30 results about "Traffic bottleneck" patented technology

The invention relates to the technical field of intelligent traffic, in particular to a vehicle driving path control system and method based on intelligent traffic big data, and the method comprises the following steps: a, collecting the traffic data of the real-time position, the road traffic flow and the congestion condition of a vehicle through a real-time traffic data collection module; b, analyzing the historical traffic data through a big data analysis and prediction module, and predicting peak periods, accident risk areas and traffic bottleneck points; according to the invention, through dynamic acquisition and big data analysis of real-time traffic data, the peak period and the congestion area can be accurately predicted, so that the driving path of the vehicle is optimized, congestion is reduced, in addition, through intelligent regulation and control of a signal lamp and a freight vehicle shunting mechanism, the system avoids interference of freight vehicles to public transportation in the peak period, and the traffic safety is improved. The road traffic efficiency is improved, the system also provides real-time path adjustment suggestion and early warning service, and more convenient and safer travel experience is provided for drivers.

A heavy traffic abnormal event detection and state discrimination system belongs to the field of intelligent traffic and comprises a data acquisition and labeling module, an image processing module, a traffic state analysis module, a state determination and abnormal event identification module and a transfer learning module. The data acquisition and labeling module comprises a data acquisition unit and a data labeling unit; the image processing module comprises an image enhancement unit and an image compression and reconstruction unit; the traffic state analysis module comprises a traffic bottleneck identification unit and a traffic flow wave analysis unit; the state judgment and abnormal event recognition module comprises a vehicle statistical unit, a congestion state recognition unit, a state transition analysis unit and a compressed sensing model establishment unit; and the transfer learning module comprises a transfer learning unit. According to the method, the false detection rate of the traffic flow abnormal events in bad weather can be reduced, so that the traffic abnormal events can be detected all day long, and the occurrence probability of the traffic events in bad weather conditions can be reduced.

The invention discloses an urban traffic distribution analysis method and system based on multi-source big data, and the method can comprehensively describe the travel general situation of urban residents through integrating the big data of multiple traffic modes of buses, subways, taxis and shared bikes in a city, avoids the deviation caused by a single data source, and improves the accuracy of urban traffic distribution analysis. According to the data preprocessing, original data are converted into a unified spatial point data format, accurate analysis is ensured, OD extraction not only records a travel starting point and a travel ending point, but also calculates travel duration, a key basis is provided for travel behavior analysis, spatial and temporal distribution feature analysis comprehensively considers time and spatial scales, and a spatial interaction network of various traffic modes is revealed finely. In the step of interaction matrix construction, OD is matched to space units, an OD interaction matrix between stations or grids is constructed, travel behaviors are quantified, travel characteristics are compared to reveal travel differences in different time periods, traffic bottlenecks and hot spots are recognized, and in the step of travel mode recognition, high-frequency OD pairs and interaction areas are found, and scientific guidance and suggestions are provided for urban traffic planning and management.

The invention discloses a self-intelligence network evaluation method and device based on IOAM flow detection, and relates to the technical field of network detection. According to the method, the IOAM flow detection technology is utilized, and the real-time monitoring and feature data analysis of the network flow session are combined, so that the network service quality can be accurately evaluated in the target network in real time. Compared with a traditional network quality detection scheme, the method has the advantages that higher detection precision and instantaneity can be provided, and the stability and high efficiency of network flow are ensured. By performing dynamic analysis and feature extraction on the network traffic session data, the abnormal traffic and quality problems in the network can be effectively identified, and the problems of traffic bottleneck, traffic abnormality and the like can be found in time. In combination with real-time evaluation of network load pressure, network configuration, flow control and resource allocation can be automatically adjusted, and high efficiency and reliability of network service are ensured.

