1238 results about "Urban transportation" patented technology

A transportation system and a method for operating the transportation system are provided. The transportation system provides a flexible, on-demand, and door-to-door transportation service to improve the accessibility of an existing public transit system. The transportation system may be operated by or in conjunction with the transit system and is uniquely different from either taxi, shuttle, or bus service. A vehicle of the transportation system transports a passenger between a pickup location and a predetermined node in response to a service request initiated by the passenger according to instructions that are dynamically determined in real time in consideration of the pickup location, a current location of the vehicle, and a predetermined travel time between the pickup location and the node.

A system of cell phone positioning in real time is provided with specialized location device installations on multiplicity of base stations BSs in CDMA and TDMA cellular communication networks. The purpose of the positioning system is to enable tracking and locating large quantities of anonymous mobile cell phones MS in any number of network cells to be used for real time traffic-forecasting systems, emergency services E911, and other client-initiated position requests. Location data thus obtained can be continuously updated from vehicular-based cellular phones, collected, processed and used as a basis for input to intelligent transportation systems, such as real time urban traffic guidance for vehicular congestion and intelligent traffic control systems. The system is capable of covering large urban geographical areas and number of independent cell structures serving thousands of mobile cell phone clients. It is an independent plug-in solution with specialized synchronized location device installations in each cell BS. Centrally located specialized location software based on Time of Arrival (TOA) and Time Difference of Arrival (TDOA) methods for high speed location processing in central Location Database Server (LDS). The inventive system consists of number of component functions: Operator-initiated functions, location device functions and software enabled positioning functions.

System and method for real-time, automatic, recognition of large time-scale driving patterns employs a statistical pattern recognition framework, implemented by means of feed-forward neural network utilizing models developed for recognizing, for example, four classes of driving environments, namely highway, main road, suburban traffic and city traffic, from vehicle performance data. A vehicle control application effects changes in vehicle performance aspects based on the recognized driving environment.

The invention relates to a traffic control method and system based on an intersection group.The method comprises the steps that 1, 360-degree panoramic video of intersections is dynamically collected in real time through an intelligent robot, an intersection operation model is established according to video data, and traffic characteristics of the intersection group are analyzed according to the intersection operation model; 2, intersection index evaluation and online simulation analysis are conducted according to the traffic characteristics, and the traffic operation state of the intersection group is identified; 3, supersaturation state intersection signal timing control scheme optimization is conducted on critical paths of a supersaturation state intersection group, and a supersaturation state intersection group traffic signal control strategy is adjusted; 4, the adjusted intersection group traffic signal control strategy is operated, and steady-state operation of an intersection control signal timing optimization scheme and linkage command of the intelligent robot are achieved.By means of the traffic control method and system based on the intersection group, traffic efficiency and service level of intersection single point control can be improved, and therefore the operation efficiency of an urban traffic system is greatly improved, and urban traffic jams are relieved.

The invention discloses a dynamic traffic network urban road feature model and a modeling method thereof. The dynamic traffic network urban road feature model comprises a dynamic traffic network urban road model, a dynamic traffic network running model, a dynamic traffic urban vehicle model and a control center model. The invention aims to solve the problems of poor data consistency and incapacity of carrying out real-time dynamic regulation of a conventional urban traffic network data model, and provides a GIS-T (Geographic Information System for Transportation) data hierarchical model, and the dynamic traffic network urban road feature model is established. Compared with the conventional urban traffic network data model, the dynamic traffic network urban road feature model comprehensively describes urban traffic feature information from macroscopic view to microscopic view, realizes optimized expression of the feature model and optimization of a road network structure and realizes dynamic simulation and regulation of traffic objects.

An urban transportation system which comprises a pool of rental urban vehicles all having the same size and physical configuration and having a range of driving which is limited as compared to the range of vehicles that are powered by internal combustion engines. The system includes a computer-controlled mechanized facility for delivering, receiving, processing, servicing and storing the vehicles. Vehicles are automatically recharged while in storage. The system includes areas for on-street parking of the vehicles. There are also several features to combat theft of the vehicles. A user may obtain the most readily available vehicle from any place in the system and return the vehicle to any other place within the system.

The invention provides an organic calculation based coordination control method for a traffic signal of a zone boundary and relates to the field of urban traffic signal control and manual intelligence. A device comprises a traffic flow monitoring module, a self-optimizing module, a self-organizing module, an evolution learning module, a self-assessment module and a self-setting module. According to the method provided by the invention, the coordination among intersections of the zone boundary is realized and the traffic jam at the intersections of the zone boundary is avoided.

