64 results about "Trip chain" patented technology

The invention discloses a subway passenger flow simulation deduction method considering an individual trip chain and an activity mode, and relates to the technical field of subway operation management, and the method comprises the following specific steps: constructing a topological framework, calculating an external factor attention weight, constructing an individual trip behavior library, carrying out dynamic topological adjustment, and executing passenger flow simulation and deduction. By constructing the space-time heterogeneous topology model and dynamically adjusting the weight values of the nodes and the edges, the precision and the real-time performance of subway passenger flow simulation are improved, physical parameters and historical operation data of urban subway lines are collected, an external factor input layer and a calibration layer are additionally arranged, and the accuracy of subway passenger flow simulation is improved. The spatial-temporal heterogeneous topology model can reflect the influence of external factors on the subway passenger flow in real time, the station retention weight and the line passing weight are dynamically corrected by calculating the attention weight of the external factors and combining data in the individual travel behavior library, and the prediction of the subway passenger flow change is realized. And a scientific basis is provided for subway operation managers.

The invention discloses a truck trip chain identification method based on trajectory data, which comprises the following steps: S1, acquiring GPS trajectory data of a truck, and preprocessing the GPS trajectory data, including data cleaning, noise removal and trajectory segmentation; s2, by setting a speed threshold value and a time threshold value, the stop point of the truck is recognized; s3, spatial matching is carried out on the stay point and road network data, and the accurate position of the stay point is determined; s4, segmenting the track of the truck based on the time sequence and the space distribution of the stop points to form a plurality of travel segments; s5, analyzing the driving characteristics and the stop characteristics of each travel section, and removing stop points related to non-freight; and S6, identifying key staying points related to loading and unloading activities, and connecting the key staying points according to a time sequence to form a truck travel chain. According to the method, the actual running path and the logistics movable chain of the truck can be clearly restored, and high-precision technical support is provided for freight path analysis and industrial chain space structure recognition.

The invention discloses an urban circle traffic demand prediction method and system, and belongs to the technical field of traffic data processing, and the method comprises the steps: taking a four-stage theory as a basis, efficiently obtaining a travel demand OD matrix of urban circle global traffic middle area granularity in the future, and mastering the overall traffic demand pattern of an urban circle from a macroscopic level; on the basis of a trip chain theory, a city traffic cell granularity trip demand OD matrix with higher time-space precision in the future year is accurately obtained, and the method can adapt to more application scenes and reflect traffic prediction and evaluation analysis of traffic demand details; through a traffic distribution algorithm of motor vehicles and public traffic, traffic flow indexes borne by urban circle traffic supply facilities are obtained, and continuity and consistency of spatial and temporal distribution of the current situation and indexes such as traffic supply facilities, travel demands, road flow and bus passenger volume in the future are achieved. And therefore, the traffic demand prediction and evaluation index result of the urban circle in the future year can scientifically meet the needs of users.

The invention provides an electric vehicle charging load prediction method based on travel characteristics, which adopts a Monte Carlo method to simulate travel and charging behaviors of electric vehicles, and comprises the following steps: based on a travel chain model, extracting the initial travel time, the initial travel place, the initial charge state and the travel purpose of each time period of each electric vehicle; according to the travel purpose, extracting the functional area which each electric vehicle needs to reach in each time period; according to the functional area needing to be reached, path planning is carried out on the driving path of each electric vehicle; calculating the driving energy consumption on the driving path, and updating the charge state of the electric vehicle; the charging probability of the electric vehicle is judged according to the charge state; and finally, according to the charging probability of each electric vehicle in each area in different time periods, obtaining the charging load space-time distribution of the electric vehicles. By comprehensively considering various travel characteristics, the reliability of the prediction method is enhanced, and the problem of insufficient prediction precision is solved.

The invention provides a traffic mode prediction method considering parking lot charging and related equipment thereof. The method comprises the following steps: acquiring a grid map of a target area, parking lot data and resident trip chain data; calculating the grid parking fee of each grid unit in the grid map based on the parking lot data and the resident trip chain data; based on the resident trip chain data and the grid parking fee, constructing a private car generalized trip cost expression; constructing a multivariate utility selection model according to the private car generalized travel cost expression and a plurality of preset other travel cost expressions; generating a travel distribution matrix among the grid units according to the grid map and the resident travel chain data; and carrying out traffic mode prediction on the travel distribution matrix according to a multivariate utility selection model to obtain a refined travel distribution matrix considering traffic mode selection. According to the invention, by considering the influence of the parking fees on resident travel mode selection, the accuracy of urban traffic travel prediction is improved.

