157results about How to "Improve traffic efficiency" patented technology

The invention discloses a road traffic signal control system and method, and the method comprises the steps: collecting real-time data through a sensor, a camera and a geomagnetic device, combining historical data with emergency analysis, and predicting a flow mode and a change trend; green light time distribution is dynamically optimized, a fixed timing, inductive and adaptive control strategy is adopted, and traffic light duration and priority adjustment is achieved; through regional cooperation and linkage control, green wave bands and branch intersection red light optimization are set, and peak period traffic signals are intelligently dispatched. A machine learning model is used to predict a flow peak value, environment and accident detection data are combined, a signal lamp period is dynamically adjusted, vehicle waiting time, traffic efficiency, carbon emission and traffic satisfaction are evaluated, and a traffic control strategy is comprehensively optimized. According to the road traffic signal control system and method, the traffic flow in different time periods is predicted by adopting the flow prediction model based on big data analysis and machine learning, and the signal period is adjusted in advance.

The invention provides an underground tunnel driving risk evaluation method based on a dynamic Bayesian network. The method comprises the following steps: constructing an underground tunnel driving risk evaluation index system; determining Bayesian network nodes according to the underground tunnel driving risk evaluation index system, and constructing an underground tunnel driving risk dynamic Bayesian network model; obtaining a driving risk assessment result according to the dynamic Bayesian network model of the driving risk of the underground tunnel; and performing driving improvement according to the driving risk assessment result. The method can effectively identify and predict the driving risk probability of different road sections of the underground tunnel, provides risk early warning for drivers, and provides data support for a traffic management department to optimize an induction system and an accident prevention strategy, thereby improving the driving safety and traffic efficiency in the tunnel.

The invention relates to the technical field of intelligent traffic, and discloses a deep learning-based expressway traffic flow detection method and system, and the method comprises the steps: obtaining multi-modal traffic data and environment data in real time from each key road section of an expressway, and carrying out the preprocessing of the multi-modal traffic data obtained in real time, including the time-space alignment of the multi-modal traffic data; inputting the preprocessed multi-modal traffic data and environment data into a pre-trained deep fusion network model, and outputting various vehicle parameters for representing expressway traffic flow characteristics; inputting the various vehicle parameters into a pre-trained high-speed traffic flow detection model, and outputting traffic flow grade information; dynamically updating the deep fusion network model at the key road section by adopting a model fusion mode according to the vehicle parameter change condition at each key road section within the preset time; and updating the high-speed traffic flow detection model according to external conditions introduced by the traffic flow grade information in actual use. And the accuracy and real-time performance of high-speed traffic flow detection are improved by combining deep learning.

The invention provides a vehicle-mounted detecting and guiding system during advancing, and belongs to the field of vehicle control. The problem that the vehicle auxiliary driving efficiency is low is solved. Specifically, the data acquisition module acquires driving data; the positioning correction module is used for carrying out primary analysis on the driving data and correcting the position of the vehicle; the driving assistance module carries out primary adjustment on the vehicle speed; analyzing the power oil consumption change of the vehicle according to the driving data, obtaining the remaining driving time of the vehicle to the destination, and carrying out secondary adjustment on the driving speed of the vehicle; the continuous adjustment module continuously obtains driving data and destination information of the vehicle and adjusts the positioning and driving speed of the vehicle; according to the invention, the positioning and speed of the vehicle are adjusted by acquiring, analyzing and processing related data of vehicle driving, so that the vehicle is stable in driving.

The invention discloses an access control system based on face recognition, relates to the technical field of biological recognition, and solves the problem that feature parameters are difficult to extract according to face images and iris images. Secondly, face comparison detection is difficult to carry out according to the extracted feature parameters of the face image; then, it is difficult to analyze the iris image of the incomer according to the face comparison result, and it is difficult to analyze the abnormal behavior according to the monitoring image; and finally, the risk coefficient is difficult to comprehensively analyze, and the risk level of the access control system is difficult to assess. By analyzing the face image and the iris image, the identity verification accuracy of the access control system is improved; abnormal behaviors of strangers in a monitoring image are analyzed by using a deep learning model, and a risk coefficient is comprehensively analyzed according to an identification failure rate and a potential abnormal coefficient, so that the risk level of the access control system is evaluated.