The invention relates to an idle parking space sharing method. The method includes the steps of: S1. idle parking space acquisition; S. idle parking space query; S3. formation of a temporary parking order; S4. parking space reservation; S5. verification and parking; and S6. vehicle pickup and settlement of a parking fee. The system utilizes a server and a network to perform intelligent operation, provides temporarily idle parking spaces of vehicle owners to automobiles that continue to park temporarily for use, and can activate household parking spaces without influencing parking use of fixed parking space owners, thereby generating economic benefits, giving full play to the utilization rate of the temporarily idle parking spaces, realizing complementation of supply and demand, improving urban transportation efficiency, reducing stress of a traffic department, and promoting economic development and social stability. Moreover, the fixed parking space owner and a temporary parker only need to install system software and perform simple operation, the operation is simple, and the situation that ''parking spaces are everywhere with one device in hand'' is truly realized.

The invention discloses a graph neural network traffic flow prediction method and system based on an attention mechanism. The method comprises the steps of obtaining to-be-predicted urban traffic flowdata; constructing a road network map according to the road connection relationship; preprocessing the urban traffic flow data to be predicted; and inputting the road network map and the preprocessedresult into a pre-trained neural network based on an attention mechanism, and finally outputting a prediction result of the urban traffic flow. Roads and checkpoints are encoded according to the roadnetwork information, a road network graph structure is established according to the upstream and downstream relationship of the roads, vehicle passing data of the checkpoints is counted under different time dimensions nd summarized to form a road network traffic flow data table; a graph neural network formed by stacking multiple layers of attention modules is constructed, a time sequence attention mechanism and the graph attention network are used for modeling the traffic flow in the whole road network, and the future traffic flow condition of a specified checkpoint is predicted.

The invention relates to an urban intelligent traffic and transportation system, which comprises a full-interchange totally enclosed light elevated road passage, unmanned electric intelligent trolleys, nodal stations, an intelligent dispatching system, a solar power generating system and a logistic service system, wherein power supply rails are laid on a road surface of the passage; positioning bar codes and navigational lines are laid on the top surface in the passage and the road surface of the passage; and solar power generating plates are laid at a passage top. Passengers swipe cards at nearby nodal stations and select destinations, and the intelligent trolleys generate the best driving paths according to the destinations and the dispatching information and automatically run with the help of the locating bar codes and the navigational lines, so as to directly send the passengers to the destinations. The urban intelligent traffic and transportation system aims to solve the problems of urban traffic congestion, low efficiency, serious pollution, high traffic cost, low safety factor and the like, and has the characteristics of full interchange, full throughway, intelligent driving function, intelligent dispatching function, rapidity, high efficiency, environmental friendliness, conservation, convenience, safety and the like.

The invention relates to an intelligent transportation integration control platform system based on a Web geographical information system technology. The system comprises an equipment integration system portion, an integration application system portion and a center database. The equipment integration system portion comprises a signal control subsystem, an electronic police subsystem, a high definition bayonet subsystem, a video monitoring subsystem, a transportation guidance subsystem and a transportation information acquisition subsystem. The integration application system portion comprises an integration transportation geography information subsystem, a police facility equipment management subsystem, a guard plan management subsystem, a real-time traffic condition issuing subsystem, a bayonet deploying and monitoring alarm subsystem and an integration transportation information service subsystem. By using the intelligent transportation integration control platform system based on the Web geographical information system technology, based on Web GIS, various kinds of intelligent transportation system information resources are highly integrated, and unified command and scheduling of urban transportation security management are realized. Operation and monitoring are convenient and fast. Working performance is stable and reliable. A suitable scope is wide.

The invention discloses a short-term traffic flow prediction method based on a generative confrontation network. The prediction method can be applied in an urban traffic road network, wherein historical traffic data is modeled into a matrix sequence, a depth model is used for learning spatial-temporal correlativity in the data, a generator and discriminator in the network are iteratively trained,the trained model can predict the traffic conditions of all road segments in the road network in a short time in the future. The short-term traffic flow prediction method based on the generative confrontation network fully utilizes the spatial-temporal characteristics of the traffic data, the predicted object is extended from a single road segment to the entire traffic network, and the accuracy ofthe prediction is significantly improved.