The invention discloses a regional charging load prediction method, device and system, and a storage medium. The method comprises the following steps: S1, constructing a travel chain model, a working condition prediction model, an energy consumption prediction model and a charging decision model; and S2, according to coupling of the stroke chain model, the working condition prediction model, the energy consumption prediction model and the charging decision model, prediction of the regional charging load is carried out. By adopting the technical scheme of the invention, the charging load of the selected area can be predicted more accurately.

The invention relates to the technical field of data analysis, and discloses a trip chain supplement method and device based on user riding data, and the method comprises the steps: obtaining the riding data corresponding to a user; judging whether a to-be-supplemented riding record in the riding data meets a combined travel mode condition or not; when it is judged that the to-be-supplemented riding record meets the combined travel mode condition, determining a target combined travel mode matched with the to-be-supplemented riding record according to a mode classification condition; based on a trip chain supplement formula corresponding to the target combined trip mode, analyzing the riding data to obtain first supplement information; when it is judged that the to-be-supplemented riding record does not meet the combined trip mode condition or the trip chain supplement formula has no solution, the riding data are analyzed based on a trip chain prediction algorithm, and second supplement information is obtained; and supplementing the trip chain according to the trip chain supplement information. Therefore, by implementing the method, the supplement accuracy of the trip chain can be improved, so that the accuracy and the reliability of the trip chain are improved.

The present invention discloses an electric vehicle charging scheduling method considering demand matching, relates to the technical field of power system control, and alleviates the problems of under-response, over-response and slow solution convergence speed in the process of electric vehicle charging scheduling. The method comprises the following steps: establishing an electric vehicle charging load prediction model of a travel chain; inputting the sorted information into the electric vehicle charging load prediction model to obtain a prediction result; establishing an electric vehicle scheduling model of demand matching; inputting the prediction result in step 1 into the scheduling model in step 2, solving the problem through a fuzzy cuckoo algorithm, and obtaining a scheduling plan; the present invention utilizes an improved travel chain to perform electric vehicle load prediction, and predicts the charging load of electric vehicles at different times and spaces. The method can take into account multiple factors such as the number of electric vehicles and battery capacity, making the prediction result more realistic and reasonable.

The application provides a low-carbon travel carbon emission reduction amount verification method and system based on travel chain big data, which comprises the following steps: step 1) obtaining sample data of travel data; step 2) when the sample data is walking data, going to step 3); when the sample data is bicycle data, going to step 4); when the sample data is electric bicycle data, going to step 5); step 3) verifying the sample data by using a walking travel distance verification method and removing duplicates to obtain a final effective travel distance; calculating the carbon emission of walking according to the final effective travel distance; step 4) verifying the sample data by using a bicycle travel distance verification method and removing duplicates to obtain a final effective travel distance; calculating the carbon emission of the bicycle according to the final effective travel distance; step 5) verifying the sample data by using an electric bicycle travel distance verification method and removing duplicates to obtain a final effective travel distance; and calculating the carbon emission of the electric bicycle.

This invention relates to the field of electric vehicle trip chain simulation technology, specifically to a method and system for simulating the trip chain of electric heavy-duty trucks considering charging strategy optimization. The steps are as follows: Real-time updates of target station distance and battery charge; upon arrival at a loading / unloading station and when the loading status meets loading / unloading conditions, updating the loading / unloading status and executing an event; after the event, updating to a driving status; upon arrival at a charging station and when the battery charge meets charging conditions, if there is an available charging pile or the overall satisfaction level is greater than a threshold, updating the charging status and executing an event; after the event, updating to a driving status; if the overall satisfaction level is less than the threshold, directly updating to a driving status; updating the target station for the vehicle in the driving status and repeating the above steps until the entire trip chain simulation is completed. This invention has significant reference value for electric heavy-duty truck capacity demanders in terms of operational scheduling plans and station configuration, and can promote improvements in fleet operating efficiency and economy.

