304 results about "Bus transit" patented technology

The invention discloses a public traffic composite traffic network key node identification method based on an improved K-shell algorithm, and the method comprises the steps: firstly extracting the line, geographic position, vehicle arrival interval and rated passenger capacity information of a subway and a bus station from multi-source data, and constructing a weighted composite traffic network considering the intra-layer transportation capacity and inter-layer transfer relation; node stripping is carried out based on a K-shell algorithm, and a shell fine position KP value is calculated in combination with a node removal sequence; further introducing a neighborhood structure overlap ratio, and constructing a node local propagation influence index; meanwhile, a comprehensive correction coefficient is constructed by considering the difference between intra-layer node strength and transfer attributes and the transportation capacity difference between subway and bus layers; and finally, a node comprehensive importance index IKA is formed and is used for sorting and identifying network key nodes. Finally, the recognition effect is verified through network efficiency, connectivity and SIR (susceptibility-infection-recovery) propagation simulation, and the result shows that the method is superior to a traditional centrality algorithm in the aspects of recognition precision and propagation influence measurement.

The invention discloses a bus passenger getting-on station estimation method based on multi-source data fusion, and relates to the field of public traffic operation planning and management, and the method comprises the steps: obtaining multi-source sample data, and carrying out the integration processing, and obtaining database storage information; processing and extracting by using the public transport data preprocessing terminal to obtain a public transport operation time parameter; segmenting and constructing a fixed threshold time window; matching the intelligent card data to obtain an intelligent card data set of the candidate site; performing first data screening, constructing a candidate site group, and calculating by using a maximum likelihood estimation model to obtain a first data set of a determined site; performing second data screening and division to obtain a time sequence subset, and performing prediction by using a prediction neighbor information algorithm to obtain site information of the second data; and carrying out unified site numbering to obtain a site matching data set, and carrying out verification by using a multi-dimensional data terminal to obtain an evaluation result of the multi-dimensional data. According to the method, key field dependence is broken through, and data adaptability is improved.

The invention provides a conventional bus resource optimal configuration method and system considering subway toughness improvement, and belongs to the technical field of urban rail transit emergency connection. According to the method, actual factors such as a rail transit and bus network structure, conventional bus transportation requirements, metro emergency connection requirements, limited bus transportation capacity and bus system extension cost are comprehensively considered, and conventional bus service planning and metro interruption emergency response are collaboratively optimized; according to the method, stable operation of conventional buses can be guaranteed, meanwhile, passengers detained in the metro can be evacuated to the maximum extent, the service quality of a rail transit and bus connection mode can be improved, and the public transport network toughness can also be enhanced to the great extent. Meanwhile, in the collaborative optimization process of the conventional bus service and the bus connection service, a Pareto optimal method is introduced in view of the complexity of a dual-objective planning problem, so that efficient solving of an actual scale case is realized, and the method is closer to an actual problem.

The invention provides a bus automatic driving method and system, and relates to the technical field of intelligent public transportation, passenger flow prediction is carried out on a target area, route planning is carried out according to a passenger flow prediction result, and a bus operation baseline is constructed; collecting travel reservation requests of users, performing spatial-temporal clustering analysis, and constructing a travel demand cluster; and performing scheduling decision analysis on the travel demand cluster and the candidate vehicle information, establishing a bus scheduling strategy, issuing and executing the strategy, receiving a real-time travel request of a user, performing matching analysis on the real-time travel request and the initial running path, and performing dynamic insertion updating on the initial running path according to an analysis result. The technical problems of low scheduling efficiency and resource waste caused by the fact that personalized and decentralized travel requirements cannot be responded in time due to fixed lines and scheduling response lag in the prior art are solved, and the technical effects of improving the bus travel service matching rate and enhancing the operation flexibility and the resource configuration efficiency are achieved.

The invention belongs to the technical field of urban traffic management, and particularly relates to an electric bus cooperative scheduling method in a charging facility time-sharing sharing mode. The objective of the invention is to solve the problem that the existing method faces a single electric bus route, neglects the fluctuation of the charging demand of private cars in each time period, and is easy to cause low utilization rate caused by excessive charging facility distribution number in individual time periods, or long charging queuing time of private cars caused by insufficient charging facility distribution number. The method comprises the following steps: constructing a collaborative optimization model of a bus scheduling scheme and a charging facility sharing scheme based on the daily charging cost of an electric bus line and an expression of an estimated value of earnings obtained by sharing a charging pile to an electric car every day by a bus enterprise; solving the collaborative optimization model to obtain an optimal feasible solution and a corresponding objective function value; the optimal feasible solution comprises an electric bus scheduling scheme and a charging facility sharing scheme.

