731 results about "Smart transportation" patented technology

The invention provides an active-lane-changing collision-avoidance control method and device based on vehicle-vehicle coordination and belongs to the technical field of intelligent traffic / automobile active safety control. The active-lane-changing collision-avoidance control device comprises a vehicle-vehicle communication unit, a distance measuring sensor, a three-axis acceleration sensor, a vehicle speed sensor, an electronic control unit, an ESP control unit, a driver auxiliary operation unit, a tail lamp control unit and a vehicle inner warning unit. According to the active-lane-changing collision-avoidance control method, surrounding vehicle information is obtained through the vehicle-vehicle communication unit, the safety distance between a vehicle and a front vehicle on the same lane and the safety distance between the vehicle and a front vehicle on the side lane are determined; then, whether the vehicle meets the coordinated lane-changing collision-avoidance requirement is judged, if yes, the expectant lateral and the longitudinal acceleration of the vehicle is calculated, and control over the direction and the speed of the vehicle are conducted, and if not, lane-changing danger warning is conducted. By the adoption of the active-lane-changing collision-avoidance control method and device based on vehicle-vehicle coordination, active-lane-changing collision-avoidance control is achieved, driver safety accidents caused by emergency brake and rear-end collision accidents caused by emergency brake can be avoided, manual operation errors are avoided, and the safety of driving the vehicle is guaranteed.

The invention discloses an intelligent traffic safety system based on the human condition recognition, comprising a server which supports cloud computing, a plurality of electronic terminals which are installed on an automobile and an electronic terminal installed on a traffic basic facilities, wherein the server, as a cloud computing platform, supplies basic computing and management functions to the whole intelligent traffic safety system, the cloud computing platform is connected with each electronic terminal through a wide area wireless network and / or wired network, every two electronic terminals are connected through a wireless network, and the electronic terminals installed on the automobile are used for recognizing human condition information containing a fatigue state of a driver, a stressed state of the driver, a drinking state of the driver and information on whether the driver is legal or not. The intelligent traffic safety system comprehensively applies a cloud computing technology and an internet of things and supplies strong technical support to development of intelligent traffic cause.

The present invention relates to a ‘smart traffic ticket device’ with means to 1) determine the identity of a vehicle; 2) determine the identity of the offending driver; 3) issue traffic tickets according to applicable traffic laws. The smart traffic ticket device comprises of; 1 ) an interrogator in the form of a radiofrequency (RF) reader; 2) communication means comprising of a wireless transceiver, modem and communication ports; and 3) a central processing unit comprising of a processor and memory chip that controls the functioning of the smart traffic ticket device. Transponders, comprising of radiofrequency (RF) tags, are mounted on vehicles; and contain vehicle identification information. Similarly, transponders, comprising of radiofrequency (RF) tags, are present on the driver's license; and contain driver identification information. The RF reader of the smart traffic ticket device has means to interrogate the RF tag on vehicles and the RF tag on the driver's license from a safe distance; both while the said vehicle is at rest and while the said vehicle is in motion. The information thus obtained is used to issue a traffic ticket bearing; 1 ) vehicle identification information; 2) driver identification information and; 3) nature of violation and; 4) the associated fine. According to another aspect of the present invention, the smart traffic ticket device is equipped with a traffic violation sensing device, such as a speed sensor, to form a ‘smart speeding ticket device’ . The smart speeding ticket device has means to 1) identify a vehicle driving over allowed speed limit; 2) determine the identity of the said speeding vehicle; 3) determine the identity of the driver of the said speeding vehicle; and 3) issue a speeding ticket to the said speeding vehicle according the applicable traffic laws.

The invention relates to a fast passing assisting method and device for vehicles at an intersection based on vehicle-road communication. The method comprises the following steps: 1) arranging the fast passing assisting device for the vehicles at the intersection; 2) acquiring phase timing information of a traffic light through a traffic signal acquiring module, and broadcasting to a vehicle queue waiting in the intersection because of red traffic light; 3) pressing an automatic vehicle control module button by a driver to enable the vehicle to enter an automatic control state; 4) synchronously starting vehicles through automatic vehicle control modules of all vehicle queues waiting because of the red traffic light when the traffic light turns to green; 5) keeping all vehicles at a safe distance under the control of the automatic vehicle control modules so as to form a cruising queue to fast pass through the intersection; 6) pressing an automatic control module button by the driver to finish automatic control and recover the normal driver-based control after passing through the intersection. The method and device can be widely applied to intelligent traffic management processes of various intersections.