The application provides a cross-border passenger travel chain identification method and system, which comprises the following steps: screening the base stations of the exit port / airport based on the base station position information, and preliminarily screening the potential cross-border passengers; obtaining the full-chain travel of the potential cross-border passengers in the country through the residence area pre-identification algorithm based on the mobile phone signaling of the passengers in the country; constructing a residence mathematical model, screening the residence point with the highest credibility in the exit port / airport as the cross-border entry and exit point through the residence mathematical model, and determining the position of the cross-border entry and exit point; distinguishing the entry and exit behaviors of the cross-border passengers according to the residence time of the cross-border passengers in the port / airport and the mobile phone signaling disappearance characteristics; and combining the full-chain travel of the cross-border passengers in the country, the position of the cross-border entry and exit point and the entry and exit behaviors to construct the cross-border passenger travel chain. Through the scheme, the accuracy of the cross-border passenger travel investigation data can be improved, and the coverage time and coverage range of the investigation data can be improved.

The invention discloses a charging network guiding method and system based on trip chain simulation, and the method comprises the steps: firstly, building an electric vehicle trip probability model based on a trip chain theory, generating a virtual scene containing a vehicle driving track and charge state evolution through employing a Monte Carlo simulation method, and when a charging triggering condition is satisfied, carrying out the simulation of an electric vehicle trip probability model; recording the demand information to form a charging load basic data set for charging network planning; then, acquiring short-time load prediction data of a charging station; and finally, based on the prediction data, generating a guidance index related to the charging congestion degree, issuing the index to a user terminal for charging navigation, and reporting the prediction data to a power grid system at the same time. The problems that charging network planning lacks data support and information islands exist in operation are solved, planning basis is provided through scientific simulation, information linkage among the charging stations, the users and the power grid is established, and the cooperative operation efficiency of the whole energy traffic system is effectively improved.

The present application provides a method, device, electronic device and computer program product for obtaining user travel labels, and relates to the field of data analysis technology. The method includes: performing data cleaning on signaling data based on specific information; generating and classifying travel chains according to user trajectory data, and calculating the information entropy of each type of travel chain list; extracting a travel chain from each travel chain list and combining it with the corresponding information entropy to generate a travel chain information entropy list; when it is determined that the travel regularity index of the target user meets the standard, the travel chain information entropy list is matched with the map point of interest classification table to obtain the travel label of the target user. The embodiment of the present application cleans the acquired signaling data based on specific information, and filters the user's travel information based on the extracted user travel regularity characteristics, thereby matching travel labels that can accurately reflect the user's travel habits.

The invention discloses an electric vehicle space-time charging adjustable flexibility quantification method and device, and the method comprises the steps: building a charging space-time flexibility model based on a trip chain, and covering slow charging time and fast charging space flexibility modeling; a double-criterion random dynamic user balance model is constructed, and fast and slow charging loads in a balance state are obtained; and finally, quantitatively evaluating the slow charging time flexibility and the fast charging space flexibility potential by using the flexibility model. According to the method, traffic, price and user perception differences can be comprehensively considered, the charging load space-time schedulable characteristics are comprehensively reflected, and powerful support is provided for traffic-power system cooperative scheduling.

The present invention discloses a method and system for improving the public transportation share rate of a transportation community. The method comprises: obtaining public transportation travel parameters for all transportation communities based on multi-source data analysis; selecting target transportation communities that meet preset requirements based on filtering parameters; selecting travel directions from the target transportation communities to each other area that do not meet a first preset public transportation share rate as key public transportation passenger flow channels to be analyzed; performing a travel chain analysis on the key public transportation passenger flow channels to be analyzed to obtain corresponding travel parameters, and optimizing the key public transportation passenger flow channels that do not meet the travel parameter requirements using an expert knowledge base. The present invention analyzes the public transportation share rates of the selected target transportation communities, selects key public transportation passenger flow channels from them, and performs a travel chain analysis on them, thereby regularly analyzing the public transportation share rates of transportation communities in a geographically fine-grained manner and improving and optimizing key public transportation passenger flow channels with low public transportation share rates.

