310 results about "Traffic planning" patented technology

The invention discloses a built environment and traffic accident causal effect analysis method based on bi-robust learning, and belongs to the field of intelligent traffic safety and traffic accident risk assessment. By integrating satellite remote sensing, interest point distribution and traffic accident data, a spatial-temporal feature enhanced environment index system is constructed; realizing cross-regional data collaborative analysis by adopting federal learning, and guaranteeing data compliance in combination with differential privacy encryption; a generative adversarial network is introduced to generate a high-fidelity anti-fact scene, and a dual robust learning framework is coupled to accurately quantify a condition average processing effect of a built environment on accident risks. According to the scheme, a technical tool with both theoretical preciseness and practical feasibility is provided for urban traffic planning and safety management, and a scientific basis is provided for optimizing and building environment design and reducing traffic accident risks through a multi-source data fusion and privacy cooperation mechanism; and the blank in the aspects of causal robustness, insufficient anti-fact generation precision, privacy protection and multi-source data fusion in the prior art is filled.

The invention relates to the technical field of traffic planning, in particular to a transfer connection optimization method in multi-mode traffic integrated planning, which comprises the steps of data acquisition and demand analysis, multi-mode transfer network topology design, multi-mode timetable collaborative optimization, transfer service system development and facility transport capacity dynamic matching. According to the transfer connection optimization method in the multi-mode traffic integrated planning, a dynamic and static combined multi-source data acquisition framework is constructed based on a multi-dimensional data fusion system, the data precision can be improved, the network can be perceived in real time, association modeling of weather events and demand fluctuation is realized based on construction of a multi-agent simulation system, and the optimization of the transfer connection in the multi-mode traffic integrated planning is realized. A dynamic weight mechanism is introduced based on a hypergraph model, the robustness of the algorithm is improved, a layered optimization framework processes global optimization through upper-layer integer programming, dynamic adjustment is achieved through lower-layer deep reinforcement learning, a buffer time intelligent insertion strategy can improve the late absorption capacity, and a facility utility function model is established to improve the transfer efficiency.

The invention relates to the technical field of smart city traffic, in particular to a smart city traffic indication system and a working method thereof. According to the technical scheme, the system comprises a multi-source heterogeneous data acquisition module, a data preprocessing and fusion module, a feature extraction and analysis module, a traffic flow prediction and signal timing decision module and a signal display and communication module, the acquisition frequency is dynamically adjusted according to the traffic flow. Traffic and meteorological data are dynamically collected through multiple types of sensors, the quality of data preprocessing and fusion is guaranteed, features are accurately extracted, signal timing is optimized through reinforcement learning, the display and communication module effectively transmits information and cooperates with surrounding traffic, emergency situations can be dealt with, tail gas emission is reduced, pedestrian waiting time is shortened, and traffic safety is improved. And a basis can be provided for long-term traffic planning, and the traffic efficiency, the environmental protection property and the pedestrian friendliness are comprehensively improved.

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 discloses a pedestrian behavior simulation operation control method based on an unreal engine. Efficient and vivid pedestrian behavior simulation is realized through five core modules: a three-dimensional scene construction module converts a BIM model based on an unreal engine Datasmith plug-in and constructs an interactive dynamic scene; the pedestrian model construction module defines multi-dimensional pedestrian attributes and supports data-driven initial distribution configuration; the hierarchical behavior control module realizes refined regulation and control of pedestrian behaviors through macroscopic path planning, microcosmic behavior decision and emotion driving; the dynamic resource optimization module adopts level-of-detail control and multi-thread task allocation to improve the operation efficiency of the system; and the real-time interaction and data analysis module provides scene regulation and control, data visualization and simulation data recording and playback functions. According to the method, the authenticity, the dynamic response capability and the controllability of pedestrian simulation are remarkably improved, and reliable technical support can be provided for traffic planning, emergency drilling and building design evaluation.

The invention discloses a county and rural traffic planning decision-making method and system based on a road traffic index. The method comprises the following steps: acquiring navigation API data, social economic data and POI information related to county and rural traffic planning; the method comprises the following steps: acquiring data, processing the data, then calculating a route growth index, a per-hour time index, a holiday influence index, a broken road index and a planning simulation index, carrying out weighted fusion to obtain a road traffic index, when the road traffic index is calculated, distributing an initial weight for each index, and adopting a self-adaptive weight adjustment algorithm to calculate the road traffic index. Dynamically adjusting the weight of each index according to the real-time traffic data and the simulation result; county and rural traffic planning is carried out by taking reduction of the road traffic index as a target, and a road traffic index difference value before and after planning is calculated as a decision basis. According to the invention, the problem of asymmetry of basic traffic management information is effectively solved, and the connection accuracy of the road network is improved.