A large-scale real-time traffic flow prediction method of the present invention is based on fuzzy logic and deep LSTM, which relates to a technical field of urban intelligent traffic management. The method includes steps of: selecting an urban road network scene to collect color images of real-time traffic flow congestion information; obtaining congestion levels of multiple intersections according to the color images, which are used in a data training set; and forming a data sensing end of FDFP through a fuzzy mechanism; establishing a deep LSTM neural network, performing deep learning on the training data set, and constructing a prediction end of the FDFP; construct a graph of road intersections and formulate a k-nearest neighbors-based discounted averaging for obtaining congestion on the edges; and inputting real-time traffic information received from a server into an FDFP model.

The embodiment of invention discloses a method, a device and a system of intelligent traffic control, wherein the method is that according to resource needs of a current task of an application platform, corresponding resource nodes are called by the intelligent traffic control device from a cloud computing basic resource pool, the corresponding resource nodes which are called is configured to form a server cluster, the cloud computing basic resource pool comprises computing resource nodes, storage resource nodes and network resource nodes, the current task is broken up into a plurality of subtasks by the intelligent traffic control device, then the plurality of subtasks are respectively configured to the server cluster and are concurrently processed, and then a plurality of subprocess results are obtained, the plurality of subprocess results are aggregated through the intelligent traffic control device, and a processed result of the current task is obtained and is returned to the application platform. According to the intelligent traffic control method, device and system, resources are shared among all the application platforms, resource utilization rate is improved, and event processing efficiency is improved.

The invention discloses a method for rapid tracking a target based on radar trace points, and belongs to the field of vehicle tracking of intelligent traffic. The method transmits position information of a target vehicle based on a vehicle-mounted radar in a trace point manner. All first-time incoming trace points after start-up of the radar are taken as track initiation, and a wave gate corresponding to each trace point is calculated. An incoming trace point at the next moment matches the wave gate of the previous moment, the track initiation is successful if matching is successful, and the trace points saved at the previous time are clear away if the matching is not successful. If a plurality of the trace points are disposed in a certain wave gate, agglomeration processing needs to be performed on the trace points; and, if a certain trace point falls into the different wave gates respectively, each corresponding wave gate processes the trace point. Through design of a relationship between the trace points, the rapid track initiation is achieved. The scheme further designs an improved data association algorithm, and the objects of improvement of the correct rate of associations and reduction of the computational complexity are achieved.

The invention discloses a deep learning and intelligent street lamp-based traffic signal lamp intelligent control system, and belongs to the field of intelligent public traffic. The system comprises a roadside unit and an intelligent traffic signal lamp, wherein the roadside unit and the intelligent traffic signal lamp are arranged at a traffic road intersection. The roadside unit comprises an image acquisition module, a vehicle detection module, a congestion judging module and a communication module. The image acquisition module collects road traffic images in each intersection direction and sends the images to the vehicle detection module. In this way, the vehicle flow in each intersection direction is obtained. Meanwhile, the congestion judging module determines the congestion level in each intersection direction in combination with the vehicle flow, and transmits the congestion level to the intelligent traffic signal lamp through the communication module. The intelligent traffic signal lamp adjusts and displays the transit time in real time based on the congestion level. According to the technical scheme of the invention, the road traffic condition is detected in real time. For different road sections, different congestion level threshold values are set flexibly. Therefore, the congestion condition can be automatically judged and the transit time in each direction at the intersection is adjusted. As a result, traffic signal lamps are more flexible and more intelligent to use.

The invention discloses an intelligent traffic system based on light emitting diode (LED) visible light communication, and belongs to the field of intelligent traffic systems. The intelligent traffic system comprises LED traffic lamps, a light emitter and a light receiver, wherein the light receiver and the light emitter are arranged on one vehicle; the light receiver on a back vehicle receives a signal sent by the light emitter on a front vehicle; signals sent by the LED traffic lamps comprise a road condition and traffic lamp conditions; the signal sent by the light emitter comprises a distance between the front vehicle and the rear vehicle and a warning signal; and the light receiver receives the signals sent by the LED traffic lamps and the light emitter. Due to the method, a visible light intelligent traffic network is formed, so that road condition information, the distance between the front vehicle and the rear vehicle, the warning signal and the traffic lamp conditions which are sent by the LED traffic lamps can be received immediately; and therefore, the traffic safety is guaranteed, and the probability of traffic accidents is reduced.

