98 results about "Travel behavior" patented technology

An AI-powered personalized travel recommendation system for tourists, consisting of: a user interface configured to receive user input regarding travel preferences, interests, budget constraints, and desired length of stay; a data processing unit comprising one or more machine learning algorithms configured to analyze user data, including but not limited to past travel behavior, demographic information, and real-time location data, to generate personalized travel recommendations; a recommendation engine that is operationally connected to the data processing unit and configured to synthesize and aggregate various data sources, including social media activity, online reviews, weather forecasts and local events, to compile a list of tailored destinations, activities, accommodations and dining options; an adaptive feedback loop that changes recommendations based on user interactions and feedback, with the feedback being used to improve the machine learning algorithms over time; a travel itinerary generator that uses the results of the recommendation engine to create an optimized itinerary, making it easier to discover popular tourist attractions, hidden gems, and off-the-beaten-path experiences; and a sharing module that allows users to share their personalized itineraries and ratings with other users of the system, thus promoting a community-oriented approach to travel planning.

The invention belongs to the field of urban rail transit network passenger flow simulation, and particularly relates to an urban rail transit passenger travel behavior characteristic estimation method. Obtaining, analyzing and processing travel data; searching a feasible path set meeting the maximum transfer frequency constraint between the OD pairs by adopting a breadth-first algorithm with constraint; reconstructing a passenger trip chain; through a path time similarity index, passengers on a non-transfer single path and passengers on a single-transfer single path are distributed to a train, and then a travel chain of the two types of travel passengers is obtained; and taking the standard deviation of the train arrival time as the standard deviation of the waiting time of the passengers on the platform, and calculating to obtain passenger in-out walking time distribution and transfer walking time distribution. According to the passenger travel behavior characteristic estimation method for the urban rail transit, station entering and exiting walking time distribution of passengers in all stations in an urban rail transit network, transfer walking time distribution of the passengers in each transfer direction of a transfer station and waiting time distribution of each station can be accurately estimated.

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

The invention discloses an optical storage charging station robust optimization method considering the travel behavior deviation of a charging user, and the method comprises the steps: depicting the path selection and charging behaviors of an electric vehicle user under different perception deviations through collecting the multi-dimensional data of a power network and a traffic network in real time, and employing a random user balanced mixed traffic flow distribution model; coupling the traffic and the power network through a traffic-power coupling network time-space correlation constraint; and establishing a two-stage robust optimization model of the optical storage charging station by integrating the photovoltaic output of the charging station and the behavior perception deviation multi-dimensional uncertainty of the travel user of the traffic network. An efficient solving algorithm is designed for a specific model, an optimal operation strategy of the power distribution network and photovoltaic charging station equipment in a worst scene is finally generated, dynamic optimal balance of operation economy and robustness of the power distribution network is achieved, the operation cost is reduced while the safety margin of the system is guaranteed, and the system reliability is improved. And the cooperative regulation and control capability of the complex coupling system is obviously improved.

The invention relates to the technical field of urban rail transit station-city collaborative planning, in particular to an ontology-based station-city collaborative data integration and planning prediction method, which comprises the following steps of: obtaining rail transit station passenger flow data, resident travel behavior data and station periphery built environment index data; forming a space-time sample sequence according to the unified space-time granularity of the site walking service area; constructing an urban rail transit station-city cooperation ontology, and carrying out semantic annotation and semantic fusion on the space-time sample sequence to generate a feature sequence; inputting the feature sequence into a multi-task space-time diagram convolutional neural network prediction model to output a passenger flow prediction result and establish an environment index prediction result; and calculating a feature contribution degree based on a Shapley additive interpretation value, optimizing a background sample set by using a genetic algorithm to determine a key action element set, outputting a planning index threshold and an intervention measure parameter, and realizing an interpretable station-city collaborative prediction and planning decision closed loop.