The invention discloses a traffic video AI intelligent analysis method based on a credential environment, and relates to the technical field of intelligent traffic. The problems that in the prior art, accident data processing relates to multiple steps and is complex, errors are prone to being introduced, the recognition accuracy of traffic participants is affected, and a fixed smoothing coefficient cannot adapt to prediction of dynamic traffic data are solved. By collecting and processing traffic video data in real time and accurately identifying and classifying vehicles, pedestrians and the like, the traffic monitoring efficiency and accuracy are greatly improved, potential traffic accident risks are found in time, measures are taken for prevention, the response speed to emergency situations is increased, and the traffic safety is improved. The traffic flow, the vehicle speed and the congestion condition are predicted in advance by establishing the traffic prediction model, the traffic flow and the signal lamp timing are adjusted according to the prediction result, the traffic congestion is effectively relieved, the road passing efficiency is improved, a basis is provided for urban traffic planning, and the rationality of urban planning is improved.

The invention relates to the technical field of traffic planning and management optimization, and provides a regional hub and urban rail transit interchange connection optimization method, which comprises the following steps of S1, adopting different models for performing short-time arrival passenger flow prediction aiming at different travel modes to obtain respective interchange passenger flow; s2, modeling is conducted on the environment layout and pedestrian behaviors of an actual station through Anylogic software, a dynamic evaluation index system is constructed on the basis of the whole hub and the basic transfer facility state, passenger flow prediction data are input into simulation software, and transfer connection efficiency is obtained; and S3, constructing a maximum comprehensive benefit function from three aspects of a comfort index, an economic index and a facility traffic capacity balance degree, and optimizing transfer service facility configuration by adopting a genetic algorithm to optimize transfer connection efficiency. According to the optimization method provided by the invention, the transfer passenger flow volume can be accurately predicted, and the resource allocation is reasonably optimized, so that the transfer efficiency is improved, and the experience feeling of passengers is improved.

A method to determine if a traffic situation comprising a plurality of heavy-duty vehicles following a plurality of guided vehicle trajectories in a defined area will lead to a vehicle deadlock is described. For example, one method includes generating a traffic situation representation for the defined area comprising, for each of the plurality of vehicles, an initial vehicle trajectory segment location and a subsequent deadlock status, inputting the traffic situation representation to a traffic situation deadlock classifier which has been trained to output a traffic situation deadlock classification based on an input representation of a traffic situation in the defined area, and, based on the input traffic situation representation, generating a predicted deadlock classification of a subsequent traffic situation for that input traffic situation representation.

The invention discloses a multi-channel arbitrary regulation and control full-connection space photon Isin machine, and belongs to the field of photoelectric intelligent calculation. The photon Isin machine comprises a laser light source module, a modulation multiplexing module and a detection electric control module. The photon Isin machine provided by the invention realizes multichannel multiplexing of a spatial spin matrix based on a diffraction beam splitting element and polarization multiplexing, realizes arbitrary full-connection coupling weight loading through a matrix decomposition approach, and finally realizes the full-connection space photon Isin machine with arbitrary regulation and control. According to the spatial multiplexing working mode, on one hand, calculation acceleration of the Isin model in any coupling connection can be achieved in a matrix decomposition mode, on the other hand, the modulation rate of the modulator is not sacrificed, and the defect that the calculation speed is low due to time division multiplexing is overcome. Therefore, the full-connection space photon Isin machine with multi-channel arbitrary regulation and control has important practical value in the fields of traffic planning, resource allocation, financial prediction, circuit design and the like.

The invention belongs to the technical field of urban traffic planning and intelligent traffic systems, particularly relates to an intersection phase structure optimization method based on a large language model, and aims to improve the intelligent level and operation efficiency of traffic signal control. According to the method, semantic mapping cues of traffic flow and a phase structure are constructed, and a large language model is guided to generate a phase structure scheme adapted to an actual traffic state. Compared with a traditional scheme depending on artificial experience and a fixed structure, the method can automatically generate diversified and data-driven phase structure combinations, and has higher adaptability and generalization ability. In the aspect of technical implementation, the method fuses prompt engineering and guides a large language model to generate an initial phase scheme, and performs evaluation and feedback by using a value network, so that optimization of a phase structure is realized, and the overall operation efficiency of a traffic system is improved.