The invention discloses a public transportation auxiliary unmanned aerial vehicle cooperative distribution method considering client point dynamic demands, introduces a public transportation auxiliary unmanned aerial vehicle with large service range coverage to carry out express distribution, and aims to reduce the terminal logistics distribution cost and improve the distribution timeliness. The method comprises the following steps: constructing a generation model which simulates a dynamic customer demand and considers a time window in combination with an actual scene, and establishing a bus auxiliary unmanned aerial vehicle cooperative distribution optimization model which considers the customer point dynamic demand based on a customer point generation mechanism and a bus unmanned aerial vehicle cooperative operation mechanism in the generation model; and aiming at the established model, designing a dynamic information processing strategy based on bus departure time, and fusing the dynamic information processing strategy into an adaptive large neighborhood search framework for solving, thereby obtaining a heuristic algorithm suitable for solving a bus-assisted unmanned aerial vehicle cooperative distribution problem considering the dynamic demand of a client point. The distribution path can be dynamically planned according to the demand points generated at any time, the logistics delivery efficiency is improved, and the distribution cost is reduced.

The invention discloses an intelligent bus scheduling system and method based on an AI large model, and the system comprises a data collection unit which is used for collecting multi-source urban traffic data; the data preprocessing unit is used for performing cleaning, standardization and feature extraction on the acquired multi-source urban traffic data; the knowledge graph construction unit is used for constructing the processed data into a knowledge graph containing entities, relationships and attributes; the AI reasoning unit is used for generating a natural language scheduling suggestion based on the knowledge graph state; the strategy optimization unit is used for optimizing a scheduling strategy based on a reinforcement learning algorithm; and the execution feedback unit is used for executing the scheduling instruction and collecting execution effect feedback. The problems that an existing bus dispatching system is insufficient in data utilization and lacks deep understanding and reasoning ability for a complex traffic environment are solved.

The invention provides a parallel main cabinet type direct current charging pile system, which comprises a main control unit, at least one power distribution matrix unit, at least one main cabinet and a plurality of charging terminals, and is characterized in that each main cabinet is internally provided with a plurality of pluggable power supply modules, and each charging terminal is provided with at least one charging gun; the switching control logic of the power distribution matrix unit supports any power scheduling across a main cabinet, across a charging terminal and across a charging gun; each power distribution matrix unit is constructed as an M * N full-connection switch matrix, and the power distribution matrix unit dynamically switches the output power of any one power supply module to any one charging gun. The main control unit dynamically dispatches the output ports of the SCU to be connected in parallel according to the number of the charging gun, automatic superposition of power of multiple main cabinets is achieved, the megawatt charging requirements of electric heavy trucks, buses and the like are met, any power module is supported to be correspondingly connected with the charging gun of any number, and the physical boundary among the main cabinets, the charging terminals and the charging gun is broken.

The invention discloses a subway interval interruption scene-oriented planned operation bus route fine adjustment method, which mainly comprises the following steps of: constructing a candidate bus route set and a rail station set capable of being conveniently transferred, and generating a multi-point connection path; on the premise of restraining the total demand of the detained passengers, the number of passengers getting on and off each bus station, the dynamic passenger capacity in the bus, the bus route arrival and departure time and the detour connection time are calculated, and the conventional operation service level is guaranteed; and constructing an optimization model aiming at minimizing the total delay of all system passengers, and solving the optimization model. By scientifically deploying existing operation bus resources, a multi-station dispersed transportation strategy is implemented, and travel requirements of passengers are accurately matched. The method effectively avoids the problem of congestion caused by centralized transportation of a single destination, and the constructed optimization model gives consideration to the operation efficiency of the public transportation system and the evacuation demands of detained passengers in a subway, remarkably reduces the overall delay cost, greatly improves the emergency response speed and the toughness of the public transportation system, and powerfully supports the safe and stable operation of an urban traffic system.