The invention discloses a business travel reimbursement management system, and relates to the technical field of business travel management, and the system comprises the following components: a data collection module which collects business travel demands and historical business travel records of a user, analyzes the relation between data through an algorithm model, and displays an analysis result to the user and an enterprise in a visual manner, according to the invention, by integrating the business travel related data and combining with the data cleaning and statistical analysis technology, the highly personalized business travel planning service is provided for the user, the historical travel data of the user is classified by applying the K-means algorithm, different user groups and travel modes thereof are accurately identified, and the user experience is improved. According to the method, flight, hotel and travel destination recommendation and association rule mining algorithms which better meet the actual requirements of users are provided for the users, and association rules between travel behaviors of the users and expenses are deeply mined, so that more accurate cost budget and control basis are provided for enterprises, unnecessary expenses are effectively avoided, and the user experience is improved. And the financial management efficiency of enterprises is improved.

The invention discloses an urban community public space residence vitality measurement method based on spatiotemporal behaviors, and the method is characterized in that the method comprises the following steps: S1, determining a sample; s2, defining a space range; s3, acquiring resident resident activity data; s4, determining community public space information; s5, calculating a residence vitality index; S6, constructing a residence vitality measurement model; and S7, introducing the data and calculating the residence vitality of the public space of each community. According to the method, data acquisition, statistics and analysis are carried out based on actual daily travel behaviors of community residents, and the resident activity distribution condition of the community public space obtained through representation can more truly describe the use condition of the residents on the community public space, so that the actual demands of the residents are mined, and the resident experience is improved. Therefore, the rationality and effectiveness of urban community public space planning are evaluated more accurately, and targeted technical guidance and theoretical support are provided for updating and optimization of the mountain community public space.

The invention relates to a multi-mode network accessibility calculation method for comprehensive traffic, a calculation device and a storage medium, and the method comprises the steps: constructing a network structure of a comprehensive traffic network which comprises a road network, a railway network and an aviation network; recording longitude and latitude coordinates of a starting point and an ending point; a heuristic function of an A * algorithm is improved; the hierarchical search algorithm comprehensively considers travel paths in the traffic network, including intra-city travel paths of cities where a starting point and a terminal point are located, and inter-city travel paths connecting the cities where the starting point and the terminal point are located; calculating total travel time; and calculating reachability and diagnosing reachability depression. According to the method, the path search algorithm is improved with the aim of improving the efficiency, and the path search algorithm suitable for the multi-mode traffic network is constructed from the two aspects of optimizing the search path and improving the search strategy. And by introducing a hierarchical search strategy, unnecessary repeated search is effectively reduced, and the search efficiency of the algorithm is improved. Meanwhile, the algorithm fusion considers the influence of the transfer times and the train connection relationship on the travel path selection, and the actual travel behavior of the traveler is simulated more accurately.

The invention belongs to the field of big data processing, and discloses a crowd travel mode identification method and system for multi-view subspace learning based on dynamic and static feature fusion, and the method comprises the steps: carrying out the preprocessing of original signaling data, and cleaning invalid data; extracting a travel track of each user, and processing possible abnormal conditions of ping-pong data and drift data; after track data of a single user in a sample time period is obtained, segmenting the track data, and extracting a single trip chain track; extracting static characteristics such as travel behaviors and travel traffic of a travel chain in combination with multi-source data such as mobile phone signaling data, an OSM road network and public traffic data; extracting time sequence features by using an LSTM model, and modeling dynamic features of a travel track; static and dynamic features are fused, and travel modes are captured from multiple views. According to the method, the complex travel mode can be more accurately captured, data redundancy and noise are reduced, and the accuracy of travel mode identification is remarkably improved.

The invention provides a business travel data management method and system based on cloud computing and scene matching and computing equipment. The method comprises the following steps: acquiring at least one geographic image uploaded by a user in a business travel plan execution process; generating a compliance geographic region set according to the travel plan; matching the geographic image with the compliance geographic region set to obtain a first matching result; identifying and extracting visual scene features in the geographic image, and performing consistency cross validation comparison on the visual scene features and a geographic scene expected based on geographic coordinates to obtain a first comparison result; generating an authenticity verification result of the travel behavior based on the first matching result and the first comparison result; and when the authenticity verification result is that the verification is passed, automatically generating an electronic audit voucher containing a spatial trajectory chain and a geographic image. According to the technical scheme of the invention, the accuracy of authenticity verification of travel behaviors can be improved, a high-credibility electronic audit voucher is automatically generated, and a data closed loop is provided for intelligent travel management.