The invention relates to the technical field of passenger flow information acquisition, and discloses a passenger flow information acquisition system and method based on multiple scenes, and the method comprises the steps of data acquisition, user classification, real-time analysis, regional analysis and information display.In the aspect of crowd flow analysis, mobile signaling data are accurately acquired, and timestamps and base station information are combined, so that the crowd flow analysis efficiency is improved; high-temporal-spatial-resolution analysis is realized, a crowd real-time flow diagram can be accurately generated, and urban traffic planning, large-scale activity security and the like are assisted; and in area evaluation, key inflow and normal flow areas are determined, a key area map is generated, and a basis is provided for commercial layout and public facility construction. User classification is accurate, and tourism management and local public service optimization are facilitated. An analysis result is visually presented through visual display, scientific decision making is assisted, and the method is suitable for multiple fields such as urban emergency management; a real-time updating mechanism ensures the timeliness of data, and improves the flexibility of city management and the capability of dealing with emergencies.

The invention relates to the field of intelligent robots, and discloses a multi-vehicle AGV traffic planning method, device and equipment and a storage medium, and the method comprises the steps: determining a driving route of a standard vehicle AGV as a standard driving route; the driving routes of the AGV of other vehicle types are associated with the standard driving route, and a physical location area is determined; and generating a traffic planning scheme based on the preset association point and / or the physical location area. Through the above mode, the driving route of the AGV of the standard vehicle type is obtained and the standard driving route is determined, so that other driving routes of the AGV of other vehicle types can be associated with the standard driving route, and the physical location area is determined according to the associated other driving routes and the standard driving route. According to the invention, the traffic planning scheme can be generated based on the preset association point and / or the physical location area, thereby achieving the effective management and control of the AGV system, and improving the efficiency of material handling.

The invention relates to the technical field of traffic engineering, and discloses a reinforcement learning-based virtual aircraft multi-agent co-simulation and optimization method and system, and the system comprises a virtual environment construction module which is used for constructing a virtual city environment through UE4 and AirSim simulation software; the aircraft model construction module is used for designing an eVTOL virtual aircraft agent model by using 3ds Max; the agent state sensing module is used for sensing the state of the agent in real time through an AirSim plug-in; the reinforcement learning training module is used for applying a DDQN algorithm to train an agent to make an autonomous decision; and the collaborative task design module is used for multi-agent collaborative task execution, such as traffic planning, route optimization and the like. Through a virtual simulation method, an innovative perspective of cooperative operation of a large number of virtual aircraft agents in a complex urban environment is provided. The method has remarkable advancement in urban air traffic (UAM) system design and verification research, and dynamic characteristics of urban air traffic can be truly represented and analyzed in a simulation environment.

The invention discloses a traffic planning method and device, a storage medium and a program product, and relates to the technical field of automatic driving. The method comprises the following steps: acquiring vehicle driving data and a real-time traffic condition; according to the vehicle driving data and the real-time traffic condition, a plurality of particles are initialized, and each particle represents an acceleration sequence of the vehicle in a future preset time period; iteratively updating the particles until convergence, and obtaining a preliminarily optimized acceleration sequence; and a quadratic programming algorithm is applied to smooth the preliminarily optimized acceleration sequence, a target acceleration sequence is obtained, and the target acceleration sequence is used for controlling the vehicle to run. The technical problem that a rule-based speed planning method in the prior art lacks flexibility and is difficult to deal with a complex traffic environment is solved.

The invention relates to the technical field of traffic planning, in particular to a road congestion shunting system suitable for urban design, which comprises a node congestion identification module, a signal time sequence adjustment module, a path interference screening module, a signal priority ordering module and a linkage time period correction module. According to the method, a multi-direction traffic pressure concentration area is formed through path overlapping structure identification, an association boundary of a path group and a node state is opened, a green light starting time sequence is adjusted in combination with a column head static continuous behavior, a vehicle response and period synchronization relation is reconstructed, and structural proximity judgment is introduced in path conflict screening. High-interference paragraphs are identified by utilizing the correlation degree of guidance, speed and lane parameters, a node sorting mechanism dynamically generates priorities through the consistency of a frequent path and a queuing trend, real-time refreshing of a signal response sequence is realized, linkage rhythm correction is bidirectionally verified according to release delay and an adjacent node blocking state, a starting segment is dynamically adjusted, and the linkage rhythm is corrected. Chained dislocation is avoided.