A ramp confluence area mixed traffic flow cooperative control method based on space-time network reinforcement learning comprises the steps that firstly, a historical state sequence with a fixed length is constructed through a sliding window mechanism, and time sequence information is introduced to capture the dynamic evolution process of traffic flow; secondly, designing a spatial-temporal feature extraction structure combining multi-head space attention and mask time attention, respectively modeling the correlation of the vehicle in space and time dimensions, and enhancing the state expression ability; on this basis, a deep reinforcement learning strategy network based on Actor-Critic is constructed, and optimization of vehicle decision is realized; in order to guarantee the decision safety, a minimum safety distance constraint and action safety mask mechanism is further introduced, and action selections with collision risks are screened out. And finally, the learning efficiency and stability of the multi-agent system are improved in combination with a local reward mechanism. According to the method, the cooperative control performance of the mixed traffic flow in the ramp confluence traffic bottleneck area can be improved, the collision rate is reduced, and the safety and passing efficiency of a traffic system are enhanced.

The invention discloses a path planning method based on GVD and an improved Theta-star algorithm. The method comprises the steps that a building projection plane graph in a corresponding area is acquired; determining the resolution of the grid map according to the area size and range, and converting the building projection plane graph into the grid map; generating a recommended flight route in the unmanned aerial vehicle region by using a generalized Voronoi diagram method, and updating the recommended flight route to the grid map; calculating the cost of different grids through a certain mathematical function according to the distribution of buildings and recommended routes in the regional space, and generating a non-uniform cost grid map; and traffic bottlenecks and accident hidden dangers possibly existing in the generated flight recommendation route are calculated and analyzed through intermediary centrality. The method improves the low-altitude flight path planning speed and efficiency of the unmanned aerial vehicle, balances the flight safety and speed of the unmanned aerial vehicle, provides data support for low-altitude airspace management, and is suitable for large-scale application of urban unmanned aerial vehicles.

The invention relates to the technical field of urban traffic management, in particular to a prediction system and method based on traffic management, and the system comprises a data collection module, a data preprocessing module, a feature extraction module, a model construction module, a prediction analysis module and a management strategy making module. A sensor network is deployed at a traffic road section and a road intersection, traffic flow data under rain and snow weather conditions and during holidays and festivals are collected in real time, and then traffic flow prediction is performed through data preprocessing, feature extraction and a traffic flow prediction model based on a random forest algorithm. And the traffic flow density, the vehicle speed, the traffic bottleneck position and the potential congestion point can be accurately predicted. And based on the prediction result, a corresponding traffic management strategy is formulated and implemented, so that traffic congestion is effectively relieved, the traffic passing efficiency is improved, and the traffic safety is ensured. According to the invention, dynamic, accurate and intelligent traffic management can be realized, and the system is especially suitable for high-risk time periods such as special weather conditions and holidays and festivals.

The application belongs to the technical field of expressway, and particularly relates to an expressway traffic congestion prediction method and system based on deep learning. The method comprises the following steps: acquiring traffic flow characteristic information, identifying a traffic bottleneck node based on the traffic flow characteristic information, summarizing reference characteristic values of the traffic bottleneck node to form an overall traffic flow characteristic data set, performing discrete degree analysis on the data set to obtain a characteristic index, extracting a traffic congestion concentrated interval according to the characteristic index, identifying a risk time point in the interval, and outputting a predicted expressway congestion period and a corresponding congestion area according to the risk time point and the traffic congestion concentrated interval. The application can improve the accuracy of congestion root identification by identifying a traffic bottleneck node and quantitatively analyzing the evolution trend of the bottleneck state, and can output a region to be regulated and a regulation period, thereby providing decision support for traffic relief.

The invention relates to the technical field of traffic management, and discloses a complex road network temporary construction opening influence assessment method and system. The method comprises the following steps: S1, collecting historical data of traffic flow, speed and density; s2, performing current traffic flow investigation to obtain real-time traffic data as background traffic volume; s3, combining the specific actual condition of the construction project; s4, calculating a superposed traffic volume according to the background traffic volume and the project traffic volume; s5, based on the total traffic volume, performing dynamic interaction influence analysis to obtain a dynamic analysis result; s6, proposing a traffic improvement suggestion according to a dynamic analysis result; and S7, carrying out effect evaluation on the traffic flow after construction is finished. According to the method, the dynamic traffic flow prediction technology is adopted, the traffic data are analyzed through the four-stage model, and the effect of recognizing the traffic bottleneck in the construction period in advance is achieved. Compared with a traditional static analysis method, the scheme can respond to traffic changes in time, and the problem of congestion caused by lagging decision making is solved.