The invention relates to the technical field of vehicle control, and discloses a vehicle scheduling method and device, a vehicle and a storage medium, and the method comprises the steps: obtaining the driving state information of a vehicle before the vehicle is driven to an intersection without traffic lights, and obtaining the relative positions and predicted driving path information of other vehicles in a current intersection region; in each sampling period, determining a corresponding multi-objective optimization function based on the traffic interaction scene where the vehicle is located; wherein the traffic interaction scene is determined based on the driving state information of the vehicle and the relative positions and predicted driving path information of other vehicles; constructing a vehicle longitudinal dynamic model based on the driving state information of the vehicle, and constructing a model prediction control problem in combination with a multi-objective optimization function and a safety constraint condition; and solving the model prediction control problem to obtain a target control input sequence in a control time domain, and outputting the first control input quantity in the target control input sequence to an execution mechanism of the own vehicle to realize real-time regulation and control of the own vehicle.

The application provides a holiday emergency lane dynamic management and control method and system based on vehicle-road cloud cooperation, and belongs to the technical field of intelligent transportation, which comprises the following steps: collecting multi-source traffic data through a vehicle-mounted terminal and a roadside sensing communication module; fusing data by using a Kalman filtering algorithm by a cloud cooperative decision module, judging a congestion level based on real-time traffic state parameters and a three-level congestion threshold model, and judging emergency demand in combination with task information reported by an emergency vehicle; finally, generating a passing right regulation strategy dynamically based on the double dimensions of "congestion level + emergency demand", and automatically switching the state of the emergency lane and executing vehicle guidance through a traffic management and control execution module. The application realizes intelligent and dynamic switching of emergency lane resources between "social vehicle passing" and "emergency vehicle special use", relieves main road congestion when there is no emergency demand, and guarantees priority passing through a whole-process escort mechanism when there is emergency demand, thereby improving road resource utilization efficiency and emergency passing guarantee capacity.

This invention belongs to the field of airport public transportation allocation technology, and discloses an airport public transportation pre-allocation processing system and method based on a big data model. The method includes: acquiring historical data; using big data processing technology and machine learning and deep learning models to infer passenger demand and public transportation usage from historical data, generating a data projection model; inputting real-time weather, time, and passenger data into the data projection model, employing intelligent optimization algorithms, and dynamically adjusting public transportation resource allocation in conjunction with real-time traffic conditions; monitoring the public transportation resource allocation results in real time, continuously optimizing and dynamically adjusting the results using a feedback mechanism, and pushing the latest adjusted results to downstream third-party systems. This invention utilizes real-time data analysis and intelligent optimization algorithms to achieve a more efficient traffic scheduling scheme, and has significant practical implications and broad application prospects.

The application discloses a kind of based on space alignment and timing correction multi-intersection adaptive signal control method, belong to traffic signal control field, including: through the traffic detection sensor deployed in intersection, real-time traffic state data are collected and sent to signal lamp controller;According to road network topology, send cooperative data to downstream intersection;Traffic flow and candidate phase set are constructed;The traffic data associated with candidate phase is aggregated as demand value and executes ordering transformation to obtain combination state;To combination state, execute spatial feature alignment processing and map as state feature vector;State feature vector is input into near-end strategy optimization controller, and output target phase and basic phase duration;The traffic flow information of last phase period on upstream intersection is combined to carry out timing correction to basic phase duration;Drive signal lamp hardware to execute and generate reward feedback and update near-end strategy optimization controller parameter.The application realizes the cooperative optimization of multi-intersection traffic signal, improves the traffic efficiency and adaptive capacity.

The utility model belongs to the field of access control equipment, and particularly relates to an access control device for security and protection, which comprises a connecting wall, the side wall of the connecting wall is provided with a rotary gate, the top of the rotary gate is provided with a rotary boss, the rotary boss is provided with a synchronous assembly, and the synchronous assembly comprises a synchronous top seat. A synchronous top seat is arranged on the rotating boss, a stress rod is fixedly connected to the outer wall of the synchronous top seat, a pushing rod is arranged at the top of the rotating gate, a guide sliding block is slidably connected to the inner wall of the synchronous top seat, and a metal sheet is fixedly connected to the outer wall of the guide sliding block; and the inner wall of the synchronous top seat is fixedly connected with a metal elastic sheet switch. According to the access control device for security and protection, when a person climbs the rotary gate, the hand applies force to the blocking rod, the compression spring is stressed and compressed, the metal sheet is driven to move downwards, the metal elastic piece switch is triggered, the alarm immediately gives out a sound-light alarm, the response time is fast, and the climbing person is scared away before climbing work is completed.