The application provides a travel service recommendation method and device, an electronic device and a storage medium. The method comprises the following steps: performing regional clustering based on station position information of a traffic station to obtain a plurality of candidate travel region pairs, and constructing a candidate travel chain; obtaining first historical travel data in a preset historical period, and extracting corresponding second historical travel data for a preset operation time interval; for each candidate travel region pair, determining a maximum number of transfers, and obtaining a target historical passenger flow in a continuous historical time interval; combining a preset screening condition to screen a target travel region pair; extracting third historical travel data, generating a travel inappropriateness evaluation index of each operation time interval based on the first and third historical travel data for each target travel region pair, and recommending a travel service to a travel object according to the travel inappropriateness evaluation index. The application can provide personalized traffic services for travel objects with the same travel demand and provide a good travel experience.

The invention relates to the technical field of intelligent traffic, and particularly discloses a traffic flow prediction system based on trip chain dynamic evolution. The system comprises a trip chain real-time sensing and reconstruction module, a trip decision behavior dynamic deduction module, a group trip demand evolution calculation module and a macroscopic traffic flow dynamic prediction module which are connected in sequence. Through real-time sensing and reconstruction of an individual trip chain, individual behaviors are dynamically deduced, group demands are simulated and aggregated based on an intelligent agent, and a macroscopic model is driven to carry out prediction. The system constructs a complete technical closed loop from microscopic individual behavior real-time perception to macroscopic road network flow dynamic prediction. According to the system, through cooperation of the travel decision behavior dynamic deduction module and the group travel demand evolution calculation module, fine simulation of a group travel behavior emergence effect under an emergency situation is realized.

The application discloses a kind of motor vehicle trip chain intelligent generation method based on attention mechanism, including obtaining the trip observation data of individual vehicle using AVI detector, constructs the association system of trip observation and behavior attribute, and generates embedding vector representation by semantic and syntax embedding method;Combined with the characteristics of fragmented data, the multi-head attention mechanism of spatio-temporal association is fused, the coding and decoding structure is improved, and the trip chain is generated;Adopt the multitask pre-training strategy of autoregressive generation and contrast learning;Through trip group clustering and hierarchical fine-tuning strategy, implement individual heterogeneity adaptation using lightweight adapter, generate candidate trip chain in instruction-reply form, filter high-confidence results according to generation probability and spatio-temporal consistency, provide accurate and diversified support for motor vehicle trip chain reconstruction;The application can efficiently and accurately infer and generate complete motor vehicle trip chain under fragmented observation conditions, and consider the generalization ability and individual difference adaptability of the model.

The invention provides a cross-border passenger trip chain identification method and system, and the method comprises the steps: screening out a base station of a port / airport based on the position information of a base station, and carrying out the preliminary screening of potential cross-border passengers; the method comprises the following steps of: acquiring full-chain travel of potential cross-border travelers at home through a residence area pre-identification algorithm based on mobile phone signaling when the travelers reside at home; constructing a residence mathematical model, screening a residence point with the maximum credibility in the port / airport through the residence mathematical model as a cross-border access point, and determining the position of the cross-border access point; distinguishing entry and exit behaviors of the cross-border passengers according to the residence time of the cross-border passengers at the ports / airports and the mobile phone signaling disappearance characteristics; and merging full-chain travel, cross-border entry and exit point positions and entry and exit behaviors of the cross-border passengers in the home, and constructing a cross-border crowd travel chain. Through the scheme, the accuracy of the cross-border passenger travel survey data can be improved, and the coverage time and coverage range of the survey data are improved.

The invention discloses a bus OD derivation and road network flow matching method based on multi-source data and electronic equipment, and the method comprises the steps: obtaining and preprocessing bus multi-source data in a target area, and building a station-line-vehicle three-dimensional mapping relation with direction information; the bus multi-source data comprises bus GPS (Global Positioning System) data, station GIS (Geographic Information System) data and IC (Integrated Circuit) card swiping data; determining passenger types and historical travel laws according to the IC card swiping data, analyzing travel chain features according to the historical travel laws, and then deducing getting-off stations and time of passengers in combination with POI types of stations; according to the deduced get-off station of the passenger and the corresponding route, determining a station sequence interval of the travel path of the passenger on the bus route; then, according to the corresponding relation between the line station and the road section number in the road network, the passenger travel path is mapped to the actual bus road network, the total passenger flow of each road section is counted, and flow statistical data with the road section as the unit is generated.