The invention discloses a dynamic traffic bottleneck identification method based on a space-time diagram attention network, and belongs to the field of intelligent traffic, and the method comprises the following steps: obtaining road network data and track data, and carrying out the preprocessing of the track data based on the road network data, and obtaining the preprocessed track data; constructing a space-time diagram attention network, and inputting the road network data and the preprocessed trajectory data into a space-time diagram attention network model to obtain node representation with time sequence dynamics; and a dynamic traffic bottleneck identification algorithm is adopted to process the node representation with the time sequence dynamics to identify the dynamic traffic bottleneck. According to the method, the road network node space proximity can be effectively extracted, the traffic flow propagation space-time dynamics can be accurately captured, the operation speed of the recognition algorithm is high, and the real-time requirement is met.

The invention discloses a traffic bottleneck identification method based on reverse influence sampling and an evolutionary algorithm. The method comprises the following steps: acquiring historical traffic data of a city; a city map is divided into grids to construct a road network, each grid comprises a central area and road sections communicated with the central area, the traffic flow of each area grid of the road network is constructed according to GPS information and traffic flow data of each grid, the traffic state of the area grids is further obtained, a vehicle density-congestion degree formula is obtained, and the traffic flow of each area grid is calculated according to the vehicle density-congestion degree formula. Establishing a relationship between the degree of congestion and the traffic flow; a reverse influence sampling method is adopted to establish a proxy model for the traffic bottleneck identification problem, the proxy model uses a two-dimensional adaptive value to describe the quality of a candidate solution, the maximum estimated value of the congestion degree of the region is selected, and the key traffic bottleneck region is identified by taking the minimum estimated experience error as the target. The problems that in the prior art, when the influence maximization problem on the road network is solved, the search efficiency is low, and the final solution quality is poor are solved.

The invention discloses a compliance automatic transportation and monitoring system which comprises an algorithm fusion module and an excitation module. The algorithm fusion module is used for calculating the path length in an AGV dynamic scheduling algorithm according to a cost map updated in real time, and ensuring that each planned path is physically shortest and optimal in compliance; the algorithm fusion module comprises a fusion type AGV dynamic scheduling module, an intelligent elevator cooperative control module and a compliance path planning module. According to the invention, the information barriers among the AGV scheduling subsystem, the elevator control subsystem and the indoor path planning subsystem are thoroughly broken through the central compliance decision algorithm, and cross-system global resource optimization and cooperative command are realized. The system ensures absolute compliance of whole-process transportation and material safety. Meanwhile, the system achieves active and reserved cooperation of the AGV and the elevator, the waiting time is greatly shortened, and the traffic bottleneck of the building is eliminated.

The application discloses an inner-cross type tire crane instruction scheduling method based on multi-dimensional dynamic priority and predictive scheduling, which comprises the following steps: step S1, candidate instruction set generation and hard constraint filtering; step S2, opportunity overtaking window evaluation; step S3, nonlinear dynamic sorting of regular instructions; and step S4, instruction issuing and rescheduling. The application quantifies and decides whether to execute the overtaking instruction which can bring higher net benefit through the opportunity overtaking window evaluation, so as to improve the work efficiency. The application introduces the instruction waiting time factor and the maximum tolerance threshold with exponential growth characteristics, so as to ensure that the priority of the instruction waiting for a long time can be systematically improved, and the scheduling fairness can be ensured. In addition, the local congestion factor is used to reduce the priority of the instruction in the congestion area, so as to relieve the traffic bottleneck.

This invention discloses a method for estimating road capacity and bottleneck status considering the influence of longitudinal slope. The steps include: 1. Collecting longitudinal slope data of the target road; 2. Identifying longitudinal slope regions using wavelet transform; 3. Collecting traffic flow information under free-flow conditions of the target road; 4. Identifying macroscopic traffic flow velocity regions using wavelet transform; 5. Constructing a capacity model considering the influence of longitudinal slope; 6. Predicting traffic bottlenecks of the target road based on the current state and inflow traffic volume. This invention achieves accurate matching of road capacity under different road longitudinal slopes and free-flow velocities. By using initial state and real-time inflow statistics, it enables the prediction of bottleneck road segment status, providing important data for traffic management.