The embodiment of the invention discloses a bus scheduling method and device based on the battery health state, and the method comprises the steps: obtaining the battery health state parameter, the battery remaining capacity, the real-time position information and the line information of each bus in real time, carrying out the loss coefficient evaluation of each bus line based on the line information, and obtaining the loss coefficient of each bus line; matching each loss coefficient obtained by evaluation with the battery health state parameter of each bus, and determining a plurality of dispatchable vehicles on each bus route; classifying the plurality of dispatchable vehicles according to the battery remaining capacity and the battery health state parameter of each dispatchable vehicle and the route information of the corresponding bus route, determining running vehicles and alternative vehicles on each bus route, and when the battery remaining capacity of the running vehicles is smaller than a preset capacity threshold value, sending the alternative vehicles to the dispatchable vehicles; and scheduling the alternative vehicles based on the real-time position information of the running vehicles. The matching precision of each bus route and the schedulable vehicle is improved, and the rationality and timeliness of vehicle scheduling are fully guaranteed.

The invention provides an unmanned aerial vehicle terminal distribution method and system based on a public transport network, and belongs to the technical field of logistics distribution, and the method comprises the steps: obtaining customer information, public transport network information and unmanned aerial vehicle information; establishing a multi-target optimization model for cooperative distribution of the bus and the unmanned aerial vehicle by using the customer information, the public transportation network information and the unmanned aerial vehicle information; the target optimization module takes the minimum cost as a target function and considers time difference constraint, route constraint, load constraint and energy constraint; adopting a non-dominated transformer neighborhood search algorithm of a simulated annealing algorithm acceptance criterion to solve the multi-objective optimization model to obtain an approximate Pareto optimal solution set; the optimal solution set is an unmanned aerial vehicle tail end distribution scheme considering cost, time and risk. Based on the method, the invention further provides an unmanned aerial vehicle tail end distribution system based on the public transport network. According to the invention, through combination of unmanned aerial vehicle electric driving and public transportation network cooperation, multi-objective optimized logistics distribution is realized.

The invention provides an artificial intelligence-driven bus real-time scheduling and path optimization method. The artificial intelligence-driven bus real-time scheduling and path optimization method comprises the following steps: S1, multi-source data acquisition; s2, data preprocessing; s3, constructing an artificial intelligence scheduling optimization model; s4, generating a real-time scheduling decision; s5, vehicle execution and data feedback; and S6, path dynamic optimization. Through deep fusion of an artificial intelligence technology and public transportation services, the precision, dynamic and intelligent scheduling decision is realized, and finally the operation efficiency and service quality of a public transportation system are improved.

The invention relates to the technical field of intelligent transportation, in particular to a public transportation passenger and freight co-transportation scheduling system based on big data and the Internet of Things. Comprising data collection and processing, scheduling system architecture, path optimization and dynamic adjustment, and user interface and system management. Wherein the data collection and processing is used for the process of collecting and processing bus operation data by the system and is the basis of the system; the data collection and processing comprises passenger flow data, cargo flow data, vehicle state data and environment data; the scheduling system architecture is used for realizing comprehensive monitoring, real-time scheduling and resource optimization of bus passenger and freight co-transportation; according to the invention, by integrating real-time passenger flow data, cargo flow data and vehicle state information and utilizing big data analysis, an Internet of Things technology and an artificial intelligence algorithm, co-scheduling of buses and freight, path optimization and maximum utilization of resources are realized.

The invention discloses an electric bus station optimization scheduling method and system based on light storage and charging cooperation. The method comprises the following steps: acquiring parameters; according to the obtained parameters, an electric bus charging and discharging behavior model, a photovoltaic output model, an energy storage system operation model and a charging pile operation model considering a one-pile multi-gun sharing mode are established; by taking the minimization of the total operation cost of the station as a target and taking the established unit models as constraints, constructing an optical storage and charging integrated electric bus station optimization scheduling model, and solving to obtain a scheduling plan in the station; and issuing the solved scheduling plan to a station operation control system to generate a scheduling instruction and a charging and discharging instruction of the electric bus so as to realize cooperative scheduling of the station optical storage and charging resources. According to the method, the physical matching relation of the vehicle pile gun is depicted finely, the problem of symmetric solution space explosion caused by neglecting equipment homogenization in a traditional model is solved, and the solving efficiency and feasibility of a large-scale scheduling problem are remarkably improved.