The invention relates to the technical field of intelligent express management, and discloses a community intelligent express management method and system, and the method comprises the steps: obtaining the historical travel behavior data, express pickup behavior data and address information of community residents, constructing a multi-express cabinet deployment space model, and outputting a delivery cabinet candidate set; determining a target cabinet number; pushing warehousing state data to residents; a long-short-term memory network is adopted for temperature prediction, and temperature is regulated and controlled in combination with parcel temperature control requirements. Compared with an express cabinet distribution method based on a static address or a fixed path in the prior art, the technical problem that efficient cabinet grid scheduling and environment adaptation are difficult to realize particularly under the conditions that multiple express cabinets exist in a community and resident travel habits are diversified is solved. A better express delivery decision is realized, so that the problems of mistaken delivery, delayed delivery or improper temperature control of the express delivery are avoided, and the delivery accuracy and the package safety are improved.

The invention provides a flexible bus route planning method based on user travel behavior prediction. The invention discloses a flexible bus route planning method based on user travel behavior prediction. The method comprises the following steps: S1, collecting and integrating multi-source travel data; s2, user behavior analysis and demand prediction; s3, dynamic service area division; s4, generating and optimizing a flexible line; s5, performing real-time dynamic scheduling and adjustment; s6, ticket price dynamic generation and feedback; and S7, service effect evaluation and iterative optimization. The flexible bus route planning method based on user travel behavior prediction provided by the invention has the advantages of being capable of obtaining balance between global optimum and calculation efficiency, reducing the vehicle empty driving rate, improving the route operation cost recovery rate, quickly responding to newly added demands and shortening the average waiting time of passengers.

The invention provides a railway passenger identification method and device, electronic equipment and a computer readable storage medium, and relates to the technical field of big data. According to the method, the signaling set of the target area within the specified time and the signaling interaction range of the railway station are utilized to preliminarily lock the to-be-determined users which stop in the railway station and generate signaling interaction, and then non-railway passengers which stop in only one railway station are excluded based on the residence information of the to-be-determined users in combination with the travel behavior characteristics of the railway passengers, so that the non-railway passengers are not stopped in the same railway station. And finally, various misjudgment conditions are comprehensively considered to exclude the journey of non-railway travelers, so that each section of railway journey of the railway travelers within the specified time is accurately identified.

The invention provides a user incentive and point management method and system for a carbon emission reduction behavior, and relates to the technical field of carbon emission reduction behavior management, and the method comprises the steps: obtaining the original track information of a user in a target travel behavior, and synchronously collecting corresponding environment feature data and equipment motion state data; carrying out fragment splitting treatment; performing exception recognition processing on each track segment; performing fragment consistency reconstruction processing on the track fragments containing the abnormal fragment marks; executing travel mileage calculation processing to obtain an actual travel mileage of the user in the target travel behavior, and generating corresponding carbon emission reduction data; integral generation processing is executed according to the carbon emission reduction data, and the carbon emission reduction data are converted into user integral records; mapping the user integral to a preset incentive resource pool to generate incentive resource availability data; according to the invention, autonomy and accuracy of carbon emission reduction behavior management are improved.

The invention relates to the technical field of electric power-traffic network cooperative operation, and discloses a traffic-electric power network space-time coupling optimization method and system based on user preference empowerment, and the method comprises the steps: collecting and preprocessing the behavior preference data of electric vehicle user charging and traveling in a predetermined region; the method comprises the following steps: modeling and quantifying electric vehicle user preferences based on an information enhanced generative adversarial network deep learning architecture, and generating preference weight vectors with spatial-temporal characteristics; constructing a traffic network model by using the weighted directed graph, establishing a power distribution network model, and constructing a traffic-power coupling network model based on the space-time mapping relationship; and constructing a multi-target optimization model, and solving the multi-target optimization model by using an optimization algorithm based on the user preference weight of the electric vehicle to realize dynamic optimization of charging scheduling and path planning. According to the invention, efficient coupling optimization of traffic and a power network is realized, and the overall performance and user satisfaction of the system are improved.