The invention discloses a vehicle-road cooperative road traffic system, which comprises a road side system, an edge computing system, vehicle-mounted terminal equipment, a central cloud platform and an intelligent traffic management and control system. The road side system is deployed on a traffic road and is used for acquiring vehicle information in real time and acquiring multi-dimensional traffic information; the edge computing system is deployed on the edge side, close to the traffic road and a traffic data source, of the front end of the vehicle-road cooperative road traffic system, cooperates with the central cloud platform, and receives and executes an adjustment vehicle-road cooperative optimization control strategy of the central cloud platform; the vehicle-mounted terminal equipment is arranged in a vehicle, is in communication connection with the road side system or / and the edge computing system and is used for acquiring vehicle information and multi-dimensional traffic information in real time, and the central cloud platform is in communication connection with the edge computing system and is used for receiving edge node information of the edge computing system; and theedge computing system manages and controls traffic facilities according to the adjustment vehicle-road collaborative optimization control strategy of the central cloud platform and a control scheme,thereby realizing management and control of vehicle-road collaborative road traffic.

The invention belongs to the field of short-time intelligent traffic control, and particularly relates to a short-time traffic flow control method based on deep learning and spatio-temporal data fusion. The method comprises the steps of: obtaining a data source; analyzing the time and space relevance of the historical data of an intersection; respectively establishing a time dimension GRU model and a space dimension CNN regression model according to the historical data of a prediction checkpoint and the related intersection thereof; fusing output results of the time dimension GRU model and thespace dimension CNN regression model to obtain an adaptive spatio-temporal data fusion model; and counting the prediction result of the adaptive spatio-temporal data fusion model, and sending the prediction result to a traffic department. According to the method, on the space-time level, the space-time dependence of a road traffic flow in a complex road network is analyzed, the network representation learning is used for screening the traffic data, and the screened data is used for inputting the model, so that the accuracy of the traffic flow prediction result of the intersection is improved.

The invention belongs to the field of intelligent traffic, and relates to an urban road vehicle running speed forecasting method based on road network characteristics. The urban road vehicle running speed forecasting method can be applied to forecasting the vehicle running speed on an urban road during a period of time, a model is improved on the basis of a k-nearest neighbor nonparametric regression method, relations between road sections in a road network are considered, and a matched time series state vector is enlarged into a multi-dimensional space-time state matrix. A sample database is built through collected historical data, real-time data are collected for serving as a template to be matched with a sample, and the vehicle speed of a next time series of a target road section in the obtained neighbor sample serves as the forecasted vehicle speed. The gaussian function is used in the model two times for setting weights for the state matrix and forecasting results in an integrated mode so that the forecasting accuracy can be improved. The model provided in the urban road vehicle running speed forecasting method has the advantages that the data of the small road network with the road section to be detected as a center serve as the state matrix; compared with a prior forecasting model which only gives consideration to data of a current forecasted road section, the model provided in the urban road vehicle running speed forecasting method is higher in accuracy of multi-step forecasting; in addition, the method can offset real-time data or can be used for forecasting under the condition that a road section speed detector breaks down.

The invention discloses an auto logo locating and identifying method and relates to the field of intelligent transportation. Work environment of the auto logo locating and identifying method is intelligent network cameras. The intelligent network cameras capture pictures and then transmit the pictures into a DSP (digital signal processor) picture processing unit, an auto license plate locating and identifying module processes to acquire exact location of license plates, and then transmits exact location of license plates and picture data to an auto logo locating and identifying module to identify logo type. The auto logo location and identification can be classified into the following types: (1), auto logo location based on multilayer category: utilizing three locating methods to accurately locate auto logos with different texture background, (2) auto logo identification based on multilayer category: utilizing BP neural network classifiers corresponding to different locating types to identify auto logos to finally acquire auto logo types accurately. With the auto logo locating and identifying method, accuracy of auto logo location and identification is greatly improved, timeliness is good, system mounting is more simple and easy to maintain.

