deep reinforcement learning-based network traffic optimization scheduling method suitable for periodic traffic characteristics. The method comprises the following stages: collecting historical periodic traffic data; a network state is monitored in real time through an SDN controller, and historical periodic flow data including time delay, bandwidth utilization rate and load information are acquired; data preprocessing: performing cleaning, normalization and time sequence segmentation on the collected historical traffic data; constructing an LSTM model, constructing the LSTM model by using the preprocessed historical data, and defining an input feature and an output tag; training an LSTM model, training the LSTM model by using the training set data, and generating traffic prediction data of a next period; constructing a state space, fusing the predicted traffic data with the real-time traffic data, and constructing the state space; training a DQN model, and training a deep Q network (DQN) model by using the constructed state space; network topology and link state information are obtained, the current network topology is obtained through a topology discovery module of the SDN controller, and the bandwidth utilization rate, time delay and packet loss probability information of a link are collected in real time through a flow monitoring module; large flows and small flows are distinguished; scheduling the small flow by using the ECMP; and scheduling the large flow by using the DQN model.

The invention discloses a smart city traffic resource optimal configuration method, a platform and a storage medium, and relates to the technical field of traffic optimization. When a road accident is detected, accident information is collected, and a traffic accident position and traffic accident characteristics are obtained; time consumption prediction is carried out according to the traffic accident characteristics, and traffic control duration is obtained; calling real-time traffic data from an intelligent traffic system to obtain associated road data; executing multi-thread traffic flow back-stepping analysis to obtain associated traffic flow prediction information; carrying out shunting performance evaluation; and dynamically updating the signal lamp configuration of the plurality of shunting candidate roads by taking the associated traffic flow prediction information as a constraint. According to the invention, the technical problems of aggravation of traffic jam and low emergency management efficiency caused by lack of scientificity and dynamic adaptability of shunting planning and lagging of signal lamp regulation in a traffic accident scene in the prior art are solved, and the technical effects of optimizing traffic resource configuration, relieving traffic jam and improving traffic emergency management efficiency are achieved.

The invention discloses an urban traffic optimization and planning system based on reinforcement learning, and relates to the technical field of computer information, and the system comprises a traffic data acquisition unit, a reinforcement learning processing unit and a traffic planning unit. The traffic data acquisition unit is used for regarding target urban traffic as a network and regarding each intersection as a node, and constructing a nonlinear traffic flow dynamic model to update a multi-dimensional tensor at each time step; the reinforcement learning processing unit is used for taking a signal timing matrix of a signal lamp of a road section between every two nodes of target urban traffic as a state space and calculating a corresponding action space when a target reward function is a maximum value; and the traffic planning unit is used for finishing the traffic planning of the target city according to the action space of each road section. According to the method, the global optimization capability, the multi-objective optimization capability, the adaptive learning capability, the data processing capability, the safety and the interpretability of traffic signal control are remarkably improved.

The invention belongs to the technical field of traffic optimization, and particularly relates to a traffic optimization method based on a three-layer Stackelberg game, and the method comprises the steps: obtaining mixed traffic data; according to the mixed traffic data, constructing a three-layer Stackelberg game model; an equilibrium solution of the three-layer game model is solved through a deep reinforcement learning algorithm, an SAC algorithm is introduced into an upper layer to generate a global constraint strategy, and a DDPG algorithm is adopted in a lower layer to optimize cooperative driving and safe obstacle avoidance driving behaviors of the intelligent vehicle; according to the global constraint strategy and the intelligent vehicle driving behavior optimization result, the traffic efficiency, safety and energy consumption and carbon emission indexes of the mixed traffic flow are optimized, the traffic state is continuously updated through behavior feedback data of a middle layer and a lower layer, and strategy optimization is carried out in combination with SAC and DDPG models. Therefore, the problems of insufficient dynamic adaptability, low efficiency of main body interaction processing, limitation of a game model and the like in the prior art are solved.