The invention discloses an urban traffic scene image understanding and multi-view crowd intelligent optimization method, which utilizes intersection driving path prior modeling of road layout random variables and a road surface semantic prior modeling method based on an FCN learning network to improve the cognitive precision of an intersection driving path and a road surface and the semantic understanding degree of the whole scene. Cognitive-driven priori model and data-driven two-dimensional model Candidate box semantic features of three-dimensional space transformation are combined with a deep neural network to realize complex traffic scene three-dimensional target detection and recognition; The method comprises the following steps of: estimating and describing a three-dimensional sceneflow for a complex road environment such as an intersection traffic scene by cooperatively considering a vehicle position posture and driving track prior model, and comprehensively characterizing theposture and motion trend of a traffic participant in the scene; Aiming at the urban complex intersection road section environment, the holographic understanding of the traffic scene is realized through multi-view crowd-sourcing optimization, the traffic environment understanding is effective and easy to realize, and a decision basis is provided for the autonomous control of the intelligent vehicle.

The invention discloses an urban road network path planning method for the non-global information and improves efficiency of the urban transportation network through balancing the traffic network flow. A path planning method in the prior art can only calculate the best path for a single vehicle or requires more information and assumptions. In response to the above problems, through periodically monitoring state changes of road segments, intelligently learning and evaluating current road segment congestion indexes and constructing a PST model, the A*R algorithm is utilized for path selection, so the road network state is made to be in a flow balancing state. The method is advantaged in that a large number of experiments show that the method can more effectively balance the traffic network flow and alleviate traffic congestion in the scene with relatively a large number of vehicles and has a good performance in reducing the average driving time and the average driving distance, in addition, the method can significantly improve traffic congestion even when relatively lots of vehicles in the road network do not accept path planning navigation.

The invention provides an urban traffic system and method. The urban traffic system at least comprising an urban traffic management control center, an information nervous system, a rail station equipment system, an auxiliary station equipment system, a rail vehicle cluster, a passenger management system and a running guaranteeing system is established in a city firstly. By the adoption of the urban traffic system, rail trains capable of running directly to a destination from a departure place are automatically grouped through the system for passengers with the same departure place, the same destination and the same get-on time point according to taking applications sent by the passengers in advance. Trains for passengers with different time, different departure places and different destinations are under the unified real-time control of the urban traffic management control center to designedly, cooperatively, accurately and efficiently run on the optimal running routes planned through a computer, the purpose that each passenger directly reaches the destination from the departure place is achieved, the highest carrying efficiency is achieved as well, and the purpose of the urban traffic system is achieved. The urban traffic system can be used as a main carrying means for urban traffic to be widely applied to construction of each city.

The invention relates to an all-region parking lot information sharing and parking position reserving method and a system for achieving the method. The system is jointly composed of a parking position reserving module, a parking position management module, a parking lot efficiency analysis module, a payment management module, a user register module, a communication module, a data centre service module, a settlement module, a bill printing module, a notification module, a parking position reserving terminal and a parking position guiding module. A client can operate the parking position reserving module to conduct parking position reserving through the parking position reserving terminal, the parking fee can be paid in advance through the payment management module, reservation can be completed by a parking position reserving service module in a time scope set when the client conducts reservation through the parking position management programming module, and notification is given to the client. If the reservation fails, the payment management module can send back the pre-paid parking fee in the same way. The parking lot efficiency analysis module is mainly used for conducting urban traffic situation auxiliary analysis on traffic flow analysis of a parking lot. The all-region parking lot information sharing and parking position reserving method and the system are low in cost, bring convenience to the client, and are convenient to operate and high in safety performance.

The invention discloses an urban traffic illegal behavior detection method based on a video monitoring system. The urban traffic illegal behavior detection method based on the video monitoring system includes the following steps of trajectory extraction, trajectory structuring, trajectory similarity calculation, trajectory clustering and modeling and abnormality detection, wherein in the trajectory extraction step, a video movement target is detected and tracked to extract a target trajectory; in the trajectory structuring step, a trajectory section is segmented and structured, and the trajectory section is represented through four structural characteristics; in the trajectory similarity calculation step, the characteristic distances corresponding to the four structural characteristics of the trajectory section are calculated respectively, and the similarity between trajectories is calculated through weighing and calculation of the relative similarity between the trajectories; in the trajectory clustering and modeling step, a similarity matrix is structured according to the similarity between the trajectories, the trajectories are clustered, the clustered trajectories are built into Gaussian model sets, and the trajectories belonging to the same class are built into one same set of Gaussian models; in the abnormality detection step, the probability of a trajectory belonging to each model is calculated, and abnormality is judged according to whether the largest probability is larger than a preset threshold or not. According to the method, traffic illegal behaviors are detected based on the video monitoring system, and the efficiency and the accuracy of detection and the illegal behavior class are improved.