The application discloses a space-time convolution short-time traffic flow prediction method based on complementary ensemble empirical mode decomposition, and comprises the following steps: acquiring original traffic flow data and performing data preprocessing; and inputting the traffic flow data into a pre-constructed CEEMD-GAT-LSTM model to perform short-time traffic flow prediction. The prediction model comprises the following steps: performing signal decomposition on the traffic flow data by using CEEMD, fully mining the spatial features of the data by using GAT, and fully mining the time features of the data by using LSTM, so that the learning efficiency of the neural network is improved. The application can effectively improve the prediction accuracy of short-time traffic flow prediction and provide favorable reference value for the traffic department.

The invention discloses a main line intersection CAV special lane setting method and a self-adaptive control system in a mixed driving environment. The method comprises the following steps: collecting information in real time; according to the current CAV permeability, the traffic capacity in the trunk line direction in three different states is calculated, the maximum net income of the traffic capacity is calculated, and then lane function switching judgment conditions are set in a mode of comparing the maximum net income of the traffic capacity; the lane function switching judgment condition is used for judging whether to open or close a CAV special lane, and determining that the CAV special lane is set as a CAV full-steering special lane or a CAV straight special lane; and iteratively optimizing the generated signal control scheme by adopting a PS-PPO-based multi-agent deep reinforcement learning algorithm, and generating an optimal signal control scheme in real time. According to the invention, the hybrid lane and CAV lane functions are dynamically adjusted according to the change of the CAV permeability, the cooperative optimization of the lane functions and the signal control scheme is realized, and the traffic efficiency of the trunk line intersection is improved to the greatest extent.

The application discloses a multi-terminal positioning intelligent cooperative management system based on Beidou satellites and relates to the technical field of operation and maintenance data intelligent management. The application collects crossroad vehicle information, sets a collection period and a monitoring range, counts vehicle information, calls historical data, extracts historical reference vehicle data, calculates historical traffic demand, analyzes the influence degree of traffic prediction, obtains traffic demand of the next monitoring period, monitors congestion crossroads, analyzes green light adjustment demand, adjusts the green light duration, monitors the congestion of adjacent crossroads after adjusting the congestion crossroads, adjusts the green light duration of the adjacent crossroads, and is used for synchronously and reversely adjusting the red light duration while adjusting the green light duration. The application accurately analyzes the traffic demand of each direction, monitors the traffic conditions of adjacent crossroads, and is adaptively adjusted to avoid the transfer of traffic pressure, so that the traffic operation of the whole region is more smooth.

The present invention provides a control method of an intelligent traffic control system based on YOLO visual perception, and belongs to the technical field of traffic control. An intelligent traffic control system based on YOLO visual perception includes a video acquisition and processing module and a control module; the present invention connects a camera at an intersection through the video acquisition and processing module, captures the captured video image, analyzes and extracts the image feature information and converts it into control information, and at the same time, the video acquisition and processing module is connected to the control module for communication, and the control module is connected to the traffic light at the intersection, so that the control module can receive the control information and adjust the duration of the traffic light at the intersection in real time according to the control information, that is, according to the traffic volume of different lane types in different directions of the intersection, the duration of the traffic light at the corresponding intersection is controlled in real time, and the duration of the traffic light at the intersection is intelligently and dynamically adjusted, which effectively avoids the problem of traffic congestion in one direction and empty traffic in the other direction in actual road conditions, and improves the traffic efficiency of the road.

The present application belongs to the technical field of intelligent transportation system, and relates to a signal-vehicle cooperative optimization method based on MPC and trust region Bayesian optimization. Firstly, traffic flow data collection and road network simulation modeling are performed, and initial path allocation of vehicles is performed, then real-time speed control is performed on intelligent connected vehicles entering the road network, and the best driving speed is calculated by using a Bayesian optimization algorithm based on a trust region according to signal light information, distance from the intersection and driving conditions of surrounding vehicles. Then, the road impedance is updated, and the best driving path of the intelligent connected vehicle is recalculated. Then, the future traffic state is predicted by using model predictive control, and the best green light time of each intersection in different phases is calculated by using trust region Bayesian optimization. The best green light time is imported into the simulation platform, and vehicle path optimization and speed control are performed again, and the cycle feedback is performed. Finally, it is judged whether the vehicle leaves the road network or reaches the simulation end time, and the vehicle control and signal optimization are ended.