The invention discloses a computer network management system based on a cognitive digital twinning technology. The computer network management system comprises an intelligent sensing layer, a multi-dimensional fusion layer and an enhanced visualization layer, wherein the intelligent sensing layer is used for outputting a state portrait to the multi-dimensional fusion layer and the enhanced visualization layer; the multi-dimensional fusion layer is used for outputting a knowledge graph to the cognitive decision-making layer and the enhanced visualization layer; the cognitive decision-making layer is used for performing second-level traceability on the fault propagation path, verifying the diagnosis hypothesis in parallel and then feeding back verification results of the fault propagation path and the diagnosis hypothesis to the enhanced visualization layer; carrying out intelligent decision making, and outputting a decision making strategy to a collaborative autonomous layer; the collaborative autonomous layer is responsible for automatically executing configuration change and flow optimization operations and feeding back the operations to the intelligent sensing layer; and the enhanced visualization layer is used for forming a dynamic visual network topology, establishing a visual map and simulating an attack path, when an administrator triggers rapid drilling analysis through an interaction instruction, a visualization verification result is fed back to the cognitive decision-making layer, and the data acquisition priority of the intelligent perception layer is adjusted.

The invention discloses a 5G + TSN fusion network asynchronous flow optimization scheduling method with high schedulability, which comprises the following steps: S1, in a 5G and TSN fusion network, firstly, a controller of a center collects overall information of the network through a network control protocol, the information comprises channel conditions and frequency resources in the 5G network, and the channel conditions and the frequency resources in the 5G network are transmitted to a server; the communication requirements of the network topology, the link rate and the flow of the TSN network are met; and S2, running an asynchronous traffic optimization scheduling model of the 5G + TSN hybrid network through the central controller, performing routing decision and transmission scheduling on the traffic collected in the previous step in the hybrid network to obtain a scheduling result, and minimizing the consumption of network resources while meeting the transmission requirement of hard real-time traffic. According to the invention, the schedulability of the TSN gateway is obviously improved, the application range of an asynchronous deterministic access mechanism is expanded, and end-to-end traffic scheduling is realized.

The invention relates to an internet of things quality of service (QoS) management method and system, and aims to solve the problem that the internet of things quality of service is unstable through a self-adaptive flow optimization technology. The system comprises a device identification module, a flow analysis module, a flow prediction module, a resource allocation module, a congestion control module, a safety guarantee module, a user feedback module and the like, predicts a flow trend by using a machine learning algorithm, dynamically adjusts resource allocation, and improves network efficiency and user experience. The method is suitable for various scales of Internet of Things environments, and is of great significance for promoting the development of the Internet of Things technology.

The invention discloses a dynamic network traffic optimization system based on artificial intelligence, and the system comprises a data collection and preprocessing module which is used for collecting and cleaning multi-source network traffic data in real time; the artificial intelligence model training and optimizing module is used for training the prediction model by using a deep learning algorithm and optimizing the performance of the prediction model; the traffic prediction and dynamic scheduling module is used for predicting future network traffic based on the prediction model and dynamically adjusting resource allocation; the intelligent monitoring and fault early warning module is used for monitoring the network flow and the network state in real time and carrying out anomaly detection and early warning; the user feedback and model continuous optimization module is used for collecting user feedback and network performance data to continuously optimize the prediction model; the data privacy and security protection module is used for ensuring the privacy and security of the data; and the system expansion and compatibility module provides a system expansion interface and supports butt joint with other network management systems. The dynamic management and optimization of the network flow are realized, the utilization efficiency of network resources is improved, and the data privacy and security are enhanced.

Methods for a network data analytics function (NWDAF) of a communication network. Such methods include receiving, from user plane function (UPF) of the communications network, a first subscription request for an analytic related to user plane (UP) traffic congestion and sending to the UPF a second subscription request for information related to traffic between the UPF and a radio access network (RAN). Such methods also include receiving the traffic-related information from the UPF, in accordance with the second subscription request, and computing the analytic related to UP traffic congestion, based on the received traffic-related information. Such methods also include sending the computed analytic to the UPF, in accordance with the first subscription request. Embodiments also include complementary methods for UPFs, as well as NWDAFs and UPFs configured to perform such methods.