The invention discloses a system for controlling traffic flow by controlling quantity of available parking spaces, which comprises a control terminal, a parking space manager and a parking space booking device. The control terminal shares information with an urban traffic management system, stores parking spaces in the current city and identifies the parking spaces one by one, the control terminal is respectively in communication connection with the parking space manager and the parking space booking device, the parking space manager receives parking space state instructions sent from the control terminal, and the parking space booking device sends requests for booking the parking spaces to the control terminal and obtaining information of the booked parking spaces. By means of the technical scheme, the control terminal releases status information of the parking spaces in the city in real time, and users can query and book the parking spaces through the parking space booking device. A traffic control system can determine quantity of parking spaces opening to public use in each road section according to control requirements of the traffic flow, and strict punishing methods for illegal parking are matched with the traffic control system, so that quantity and distribution of traveling vehicles can be accurately controlled.

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 a high-performance thermoplastic elastomer and its application. The raw material component is based on SEBS, combined with homopolypropylene, ethylene-propylene elastomer, mineral oil, mineral filling, additives, etc. Compared with polyurethane, TPV, etc., the quality of the product is more stable and more efficient; at the same time, the appropriate compounding technology can form an interpenetrating polymer network structure between the components of the material, and its tensile strength is above 13.5MPa and its compression set capacity is less than 30%. , the present invention can be applied to the manufacture of urban traffic track gaskets, etc., and has the advantages of good shockproof performance and cushioning performance, low compression set, high mechanical strength and durability.

The invention discloses a graph convolutional neural network based urban traffic flow prediction method and a medium. The method comprises that original data is obtained; a distance matrix is generated according to latitude and longitude information corresponding to each node; a reachability matrix is calculated according to a mean value of the speed limit and the distance matrix; an initial traffic flow prediction model for predicting the traffic flow velocity is established, traffic flow velocity information and the reachability matrix are input to the initial traffic flow prediction model,so that the initial traffic flow prediction model outputs a traffic flow velocity predicted value according to the traffic flow velocity information and the reachability matrix; the initial traffic flow prediction model is trained to determine a final traffic flow prediction model; and traffic flow velocity information to be predicted and the reachability matrix to be predicted are input to the traffic flow prediction model, so that traffic flow in the future is predicted via the traffic flow prediction model. Thus, spatial characteristics of the urban traffic road network are effectively extracted, the traffic flow is predicted more accurately, and the prediction method is more universal and convenient to popularize.

The invention relates to a contact predication based large-scale mobile delay tolerant network cluster-based routing method and a system thereof. The method includes: analyzing a truthful data set of a large-scale urban taxi network to disclose the intrinsic contact and clustering characteristics; designing a contact model among nodes and determining a probability distribution expressing form of contact time interval among the nodes; designing and implementing a contact predication based distributed cluster-based routing algorithm, and dividing nodes with high mutual contact probability into a same cluster under the large-scale dynamic environment; and designing and implementing a direct delivery based in-cluster routing algorithm and a flooding based inter-cluster routing algorithm to guarantee effectiveness and expandability in routing transmission on the basis of clustering. The contact predication based large-scale mobile delay tolerant network cluster-based routing method and the system thereof can be widely applied to message communication of large-scale urban transportation networks such as dynamic navigation systems, automatic vehicle driving systems and vehicular entertainment systems.

The invention discloses a multi-intersection traffic signal control method based on heuristic deep Q learning, and mainly solves problems of correlation of training data, incapability of rapid convergence of a traffic light control strategy and low control efficiency in an existing method. The method comprises steps that the urban traffic network information is read, a vehicle traffic state set ofeach intersection is established, and the read urban traffic network information is converted into an adjacent matrix to be stored; a state set, an action set and an action reward value of each intersection are acquired from a vehicle traffic state set of each intersection; according to the state set, the action set, the action reward value and the adjacency matrix, a heuristic deep Q network method is used for continuously executing actions according to the state of each intersection to obtain rewards and then to the next state, and urban road network traffic lights are controlled. The method can improve control efficiency of intersection traffic signal lamps, improves performance of the multi-intersection traffic signal controller, can be used for urban traffic management, and reduces the urban traffic congestion.

The invention discloses a method for splitting traffic trip modes on the basis of mobile phone data.The method comprises the steps that according to mobile phone behavior characteristics, on the basis of the mobile phone data, a home address community and a work community of a mobile phone user are predicted, a commuting O-D matrix between the home address community and the work community of the mobile phone user is constructed by means of communication records, produced in the home address community and the work community, of the mobile phone user on working days; the commuting trip time, trip distance and trip speed are calculated by means of time information and position information which are contained in the mobile phone data, a speed and distance rank algorithm is built through trip characteristics of different trip modes, urban traffic network information data is introduced, a Logit model based on the trip time is built to achieve large scale urban traffic trip mode splitting, and a novel economical and feasible method is provided for traffic demand prediction among the traffic modes.

