291 results about "Traffic planning" patented technology

A method generates optimized traffic movement plans. A plan monitor determines a first planning boundary for traffic based upon traffic conditions of a region. A plan generator employs the first planning boundary and repetitively generates first traffic movement plans for the traffic. The plan generator selects one of the first traffic movement plans as a first optimized traffic movement plan, and outputs the same for controlling traffic movement. The plan monitor determines current traffic conditions of the region for a planning window, and updates the first planning boundary to provide a second planning boundary for the traffic based upon the current traffic conditions. The plan generator employs the second planning boundary and repetitively generates second traffic movement plans for the traffic, selects one of the first and second traffic movement plans as a second optimized traffic movement plan, and outputs the same for controlling traffic movement.

Using data from a wireless telephony network to support transportation planning and engineering. Data related to wireless network users is extracted from the wireless network to determine the location of a mobile station. Additional location records for the mobile station can be used to characterize the movement of the mobile station: its speed, its route, its point of origin and destination, and its primary and secondary transportation analysis zones. Aggregating data associated with multiple mobile stations allows characterizing and predicting traffic parameters, including traffic speeds and volumes along routes.

The invention discloses a mobile phone location based transportation mode identification method. The mobile phone location based transportation mode identification method includes the following steps of step one, acquiring a mobile phone position coordinate through a mobile phone location algorithm; step two, determining a travel route according to the mobile phone position coordinate, and calculating a mobile phone positioning movement speed; step three, applying a multi-feature matching method to multi-mode traffic identification. According to the mobile phone location based transportation mode identification method, the multi-feature matching method for traffic mode identification is established to perform collection, analysis and spatial-temporal feature matching on mobile phone location information, so that three typical travel modes of bus travel, self-driving (or taxi) travel or walking travel can be accurately identified. By means of popularization and application of the mobile phone location based transportation mode identification method, a data support can be provided for urban traffic planning, construction and operation management, and scientific decision-making by a traffic management department is facilitated.

The invention relates to an automatic driving automobile intelligent road system, which comprises an equipment layer, an edge computing layer and a cloud computing layer. An intelligent driving automobile of the equipment layer intelligently sends acquired related information to the edge computing layer. The edge computing layer comprises a plurality of roadside units arranged along a road. Each roadside unit comprises an edge computing server, and the edge computing server performs data distribution processing on received information to generate a first data packet and a second data packet, locally stores the first data packet and uploads the second data packet to the cloud computing layer. The cloud computing layer comprises a cloud server. The cloud server performs macroscopic data processing according to the second data packet to generate a global road planning and macroscopic traffic scheduling instruction, and sends the global road planning and macroscopic traffic scheduling instruction to an edge computing layer, so that the edge computing server generates the regional traffic planning auxiliary instruction and sends the regional traffic planning auxiliary instruction to theintelligent driving automobile, the intelligent driving automobile generates an automatic driving control strategy, automobile-road coordination is fused, and the automobile perception decision control capability is improved.

The invention provides a commuting traveling characteristic analysis method based on license plate data. The method includes: (1) acquiring detection data of road network intelligent toll-gate devices and urban static information; (2) performing data quality examination on vehicle license plate original data detected by the intelligent toll-gate devices, obtaining effective vehicle license plate data, integrating the effective data according to a time sequence, and analyzing the traveling types of the vehicles; (3) extracting license plate data information of urban commuting vehicles, and constructing traveling chain data of the commuting vehicles; and (4) performing statistics on traveling information of the commuting travelling vehicles, analyzing the resident leisure activity condition, the resident commuting traveling time and distance, and the resident traveling demand, and obtaining a resident traveling rule. According to the method, the commuting traveling characteristics of resident private vehicles are analyzed through data of a lot of license plates, statistics and analysis of the resident traveling demands are realized, support data is supported for a traffic planning model, the traffic demand management level is improved, and the problem of traffic jam is effectively alleviated.