The invention relates to a multi-mode traffic network modeling and capability evaluation method and system, and the method comprises the steps: constructing a topological structure of a multi-level road and railway network based on road and railway data in combination with remote sensing images, surveying and mapping data and the like; constructing a three-section task path which simultaneously comprises a railway section, a transfer node and a road section; dynamic updating and seasonal model switching are carried out, and traffic state change is controlled based on time, weather and manual input; performing multi-target path evaluation, and performing path generation and visual display and interaction according to multiple target functions; and data persistence and interface service. According to the method, the path connection efficiency of different modes is improved, the attribute expression is multidimensional and fine, the dynamic capability is prominent, the path evaluation precision is high, the system integration degree is high, the guarantee capability is remarkably improved, traffic bottlenecks, key paths and sensitive areas are effectively identified through system modeling, and the method has a remarkable supporting effect on rapid deployment, area sealing and control, special vehicle action and the like; and the overall guarantee capability under the emergency condition is improved.

This invention discloses a road driving risk warning method for dynamic traffic bottlenecks in an intelligent connected environment. The steps include: 1. Establishing a road plane rectangular coordinate system; 2. Obtaining current dynamic traffic bottleneck and vehicle-related information; 3. Predicting next-time dynamic traffic bottleneck and target vehicle-related information; 4. Predicting the road boundary risk field value, superimposed dynamic traffic bottleneck risk field value, and superimposed vehicle risk field value of the target vehicle at the next time; 6. Predicting the total risk field value of the target vehicle at the next time; 7. Determining whether to issue a risk warning signal to the target vehicle. This invention can prevent traffic accidents and improve road traffic safety by obtaining current road, dynamic traffic bottleneck, and vehicle-related information in an intelligent connected environment, predicting the dynamic risk value of the target vehicle during its journey at the next time, and determining whether to issue a risk warning signal to the target vehicle.

Disclosed herein are systems and methods for identifying and ranking traffic bottlenecks. An example of such methods may include operating at least one processor to: receive traffic data associated with a road network comprising a plurality of road segments, the traffic data comprising vehicle speed data collected from a plurality of vehicles operating within the road network; determine a benchmark speed for each of the plurality of road segments; determine, for each of the plurality of road segments, whether a traffic disruption is present by comparing the benchmark speed thereof to vehicle speed data collected from at least one vehicle operating therealong; determine, for each of the plurality of road segments having the traffic disruption therealong, one or more road segment metrics associated therewith; identify a traffic bottleneck by aggregating a plurality of adjacent road segments having traffic disruptions therealong and the one or more road segment metrics associated therewith; and rank each traffic bottleneck based on one or more aggregated road segment metrics associated therewith.

Disclosed are systems and methods that provide a computerized framework for accurately and efficiently estimating the backhaul bandwidth and core capacity, which can be utilized to automatically and dynamically configure and / or curate network configurations and topologies to handle network loads and traffic. The framework can utilize and / or operate RF system simulators to perform the estimation of each sector's delivered UL / DL throughputs, as well as user inputs related to the traffic model, the dedicated network's topology and product specifications related to the processing limits of BBUs and private cores in order to determine network bottlenecks. Such determinations and / or predictions can be leveraged to ensure the allocation of backhaul bandwidth and core capacity, among other network features and / or characteristics, are optimized to achieve overall high throughput and remove traffic bottlenecks.

The utility model discloses a motorized multi-mode expressway toll station structure, which relates to the technical field of expressway toll station structures, and comprises a toll queue area which is divided by depending on the interior of a single lane and is used for accommodating a plurality of vehicles to be transacted to form a transaction queue; a mobile and / or fixed charging terminal is adopted, is arranged on the side surface of the charging queue area, and carries out charging transaction corresponding to the vehicles in the transaction queue; the entrance and / or exit of the charging queue area is provided with a fee-display integrated handrail to control vehicles to go in and out. According to the utility model, the toll queue areas are divided in the single lane to accommodate multiple vehicles, and the toll terminal is matched to carry out parallel toll collection, so that the traditional single-point serial toll collection is changed into multi-point multi-vehicle parallel toll collection, the traffic bottleneck problem of insufficient toll points in peak hours is solved, and the traffic efficiency and throughput of the toll station are improved.