The invention relates to the technical field of intelligent traffic, and provides a public transport priority signal and track cooperative control method and device under dynamic road right distribution, and the method comprises the steps: constructing a historical data set of traffic signal information and vehicle information; according to the phase of the historical data set and the vehicle information of the corresponding lane in the direction from the stop line to the lane entrance, constructing a motorcade according to the constraint that the maximum number of vehicles allowed by phase timing is not exceeded; inputting the vehicle information and the traffic signal information of the motorcade in the historical data set into a signal timing and road right distribution model to obtain signal timing decision information, road right distribution decision information and vehicle decision information of at least a next period; controlling traffic lights according to the signal timing decision information and the right of road distribution decision information; and controlling the vehicle to run according to the right-of-way distribution decision information and the vehicle decision information. According to the invention, the passenger passing efficiency is considered, the lane right is dynamically allocated, and the traffic signal timing is optimized.

The invention provides a bus route planning method and system, and relates to the field of route planning. Establishing a road network model based on station coordinates and inter-station operation time information, establishing Markov decision process models for a plurality of demand response type buses, and designing an improved Q-Learning algorithm; and reasonably distributing travel demands in a road network, importing the travel demands into a designed path planning algorithm, and solving to obtain an optimal running route of each demand response type bus. The problems of path overlapping and local optimization in a traditional single-agent model are solved, an order allocation mechanism is designed, and the problem of vehicle resource competition is avoided.

The invention discloses a customized bus evaluation method and device and a storage medium, and relates to the technical field of public transportation. The evaluation method of the customized bus comprises the following steps: acquiring first vehicle-road interaction data of the customized bus, wherein the first vehicle-road interaction data is interaction data between a vehicle-mounted communication unit and a road side communication unit of the customized bus; processing the first vehicle-road interaction data to obtain an evaluation value corresponding to an evaluation index under at least one evaluation dimension, the at least one evaluation dimension at least comprising a vehicle-road cooperation service level dimension, and the vehicle-road cooperation service level dimension being used for evaluating the service effect of the customized bus in the vehicle-road cooperation environment; and determining an evaluation score of the customized bus by using the evaluation value corresponding to the evaluation index, the evaluation score being used for indicating the service quality of the customized bus. According to the embodiment of the invention, the reliability of customized bus evaluation can be improved.

This invention provides a method for optimizing the shared use of dedicated bus lanes, relating to the field of transportation technology. The method includes: identifying congested road sections and obtaining vehicle speeds in each lane of the congested sections; comparing the congestion levels of dedicated bus lanes and regular lanes by calculating travel indices, and identifying routes where regular lanes are congested while dedicated bus lanes are unobstructed; establishing a multi-objective integer optimization model with the objectives of maximizing the number of shared vehicles available for reservation and minimizing overall road delays; solving for the maximum number of vehicles that can use the dedicated bus lane during a given time period; conducting simulations to verify the multi-objective integer optimization model and evaluating the impact of the shared reservation scheme on dedicated bus lanes and regular lanes; and, considering user acceptance, constructing a mechanism for regular vehicles to reserve bus lanes using carbon credits. This invention addresses the shortcomings of dynamic schemes and the one-size-fits-all approach to existing dedicated bus lane management models.

The invention discloses a transportation hub transport capacity scheduling system based on big data, and particularly relates to the technical field of traffic scheduling, which comprises a data aggregation and sensing module, an early warning and triggering module, a scheduling scheme simulation and decision module and an instruction distribution and execution monitoring module. The system collects subway, bus, taxi, online car-hailing and passenger flow data in real time through a multi-source interface and a sensor, and a unified transport capacity diagram is generated after cleaning and anomaly detection; carrying out transport capacity gap identification and early warning based on a multi-index coupling risk analysis model; a plurality of scheduling schemes are generated and evaluated through a microscopic simulation model integrated with passenger behavior prediction, and an optimal scheme is recommended; finally, the scheme is converted into a specific instruction to be distributed and executed, and real-time monitoring and feedback are performed to form closed-loop management. According to the invention, the transformation from passive response to active intervention and from experience decision to scientific scheduling is realized, and the transport capacity cooperation efficiency and passenger evacuation capability of the transportation hub are significantly improved.

