43 results about "Travel behavior" patented technology

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 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 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 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 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.

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 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 invention discloses a method, medium, and device for analyzing the influencing factors of residents' green travel behavior. Based on the theory of planned behavior and interpersonal relations theory, the method constructs a comprehensive theoretical model of the influencing factors of urban residents' green travel behavior from a static perspective. Secondly, based on a survey of 817 residents in the Yangtze River Delta region, structural equation modeling is used to empirically test the driving mechanisms of different categories of green travel behaviors among urban residents. Furthermore, a social interaction perspective is introduced, and a simulation model is constructed based on scale-free networks and opinion propagation dynamics to simulate and analyze the group response characteristics and dynamic trends of residents participating in various green travel behavior decisions under different scenario parameters. Finally, targeted policy recommendations are proposed to more effectively promote urban residents' practice of green travel.

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.

This invention discloses a spatiotemporal collaborative simulation method for a new energy transportation system, comprising: Step 1, constructing at least two urban simulation scenarios and generating a new energy transportation system network diagram; Step 2, constructing a collaborative evolution model consisting of travel subjects, energy supply subjects, and power grid regulation subjects; Step 3, generating a set of candidate travel behaviors based on individual travel plans at each time step; Step 4, selecting the travel behavior for the current time step based on a utility function and updating the transportation system state according to a preset update frequency and update order; Step 5, repeating steps 3 to 4 until the simulation process is completed; Step 6, outputting the simulation evaluation results after the simulation process is completed. This invention can realize the collaborative evolution simulation of traffic, energy, and carbon emission states within a unified spatiotemporal framework, effectively solving the problem of independent operation of each module and lack of collaborative feedback in existing technologies.

The present application relates to the field of intelligent transportation system and big data analysis technology, and more particularly to a shared bicycle rail connection analysis method considering travel stage built environment, comprising: obtaining shared bicycle order trajectory data in a target area, identifying travel behavior, performing OD mapping of the identified connection travel starting point-destination, and generating shared bicycle travel OD samples; performing shared bicycle trajectory data corresponding to the OD samples for riding path reconstruction and path restoration; for different travel stages of shared bicycle connection travel, corresponding built environment features are extracted respectively; based on the built environment features of different travel stages, prediction models for predicting shared bicycle use probability of rail transit inbound connection and outbound connection are constructed respectively; the prediction models are explained and analyzed, and key influencing factors and their action relationships are output. The present application can more comprehensively and truly reflect the formation mechanism of shared bicycle and rail transit integrated travel.

A charging management system including a transceiver and a processor is disclosed. The transceiver may receive historical inputs associated with a vehicle and user preferences associated with a vehicle user. The processor may determine a routine travel behavior of the vehicle based on the historical inputs, and an expected set of destinations from a plurality of destinations that the vehicle is expected to visit in a preset time duration based on the routine travel behavior. The expected set of destinations may include a first destination associated with a first destination tag, and a second destination associated with a second destination tag. The processor may further identify an optimal set of destinations, from the plurality of destinations, based on user preferences and routine travel behavior. The optimal set of destinations may include a third destination associated with the first destination tag, and a fourth destination associated with the second destination tag.

This invention relates to the field of intelligent express delivery management technology, and discloses a community intelligent express delivery management method and system. The method includes: acquiring historical travel behavior data, express delivery pickup behavior data, and address information of community residents; constructing a multi-express locker deployment spatial model; outputting a candidate set of delivery lockers; determining the target locker number; pushing inbound status data to residents; and using a long short-term memory network for temperature prediction and adjusting the temperature based on package temperature control requirements. Compared to existing express locker allocation methods that are mostly based on static addresses or fixed paths, especially in communities with multiple express lockers and diverse resident travel habits, this invention addresses the technical problem of difficulty in achieving efficient locker scheduling and environmental adaptation by using behavioral prediction modeling and a multi-target locker matching strategy. This achieves better express delivery decisions, thereby avoiding problems such as misdelivery, delayed delivery, or improper temperature control, and improving delivery accuracy and package security.