The invention provides a real-time warning condition data processing method and system based on edge calculation, and relates to the technical field of traffic management, which comprises the steps of collecting traffic warning condition original data, transmitting the data to a nearby edge server in real time, constructing a lightweight deep learning model on an edge side, generating a traffic situation prediction result, and simultaneously, combining police force information to obtain a real-time warning condition prediction result. And constructing a police deployment optimization matrix, uploading the matrix to the cloud platform, generating a traffic optimization strategy, and issuing the traffic optimization strategy to the edge computing node of the accident area. The method can improve the integrity and timeliness of accident information perception, optimizes the police resource distribution and traffic dispersion decision, and remarkably enhances the intelligence and cooperation capability of traffic accident emergency management.

The invention discloses a traffic signal global collaborative prediction method based on a quantum entanglement state, and the method comprises the steps: building a quantization model of a dynamic evolution path of a target traffic network in a time window in the future, and constructing a space-time diagram model; mapping the signal phase state of the intersection into a time-space integrated quantum state by adopting a layered quantum coding scheme; on the basis of a preset traffic optimization target, constructing Hamiltonian including spatial coupling, time evolution and a cost item, solving a ground state of the Hamiltonian through a mixed quantum-classical calculation method, and determining an optimal dynamic evolution path; coding the actual state of the current traffic network into an initial state, carrying out sequential measurement through an optimal evolution operator, decoding a cooperative signal control strategy of each time point in the future, and executing the cooperative signal control strategy through a rolling time domain control framework; according to the invention, global optimization is carried out by using quantum parallelism, the optimal cooperation strategy in the whole space-time range can be obtained at one time, and the overall operation efficiency, predictability and robustness of the traffic network are improved.

The invention discloses a wireless network topology visualization and fault analysis system, and the system comprises a networking state sensing module which is used for collecting the communication state data of various types of wireless transmission equipment in a network environment; the network link visualization module is used for analyzing the data acquired by the networking state sensing module, completing statistics of equipment traffic in a link and completing visual display of an equipment topological graph and traffic use conditions; the network fault management module is used for dynamically monitoring a network node communication state to identify a network fault node and giving an alarm; and the network security visualization module is used for monitoring a network security state in real time, supporting custom configuration of a firewall, and performing alarm and log storage on network security abnormity. According to the method, the connection relation and the topological structure of the equipment in the network can be visually displayed, the fault source can be rapidly positioned, and capacity planning and flow optimization can be carried out.

The invention discloses a smart city traffic optimization method and system based on a 5G network, and particularly relates to the technical field of smart traffic, the smart city traffic optimization method based on the 5G network comprises the steps: deploying a 5G base station, and installing a sensor and traffic equipment on a road to collect traffic data; the traffic data comprises traffic flow data and a traffic signal state, rapidly transmitting the acquired traffic data to a cloud platform for storage by using a 5G network, preprocessing the acquired traffic data in the cloud platform, performing deep analysis through a machine learning algorithm to identify a traffic mode, and based on an analysis result, determining the traffic mode. The invention discloses a smart city traffic optimization system based on a 5G network, which is used for predicting a traffic flow demand, pre-judging an appearing congestion condition in advance and optimizing by adjusting a signal lamp period, and comprises a data acquisition module, a data analysis module and a traffic flow control module.

The invention discloses a signal timing and vehicle trajectory collaborative optimization method for an intelligent networked vehicle, and the method comprises the steps: obtaining a parameter set through employing different vehicle following models according to different types of vehicle following modes corresponding to different types of vehicles on a road; acquiring state information of vehicles in a communication range through road infrastructures, and transmitting the state information to an upper signal timing optimization layer; according to the parameter set, taking the green time ratio of each phase as a decision variable, taking the state information of the vehicle in the communication range as basic input information, and constructing an upper-layer signal timing optimization model; on the basis of the trigonometric function optimization model, a lower-layer vehicle trajectory optimization model is constructed by judging whether the vehicle meets trajectory optimization conditions or not; and according to the upper-layer signal timing optimization model and the lower-layer vehicle trajectory optimization model, circularly optimizing signal timing and a vehicle trajectory. According to the embodiment of the invention, the traffic optimization capability can be improved, the optimization efficiency is greatly improved, and the method can be widely applied to the technical field of intelligent traffic.

