13 results about "Trip chain" patented technology

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 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 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 application discloses a bus passenger alighting station inference method based on a travel chain and an integrated learning, and comprises the following steps: collecting bus multi-source data; performing data cleaning and preprocessing on the multi-source data; fusing the multi-source data and judging a passenger boarding station according to a station arrival time window; performing preliminary inference on the alighting station based on a deterministic algorithm; establishing a deterministic algorithm to infer the passenger alighting station according to a travel chain assumption and passenger travel history; performing supplementary inference on the alighting station based on the integrated learning; introducing built environment and bus line external information for a journey that cannot be inferred by the deterministic algorithm; and establishing a multi-classification model based on an integrated learning two-layer stacking framework to further infer the passenger alighting station. The application realizes non-aggregated, station-level single-ticket bus passenger alighting station inference based on bus multi-source big data, can obtain accurate bus passenger flow space-time distribution characteristics, and provides a reference for bus operation organization optimization.

This invention provides a multi-task individual basic travel chain prediction method and system based on computational graphs, specifically comprising: Step 1: Data acquisition and construction of a data sample set, followed by preprocessing of the acquired data; Step 2: Graph structure modeling of spatial relationships between traffic zones, generating a weighted adjacency matrix at the region level, and using a multimodal graph representation learning model to generate graph embedding vectors and node embedding vectors for traffic zones; Step 3: Construction of a multi-task neural network model comprising a sequentially connected input layer, a shared layer, and multiple task output layers, with a context-aware module introduced in each task output layer to dynamically adjust sample-level weights and bias parameters; Step 4: Joint optimization of the learning model and the multi-task neural network model. This invention contributes to promoting the deep integration and practical application of artificial intelligence in the field of transportation engineering.

The application provides a kind of traveller activity-travel chain reconstruction method based on mobile phone signaling data, comprising the following steps: input original mobile phone signaling data and carry out data pretreatment to mobile phone signaling data;Potential stop point and displacement point are judged by the improved ST-DBSCAN density clustering algorithm applied to pretreated mobile phone signaling data, and potential stop point sequence and potential displacement point sequence are obtained;Travel OD is identified using OD identification algorithm based on dynamic threshold, and travel information such as O point position and stay start and end time, D point position and stay start and end time and travel duration is extracted;According to the stay information of travel O point and D point, activity duration, activity location, activity start time, activity end time activity information is extracted;Residence and work place are identified according to effective stop point information in user multi-day trajectory;The activity type of user is inferred according to the distance between user stop point position and residence and work place;According to identified travel information and activity information, activity-travel chain of traveller is generated in sequence.

This invention discloses a method for identifying air-rail intermodal travel chains based on passenger ticket data. This method constructs a continuous passenger travel sequence by fusing heterogeneous air and rail ticket data. It initially screens candidate pairs for rail-to-air or air-to-rail transfers using travel mode identifier differences. It introduces station-city mapping spatial constraints to eliminate pseudo-intermodal transfers in different locations. For candidate pairs within the same city, it instantiates minimum transfer times for three scenarios based on hub spatial distance and connection characteristics. The maximum transfer time is determined by minimizing the statistical threshold of pure transfer groups fitted by a Gaussian mixture model and the absolute physical upper limit of the travel chain, forming a differentiated dynamic effective transfer time threshold range. Candidate pairs whose transfer times fall within this range are retained, and the air-rail intermodal travel chain is output. This invention overcomes the shortcomings of existing methods, such as lack of spatial constraints and poor adaptability of fixed thresholds, and has the advantages of high recognition accuracy, low false positive rate, lightweight algorithm, and easy engineering expansion.

The application belongs to the technical field of cold chain highway freight, and discloses a cold chain freight vehicle classification method based on travel task complexity. The method steps are as follows: collecting cold chain truck trajectory point data based on a Beidou satellite positioning system; pre-processing the collected cold chain truck trajectory point data; constructing an OD pair based on the pre-processed cold chain truck trajectory point data; constructing a cold chain freight vehicle travel chain based on the constructed OD pair; extracting travel chain features based on the constructed cold chain freight vehicle travel chain; obtaining a travel characteristic portrait of the cold chain freight vehicle based on the extracted travel chain features; and constructing a vehicle multi-task scheduling optimization model based on the obtained travel characteristic portrait of the cold chain freight vehicle. The application aims to provide support for fine management and operation optimization of cold chain logistics.

The application discloses a bus individual trip decision model, obtains the trip rule of the bus individual based on the analysis of the bus individual trip data, and inputs the initial basic data of the model; the getting-off and getting-on station information of the bus individual is calculated and recognized through a time matching method, a trip chain theory and bus station characteristics, and the identification and division of the individual trip chain are completed in combination with the connotation and function level of the multi-mode bus, a virtual start and end point is defined for the departure or arrival station cluster of the individual, the analysis and processing of the individual trip data are completed, then the individual trip decision model about the departure time selection, the getting-on station selection and the bus line selection is established based on the individual historical trip information obtained through the above method, and the individual can reach the trip end point in the case that the total cost is as small as possible.

The application provides a travel survey simulation method and device, electronic equipment and readable storage medium, wherein the method comprises: obtaining a user portrait and a date condition of a target individual; the user portrait comprises at least one of age, gender, occupation, family structure, income level and vehicle ownership; based on a pre-trained inference enhanced language model, a travel inference chain and a structured travel table are generated according to the user portrait and the date condition; the inference enhanced language model is obtained by performing basic supervision fine-tuning and enhanced supervision fine-tuning on a pre-trained large language model by using a trip chain inference sample set constructed by using real travel survey data. The method significantly improves the spatio-temporal consistency and behavior explainability, effectively solves the problem of insufficient generalization ability of traditional methods in cross-city migration and cold start scene, and realizes low-cost, high-credibility, explainable and easy-to-deploy travel data generation.

This invention discloses a method for recognizing vehicle behavior in scenic areas based on travel chain state machines and unsupervised clustering, belonging to the field of intelligent transportation technology. The method first determines the entry and exit status of vehicles and groups and sorts them according to the passage data of scenic area checkpoints, constructing three types of travel chains. Vehicles are categorized into large, medium, and small vehicles, and the travel chain data for each vehicle type is aggregated and analyzed to construct vehicle behavior feature vectors. After feature optimization, standardization, and adaptive weighting, a weighted vehicle travel chain feature vector is obtained. After detecting and isolating abnormal travel chains, initial clustering results of vehicle behavior are obtained, and the optimal number of clusters is determined. Stable vehicle behavior clustering results are obtained through stability evaluation and parameter tuning. Finally, based on the distribution differences of cluster centers for different vehicle types, the final recognition result is output. This method improves recognition accuracy and adaptability, provides data support for refined traffic management in scenic areas, and can also be applied to traffic scenarios with clearly defined entry and exit boundaries.