A base station used in a traffic communication system includes a communicator configured to receive, via wireless communication, information transmitted from a vehicle traveling on a road, and a controller configured to identify, based on the information received by the communicator, a traveling behavior including a travel route of the vehicle on the road. The controller is configured to execute first processing for performing statistical learning or machine learning on a plurality of the traveling behaviors respectively identified for a plurality of the vehicles traveling on the road, and second processing for determining, based on a result of the first processing, whether a traffic obstacle is present on the road and a position of the traffic obstacle on the road.

The invention discloses a travel scene presetting and service method based on a safety seat and a child seat, and the method comprises the steps: recording the travel behavior data of a user, and generating a user activity list based on learning and / or customization; storing the user activity list, wherein the user activity list comprises conditions and feedback; monitoring parameters of a current scene according to conditions in the user activity list; matching corresponding feedback from the user activity list according to the monitored scene condition; according to the matched feedback, the safety seat is controlled to execute corresponding actions, and travel scenarized service is completed. The safety seat can provide accurate and intimate scene services by learning user behaviors or allowing user customization, and the system generates a personalized activity list according to the use habits of the user, so that the requirements of different families are met, and the comfort is enhanced; various condition signals are processed through an activity list and a learning algorithm, the hardware complexity and the number of sensors are reduced, and the cost is reduced.

The invention provides a method and device for carrying out cost optimization prediction through multi-dimensional business travel behavior analysis, and belongs to the technical field of enterprise business travel management.The method comprises the steps that business travel data are collected, cleaning, duplicate removal and entity alignment operation are carried out, and a business travel behavior data set is constructed; executing a preset query script on the business travel behavior data set, extracting characteristic indexes from preset dimensions, executing descriptive analysis, diagnostic analysis and clustering analysis, and generating a business travel behavior analysis report; and inputting the business travel behavior data set and the business travel behavior analysis report into a cost optimization prediction model to carry out business travel demand prediction, business travel price prediction and business travel travel optimization, generating an optimal travel travel scheme set, pushing the optimal travel travel scheme set to the employee user, collecting a feedback result of the employee user on the optimal travel travel scheme set, updating the business travel behavior data set, and obtaining a business travel result. And incremental training and iterative optimization are carried out on the cost optimization prediction model. Through multi-dimensional analysis and prediction, the travel cost is optimized, and the management efficiency is improved.

The invention discloses a smart home environment management system based on data analysis, which relates to the field of smart home and comprises a data acquisition module, a data analysis module and a control module. The data acquisition module is used for acquiring travel behavior data of each family member; acquiring home environment data of each family member at each moment when the family member is at home; the data analysis module is used for predicting the predicted home returning time of each family member according to the travel behavior data; analyzing a suitable environment parameter range of each family member according to the home environment data; and the control module is used for determining a target home environment parameter range of each time period according to the predicted home returning time of each family member and the suitable environment parameter range, controlling each household appliance to work and adjusting the indoor environment. According to the invention, home environment management can be carried out more intelligently, and the experience of each family member is comprehensively improved.

The present application relates to the field of data processing, and particularly relates to a user travel behavior label identification method and device, the method comprising: obtaining target user information and converting into a user feature vector; determining a travel behavior label corresponding to the user feature vector based on a plurality of preset travel behavior labels respectively corresponding to a plurality of determination conditions and a plurality of travel behavior label prediction models; if the user feature vector and any determination condition have a preset association relationship, then the travel behavior label corresponding to the determination condition is taken as the result; otherwise, the user feature vector is input into the plurality of travel behavior label prediction models, and the result is determined according to the model output. The present application sets different key feature subsets as determination conditions for different travel behaviors, and combines the two-stage architecture of rule priority and model bottoming, thereby ensuring the identification accuracy, reducing the calling frequency of complex models, and improving the overall identification efficiency.