This invention discloses a vehicle trust assessment method for cooperative decision-making at unsignalized intersections, belonging to the field of intelligent transportation and autonomous driving cooperative control technology. The method first acquires multi-source state and interaction information of the target vehicle and conflicting vehicles, constructing reference position estimates and physical interaction characteristics. Then, based on multi-source observation consistency analysis, it calculates the information reliability of the target vehicle and, combined with the spatiotemporal interaction relationship between vehicles, calculates the physical interaction trust of the conflicting vehicle pairs. Further, it determines the comprehensive trust based on the information reliability and physical interaction trust, and generates a safe time interval between vehicles based on the comprehensive trust. This interval is then used as a conflict constraint parameter input into the intersection cooperative scheduling model to achieve adaptive optimization control of vehicle passage order and entry time. This method can adaptively adjust the safe time interval between vehicles under conditions of uncertainty in vehicle state information, differences in vehicle execution consistency, and complex interaction relationships, improving the safety, stability, and traffic efficiency of cooperative decision-making at unsignalized intersections.

This invention discloses a method and terminal for intelligent coordinated control of traffic lights. The method includes: upon receiving a request for passage from an intelligent driving vehicle at a target intersection, real-time collection of pedestrian passage requests, traffic conditions in conflicting directions, and traffic conditions in the target direction; determining whether to trigger a constant green strategy based on the traffic conditions in the conflicting directions and the pedestrian passage request; if not triggered, calculating the next cycle phase sequence and duration scheme of the traffic lights using a signal control algorithm based on the intelligent vehicle passage request, the traffic conditions in the target direction, the traffic conditions in conflicting directions, the pedestrian passage request, and the default timing strategy; adjusting the traffic lights at the target intersection according to the next cycle phase sequence and duration scheme; and adjusting the traffic lights at the next preset intersection according to the driving route in the intelligent vehicle passage request and the traffic lights at the target intersection to form a green wave segment. This method can effectively improve intersection traffic efficiency and meet the passage needs of intelligent driving vehicles.

The application relates to the technical field of traffic management, and provides a smart city traffic management method and system based on a cloud platform.The method comprises the following steps: collecting a target pedestrian passage of a target city; performing real-time monitoring on the target pedestrian passage to obtain illegal monitoring data; positioning a first illegal vehicle according to the illegal monitoring data, and obtaining illegal frequency information and illegal type information of the first illegal vehicle; obtaining a preset illegal punishment scheme; performing punishment scheme matching on the illegal frequency information and the illegal type information according to the preset illegal punishment scheme to obtain a first punishment scheme; and sending the first punishment scheme to a traffic control platform and a first vehicle owner, and performing traffic punishment management according to the first punishment scheme.The application solves the technical problem that traffic events are disposed slowly in the prior art and subsequent supervision of a vehicle causing an accident cannot be performed, and achieves the technical effects of timely disposal of traffic events and improvement of road passing efficiency.

The invention provides a vehicle priority passing method, a cloud control platform and a vehicle, the vehicle priority passing method is applied to the cloud control platform, and the method comprises the following steps: responding to a priority passing request sent by a target vehicle before the target vehicle runs to an intersection; performing phase control of different arrival conditions on signal lamps of the intersection by using the intersection signal control model, and generating control strategies of different arrival conditions, so that the target vehicle passes through the intersection preferentially based on the control strategies; wherein the target vehicles comprise vehicles needing to be subjected to priority passing scheduling, and the different arrival conditions comprise the condition corresponding to the condition that the signal lamp is red or green when the target vehicles arrive at the intersection, so that the technical problem that the passing efficiency of the priority passing vehicles is relatively low is solved.