The invention discloses a dynamic road traffic optimization control method and system based on perceptual intelligence, and relates to the technical field of traffic optimization control, and the method comprises the steps: laying an intelligent perceptual network, and collecting and obtaining a multi-source road traffic operation data flow; acquiring a traffic influence factor index set; setting traffic data analysis multiple channels based on the traffic influence factor index set; performing correlation analysis to obtain a traffic multi-dimensional operation state parameter set; and constructing a traffic optimization control strategy space, carrying out optimization analysis on the traffic multi-dimensional operation state parameter set, determining traffic control strategy optimization parameters, and carrying out road traffic dynamic control through the traffic control strategy optimization parameters. The technical problems of aggravated traffic congestion and low traffic efficiency caused by insufficient traffic data acquisition and analysis capability and lack of dynamic adjustment of a control strategy in the prior art are solved, and the technical effects of dynamically controlling road traffic, improving traffic operation efficiency and relieving congestion are achieved.

The invention relates to the technical field of data transmission, in particular to an ink-jet printing data transmission system based on a TSN and an ink-jet printing system.The ink-jet printing data transmission system comprises a time-sensitive network switch used for receiving flow data frames sent by an upper computer; the time-sensitive network gateway is used for receiving the traffic data frame transmitted by the time-sensitive network switch, determining a transmission sequence of all traffic data in a preset transmission time window, and transmitting all processed traffic data; wherein the time-sensitive network gateway mathematical model is constructed based on the flow distribution balance optimization target and the system flow priority distribution optimization target by taking the total size of the flow data received by each electronic control unit as a constraint. According to the invention, the TSN technology is introduced, so that low-delay and high-determinacy data transmission under the condition of high bandwidth is realized in an ink-jet printing system; based on traffic optimization scheduling, a TSN traffic model is designed, a data transmission sequence is determined, and the flexibility and reliability of the system in the data transmission process are improved.

The invention discloses a regional traffic dynamic optimization control method based on vehicle-road cloud coordination, relates to the technical field of traffic control, and designs a self-adaptive space-time dynamic compensation algorithm and a three-level communication fault-tolerant architecture aiming at space-time asynchronization and communication interruption risks existing in multi-source data of a vehicle end, a road side and a cloud end. According to the method, accurate alignment and stable transmission of multi-dimensional heterogeneous data are realized, an attention mechanism enhanced multi-agent deep reinforcement learning architecture is adopted, online element reinforcement learning is combined, global and local optimization control of cloud-side double-loop cooperation is realized, communication is performed by adopting a block chain-based security consensus protocol, the system security and stability are effectively guaranteed, and the system reliability is improved. According to the method, the operation efficiency, the emergency response capability and the environmental friendliness of urban smart traffic are remarkably improved, and the method is suitable for a smart city global traffic optimization scene.

The invention relates to the technical field of network communication, and discloses a network traffic optimization method and device, equipment, a storage medium and a product, and the method comprises the steps: obtaining a traffic feature data set through analyzing real-time traffic, obtaining a prediction result based on a time sequence model according to a historical traffic data set and the traffic feature data set, and obtaining a prediction result according to the prediction result. The real-time flow is optimized; according to the method, the traffic characteristic data set is obtained by analyzing the real-time traffic, and prediction is performed in combination with the traffic characteristic data set and the historical traffic data set so as to deal with potential traffic fluctuation, so that the network traffic is optimized, the congestion risk is reduced, dynamic adjustment optimization and high-precision prediction are realized by using the time sequence model, and the prediction efficiency is improved. The processing effect of different characteristic flows is improved, and the data transmission efficiency is improved.

The invention provides a traffic optimization and path planning method and system based on an intelligent agent map, and a medium. The method comprises the following steps: collecting travel information of a target user, extracting travel demand data and travel preference data, obtaining a plurality of alternative travel paths according to the travel demand data, processing according to the travel preference data to obtain user target preference data, obtaining traffic characteristic data corresponding to each alternative travel path, and sending the traffic characteristic data to the user. Comprising road information data, traffic flow data and traffic signal lamp information data, obtaining real-time weather condition data, processing through a preset path optimization algorithm in combination with user target preference data and traffic characteristic data, obtaining a path recommendation coefficient corresponding to each alternative travel path, and recommending the path recommendation coefficient to the user. And obtaining an optimal path according to the path recommendation coefficient, and pushing the optimal path to the target user, thereby realizing a traffic optimization and path planning technology based on the agent map.