According to the method of the present disclosure, first, a blind spot that is present in front of an ego-vehicle and cannot be seen from the ego-vehicle is recognized from information on a surrounding environment of the ego-vehicle. Next, a traveling behavior of a blind spot passing vehicle in the vicinity of the blind spot is observed from information acquired by an external sensor of the ego-vehicle. The blind spot passing vehicle is a vehicle that passes through the blind spot before the ego-vehicle. Then, a risk caused by the blind spot is estimated based on the traveling behavior of the blind spot passing vehicle. Finally, the ego-vehicle is operated in a mode corresponding to the risk.

The invention relates to the field of business travel cost control, in particular to an employee business travel behavior optimization and cost control method and system based on consumption data, and the method comprises the steps: collecting business travel consumption data of employees within a reference time T, and carrying out the subdivision of the business travel consumption data according to the consumption types; based on the consumption data, calculating a reputation score of the employee, specifically, for each type of consumption behavior of the employee, calculating a single consumption influence score of the employee; performing weighted summation on all consumption data of the employees within the reference duration T based on the single consumption influence score in combination with the time attenuation factor to obtain dynamically updated employee reputation scores; according to the employee reputation score, performing employee reputation grade division in combination with an employee consumption frequency parameter k value; and according to the reputation level and the consumption category, carrying out differential adjustment on the travel expense standard. And the reputation score is calculated according to the consumption behavior, so that the top-order reservation phenomenon is avoided, unnecessary waste is reduced, and the business travel cost is reduced.

A method for providing an interactive music system in a vehicle includes estimating a travel time of the vehicle based at least on a learned travel behavior of a user of the vehicle; obtaining data related to at least one drive condition; detecting that the travel time of the vehicle is within a duration threshold and that the at least one drive condition is satisfied; defining a playlist based at least on the travel time in response to the travel time being with the duration threshold and the at least one drive condition being satisfied; and causing a sound to be emitted based on an audio signal indicative of a song from the playlist in an interactive mode using one or more speakers in the vehicle.

This invention discloses a method for assessing the resilience of subway travel behavior during rainstorms, taking into account the impact of the built environment. It belongs to the field of urban transportation resilience assessment. The specific steps include: S1, collecting subway travel data, meteorological data, and built environment data around subway stations under rainstorm and ideal weather conditions; S2, quantifying the resilience value of subway travel behavior using the resilience triangle method; S3, constructing a spatially weighted geographic random forest model, using built environment data as features and resilience value as the objective, establishing a nonlinear mapping relationship between the two, and outputting the spatial distribution characteristics of resilience; S4, using the model to analyze the marginal contribution of each built environment factor to the resilience value, outputting the feature importance ranking, and identifying key built environment factors. This invention can quantify the disturbance and recovery capacity of subway travel behavior during rainstorms, reveal the mechanism by which the built environment affects travel behavior resilience, and provide a scientific basis for urban transportation resilience planning and rainstorm emergency management.

The invention discloses a method and a device for identifying a cross-regional travel behavior of a vehicle and electronic equipment. The method comprises the following steps: acquiring multi-source traffic travel data of the vehicle in a target region; the multi-source traffic trip data comprises checkpoint data and expressway toll collection system data; performing vehicle travel track extraction processing on the checkpoint data and the highway toll collection system data to obtain a checkpoint vehicle travel track and a highway vehicle travel track; fusing the checkpoint vehicle travel trajectory and the highway vehicle travel trajectory to obtain a vehicle total travel trajectory; inputting the total travel trajectory of the vehicle into a target integrated learning model, and reconstructing complete travel path data of the vehicle; and performing cross-region travel behavior identification on the complete travel path data of the vehicle according to a preset region division rule to obtain cross-region travel behavior data of the vehicle in the target region. The method can realize vehicle individual travel full-chain cognition, restores the complete travel trajectory of the vehicle, and can be widely applied to the technical field of data processing.