The application discloses a dynamic traffic bottleneck identification method based on a space-time graph attention network and belongs to the field of intelligent traffic, and the method comprises the following steps: acquiring road network data and trajectory data, pre-processing the trajectory data based on the road network data to obtain pre-processed trajectory data; constructing a space-time graph attention network, inputting the road network data and the pre-processed trajectory data into the space-time graph attention network model to obtain node representation with time sequence dynamics; and processing the node representation with time sequence dynamics by using a dynamic traffic bottleneck identification algorithm to identify a dynamic traffic bottleneck. The application can effectively extract the spatial proximity of road network nodes, accurately capture the space-time dynamics of traffic flow propagation, and the identification algorithm has a high running speed and meets the real-time requirement.

The invention relates to the technical field of traffic avoidance, in particular to a traffic conflict point active collision avoidance control method and system, and the method comprises the steps: carrying out the dynamic conflict point recognition of a road intersection, and obtaining a real-time traffic conflict point distribution diagram; performing behavior feature extraction on the traffic participants according to the real-time traffic conflict point distribution map to obtain dynamic state data of the traffic participants; inputting the dynamic state data of the traffic participants into a traffic conflict risk assessment network for risk quantification processing to obtain a traffic conflict risk assessment result; and performing conflict development trend prediction processing on the traffic conflict risk assessment result to obtain a traffic conflict evolution prediction result. Through cooperative control with infrastructures such as a traffic signal control system and a traffic guidance system, the traffic light time length, lane distribution and the like can be intelligently optimized, the efficiency of a traffic management system is improved, and the problems of traffic bottleneck and congestion are avoided.

The application discloses a kind of urban traffic bottleneck area flexible boundary robust control method and equipment, belong to intelligent transportation technical field, for solving the technical problem that the interference rejection capability of urban traffic bottleneck area boundary control method of static boundary constraint is poor, robustness is insufficient.Method includes: real-time acquisition urban traffic bottleneck area's dynamic static traffic information;According to dynamic static traffic information, determine the comprehensive traffic correlation degree of each boundary section;According to the comprehensive traffic correlation degree of each boundary section, determine the all dynamic threshold control intersections of urban traffic bottleneck area;The connection of all dynamic threshold control intersections is dynamic control boundary;Through MFD model, estimate the best vehicle accumulation in dynamic control boundary range, and determine the traffic control distribution of each boundary section;According to traffic control distribution, calculate the traffic signal lamp duration of each dynamic threshold control intersection, obtain bottleneck area traffic signal flexible boundary control scheme.

The invention discloses a traffic capacity-based signal management and control evaluation method, which comprises the following steps of: determining a lane saturation flow of each lane, and determining a corresponding lane traffic capacity according to the lane saturation flow; obtaining flow direction information of each lane, determining a flow direction passing flow allowable value in combination with the traffic capacity of the lane, and determining different path passing flow allowable values of the intersection based on the flow direction passing capacity; and evaluating a signal control scheme at the bottleneck point of the path according to the path traffic allowable value, determining a flow direction congestion state of a coordinated flow direction at the bottleneck point, and judging whether the signal control scheme has an optimization space or not through a threshold value. According to the method, the saturation flow and traffic capacity analysis of each lane and flow direction is refined, so that highly refined evaluation of traffic conditions is realized, traffic bottlenecks and potential problem areas are identified more accurately, and accurate data support is provided for subsequent signal optimization; by acquiring the lane flow direction information in real time or regularly, the effect of the signal control scheme can be dynamically evaluated.

The application provides a judgment method and device for a highway maintenance construction organization mode, relates to the technical field of traffic organization, and comprises the following steps: constructing a construction road section traffic bottleneck analysis model according to core parameters; solving the construction road section traffic bottleneck analysis model through a genetic algorithm to obtain single-lane traffic capacity; calculating a first ratio and a second ratio according to a road occupation construction period, a closed construction period, a target single-lane number, a target maintenance road occupation number, daily traffic volume and the single-lane traffic capacity; determining a construction organization mode type, a first ratio threshold and a per-kilometer daily traffic fee threshold according to the second ratio and a second ratio threshold; constructing a construction organization mode comparison table according to the target single-lane number, the target maintenance road occupation number, the per-kilometer daily traffic fee threshold, the first ratio threshold and the construction organization mode type; and selecting a final construction organization mode in the construction organization mode comparison table according to to-be-measured road section traffic data.