The invention belongs to the intelligent transportation field, and specifically relates to the traffic flow statistical method facing to an urban traffic signal timing plan. The method comprises the steps of detecting and tracking a traffic object in a video by using an image processing technology, acquiring the track information of the traffic object, extracting a start-destination coordinate ofeach track to cluster by analyzing and processing the track information and the video scene information, acquiring the subarea information of the scene, and at last, acquiring the detailed traffic flow information. The statistical method provided by the invention has better precision and data richness, provides richer traffic parameter information, can be used for the early warning of the accident, the jam prevention and the automatic route planning, and still has better effect specifically aiming at the complex situation of the heavy traffic flow scene. At the same time, the signal timing plan is performed by acquiring the traffic flow information of the cross road during different time periods, so as to bring obvious economic benefic and improve the traffic passing efficiency.

The invention discloses an intelligent traffic signal control system based on a high-definition video technology, which comprises external station equipment, a communication network and central system equipment, wherein the external station equipment uses each intersection as a unit, and all directions of each intersection are respectively provided with high-definition cameras; the cameras are connected with a high-definition video detecting host machine and installed in a signal case; a signal lamp is connected into a traffic signal control machine; network equipment in an intersection signal machine is intercommunicated and interconnected with the central system; the communication network comprises three parts, namely the network connection from the intersection signal machine to a signal control communication unit, a communication unit and the internal network connection of the central system equipment; and the central system equipment is divided into traffic signal control system equipment, traffic violation processing system equipment, traffic video monitoring system equipment and an operating terminal according to the function. By utilizing the high-definition video technology, the intelligent traffic signal control system can realize the detection of dynamic traffic flow data, traffic data acquisition and traffic enforcement function.

The invention belongs to the field of intelligent transportation, and in particular relates to a method for predicting urban traffic flow based on time-space correlation. The method includes the following steps: 1) forecasting model training: generate corresponding forecasting models according to different time forecasting granularities; 2) real-time traffic forecasting: consistent with the model training process, the latest collected traffic will be used for traffic flow forecasting The data is added to the input end of the forecasting model, and then processed by the forecasting model to output the forecasted traffic for the next period. The invention accurately predicts urban road traffic flow, can realize intelligent traffic control and management, traffic information service, and provides real-time data for alleviating urban traffic congestion, and has significant social and economic benefits.

The invention relates to the field of intelligent transportation, and provides a road information fusion system and method for an automatic driving vehicle. The road information fusion system comprises an information acquisition unit used for acquiring various kinds of road information acquired by various types of road perception sensors of a vehicle; an information initial fusion unit, used for carrying out initial fusion on the various road information, and the initial fusion comprising information preprocessing and information validity verification; and an information depth fusion unit, used for carrying out depth fusion on the initially fused road information so as to output a road model of the vehicle, and the depth fusion comprising information coordinate conversion, road feature point extraction, lane line fitting, lane line calculation and information comprehensive management. According to the invention, initial fusion and secondary fusion are carried out on similar road information, repeated data processing processes are reduced, and the accuracy of an output road model is improved.

The embodiment of the invention, which relates to the intelligent traffic field, discloses a detection method, apparatus, and system of an oversized vehicle, thereby improving detection accuracy of an oversized vehicle based on simplification of hardware complexity. According to the embodiment of the invention, when occurrence of a vehicle licence plate in a current view is identified, two shooting units of a binocular camera are triggered to carry out synchronous shooting on a same to-be-detected vehicle and two image frames that are obtained by synchronous shooting are obtained; contours of the to-be-detected vehicle in the two image frames are identified, and a three-dimensional contour model of the to-be-detected vehicle under the world coordinate system is constructed according to the identified contours as well as a relative pose between the two shooting units of the binocular camera; statistics of a distribution situation of all pixel points under the world coordinate system in the three-dimensional contour model is carried out, and the size of the to-be-detected vehicle is determined according to the statistical distribution situation; and when the size exceeds a preset threshold value, the to-be-detected vehicle is determined to be an oversized vehicle. Therefore, the problems in the prior art can be solved.

The invention provides a dynamic graph convolution traffic speed prediction method which comprises the steps: step 1, matching vehicle GPS trajectory data into an urban road network, and obtaining a traffic speed time sequence of each road section; step 2, regarding road sections of the urban road network as graph nodes, regarding intersections of the urban road network as connecting edges of a graph, constructing a road network graph, and obtaining an adjacent matrix between the road sections; step 3, calculating the traffic speed similarity between adjacent road sections according to the traffic speed time sequence of each road section, and obtaining a real-time adjacent road section similarity matrix; and step 4, inputting the traffic speed time sequence of each road section and the adjacent road section similarity matrix into a graph convolution network for training to obtain a future road section traffic speed prediction result. According to the invention, spatial dependence and time dependence between road sections can be learned in real time, the change rule of the traffic speed can be captured, the speed of future urban roads can be predicted more accurately, and the methodcan be applied to intelligent traffic and smart city construction.