The invention relates to the technical field of prediction methods, and provides a user personal preference-based traffic behavior prediction method, which has the advantages that the coverage range of basic data is wide, the matched basic data is fused into a time axis, the diversity and comprehensiveness of the basic data are enriched, and the user experience is improved. The method improves rationality and reliability of traffic travel behavior prediction based on user personal preference, enables a result of traffic travel behavior prediction based on user personal preference to better fit the user personal preference, and further improves prediction reliability and personalized matching performance, and comprises the following steps: data collection and preprocessing; the method comprises the following steps: establishing an adaptation algorithm according to a data collection condition, generating transportation recommendation, generating transportation behavior prediction, generating comprehensive transportation behavior prediction, after a transportation behavior prediction scheme is generated, collecting personal actual transportation behaviors, and comparing the personal actual transportation behaviors with the comprehensive transportation behavior prediction scheme. And optimizing the fitting degree of the traffic behavior prediction based on the personal preference of the user.

The present application relates to the field of data processing, and particularly relates to a user travel behavior label identification method and device, the method comprising: obtaining target user information and converting into a user feature vector; determining a travel behavior label corresponding to the user feature vector based on a plurality of preset travel behavior labels respectively corresponding to a plurality of determination conditions and a plurality of travel behavior label prediction models; if the user feature vector and any determination condition have a preset association relationship, then the travel behavior label corresponding to the determination condition is taken as the result; otherwise, the user feature vector is input into the plurality of travel behavior label prediction models, and the result is determined according to the model output. The present application sets different key feature subsets as determination conditions for different travel behaviors, and combines the two-stage architecture of rule priority and model bottoming, thereby ensuring the identification accuracy, reducing the calling frequency of complex models, and improving the overall identification efficiency.

This application discloses a robust optimization method for photovoltaic charging stations with energy storage, taking into account the travel behavior biases of charging users. By collecting multi-dimensional data from the power and transportation networks in real time, a randomized user equilibrium hybrid traffic flow allocation model is used to characterize the path selection and charging behavior of electric vehicle users under different perception biases. The transportation and power networks are coupled through the spatiotemporal constraints of the transportation-power coupling network. A two-stage robust optimization model for photovoltaic charging stations is established, integrating the multi-dimensional uncertainty of the charging station's photovoltaic output and the perceived biases of travel user behavior in the transportation network. An efficient solution algorithm is designed for the specific model, ultimately generating the optimal operating strategy for the distribution network and photovoltaic charging station equipment under the worst-case scenario. This method achieves a dynamic optimal balance between the economic efficiency and robustness of the distribution network's operation, reduces operating costs while ensuring system safety margins, and significantly enhances the coordinated control capabilities of complex coupled systems.

The present invention provides an evaluation method and system for the relationship between niche information intervention and green travel behavior, which relates to the technical field of information intervention strategy evaluation. Based on the method of Meta-analysis, the present invention first obtains the original literature related to the relationship between niche information intervention and green travel behavior; then, based on a literature screener, the original literature is preliminarily screened and sorted, and through the detection of the titles, abstracts, and full texts of the preliminarily screened literature, the secondary screening of the original literature is realized; finally, the main effect analysis and heterogeneity test are carried out on the secondarily screened literature, and the evaluation result of the relationship between niche information intervention and green travel behavior is included in the final Meta-analysis result. The present invention accurately quantifies the relationship between the niche information intervention strategy and green travel behavior, realizes the systematic and comprehensive exploration of the differences in the intervention effects of various factors on green travel, and thus helps to guide travelers to travel in a healthier and more environmentally friendly way.

The invention relates to a travel behavior prediction method and device, a vehicle and a storage medium, and relates to the field of data analysis. The method comprises the following steps: acquiring travel initial data and a travel prediction probability model of a vehicle; the travel initial data comprises various types of theme data; the travel prediction probability model is used for representing the probability of at least one travel behavior corresponding to the subject data set; the subject data set is used for representing combinations of various types of subject data; and determining a travel behavior prediction result based on the travel initial data and the travel prediction probability model. Therefore, the accuracy of behavior prediction of a single trip of the user can be improved, the current demand of the user can be accurately identified, the potential demand of the user can be mined, and the personalized demand can be better met.