A fusion evaluation method of traffic information collected based on micro blogs comprises the steps of: constructing a data set of micro blog messages with traffic information as a theme; traversing road sections in a road network and selecting micro blog message records related to the road sections in the road network in an effective time window for establishing a micro blog message record set; extracting traffic information description indicators from the micro blog message records and formally conveying the traffic information contained in the micro blogs; performing fusion evaluation of the traffic information in the micro blog message record set and acquiring a real-time dynamic traffic information update table of the traffic information with regard to traffic restriction or traffic control; as for the traffic information with regard to road conditions, calculating clear degree and support degree of the road sections during corresponding period of time; establishing a dynamic feedback model and adjusting honesty degree of micro blog users; and circularly traversing all the roads in the road network and processing the traffic information of the whole road network covered in the micro blog message data set. The method in the invention realizes the rapid fusion evaluation of the traffic information contained in the micro blogs and provides an important data source for the collection of the urban traffic information, thereby being applicable to real-time navigation and location-based service systems, map websites, traffic planning management and emergence response plans.

The invention discloses a method for predicting the OD (Origin-Destination) passenger flow among bus stations on the basis of IC (Integrated Circuit)-card records and a device, which belong to the technical fields of urban public traffic planning, operation and management. The method comprises the following steps of: identifying passenger getting-on or getting-on and getting-off station information by utilizing a bus-mounted IC-card POS (Point Of Sale) machine and a GPS (Global Positioning System) positioning instrument, transmitting the passenger getting-on or getting-on and getting-off station information to a data center in real time to obtain time-variant passenger-flow data, and estimating the OD passenger flow by combining the characteristics of IC-card historical data and bus routes, and finally predicting the passenger-flow information in the next period of time. The method and the device are favorable to the effective utilization of urban intelligent bus data and the real-time dispatching of a bus system.

The invention discloses a resident trip OD (Origin Destination) acquisition method based on mobile phone signaling data. The method comprises the step of analyzing time-space position information in user mobile phone signaling data to recognize move and stop behaviors of a user, thus determining trip end points; and the method specifically comprises the steps of acquiring and preprocessing mobile phone signaling data according to the investigation requirement, judging the motion state of a position point, and performing zonal statistics and result sampling expansion. The method has the advantages of large investigation samples, low implementation cost, long-term continuous monitoring and the like, and can provide a technical support for acquiring large-range accurate and reliable resident trip OD data in traffic demand analysis and traffic planning making.

The invention discloses a model method of a traffic prediction. The method comprises the following steps of: (1) establishing a road traffic basic facility geographical information database, storing and updating traffic planning schemes, and establishing intersection delay determination; (2) establishing an urban planning construction geographical information database, storing regulatory detailed plans of each area, and utilizing an original unit method to construct traffic generation cases; (3) calling data of the road traffic basic facility geographical information database and the urban planning construction geographical information database, and utilizing a gravity model method to predict parallel distributed matrixes; (4) constructing a multi-element logit model, and determining the probability distribution for a single person selecting a travelling mode in specific travelling information; and (5) utilizing a capacity-limited multi-path distribution method to carry out traffic distribution. The modeling purpose, method to realization are overall planed, modeling parameter selection, basic data sources and calculation program design are coordinated, and the convenient application and operation of a traffic prediction model system are ensured.

The invention discloses a method for quantizing urban motor vehicle emission load based on resident trip analysis, belonging to the technical field of intelligent traffic. The method comprises the following steps: analyzing resident trip characteristics; predicting traffic volume; determining an emission factor by means of a road experiment; calculating the emission load of motor vehicle pollutants; and evaluating environment. In the method, the practical resident trip situation information obtained by investigation is combined with a vehicle emission model, and traffic investigation data is reasonably linked with motor vehicle environment evaluation so as to calculate the motor vehicle emission load within a certain area in the urban; and by utilizing the method, necessary basic data and important parameters for total wide-zone emission load estimation of the urban traffic can be accurately and objectively provided, and practical data and decision support can be provided for controlling traffic pollution and realizing the urban traffic planning for sustainable development and the like. The method has the advantages of high accuracy and high efficiency, and is convenient to implement on the basis of fully utilizing the existing traffic investigation data.