The invention discloses a traffic jam state prediction and early warning system and method based on city portraits. The system comprises the following modules: a data collection and processing module,a deep learning module, a historical record module and a front-end display module. Recording road condition information of each street and intersection in real time by utilizing data provided by a traffic camera; streets are abstracted into edges in an undirected graph, intersections are abstracted into points in the undirected graph, road condition information of the streets and the intersections is sorted into edge weights and point weights, the edge weights and the point weights are input into a deep learning model of a dynamic graph, a real-time city portrait is constructed, city trafficcongestion state propagation at the next moment is predicted, and early warning information is reported to a congestion area.

The invention discloses a device and method for guiding a vehicle to pass across an intersection without stopping based on vehicle speed induction strategy, and belongs to the field of urban transportation control and automobile auxiliary driving. The method is characterized by, to begin with, a signal machine control unit receives real-time signals and position signals of traffic lights at one intersection in real time, adapts to non-periodically-changing timing information and provides road traffic information ahead; then, data transmission is realized between the signal machine control unit and a vehicle-mounted control unit zigbee wireless transmission unit; and finally, according to the obtained data information, a vehicle-mounted control unit generates a vehicle control strategy in real time according to different driving conditions to guide vehicles to speed up or slow down to pass through the intersection without stopping. The device and method realize the effect that the vehicle can reach and pass through the intersection within the green light phase, ensure the vehicle to pass through the intersection without stopping, overcomes the problems of congestion and too much delay, improve discharge capacity of the intersection, and are easy to operate.

The present invention provides an self adaptive electric vehicle charging service rate floating calculation system and method. The system comprises a sensing module, a communication module, a fee charging module and a display module. After the system is started, the communication module acquires related information from a cloud end according to registration user information of a client that initiates a charging request or vehicle information identified automatically by the system, and acquires the actual number of to-be-charged electric vehicles around a current charging pile from the sensing module; real-time electricity price is calculated by means of the calculation module; and the real-time electricity price is returned to a driver by means of the cloud end and is displayed by means of the display module. According to the adaptive electric vehicle charging service rate floating calculation system and method provided by the present invention, various factors such as drivers, operators, urban transportation, charging pile load capacity and the like are fully taken into consideration, so that intelligence of the charging pile and intelligent scheduling of the charging vehicles can be implemented, fair, efficient, convenient and intelligent electric vehicle charging services can be provided for the drivers, and intelligence of cities can be facilitated.

The invention discloses an urban traffic situation identification method based on a directed graph convolutional neural network. The method comprises the steps: carrying out the traffic situation classification of historical traffic flow information, converting an urban road network into a directed graph according to a point-edge conversion rule, and extracting a corresponding sub-graph; then, calculating the weight of a directed edge and the weight between non-directly connected nodes, standardizing the number of nodes of the subgraph, and calculating a traffic information matrix and a feature matrix of the subgraph; finally, designing a traffic directed graph convolutional neural network model, performing training and testing, using the model for classifying real-time traffic flow information to identify the real-time traffic situation of all road sections. According to the method, the incidence relation between directed road sections of different levels and different grades under the hybrid road network is fully considered, a unified standardized model input and traffic situation recognition model is designed, and good universality is achieved; moreover, the method has the characteristics of simple process, easiness in calculation, easiness in programming realization and the like, and can be suitable for complex urban road networks.

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.

Provided is an intelligent urban traffic management platform, which comprises an intelligent vehicle system, a high-precision map system, a traffic management system and a vehicle cloud system. The intelligent vehicle system comprises a 3G / 4G SIM card, a man-machine interaction module, a wireless communication module, a sensor, a microprocessor, an executor and a storage device. The high-precision map system is used for receiving information and requests sent by the intelligent vehicle system, carrying out processing on the information and sending map information according to the requests and sending the processed map information to the traffic management system; the traffic management system is used for integrating the information provided by the high-precision map system and the intelligent vehicle system, displaying traffic situations, carrying out self-learning optimization and sending the traffic information to the intelligent vehicle system according to the requests; and the vehicle cloud system is used for carrying out identification learning and fusion on the data collected by different intelligent vehicle systems and sending learning information to the intelligent vehicle systems. The platform solves the problem of information lack among vehicles, roads and traffic management systems.