The invention discloses a public transport passenger flow prediction method based on IC card data, which relates to the technical field of intelligent transportation, and comprises the following steps: respectively carrying out feature inspection on a week time sequence, a day time sequence, a time interval sequence and a holiday effect sequence, selecting an adaptive model, carrying out parameter and noise variance feature analysis on the adaptive model by using an RLS algorithm, and carrying out prediction on the public transport passenger flow. Outputting four groups of prediction result sequences; models corresponding to the four groups of prediction result sequences are converted into a state space form, a noise variance estimation value is estimated online through an RLS algorithm, and four groups of time-varying fusion weights are generated; and constructing an observation noise covariance matrix based on the four groups of time-varying fusion weights, performing recursive optimization through a Kalman filter, and outputting a hybrid prediction result by weighting and fusing observation prediction values of the four groups of sequences. According to the method, accurate fitting of passenger flow periodicity, seasonality and heterovariance is realized, and the single-model prediction precision is improved.

The invention discloses a large-model semantic-driven bus carbon efficiency intervention evaluation method, device and medium, and the method comprises the steps: carrying out the fine adjustment of a large language model through bus multi-source heterogeneous text data, constructing line-level, station-level and road-section-level cues based on the structural features of a line, inputting the fine-adjusted model to generate a corresponding semantic embedding vector, and carrying out the calculation of the semantic embedding vector. And semantic features are obtained through fusion. And further splicing the line structured feature and the intervention strategy into a joint feature, fusing the joint feature with the semantic feature to form a first embedded vector, inputting the first embedded vector into the trained deep causal network, and predicting a carbon efficiency estimated value after the intervention is implemented. Similarly, fusing the features without the strategy implementation to obtain a second embedded vector, and inputting the second embedded vector into the same network to obtain a carbon efficiency estimated value without intervention implementation. And finally, by comparing the carbon efficiency improvement rates under the two scenes, quantitative evaluation of the intervention measure effect is realized.

The application discloses a bus carbon efficiency intervention evaluation method and device driven by a large model, and a medium, comprising: fine-tuning a large language model by using bus multi-source heterogeneous text data, and constructing line-level, station-level and section-level prompt words based on line structural features; inputting the fine-tuned model to generate corresponding semantic embedding vectors, and obtaining semantic features through fusion. Further, the line structural features and the intervention strategy are spliced into joint features, and then fused with the semantic features to form a first embedding vector, which is input into a trained deep causal network to predict the carbon efficiency estimate value after the intervention is implemented. Similarly, the features without implementing the strategy are fused to obtain a second embedding vector, which is input into the same network to obtain the carbon efficiency estimate value after the intervention is not implemented. Finally, by comparing the carbon efficiency improvement rates in the two scenarios, the quantitative evaluation of the effect of the intervention measure is realized.

The invention discloses a multi-mode collaborative passenger flow acquisition and guidance method and system for a honeycomb bus port in a peak period, and the method comprises the steps: obtaining the real-time passenger flow video data, collected by hardware equipment, of a target region, and calculating the risk score of the target region according to the real-time passenger flow video data; generating a corresponding passenger flow guiding strategy based on the risk score; the passenger flow guiding strategy comprises an unmanned aerial vehicle guiding strategy and a ground guiding strategy; controlling the unmanned aerial vehicle and the ground personnel to guide the passenger flow according to the passenger flow guide strategy; according to the invention, the accuracy, high efficiency and intelligentization of passenger flow guidance are realized, the guidance efficiency and emergency response speed are effectively improved, and the system toughness and safety in a large passenger flow scene are significantly enhanced.

The present application relates to a kind of flexible public transport static and dynamic coordination scheduling method, including static scheduling and dynamic scheduling two stages, wherein static scheduling stage is gathered user demand by designing reservation time window, while considering the possibility of subsequent real-time request of each site, establishes and solves static scheduling model, determines the initial skip-stop route and timetable of flexible public transport, while the request matching result is fed back to user;Dynamic scheduling stage is based on real-time request on the basis of static scheduling, establishes and solves dynamic scheduling model, updates the skip-stop route and driving timetable of flexible public transport.The present application adds the probability of real-time request in static scheduling stage, prolongs the stop time of corresponding station, organically combines flexible public transport static scheduling and dynamic scheduling, is conducive to improving real-time request acceptance rate, and in low passenger flow area or non-peak period, through skip-stop and flexible timetable, avoid empty running and low load rate, reduce operating cost, improve service quality.