The invention discloses an urban traffic bottleneck edge global identification method in a travel demand uncertain scene, and relates to the technical field of intelligent traffic management. The method comprises the following steps: 1, constructing a bottleneck edge index and an identification framework based on traffic sensitivity; 2, defining a traffic network and flow parameters in an uncertain demand scene; 3, establishing a linear programming model for road flow distribution optimization under the worst condition in the uncertain demand scene; 4, factors such as traffic demand, traffic balance and travel uncertainty are planned as a whole, and linear programming model constraints are constructed; 5, solving the model to obtain an optimal flow distribution result; and step 6, bottleneck edge indexes are calculated according to a flow distribution result, and key bottleneck edges are obtained through sorting. According to the method, the key bottleneck road sections in the urban traffic network can be accurately identified, the influence caused by the uncertainty of travel demands can be effectively handled, the method can be widely applied to the fields of urban traffic planning, congestion management and road network optimization, and scientific decision support is provided for traffic management departments.

Road traffic is monitored in a road network 315 leading to a selected destination 303 to obtain from live and historical data an estimate of the rate of arrival of vehicles at the destination, which may be a traffic bottleneck. If the estimated rate exceeds a desired rate, preferably selected to avoid a slow or static queue, vehicles are instructed to diverge from their intended route and may be directed to a waiting area 305. Diverted vehicles are assigned to a virtual queue and are given permission to proceed to destination 303 or elsewhere 304 in accordance with their places in the virtual queue.

The invention discloses a traffic bottleneck identification method based on urban road network base and bayonet data, and relates to the technical field of traffic monitoring. Accurate association of data and road sections is realized by building an urban road network base and configuring and binding bayonet equipment; acquiring long-term historical checkpoint data, and counting vehicle passing data volume and the proportion of large vehicles to small vehicles; based on the historical checkpoint data, calculating road section traffic capacity and theoretical traffic time by adopting a preset formula, and jointly taking the road section traffic capacity and theoretical traffic time as road section design capacity data; counting vehicle intersection average queuing time, road section average passing time and road section vehicle occupancy at preset time intervals, and completing traffic bottleneck monitoring index counting; based on road section design capability data and traffic bottleneck monitoring indexes, potential, short-term and long-term traffic bottlenecks are judged, congestion chains are tracked through a road network topological relation to identify cascade propagation attributes, and traffic bottleneck generation reasons are analyzed. According to the method, the traffic bottleneck can be quickly and accurately identified, and the cause of the traffic bottleneck is analyzed from multiple dimensions.

A framework for modeling traffic speed in a transportation network analyzes both the spatial and temporal dependencies in probe-based traffic speeds, historical weather data, and forecasted weather data, using multiple machine learning models. A decentralized partial least squares (PLS) regression model predicts short-term speed using localized, historical probe-based traffic data, and a deep learning model applies the predicted short-term speed to further estimate traffic speed at specified times and at specific locations in the transportation network for predicting traffic bottlenecks and other future traffic states

The present invention discloses a road network traffic bottleneck identification method integrating wavelet transform and image binarization. The main steps include: inputting speed time series data, searching for speed mutation points according to the wavelet transform principle, and outputting the time and position of the speed mutation points; secondly, sorting the speed values ​​of the speed mutation points in ascending order, selecting the speed threshold according to the quartile method, and converting the speed time series data into a speed spatiotemporal heat map; then, setting the speed threshold as a parameter for image binarization, and dividing the speed spatiotemporal heat map into congested areas and non-congested areas through image binarization; finally, for the irregularly scattered congested areas in the binarized image, considering that they have little impact on the overall congestion of the traffic road network, using median filtering to eliminate irregular, sporadically distributed pixels and small bottleneck points, so as to improve the calculation efficiency and recognition accuracy of frequent traffic bottlenecks in the urban road network.