The embodiment of the invention provides an intelligent traffic-oriented spatio-temporal data feature mining method and system, and the method comprises the steps: generating a traffic spatio-temporal knowledge network through the real-time obtaining and processing of the dynamic traffic flow data of a plurality of traffic participants, and marking and analyzing a first spatio-temporal vertex and a target dominant participant in the traffic spatio-temporal knowledge network. According to the invention, the method can accurately recognize the key participant which has the greatest influence on the traffic flow, carries out the deep feature mining of the main body moving state track data of the key participant, can find potential traffic accident risk points in advance, remarkably improves the early warning capability of an intelligent traffic system, and facilitates the reduction of traffic accidents. Based on the dynamic traffic flow data and the analysis result of the key participants, traffic resources are allocated more scientifically and reasonably, a traffic signal control strategy is optimized, the road passing efficiency is improved, traffic congestion is reduced, and the efficiency and effect of urban traffic management are enhanced.

The utility model relates to a kind of offshore wind power platform anti-collision device, including foundation platform and surrounding multiple foundation columns, and there is protective ring frame outside foundation column, which is composed of large and small arc frames with buffer protection parts, large arc frame is fixed in vice foundation column, and notch is arranged at the position of main foundation column;Small arc frame is movably connected to main foundation column and located in the notch, and the notch is opened and closed by driving device;Protective ring frame is composed of large and small arc frames, under normal condition, small arc frame closes the notch, so that protective ring frame forms complete annular structure, can be all-around around foundation column, intercepts impact object from all directions;When personnel need to go up and down platform through the ladder of main foundation column, small arc frame is driven to move by driving device to open the notch, and the ladder position is directly exposed, without bypassing anti-collision structure, significantly improve traffic efficiency and safety, especially suitable for emergency repair or emergency evacuation scene.

The invention discloses a multi-AGV intelligent path planning and scheduling optimization system, and belongs to the technical field of intelligent manufacturing and automatic logistics. The system comprises a central controller, a task distribution module, a path planning module, a traffic flow prediction module, a scheduling optimization module and an AGV state monitoring module. In order to solve the problems that in the prior art, in a multi-layer complex environment, path planning and task scheduling are disjointed, congestion conflicts are prone to occurring, and the centralized scheduling efficiency is low, path planning and task scheduling are unified and coordinated, and a three-dimensional path planning algorithm suitable for a multi-layer space and a scheduling optimization mechanism based on distributed negotiation are adopted; and intelligent coordination of multi-AGV operation is realized. According to the system, path conflicts can be effectively predicted and avoided, resource utilization is optimized, and therefore the overall operation efficiency, safety and adaptability of the multi-AGV system in a complex dynamic environment are remarkably improved.

The utility model discloses a wharf portal truck identification system, include: set up on the safety island of each wharf portal both sides low frequency activator and camera device, set up on each wharf portal top microwave active RFID read-write ware, set up in control room host computer, low frequency activator activates the microwave active label of truck license plate built -in, makes it to empty send data packet, microwave active RFID read-write ware receives data packet, camera device gathers image data, host computer receives data packet, truck's image data, carries out comprehensive analysis, identifies truck information, the portal that truck went in and truck advancing direction to, and verifies truck information, the utility model provides a wharf portal truck identification system, has combined RFID transmission technology, low frequency activation technology and image recognition technology, carries out accurate identification and check to the container truck information and the direction of passage of wharf portal, can promote the efficiency of portal traffic, ensures the effective supervision of port wharf to truck vehicle.

The invention provides a high-speed electronic toll collection method and system based on mobile phone Bluetooth, and the method comprises the steps: carrying out the interactive communication based on Bluetooth disposed at an entrance lane, a portal system, an exit lane and a mobile terminal, collecting and recording vehicle information, entrance information and passing portal information, and carrying out the data collection and charging transaction at the exit lane. In a scene without ETC equipment and a composite pass card, high-efficiency pass charging transaction is realized, and the pass efficiency is improved.

The invention discloses a path planning method and device based on a variable expansion coefficient, and a storage medium. The path planning method comprises the following steps: obtaining state information of a vehicle and environment obstacle information; according to the state information of the vehicle, a dynamically-changing expansion coefficient is calculated, and the dynamically-changing expansion coefficient is at least in positive correlation with the current speed of the vehicle; performing expansion processing on an obstacle by using the dynamically changing expansion coefficient to generate an expansion area; generating a group of candidate paths based on the road reference line; candidate paths intersecting with the expansion area are removed, and an optimal path is selected from the remaining candidate paths to serve as an obstacle avoidance track; and outputting the obstacle avoidance track to a vehicle control system for execution. According to the method, the expansion coefficient dynamically related to the vehicle speed, the steering angle, the path curvature and other factors is designed, self-adaptive adjustment of the safety distance is achieved, and therefore the obstacle avoidance track which is safer and smoother and better conforms to the vehicle dynamic characteristics is generated.