To improve a technique of an operation management system for managing an operation of an automatic driving vehicle.SOLUTION: An information processing device 30 for managing an operation plan of one or more automatic driving vehicles includes a database for allowing one or more users, the one or more automatic driving vehicles, one or more operation diagrams, and one or more points on each one of routes of the operation diagrams to be stored in association with information of at least one area. The information processing device 30 allows the one or more operation diagrams associated with the information of at least one area related to the user to be displayed on an incorporated display or a screen of an external device for each one of the users.SELECTED DRAWING: Figure 6

The present invention belongs to the field of urban smart logistics technology and relates to an urban logistics distribution method that collaborates with buses and drones. The present invention is a new bus-drone collaborative distribution model suitable for non-peak traffic scenarios, and designs and constructs a two-layer decision-making framework. The upper-layer decision-making adopts the Dueling DQN algorithm that integrates a multi-head attention mechanism to reasonably match the package with the bus it should take. After the package is transported to a specific drone station by bus according to the upper-layer decision-making result, the lower-layer decision-making uses a heuristic algorithm to plan the paths of each drone. The present invention forms an efficient network of "trunk transportation + flexible terminals" by coordinating the idle transportation capacity of buses during non-peak periods with drone terminal distribution, which not only activates bus resources but also improves distribution efficiency, reducing traffic and pollution problems caused by additional vehicles in traditional solutions.

Roof construction, especially for a freestanding roof, for example bus shelters or carports, characterized by a (leak-proof) basin having a frame (10) with an angle profile, and in which a profiled sheet (21), in particular a corrugated sheet, with channels (22) and webs (23) is arranged, wherein at least one outer edge of the profiled sheet (21) has a distance (d) to an adjacent inner edge of the frame (10) so that water circulation between at least some of the channels is possible, and a preferably rigid membrane element (30) which is arranged on the profile plate (21), in particular on at least some of the webs, such that the basin is divided into a functional area, in particular a planting area (40), and a storage space (20), wherein the storage space (20) is bounded from below by the profile plate (21) and the frame (10), laterally by the frame (10) and upwards by the membrane element (30).

The invention discloses a bus dedicated path planning method and system for coping with multi-dimensional consideration, and relates to the technical field of path planning, and the method comprises the steps: synchronously collecting the multi-dimensional data of a target road section, and calculating a real-time traffic entropy value based on an information entropy theory; determining participants, performing auditing to generate an effective bidding list, regarding road use right distribution as a cooperative game problem, calculating marginal contribution of each game subject by adopting a Shapley value algorithm, and screening an optimal bidding combination; dividing traffic state intervals, combining the optimal bidding combination, the real-time traffic entropy and the exclusive lane occupancy rate, deciding the right to use according to an interval allocation rule, and generating a standardized allocation scheme; and converting the distribution scheme into a V2X broadcast instruction to be pushed, completing dynamic lane mark switching and passing guidance, collecting feedback data in real time to judge whether deviation occurs, and if the deviation is judged, dynamically adjusting the scheme and re-executing the scheme, thereby realizing dynamic optimal configuration of bus lane resources and system stability.

The invention provides a user privacy protection method and system for bus payment, and relates to the technical field of information security protection, and the method comprises the following steps: a user sends an anonymous two-dimensional code account application request to a bus taking code APP through a user side, generating corresponding anonymous assembled message data at a user side, and sending the anonymous assembled message data to a privacy protection background for analysis; the privacy protection background receives and analyzes the anonymous assembled message data and sends response data of the privacy protection background to the riding code APP, and the riding code APP requests a code platform and completes opening of an anonymous two-dimensional code account; the user uses the anonymous two-dimensional code account, the anonymous wallet account and the recharging request to generate a corresponding message response privacy set and then completes anonymous code scanning and recharging service for the anonymous wallet account; the payment behavior of the normal user does not leak the specific identity of the user.

The invention belongs to the technical field of urban planning, and particularly relates to a traffic station region land utilization efficiency evaluation method, which comprises the following steps of: acquiring and preprocessing point-of-interest data and a traffic station region in an analysis region; extracting land utilization layout features based on the null data set; constructing a multi-mode traffic network including walking, bicycles, buses and rail transit based on the layout features; establishing a land coupling simulation model of traffic based on the regional reachability index; causal inference and model calibration are carried out based on historical traffic intervention events of dynamic evolution; adopting a multi-objective optimization algorithm to generate traffic and land utilization configuration schemes meeting different planning objectives; according to the method, the evaluation result is subjected to visual display and decision output, unified modeling of traffic and social economic data is achieved through multi-source heterogeneous data fusion and standardization processing, and the effect of improving data space-time matching precision and calculation reliability is achieved.