The invention discloses marking equipment for road traffic planning, and relates to the technical field of road marking. The marking equipment for road traffic planning comprises a spray head pipe, and a gas pipe and a material injection pipe are arranged on one side of the spray head pipe and used for gas injection and material injection respectively; the needle valve rod is located in the spray head pipe, a blocking ball is arranged at the lower end of the needle valve rod and used for blocking or not blocking the spray hole, a piston plate is fixed to the upper end of the needle valve rod, and a reset spring is arranged between the piston plate and the inner top end of the spray head pipe. The movable valve plate is jacked to open the pressure relief pipe while the three-way valve cuts off the material injection pipe, so that the air pressure in the nozzle pipe is reduced, the paint spraying inertia caused by sudden pressure drop is effectively eliminated through the stepped reduction of the air pressure, and the blocking ball is pushed to block the spraying hole through the synergistic effect of the reset spring and the air pressure difference, so that physical secondary sealing is formed. Mechanical-pneumatic double-drive sealing is achieved, so that paint leakage is reduced, and paint point accumulation caused by sudden pressure increase or sudden pressure drop at the starting end and the stopping end of the marking line is avoided.

The invention relates to the field of traffic planning, and discloses an urban road network multi-path coordination control method and system considering multi-dimensional dynamic characteristics, and the method comprises the steps: obtaining the traffic flow data, congestion indexes and bidirectional green wave bandwidth difference data of each path in a road network at an intersection; calculating the multi-dimensional dynamic weight of each path at the intersection according to the traffic flow data, the congestion index and the bidirectional green wave bandwidth difference data; constructing a target function by taking the maximum weighted green wave bandwidth sum as a target, and embedding a multi-dimensional dynamic weight into the target function; constructing a collaborative optimization system comprising a high-weight path guarantee constraint, a dynamic adaptability constraint and an auxiliary constraint; and solving an optimal signal timing parameter based on the objective function and the collaborative optimization system, and generating a multi-path coordination control scheme. According to the method, through high-weight path guarantee constraint, dynamic adaptability constraint, flexible breakpoint constraint and the like, the length of a green wave band is increased, and the continuity of green waves and the anti-interference capability of a road network are improved.

The invention provides a multi-destination travel itinerary planning method, system and device and a storage medium. The method comprises the following steps: receiving a travel planning request of a user; based on keywords in the tourism planning request, matching a plurality of related scenic spots and tourism destination cities to which the scenic spots belong; at least according to the traffic information between the tourism destination cities, generating a tourism sequence based on the tourism destination city playing sequence, inter-city traffic planning between the tourism destination cities and tourism sub-planning of each tourism destination city, and obtaining daily tourism tracks, daily accommodation hotels and scenic spot routes; and generating a travel itinerary plan according to the daily travel track, the scenic spot itinerary, the daily accommodation hotel and the inter-city traffic plan, and feeding back the travel itinerary plan to the user. According to the invention, travel, accommodation and travel time arrangement and route planning schemes in the travel itinerary of the user can be finely designed, the travel time cost of the user is reduced, and the experience in the travel itinerary is improved.

The invention relates to the field of electric digital data processing, and discloses a signal global induction real-time control method and system. The method comprises the following steps: collecting state data in real time through a thunder detector, and generating a state value through fuzzy comprehensive evaluation; calculating an intersection credit value based on a multi-stage enhanced game model in combination with the state value and the historical compromise rate; dynamically adjusting the cooperation range by adopting a cooperation suppression distance field mechanism; and distributing signal duration according to a three-level arbitration strategy, updating a credit value, and optimizing the system through a credit entropy stabilization mechanism. According to the method, the problem that signal control lacks global collaboration due to the fact that traffic association of intersections of the whole network is not fully considered and data processing is limited to local is solved, a universal global collaborative planning control method is realized, and the traffic planning efficiency and the system stability are improved.