The invention discloses an electric vehicle charging scheduling method and electronic equipment, relates to the technical field of new energy, and can accurately understand a travel intention and a charging demand of a user by acquiring information such as a starting point, a terminal point, a departure moment and an initial charge state of a target electric vehicle. And the charging scheduling scheme and the actual travel plan of the user are ensured to be closely fit. The trip chain is constructed based on the starting point and the ending point, and the optimal path is generated, thereby ensuring the reasonability and high efficiency of path selection. And the optimal path and the initial charge state are combined to accurately predict the time, the place and the required charge state of the charging service of the electric vehicle, so that the charging station can be planned in advance, and the time cost for searching the charging pile is reduced. Charging can be reasonably arranged in an idle window of a user travel by calculating a charging schedulable time period. And a moss growth optimization algorithm is adopted to solve the charging power scheduling model, so that the feasibility of a charging scheduling scheme and the stability of the whole system are ensured.

The application discloses an intra-city traveler portrait system based on mobile signaling data, comprising a data preprocessing module, a travel chain extraction module, a travel mode recognition module, a daily travel mode recognition module, a travel path flow recognition module and a traveler portrait module. The system loads the dwell point data processed by the mobile signaling data supplier and combines the administrative division data for preprocessing, extracts the travel chain, recognizes the travel mode by using the Gaode map API and the log Gaussian mixture model, recognizes the daily travel mode by combining the convolution self-encoder and the K-means algorithm, counts the travel path flow, and finally summarizes the labels of the travel characteristics to construct the traveler portrait. The system optimizes the data processing flow, improves the accuracy of the travel mode recognition, enhances the interpretability of the travel mode recognition, and associates the macro traffic flow with the individual travel path, thereby providing a precise and efficient analysis tool for traffic demand management.

The invention specifically discloses a scenic spot vehicle behavior identification method based on a trip chain state machine and unsupervised clustering, and relates to the technical field of intelligent traffic. The method comprises the following steps: firstly, judging vehicle access states according to passing data of scenic spot checkpoints, grouping and sequencing, and constructing three types of trip chains; dividing the vehicles into large vehicles, medium vehicles and small vehicles, aggregating and analyzing travel chain data of each vehicle type, and constructing vehicle behavior feature vectors; obtaining a weighted vehicle trip chain feature vector after feature optimization, standardization and adaptive weighting; after the abnormal trip chain is detected and isolated, an initial clustering result of vehicle behaviors is obtained, and the optimal clustering number is determined; obtaining a stable vehicle behavior clustering result through stability evaluation parameter adjustment; and finally, outputting a final recognition result according to the distribution difference of the vehicle type clustering centers. The method improves the recognition accuracy and adaptability, provides data support for fine management of scenic spot traffic, and can also be applied to traffic scenes with clear access boundaries.

The invention discloses a public transport node performance analysis method based on multivariate data and interpretable machine learning, relates to the field of machine learning, and aims to solve the problem of incomplete performance analysis caused by single data source, insufficient spatial analysis granularity and inaccurate matching between individual travel behaviors and public transport node microscopic features in the prior art. Comprising the following steps: S1, collecting and preprocessing multi-source data; s2, generating a main trip mode label through trip chain codes; s3, constructing a bus node space based on 150-meter buffer area superposition and a line density threshold value; s4, counting a section POI proportion, and generating a node feature vector by fusing a location index; s5, associating a travel starting point with a node space through line matching and a distance threshold value, and fusing individual and node features to construct a data set; and S6, quantizing the nonlinear influence of the node function layout on the travel selection by adopting a hierarchical LightGBM model in combination with SHAP explanation. The method has a good application prospect in the field of urban traffic planning.

The invention discloses a user trip chain synthesis and labeling method based on an NFC-SIM card, and relates to the technical field of data processing, and the method comprises the steps: responding to a selection operation of at least one target card swiping record in a labeling task set, and in each track point of signaling track data corresponding to the target card swiping record, labeling the target card swiping record in the labeling task set; determining a candidate alignment point which is in space-time alignment with the target card swiping record; determining a travel OD segment based on the candidate alignment points and the signaling trajectory data; in response to a travel mode label carried by the travel mode labeling operation, associating the travel mode label to the travel OD segment; and in the multiple travel OD segments of the same user, determining time-space continuous target OD segments, synthesizing the target OD segments into a composite travel chain, generating a combined tag based on the travel mode tag of each target OD segment, and associating the composite travel chain. According to the invention, the accuracy of user trip chain labeling is improved.