The invention discloses a travel behavior analysis management method and system based on federated element learning, and relates to the technical field of federated element learning, and the method comprises the steps: a travel service center issues a travel service in combination with the characteristics of travel behavior analysis, sets a to-be-trained federated element learning task and a training strategy, and transmits the tasks and the training strategy to a server; the server divides training users into trainers and testers, and issues models and training parameters to the trainers according to a training strategy; the trainer trains the local model and uploads the trained local model to the server; the server aggregates the local models of all trainers to obtain a global model, and issues the model and deployment parameters to the testers; a tester tests the global model and uploads test performance to the server; when the model performance reaches the standard, the travel service center opens the corresponding service; the traveler subscribes to the corresponding service in the travel service center, and the local deployment is quickly completed. According to the invention, the privacy of the user can be protected, and personalized and diversified travel services can be provided.

This invention provides a user incentive and points management method and system for carbon emission reduction behavior, relating to the field of carbon emission reduction behavior management technology. The method includes: acquiring the user's original trajectory information in the target travel behavior, and simultaneously collecting corresponding environmental feature data and equipment motion status data; performing segmentation processing; performing anomaly identification processing on each trajectory segment; performing segment consistency reconstruction processing on trajectory segments containing anomaly segment markers; performing travel mileage calculation processing to obtain the user's actual travel mileage in the target travel behavior, and generating corresponding carbon emission reduction data; performing points generation processing based on the carbon emission reduction data, converting the carbon emission reduction data into user points records; and mapping user points to a preset incentive resource pool to generate incentive resource availability data. This invention improves the autonomy and accuracy of carbon emission reduction behavior management.

The invention discloses a rainstorm weather subway travel behavior toughness evaluation method considering the influence of a built environment, and belongs to the field of urban traffic toughness evaluation, and the method specifically comprises the steps: S1, collecting subway travel data, meteorological data and built environment data around a subway station under rainstorm and ideal weather; s2, quantifying a subway travel behavior toughness value by adopting a toughness triangle method; s3, constructing a spatial weighted geographic random forest model, establishing a nonlinear mapping relationship between the environmental data and the toughness value by taking the established environmental data as a characteristic and the toughness value as a target, and outputting spatial distribution characteristics of the toughness; s4, analyzing the marginal contribution of each built-up environment factor to the toughness value by using the model, outputting a feature importance sequence, and identifying a key built-up environment factor; according to the method, the disturbance and recovery capability of the rainstorm weather to the subway travel behavior can be quantified, the action mechanism of the built environment to the travel behavior toughness is revealed, and a scientific basis is provided for urban traffic toughness planning and rainstorm emergency management.

The invention discloses an early warning service-oriented microblog user travel behavior identification method. According to the method, after multi-modal features of image-text data published by microblog users are extracted, a gating mechanism is introduced into cross attention, fine alignment is performed on image and text features, the utilization efficiency of gating text features and gating image features is improved, then intra-modal distinguishing and dynamic enhancement are performed on the gating text features and the gating image features by adopting self-adaptive category embedding, and the accuracy and the robustness of the method are improved. And finally, deep fusion is carried out on the two features through multi-layer Transform, progressive integration of global contexts is realized, feature alignment and fusion precision are controlled in stages in the whole process, and the recognition effect is greatly improved, so that when the tourism behavior recognition method is used on a microblog image-text data set, the recognition efficiency is greatly improved, and the recognition efficiency is improved. Whether the microblog user has a tourism behavior can be accurately identified, the weather early warning information is timely issued to the user, and the tourism experience of the user is improved.