The invention discloses a smart city traffic planning method and system based on deep learning, and relates to the technical field of city traffic management, and the method comprises the steps: recognizing an activity region of a traffic disturbance activity, constructing a disturbance region graph Ga, collecting vehicle driving related data and lamp control cycle data, and constructing a standardized data set H; coupling modeling is carried out on path length change and direction deviation of the traffic driving path before and after the traffic disturbance activity so as to quantify the traffic state of the activity area, a path disturbance deviation coefficient Py is acquired, the traffic state of the activity area is evaluated in combination with a path disturbance threshold Pyyz, and when the traffic state is in an abnormal state, the traffic disturbance deviation coefficient Py is calculated. According to the method, signal lamp cycle rhythm analysis is carried out, a phase dislocation propagation index Xw is obtained, and coupling modeling is carried out to obtain a comprehensive rhythm adaptation index Zh, so that the risk level of a traffic signal control system is divided, a system adjustment instruction is generated, intelligent adjustment is carried out, and the intelligent, precise and real-time level of smart city traffic planning is improved.

The invention relates to a method for constructing a right-turn motor vehicle traffic capacity correction model under the influence of non-motor vehicle cross-line illegal parking, which comprises the following steps: taking two illegal parking characteristics of non-motor vehicle intrusion distance and illegal parking quantity as entry points, obtaining that two illegal parking factors both have influence on the speed of a right-turn motor vehicle through correlation analysis, and taking the right-turn speed as a dependent variable to obtain a right-turn motor vehicle traffic capacity correction model; and constructing a right-turn motor vehicle speed interference response model by taking the illegal parking quantity and the intrusion distance as independent variables. And based on the established speed interference response model, constructing a right-turn motor vehicle traffic capacity correction coefficient algorithm by applying a traffic wave theory and a gap acceptance theory, and integrating the right-turn motor vehicle traffic capacity correction coefficient algorithm into an HCM model to establish an improved HCM traffic capacity correction model. According to the method, the influence of the number, position and shape of non-motor vehicle cross-line illegal parking under different phases is considered, and reference is provided for traffic planning and road design.

The invention provides a data reorganizing method and device for retrieval enhancement generation in the field of comprehensive traffic planning, in the data reorganizing method, statistical yearbook data in a PDF format can be subjected to format conversion to obtain statistical yearbook data in an Excel table form, the statistical yearbook data in the Excel table form is standard in format, and the statistical yearbook data in the Excel table form can be stored in the data reorganizing method. According to the method, the statistical yearbook data in the Excel table form can be conveniently and subsequently subjected to automatic multi-dimensional row and column hierarchical dimension reduction, then the statistical yearbook data in the one-dimensional Excel table form which is more convenient to understand and good in readability is obtained, then the statistical yearbook data in the one-dimensional Excel table form is subjected to information optimization processing, the statistical yearbook data in the target one-dimensional Excel table form is obtained, and finally, the statistical yearbook data in the target one-dimensional Excel table form is obtained. In addition, the index knowledge graph is constructed according to the statistical yearbook data in the target one-dimensional Excel table form, modeling of the index entities in the statistical yearbook data is achieved through the index knowledge graph, the relation between the index entities is extracted, and the effect of subsequent knowledge retrieval can be enhanced.

The invention discloses an urban traffic intelligent planning auxiliary system and method based on big data analysis, and relates to the technical field of traffic planning. Historical travel habit data of residents in a city and historical traffic flow data of urban traffic are collected, and feature extraction is performed on the historical travel habit data and the historical traffic flow data; the method comprises the following steps: constructing a historical feature set, training a travel demand prediction model for predicting passenger flow among different urban areas based on the historical feature set, identifying high travel demand stations in each time period according to a prediction result of the travel demand prediction model, and determining the travel demand of each time period by using a shortest path algorithm according to position distribution of the high travel demand stations in each time period. Planning a new travel route; and the service quality and efficiency of urban public transportation are improved.

The invention provides a traffic state prediction and traceability method, device and equipment based on multi-modal data, which can be applied to the field of space-time prediction and traffic planning and management. The method comprises the steps that a topological graph representing a road connection relation and multi-mode traffic sequence data of a target historical time period are acquired, the topological graph comprises road nodes and edge relations, and the road nodes represent roads; processing each traffic sequence data and the topological graph by using a graph neural network to obtain traffic spatial-temporal characteristics; the traffic spatial-temporal features corresponding to the traffic sequence data are processed based on a cross attention network, a congestion prediction result is obtained, and the congestion prediction result represents whether the road nodes are congested or not; and processing the congestion prediction result based on a Shapley value algorithm to obtain a traceability result, the Shapley value algorithm being used for calculating a contribution value of each traffic sequence data to the congestion prediction result.