The application discloses a user travel chain synthesis and labeling method based on an NFC-SIM card, relates to the technical field of data processing, and comprises the following steps: in response to a selection operation on at least one target card swiping record in a labeling task set, determining candidate alignment points that are aligned in space and time with the target card swiping record from each track point of signaling track data corresponding to the target card swiping record; determining a travel OD section based on the candidate alignment points and the signaling track data; in response to a travel mode label carried by a travel mode labeling operation, associating a travel mode label with the travel OD section; in multiple travel OD sections of the same user, determining target OD sections that are continuous in space and time, synthesizing the target OD sections into a composite travel chain, and generating a combined label based on the travel mode labels of the target OD sections and associating the composite travel chain. The application improves the accuracy of user travel chain labeling.

The present application relates to a kind of electric vehicle charging power calculation method, the method includes the following steps: using the database data of Internet of Vehicles to the space-time characteristic parameter in trip chain is statistically analyzed and is synthesized and establishes mathematical model to simulate user travel regularity;Different probability distribution functions are constructed to simulate the single charging behavior characteristics of electric vehicle user in real time;Determine the power size of charging and the duration length of charging;The charging power of single electric vehicle in daily is estimated and calculated;Obtain the space-time distribution curve of electric vehicle charging power in single day.The present application considers the charging behavior habit of user, the state of electric vehicle battery and the anxiety psychology of user to potential trip, establishes more real user charging decision model, can more practically and accurately estimate the space-time distribution of electric vehicle charging power in single day, has strong practicability.

The invention provides a low-carbon travel carbon emission reduction verification method and system based on travel chain big data, and the method comprises the following steps: obtaining travel data from a plurality of data sources, and carrying out the preprocessing of the obtained travel data; through a multi-source data fusion technology, trip chain big data is comprehensively collected, the integrity and accuracy of trip data are effectively improved, and a reliable data basis is provided for accurate calculation of subsequent carbon emission reduction; through a travel mode identification model based on deep learning, travel modes under various complex conditions can be accurately identified, so that the calculation of carbon emission reduction is more accurate; the carbon emission reduction calculation model is established by comprehensively considering various actual factors for different travel modes, so that the carbon emission reduction conditions of different travel behaviors can be reflected, and the scientificity and accuracy of carbon emission reduction accounting are improved; through the application of a real-time data feedback mechanism and an online learning algorithm, model parameters are continuously optimized, and the accuracy and timeliness of carbon emission reduction verification are ensured.

The present application discloses a method and device for processing a travel chain, a storage medium, and a processor. The method includes: performing cluster analysis on the positioning points in the target location service data based on a spatiotemporal clustering algorithm to obtain a plurality of candidate stay point sets of the target object and a non-stay point set of the target object; screening and processing the plurality of candidate stay point sets to obtain a starting point set and a destination point set of the target object, and connecting the starting point in the starting point set, the destination point in the destination point set, and the non-stay point in the non-stay point set to obtain an initial travel chain; based on the first feature information set between the positioning points in the initial travel chain, identifying the travel mode to obtain a target travel chain. The present application solves the technical problem in the related art that most of the travel chains are determined based on dense and uniform trajectory data, which are easily interfered by noise points and outliers, resulting in relatively low accuracy of the travel chain.

The invention discloses a railway traffic distribution method based on a cellular network gradient algorithm. The method comprises the following steps: (A) vectorizing a railway basic network; (B) traffic cell construction based on a cellular network; (C) extracting a trip chain based on cellular network big data; (D) establishing a railway traffic flow distribution model based on a cellular network gradient algorithm; and (E) solving the model. According to the method, the mathematical model established through the cellular network gradient algorithm can accurately describe the topological structure and the operation rule of the railway network, constraint conditions such as train operation time, speed and capacity are considered, and the target function for maximizing the throughput and the operation efficiency of the railway network is also considered, so that the distribution of railway traffic flow can be effectively optimized.