The invention discloses a traffic signal area networking intelligent passing system, which is characterized in that the system includes a computer, a communication system, a traffic signal control machine and a vehicle collector, traffic lights and a traffic display screen which are connected with the traffic signal control machine; and the communication system is used for realizing networking of the traffic signal control machine and the computer in an area, so as to complete collection and sending of remote data of a computer intelligent traffic control system, the computer is used for receiving traffic information transmitted by the traffic signal control machine, processing related data through a background intelligent algorithm and displaying the data and performing remote control on the traffic signal control machine, and the traffic signal control machine is used for collecting traffic information through the vehicle collector and controlling the traffic lights and the traffic display screen. The traffic signal area networking intelligent passing system provided by the invention is novel in design and convenient to use, can implement intelligent passing and vehicle guiding through area networking, thereby effectively utilizing road resources, and reducing road congestion.

The invention belongs to the field of traffic safety and intelligent traffic management control, in particular to a real-time estimating method for high-grade road traffic flow running risks. The real-time estimating method for the high-grade road traffic flow running risks considers the problem that a high-grade road short of fixed-point traffic flow collecting facility is incapable of acquiring the road traffic flow rate, occupancy and the like traffic parameters. The real-time estimating method for high-grade road traffic flow running risks includes that using traffic flow speed data acquired through different traffic information collecting technologies to build a real-time accident forecasting model, using a dynamic Bayesian network model to consider the speed state data of several time periods, building relationships between the traffic flow state and accident risks, and estimating the accident in real time so as to early warn or regulate a vehicle to avoid an accident. The real-time estimating method for the high-grade road traffic flow running risks has good forecasting precision for the high-grade road accident risks through speed data, and the real-time estimating method for the high-grade road traffic flow running risks has broad practical application value.

The invention discloses an intelligent traffic path planning method based on federated learning and digital twinning. The method comprises the following steps of: S1, carrying out vehicle registration in a vehicle system, and verifying the identity information of vehicles; S2, according to the local historical data of the vehicles, participating in federated learning, and training a local model; S3, aggregating the local models of all the vehicles to obtain an aggregation model; S4, judging whether the aggregation model converges to preset precision or exceeds a time limit or not, if yes, entering the step S5, and otherwise, returning to the step S2; S5, establishing an Internet-of-vehicles global digital twinning model; S6, periodically updating the Internet-of-vehicles global digital twinning model; and S7, initiating a path finding request to a roadside unit, and updating the optimal path and the local prediction model in real time. The planning method provided by the invention is applied to the field of Internet of vehicles, so as to solve the problems of low flow prediction and path planning accuracy, high time delay and privacy leakage risk in the current road traffic system.

The invention relates to a method and a system for automatically generating an urban road traffic guidance strategy. According to the method and the system, by applying the intelligent traffic theory, the spatial autocorrelation theory, the fuzzy mathematic theory, an statistical method and the computer technology, real-time traffic flow data and historical data are researched and processed, and therefore, under the condition that a traffic incident occurs in a road traffic network, the traffic guidance strategy which can be released in time through VMS (variable message signs) on a road is automatically generated. According to the method and the system, by taking historical artificial guidance information and the real-time traffic flow data as input data, on the basis that historical guidance empirical laws are analyzed and real-time traffic flow features are excavated, a final guidance release strategy is determined by applying a traffic state spatial inequality analysis method provided by the invention. A proposal and strategy scheme can be provided for the quick release of the guidance information of an urban traffic management department when certain traffic incidents occur, such as road accidents, congestion, severe weather, management and control, and the like.