The invention discloses a population identifying method based on cellular signal data and belongs to the field of traffic planning data analysis. The method comprises: (1) preprocessing cellular signaling data to quickly extract available fields so as to form a storage format that uses a user ID (identifier) as a key field; (2) sorting by time, mobile positions of users, and denoising by a speed and angle anomaly judging method; (3) using the DBSCAN (density-based spatial clustering of applications with noise) algorithm to form a dense point, and identifying each user's all daily retention points and retention start and stop times; (4) classifying the retention points monthly to identify permanent population, employed population and short-term floating population. The method of the invention makes full use of cellular signaling data and quickly and accurately calculates, via the above steps, the permanent population, employed population and short-term floating population, thereby providing data support for urban population monitoring and traffic planning.

The application discloses a traffic planning method and equipment, traffic indication equipment and a traffic planning system. The traffic planning method comprises the following steps: receiving a path planning request for an emergent vehicle, and generating a running path for the emergent vehicle; transmitting the running path to the emergent vehicle, in order that the emergent vehicle runs according to the running path; before the emergent vehicle arrives at a first type of path points in the running path, controlling an indication device to give a prompt for at least one vehicle lane corresponding to the first type of path points in order to prompt non-emergent vehicles to run away from the vehicle lane; and after the emergent vehicle arrives at the first type of path points, controlling the indication device to stop giving the prompt for the vehicle lane. Through adoption of the traffic planning method, the emergent task execution efficiency of the emergent vehicle can be improvedeffectively.

The invention relates to a flow scheduling method of a regional air traffic network, which is researched on the basis of the air traffic network. From the standpoint of air traffic flow, the invention researches the relation between the change of weather and the change of flight segment capacity, thereby establishing the relation between flight segment flow and travel time, discussing the relation between the reliability of the travel time of a scheduled flight and a scheduling rule under uncertain weather conditions, establishing a flow scheduling model based on Wardrop balance in the regional air traffic network, providing a method for balanced distribution of flow of each flight segment and scheduling policy of each flight route, providing decision support for air traffic planning management, and achieving the optimal scheduling of the scheduled flight.

The invention provides a taxi passenger carrying site selection method and a system. The method comprises steps: taxi driving data corresponding to a pre-selected closed region are acquired; the closed region is uniformly divided into multiple basic grids; according to the driving data, a taxi stopping number in each basic grid is acquired; basic grids whose taxi stopping numbers are larger than a preset stopping number threshold are marked; the marked basic grids and map data are matched; and POI points matched with the marked basic grids in the map data are selected to serve as taxi passenger carrying site. Driving data serve as a reference to dynamically select the taxi passenger carrying sites, and scientific decision making, prediction analysis and technical support are provided for taxi taking, traffic planning and path navigation.

The invention discloses a remote-sensing image road network extract method based on an improved C-V mode, comprising the following steps: (1) pre-processing initial remote-sensing images; (2) selecting seed points and segmenting a first road network subimage; (3) using the level set method of the improved C-V model to extract a road network area; (4) using the method of morphologic detailing to extract the center line of the road network; (5) segmenting the next road network subimage according to the subimage position criterion rule to automatically obtain the road network initial curve according to threshold segmentation and the morphologic method. The method of the invention can be employed to effectively extract the road network from the remote-sensing image, upgrade urban road network in real time and effectively assist decision-makers in formulating traffic planning.

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 an experience route generation method based on probe vehicle data. Firstly, original probe vehicle data are extracted and processed, and invalid and abnormal points belonging to a light condition and at the positions which do not belong to service scopes of a target starting point and a target terminal point are removed; a model is built according to the principle that direction element weighting is higher than distance element weighting to enable locating points to be matched on a road section; then an experience route threshold value is calculated according to numbers of the location points and the length of a shortest path which are matched to the road section, the road section is considered to be an experience route section if the matched locating point number per kilometer exceeds the experience route threshold value, and an experience route net is composed of each experience route section; and finally a simple loop-free route calculation algorithm is loaded on the experience route net to obtain an experience route. The experience route generation method combines characteristics of sparsity, drifting, magnanimity and the like of the probe vehicle data, a generated route set can be used for guiding a traveler to reasonably select traveling routes, reasonable route set references can be provided for traffic designers, and the method serves as a tool for traffic administrators to formulate route induction strategies.