The invention provides a road signal holographic optimization control system based on an SCATS system, and relates to the technical field of traffic control, the control system comprises a roadside sensing module, the roadside sensing module is arranged at one side of a road intersection, and the roadside sensing module is configured to obtain traffic data of a current road; the model construction module is configured to construct a traffic optimization model according to the traffic data; and the scheme generation and issuing module is configured to import real-time traffic data of a current road into the traffic optimization model so as to generate a road optimization scheme, and the road optimization scheme is used for controlling a corresponding traffic signal controller based on the SCATS system. Through the control system, the problem that an SCATS system at the present stage cannot singly overcome traffic state control at the present stage is solved.

The invention discloses an intelligent traffic optimization method and system based on vehicle state perception, and relates to the field related to intelligent traffic optimization, and the method comprises the steps: collecting the vehicle state data of a target intersection in real time through a multi-mode sensor group, and generating a real-time traffic situation matrix; inputting the real-time traffic situation matrix into a digital twinborn decision model for dynamic road right distribution, and outputting a dynamic road right distribution scheme; according to the dynamic road right distribution scheme, multiple actuators are synchronously regulated and controlled to form a reconstructed physical layer traffic flow; and executing social vehicle delay feedback according to the reconstructed physical layer traffic flow, generating a compensation voucher, and pushing the compensation voucher to an affected social vehicle terminal. The technical problem of difficulty in realizing dynamic balance between the priority accurate quantitative distribution and the group fairness in the existing traffic optimization method is solved, and the technical effect of dynamic balance between the priority accurate quantitative distribution and the group fairness is realized.

The invention relates to a large-scale network traffic optimization method and system, and the method comprises the steps: employing a hierarchical traffic engineering strategy, carrying out the precise optimization of an object flow through linear programming, carrying out the rapid distribution of a mouse flow through a heuristic algorithm, and improving the calculation efficiency; a lightweight routing update strategy is adopted, L1 norm constraint is introduced into a linear programming optimization model, the range of routing configuration change is limited, and routing table update overhead is reduced; the robust traffic engineering strategy adopts a fault recovery mechanism for neighborhoods, realizes quick response to link faults, ensures the stability of network performance, and realizes millisecond-level recovery only by local re-optimization in the n-hop neighborhoods of the robust traffic engineering strategy; according to the method, the efficiency, the stability and the robustness of a large-scale network are remarkably improved while the optimal flow distribution quality is kept, the routing table updating amount is greatly reduced, and the response speed to link faults is improved; the method can be widely applied to flow optimization and management in a large-scale network, and is particularly suitable for scenes such as a data center network and an operator wide area network.

The invention relates to the technical field of resource management of mobile data communication service, and discloses a network flow optimization service method based on artificial intelligence, which comprises the following steps: collecting a hybrid automatic repeat request sequence of a target wireless link in a preset time window, counting the flipping frequency between an acknowledgement state and a non-acknowledgement state, and extracting polarity flipping characteristics; monitoring a physical resource block adjustment step length of the scheduling authorization instruction in real time; when the step length exceeds a preset disturbance threshold value, determining a blanking weight coefficient inversely proportional to the step length; correcting the polarity flipping feature by using the coefficient to obtain a purified feature vector; inputting the vector into linear discrimination logic to determine a traffic burst bias item; according to the method, closed-loop suppression of scheduling decision action and feedback disturbance is realized, and resource allocation oscillation generated by control feedback self-excitation is eliminated.

The invention discloses a vehicle network interaction flow optimization method based on information and physical fusion, and belongs to the technical field of intelligent traffic and energy internet crossing. Comprising the steps that a vehicle network is divided into a plurality of grid areas, and each grid area comprises physical layer operation data and information layer operation data; constructing a vehicle network diagram structure, and calculating the schedulable potential of the EV in the stay duration of the charging station; according to the schedulable potential and the information layer operation data of the EV in the stay time of the charging station, the method aims at minimizing the peak-valley difference, maximizing the renewable energy consumption, the vehicle network interaction income and the operation income. Constructing an objective function of the multi-objective flow optimization model, an EV charging and discharging power constraint, an EV battery charge state constraint, a charging pile capacity constraint and a power grid safety constraint; and solving the multi-target traffic optimization model to obtain a vehicle network interaction traffic optimization result. According to the method, the prediction error of the EV arrival time is reduced, and the V2G flow regulation and control are refined.