The invention discloses a height and width limiting device for a newly-built highway of highway engineering. The height and width limiting device comprises supporting legs symmetrically arranged on the two sides of a road surface, an intercepting rod horizontally installed between the top ends of the pair of supporting legs, a displayer right facing the vehicle coming direction, a distribution box integrated with a control component and a base embedded and fixed in a roadbed. Wherein the supporting legs and the intercepting rods are both of telescopic structures, and the bottoms of the supporting legs are in sliding connection with the base; an identification module and an intelligent control module are integrated in the distribution box, the intelligent control module is electrically connected with the supporting legs, a driving part of the intercepting rod and the displayer, and the vertical height of the supporting legs and the transverse width of the intercepting rod can be synchronously adjusted according to vehicle size detection signals. The system has the beneficial effects that full-process intelligent management and control of early warning, active avoidance, urgent danger prevention and remote alarm can be realized, and a rigid collision accident between a vehicle and the device is avoided.

This invention relates to the field of traffic situation awareness technology, and discloses a traffic situation awareness system and method based on a large model, comprising the following modules: a data acquisition module, including a swarm of drones and multiple sensors, wherein the swarm of drones and multiple sensors are used to acquire various data in tidal flow lanes and adjacent lanes within a target area; a processing module, which preprocesses the acquired data and constructs a tidal flow lane traffic situation model, and calculates suggested values ​​for tidal flow lane traffic lights using the tidal flow lane traffic situation model; and a data analysis module, which generates corresponding response decisions based on the suggested values ​​for tidal flow lane traffic lights and various data of the opposite lanes within the target area. Through the swarm of drones and multiple types of sensors, various data of each lane in the target area are acquired, and the traffic situation model is constructed by the processing module to calculate suggested values ​​for tidal flow lane traffic light delays.

The application discloses a kind of associated multi-intersection traffic flow prediction methods.The steps of the present application are as follows:1, collect the traffic flow data of associated intersections, and divide the intersection traffic data into training set, validation set and test set after preprocessing;2, use CNN to extract spatial features from the input intersection traffic data;3, use Transformer to extract time features by inputting spatial features;4, after the Decoder layer is fully executed, the three time window data are finally input into three vectors, and the three vectors are stacked and input into the average pooling layer;5, set the model parameters;6, train the model until the maximum training period, and use the final model to predict the traffic flow of associated multi-intersections.The application uses CNN and Transformer to extract the spatial and temporal features of associated multi-intersections.Learning time encoding is used to embed the position encoding of Transformer, which injects position information and time information into the model together, helping the model to better learn the time features of traffic volume.

This application relates to the field of automotive technology, and provides a method, device, electronic device, and readable storage medium for preventing vehicles from cutting in. The method includes: acquiring driving parameter information of second vehicles surrounding a first vehicle; issuing a cutting-in warning message when the second vehicle is detected to have the intention to cut in based on the driving parameter information; controlling the first vehicle to decelerate based on the driving parameter information when a driver's input command regarding the cutting-in warning message is received within a preset time period, until the driving parameter information indicates that the second vehicle has successfully cut in; and controlling the first vehicle to accelerate based on the driving parameter information when no driver's input command regarding the cutting-in warning message is received within the preset time period, until the driving parameter information indicates that the second vehicle does not meet the conditions for cutting in, effectively solving the problem of vehicles being unable to drive normally due to surrounding vehicles cutting in.

This invention provides a method, device, terminal, and storage medium for bus priority control at multiple intersections. The method includes: acquiring the approach lane status of each approach lane at each intersection within a target area at the current time, and the traffic light prediction model for each intersection; determining the target bus closest to its current intersection on each approach lane, and acquiring the bus status and bus prediction model for the target bus at the current time; predicting the traffic light phase status of each intersection at the next time based on the approach lane status, bus status, and traffic light prediction model; and predicting the bus acceleration corresponding to each target bus at the next time based on the approach lane status, bus status, and bus prediction model. This application can adjust the traffic light phase status and bus acceleration using traffic light prediction models and bus prediction models in a multi-intersection environment, thereby improving bus traffic efficiency.