The invention discloses a method for predicting traffic flow extracted by an improved C-V model-based remote sensing image road network, which comprises the following steps of: (1) preprocessing an original remote sensing image; (2) selecting a seed point and segmenting a first road network sub-image; (3) extracting a road network area by an improved C-V model-based level set method; (4) extracting a road network central line by a morphological thinning method; (5) segmenting a next road network sub-image by using a sub-image position decision rule and automatically acquiring a road network initial curve in the road network sub-images by a threshold segmentation and morphological method; (6) vectorizing a road network; and (7) predicting the traffic flow. By integrating technology such as remote sensing, geographic information system (GIS), image identification, traffic planning and the like, an urban road network can be more accurately, efficiently and cheaply updated in real time, the traffic flow prediction cost is lower, the traffic flow prediction accuracy is higher and the traffic flow prediction period is shorter, so that decision makers can be effectively assisted in making traffic planning decisions.

The invention belongs to the technical field of urban traffic planning, in particular to a parking space surplus monitoring and management system and a method for finding a parking space. The parking space surplus monitoring and management system comprises parking space information subsystems, a central processing system and a parking space query system, wherein the parking space information subsystems are arranged in parking lots; each parking space information subsystem is in data transmission with the central processing system through the internet, and the parking space query system is in data transmission with the central processing system through the internet; each parking space information subsystem comprises a parking space monitoring device and a subsystem processor, and the parking space monitoring device is in data transmission with the subsystem processor in a wired or wireless way; the parking space query system comprises a mobile terminal accessing to the internet and a parking space query application. After the parking space surplus monitoring and management system is adopted, a driver can quickly obtain the parking space condition of the parking lot, and the nearest quick parking is realized. The use ratio of the parking spaces of the parking lot is increased, and the income of the parking lot is increased.

The invention discloses a road network node importance evaluation method based on a knowledge graph, and belongs to the field of intersection of a graph neural network and an urban traffic planning technology. By means of the technology, the problem that importance evaluation cannot be conducted on all nodes in a road network due to the fact that data in the road network are not comprehensive enough can be solved. According to the method, firstly, frequently congested road sections and congested propagation modes thereof in a road network are mined from existing trajectory data, and each roadsection is scored according to a congested propagation probability graph of each road section; secondly, a traffic knowledge graph of a corresponding region is constructed, a graph neural network-based method is used on the knowledge graph, scores and features of known nodes are propagated, and importance of the nodes is adjusted by using trajectory data, so that importance of other nodes in the road network is predicted.

The invention relates to a method for positioning a moving track of a user based on mobile pipeline data. The method comprises the following steps of collecting operator pipeline data (mainly comprising S1-U port data and GN port data), parsing the latitude and the longitude which are reported by the user through utilizing a positioning type APP, combining a cell sequence for user mobility, computing an actual treading track of the user, user movement speed and a current position of the user through a hidden Markov chain, and judging a travel vehicle for the user accordingly in order to provide a data support for traffic planning.

A vehicle model identification method based on high-resolution remote sensing data includes acquiring raster remote sensing data including a vehicle region of interest and map vector data including the region of interest, performing rough alignment on the raster remote sensing data and the map vector data, determining a to-be-analyzed region, performing automatic extraction of center separation lines, lane or carport vacant region recognition, vehicle region detection and vehicle model identification, and outputting. On the basis of different individual analyses of the high-resolution remote sensing data, multi-source data fusion and cross validation can be performed prior to outputting. Vehicle models are automatically identified by means of priori utilizing knowledge provided by short separation lines of road centers or parking lots and integrating relevant vehicular exclusive information provided by various high-resolution remote sensing data in the context of roads or packing lots where the vehicles are located, and accordingly basic data are provided for traffic volume survey, traffic planning and the like.

The invention discloses a comprehensive transport system passenger transport mode sharing rate-distance transfer curve drawing method. The method includes the following steps that: a stratified random sampling survey method is adopted to obtain regional passenger travel information, a regional traffic travel database is constructed; according to a discrete selection behavior analysis method, a passenger transport mode selection model is constructed, and the parameters of the model are calibrated; the travel utility functions and selection probability expressions of each transport mode are determined according to the total structure of the regional transport modes, sample structures and parameter calibration results; and the selection probabilities of the transport modes under different transport distances are determined, and a regression analysis method is adopted to calibrate the selection probabilities of the transport modes, and a transport distance-based transport mode selection probability expression is determined. With the method of the invention adopted, the research gap of integrated transport system passenger transport mode sharing rate-distance transfer curve determination methods in China can be filled, and key theoretical support and technical support can be provided for comprehensive traffic planning software research and development. The method of the invention has a very broad application prospect.