The invention belongs to the field of intelligent transportation, and especially relates to a vehicle pedestrian-avoiding detection method based on video analysis. The method comprises the following steps: (1) sample acquisition: intercepting and acquiring positive and negative samples of vehicles and pedestrians from an actual scene; (2) sample preprocessing: graying the acquired samples, and performing scaling; (3) feature extraction: calculating the image gradient, performing gradient voting, calculating and optimizing gradient voting, normalizing a histogram, and collecting feature vectors; (4) classifier training; (5) quick target detection: detecting all vehicle and pedestrian targets in a calibrated area, and returning the center coordinates of the targets; and (6) making illegality judgment. Through the method, the potential conflict between vehicles and pedestrians at an intersection without traffic signal lights can be detected actively, and drivers can be reminded to avoid pedestrians so as to reduce the occurrence of traffic accidents; and drivers can be urged to develop a civilized and safe driving habit, and thus, the civilization of cities is improved.

The invention relates to the field of intelligent transportation forecasting and in particular relates to a short-term traffic flow forecasting method. The short-term traffic flow forecasting method comprises the steps of 1), collecting data of flows and speeds of historical records and presetting traffic states according to combinations formed by the flows and the speeds; 2), building a forecasting model; 3), inputting the traffic states of two historical moments x(t-1) and x(t) corresponding to actual forecasting traffic states, scheduling the forecasting model for forecasting and outputting a forecasting state; and 4), recording an actual state, calculating an error between the forecasting state and the actual state, and recording and saving an error result. The short-term traffic flow forecasting method has the beneficial effects that the method is low in calculation complexity and capable of reflecting road condition changes in real time; meanwhile, the historical data of 10 minutes near the future moment are only taken into account by the algorithm; the method is good in real-time performance; a 5-minute forecasting time duration meets delay of transmission and processing and is applicable to real-time display of a traffic induction screen; the forecasting model is simple and quick to build, applicable to traffic road conditions with various complexities and high in forecasting precision.

The invention discloses an intelligent traffic simulation system and method for an intelligent vehicle, and the system comprises a three-dimensional virtual driving environment module which is used for simulating the driving state of the intelligent vehicle; a training data acquisition module which is used for acquiring training data from the three-dimensional virtual driving environment; a training model module which is used for training a driving model according to the training data; and the three-dimensional virtual driving environment module is provided with an external control interface for controlling the intelligent automobile. According to the invention, the three-dimensional virtual driving environment module is used for simulation training, and compared with real vehicle trainingin a real scene, the trial and error cost is reduced, and the development safety is improved. The method can be widely applied to the technical field of computer simulation.

The present invention provides a cross-city traffic flow joint prediction method based on deep migration learning, the method comprises three parts: firstly, dividing a city into regions, using a heatmap to represent city traffic, and abstracting traffic data at different time points into "image frames"; then using a ConvLSTM method to learn spatial dependence and time dependence of the data; andfinally, using condition maximum average difference to measure distribution distance of different spatio-temporal data. The method achieves the purpose of knowledge migration by introducing the condition maximum average difference to reduce difference between a source domain and a target domain. The method proposes to use the ConvLSTM and the conditional maximum average difference for the first time, uses an idea of migration learning to apply to urban traffic flow prediction, solves the problem of prediction difficulty caused by insufficient data in similar or related cities, improves accuracy of prediction and is of great significance to construction of smart transportation system, transportation planning and risk prediction for future cities.

The invention provides a city traffic vehicle dynamic control system. It can realize comprehensive management of traffic, vehicle, security and charging. It includes intelligent e4lectronic vehicle plate and collecting base station. It has two modes of wireless and infrared, four receiving and transmitting modes: infrared-wireless, infrared-infrared, wireless-wireless, and wireless-infrared. The system has report mode, request mode, paging model searching mode. The electronic vehicle plate uses one-chip system integrated technology, low power consumption, high reliability.

The invention belongs to the field of urban intelligent traffic, and discloses a mixed traffic flow running method used in an intelligent network connection environment, which comprises the following steps: researching and analyzing the characteristics of a mixed traffic flow; and determining a mixed traffic flow operation strategy in the intelligent network connection environment, wherein the mixed traffic flow operation strategy comprises an intermittent bus lane passing strategy at a common road section and an intermittent bus lane passing strategy at a traffic station. On the basis of intelligent network connection environment traffic flow characteristic analysis and intermittent bus special lane research, the intermittent bus special lane passing method in the intelligent network connection environment is provided, intelligent network connection vehicles are reasonably distributed to bus lanes, the road traffic flow operation efficiency and application conditions in the method are researched, the utilization rate of the bus lane is effectively improved while relative priority of buses is guaranteed.