The invention discloses an evaluation index system construction method for a construction time sequence of an urban composite channel, relates to the technical field of traffic engineering and urban planning, and solves the problems of facility layout conflict, traffic operation disturbance, low efficiency of resource allocation and the like caused by the lack of scientific time sequence overall planning of the construction time sequence of an existing urban rail-road composite channel. The construction method comprises the following steps: S11, dividing a city composite channel construction process into a plurality of evaluation stages of construction time sequence influence; s12, determining an evaluation dimension in each evaluation stage; s13, extracting an evaluation criterion based on each evaluation dimension; s14, refining a plurality of secondary evaluation indexes according to each evaluation criterion, and constructing an evaluation index system according to the secondary evaluation indexes; and S15, determining the meaning, the calculation method and the numerical value acquisition mode of each secondary evaluation index. The invention further discloses an evaluation method for the construction time sequence of the urban composite channel. According to the invention, the scientificity of traffic planning decision and the efficiency and benefit of engineering implementation can be improved.

The invention relates to a traffic facility attribute mining method and system based on multi-modal network open source data. The method comprises the following steps: extracting traffic facility information from a webpage to construct a knowledge graph; extracting positions and appearances of traffic facilities from the images, and analyzing streetscape images to obtain attributes such as road traffic; collecting map images, tiles and vector data to construct a road network topology and attribute database; associating webpage texts, pictures, streetscape images and network map multi-source data according to the spatial position of the traffic facility; through comprehensive and deep attribute mining, different modal data are integrated to improve the accuracy and reliability of attribute mining, the real-time and dynamic updating capability, the convenient visualization and interaction operation, the data sharing and integration convenience, and the traffic facility management efficiency and collaboration are improved. According to the method, comprehensive, accurate and real-time traffic facility attribute data support can be provided for urban traffic planning, traffic management and intelligent traffic system construction, so that the efficiency and the intelligent level of traffic facility management are remarkably improved.

The invention relates to the technical field of comprehensive traffic planning and logistics optimization, in particular to a multimodal transportation junction transportation path collaborative optimization system and a method thereof.The multimodal transportation junction transportation path collaborative optimization system comprises a data layer, a model layer, an application layer and a feedback layer, the data layer comprises a data acquisition module and a data processing module, the data acquisition module mainly acquires node basic data, network topology data, freight demand data and cost aging data, and the processing content of the data processing module comprises standardization processing, space-time alignment, abnormal value cleaning and data storage processing; and the model layer is used for multi-objective optimization model and intelligent algorithm processing. According to the method, the multi-objective optimization model is constructed, so that the scientific site selection of the transit hub, the intelligent matching of the transportation mode and the path and the collaborative optimization of the whole link cost and the time efficiency are realized, meanwhile, various resource constraint conditions are met, and the overall operation efficiency of the multimodal transportation network is improved.

The invention discloses a traffic planning method, which realizes real-time monitoring, regulation and optimization of a traffic system by collecting, analyzing and processing real-time traffic data and combining an advanced traffic management algorithm. According to the invention, by using a data processing algorithm, the real-time data of the traffic system can be analyzed according to the timely acquired data, and the real-time monitoring and response to the traffic condition can be realized; through deep analysis and mining of big data, traffic demands and road condition changes can be accurately evaluated, and accurate decision support is provided for traffic planning and management. Flexible traffic management strategies and regulation and control means are adopted, and targeted traffic management and adjustment can be realized according to traffic characteristics of different time periods and regions; multiple factors such as traffic flow, road conditions, traffic facilities and the like are comprehensively considered, and the operation efficiency and safety of the traffic system are comprehensively optimized.

The invention provides an urban traffic carbon emission correlation analysis method and system based on a space syntax, and the method comprises the steps: obtaining the road network vector data of a target city, carrying out the preprocessing of the road network vector data through ArcGIS software, and generating a road grid unit which comprises the road network density, constructing a road network topological space model by adopting a line segment model through DepthmapX software, and calculating a road segment-level space syntactic index of each road segment; aggregating the road section level space syntax indexes and the road network density of the road grid units to all the road grid units; based on the economic activity intensity, generating a road grid unitized carbon emission distribution diagram by using the total urban traffic carbon emission amount; and constructing a multiple regression model, determining a quantitative relationship between the road structure and carbon emission based on the multiple regression model, and making a traffic planning scheme. According to the method, the high-carbon emission area and the key road nodes are accurately recognized, and the method is high in universality and can be popularized to traffic carbon emission analysis and optimization practice of different cities.