The present application relates to heavy load AGV cooperative control technical field, especially in a kind of heavy load AGV group's cooperative control method, the motion state parameter of each heavy load AGV is collected to construct motion state vector, local traffic congestion parameter is predicted, and then local congestion index is calculated and dynamic coordination trigger threshold is determined;Distributed speed coordination is triggered based on local congestion index and dynamic coordination trigger threshold, to determine the reference coordination speed of each heavy load AGV;The reference coordination speed is passed to relevant heavy load AGV, to generate transmission coordination speed;Based on ground state parameter, the motion state vector of each heavy load AGV and transmission coordination speed, the traffic optimization sorting of each heavy load AGV is determined, to modify transmission coordination speed and determine modified coordination speed;Based on the modified coordination speed of each heavy load AGV, the timing and number of subsequent local traffic congestion are predicted, to determine the effective duration of coordination control.The present application improves the running efficiency and safety of heavy load AGV group in complex traffic environment.

The invention provides a network traffic intelligent scheduling method and related equipment thereof. The method comprises the following steps: acquiring traffic data of a target terminal; carrying out classification identification processing on the traffic data, and determining a classification result; constructing a traffic optimization model based on the classification result; performing optimization processing on the traffic data through the traffic optimization model, and determining an optimization result; and adjusting and scheduling the network configuration corresponding to the flow data based on the optimization result. Through the steps of the method, identification and modeling of traffic of different service types can be realized, and an optimal scheduling scheme is generated in combination with a multi-objective optimization algorithm, so that dynamic resource allocation and adaptive scheduling are realized in a complex network environment, the network delay and packet loss rate are reduced, and the bandwidth utilization rate and the service quality of services are improved.

The invention discloses a method for realizing source address route dynamic selection and flow optimization based on a VRF (Very Radio Frequency), which comprises the following steps of: guiding flow to different VRF instances based on conditions such as a source address and the like, and realizing accurate routing and isolation of IP (Internet Protocol) flow of different source addresses by configuring strategies and rules on network equipment. The requirements of multi-hosting and multi-path networks in environments such as enterprise networks are met, and the security and independence of data are ensured.

A mmWave communication network (1; 2) comprising a plurality of distribution nodes (DP1-DPn), each distribution node comprising: a plurality of N sectors (S1-S4), where N is an integer being at least three, each of the N sectors comprising a mmWave transceiver (4) arranged for mmWave communication with an adjacent distribution node; and a network processing unit (5) configured to route data traffic between sectors of the N sectors (S1-S4) of an individual distribution node such that the individual distribution node is in an operation mode of either: a default bridge mode in which data from any one of the N sectors (S1-S4) is routed to all other sectors (S1-S4); or a non-default bridge mode (B) in which data from one of the N sectors (S1-S4) is routed to a selected subset of the other sectors (S1-S4); wherein the number of sectors in the selected subset is I with 0 ≤ I ≤ N-2; wherein at least two distribution nodes of the plurality of distribution nodes (DP1-DPn) are in a non-default bridge mode (B).

The invention discloses a traffic signal timing optimization method for an urban updating area, belongs to the technical field of traffic optimization, and can solve the problem that the overall operation level of a road network of the urban updating area is poor due to the fact that the efficiency is low when a traditional traffic flow control method is used for processing traffic problems of the urban updating area. The method comprises the following steps: S1, determining the number of current running vehicles and the number of ideal running vehicles in a road network of the urban updating area according to traffic flow information of the urban updating area; s2, according to the traffic flow information, the number of current running vehicles and the number of ideal running vehicles, determining the traffic optimization duration of each boundary intersection road entering the interior from the periphery of the boundary line in the urban updating area; and S3, optimizing the initial traffic duration of each boundary intersection road by using the traffic optimization duration to obtain an optimized target traffic duration. The method is used for timing optimization of traffic signals in an urban updating area.