The invention provides a method for quantifying the spatial and temporal distribution of traffic accidents based on a built environment and micro-blog data, which belongs to the technical field of urban traffic planning and traffic safety system. A built environment is added as an explanatory variable of the spatial and temporal distribution of traffic accidents, and the interpretability of the built environment to the temporal and spatial distribution of traffic accidents is proved by examples. The method considers the built environment variable as the explanatory variable of the spatial andtemporal distribution of traffic accidents, and proves the interpretability of the built environment variable to the temporal and spatial distribution of traffic accidents. Communities from differentgeographical locations show spatial heterogeneity under the influence of different built urban environments. The invention further presents a method for quantifying the spatial and temporal distribution of traffic accidents.

A traffic zone partitioning system based on a Louvain algorithm, and the system comprises a building space topological relation judging module, a building group partition road grade calculating module, a building group topology distance calculating module, a complex network edge weight calculating module, a Louvain algorithm executing module and a district partitioning result displaying module. The traffic district partitioning system provided by the invention reduces the complexity of the traffic system by analyzing the factors of the traffic district partitioning in the urban public transport system. The invention takes the urban building group as the smallest partition unit, probes into the abstract method of the urban building group network, designs the calculation method of the network structure parameters, constructs the complex network with the building group as the node, and divides the building group with similar traffic attributes into the same traffic district based on the network. The results of traffic plot division will affect the accuracy of traffic demand analysis and forecasting, which is of great significance to ensuring the feasibility and accuracy of a traffic planning scheme in traffic zone partitioning and optimization.

The invention relates to an urban traffic planning simulation method and system based on a multi-agent activity model. The method comprises the following steps: firstly acquiring urban road information to generate a road network information file; secondly, generating initial traffic requirements and an initial daily activity plan according to the acquired survey data; thirdly, loading the daily activity plan to the road network information file; fourthly, calculating a utility value and planned selective probability for each surveyed object; and finally, performing an iterative process to output the simulation result. The system comprises a client end and a server client which communicate in an http communication manner. According to the invention, a four-stage traffic simulation method is discarded; a traffic requirement generating and distributing method based on the activity model is completely adopted; a multi-agent technology and a genetic evolution algorithm are integrated for use; utility optimal limitation is used to generate and select an optimal path, so that maximum utilization efficiency of a vehicle and a road network is achieved, and traffic accidents and traffic jams are reduced.

The invention provides a coordinated trunk line planning method and configuration system based on traffic flow data, Integrating the Internet data and the passing data of the video number plate identification device, and relying on the traffic flow data in the non-congestion period to determine the key direction, so as to identify and configure the coordination direction of each control period, and then planning and configuring the coordination trunk line; The invention recognizes the non-congestion time period based on the traffic state data of the Internet, By analyzing the trunk configuration in this time period, the problem of low efficiency of coordination trunk caused by traditional trunk configuration according to network traffic planning and considering the coordination direction of each intersection for saturated time period and unsaturated time period is avoided, thus the accuracy of coordination trunk in different time periods is improved, and the coordination optimization is achieved. The invention identifies and analyzes the configuration of the trunk lines in different time periods, improves the implementation efficiency of the coordination trunk lines, and provides auxiliary support for the coordination and optimization of the regional signals.

The invention discloses a local abnormity factor-based urban heavy-traffic road detection method. The method is characterized by comprising the steps of: firstly dividing urban bus GPS data into space-time segments from the space and time perspectives by using bus route and station data, extracting, from the space-time segments, characteristic values which reflect the road traffic condition, calculating a local abnormity factor of each space-time segment by using the characteristic value, calculating an abnormity index of each road section, ranking the abnormity indexes, and finally obtaining the heavy-traffic road of the urban traffic. The local abnormity factor-based urban heavy-traffic road detection method of the invention has the characteristics of strong feasibility, wide application range and low manpower consumption, makes it possible to automatically detect the heavy-traffic road of urban traffic by using data and provides effective information for urban traffic planning.