The invention relates to a vehicle path selection behavior modeling method based on bounded rationality, and the method comprises the steps: obtaining vehicle automatic recognition data which comprises the data collection time, the vehicle ID, the intersection ID and the entrance direction of a vehicle on each collection node; based on the vehicle automatic identification data, considering vehicle behavior conditions, preprocessing the vehicle automatic identification data and extracting an individual vehicle path set; and according to the individual vehicle path set, extracting path attributes and inertia variables, and establishing a vehicle path selection behavior model considering inertia according to bounded rationality and heterogeneity of travel behaviors of travelers. Compared with the prior art, the method has the advantages that the influence of inertia is considered, and the vehicle path selection behavior model considering inertia is established, so that the path selection behavior of the vehicle is accurately described.

In one aspect of a vehicle control device and a vehicle control method according to the present invention: a storage unit stores, from the present time to a predetermined time in the past, lane information including the position and orientation of a host vehicle relative to the traveling lane of the host vehicle, acquired by an external environment recognition sensor, travel behavior information relating to travel behavior of the host vehicle, and road information including the curvature of the traveling lane; a degree of deviation between odometry information obtained using the stored information, and the lane information, is obtained; an initial value of the odometry is determined on the basis of the lane information and the degree of deviation; and lane keeping control is executed on the basis of either the odometry result or the lane information. As a result, even if the external environment recognition sensor outputs inaccurate demarcation line information, highly accurate demarcation line information can be used for lane keeping control.

This invention discloses a method for analyzing the travel demand of public transport commuters based on spatiotemporal features, comprising: acquiring historical travel data of public transport passengers and constructing a personal travel behavior dataset; filtering commuter passenger groups based on travel frequency and analyzing spatiotemporal patterns of travel based on passenger travel time, entropy values ​​of travel stops, and travel chain similarity; extracting travel chain dimension features, time dimension features, and spatial dimension features related to the prediction target based on the spatiotemporal patterns of travel; constructing a feature matrix based on the extracted features; training the features in the feature matrix using a distributed gradient enhancement model; and predicting and outputting the travel demand of commuters based on the trained travel demand prediction model. This invention achieves efficient and accurate prediction of commuter passenger travel demand by integrating multidimensional travel features of commuters with machine learning algorithms.

The application discloses a kind of reachability evaluation method, system, medium, equipment based on traffic travel willingness, and the present method is based on real travel willingness data, identifies the crowd of actual use facility, makes evaluation result more close to reality.Constructed the associated dataset of travel willingness and facility use, the mapping relationship between travel demand and facility supply is established, which provides data basis for subsequent multidimensional analysis.The calculation of personalized travel time fully considers the actual travel mode of each travel record, rather than uniformly adopting a certain preset mode, so that the reachability calculation is more in line with the real travel behavior of individuals.Meanwhile, the actual service travel total and theoretical service range are calculated, and evaluation indexes are generated based on the comparison between the two, revealing the difference between the actual service efficiency and theoretical reachability of the facility, and providing quantitative basis for identifying facility use problems.

A charging management system including a transceiver and a processor is disclosed. The transceiver may receive historical inputs associated with a vehicle. The processor may obtain the historical inputs from the transceiver, and determine a routine travel behavior of the vehicle based on the historical inputs. The processor may further determine a parking and charging location associated with the vehicle based on the routine travel behavior, and estimate a future departure time from the parking and charging location and a future arrival time at the primary parking and charging location based on the routine travel behavior. The processor may further estimate an amount of energy required by the vehicle to travel between the future departure time and the future arrival time based on the routine travel behavior, and perform a predetermined action based on the estimated amount of energy.

This invention discloses a method for recognizing travel behavior of Weibo users for early warning services. After extracting multimodal features from the image and text data posted by Weibo users, this invention introduces a gating mechanism in cross-attention to finely align image and text features, improving the utilization efficiency of gated text and image features. Subsequently, adaptive category embedding is used to perform intramodal differentiation and dynamic enhancement of both, improving the expressive power of single-modal information. Finally, a multi-layer Transformer is used to deeply fuse the two features, achieving progressive integration of the global context. The entire process controls the feature alignment and fusion accuracy in stages, greatly improving the recognition effect. When used on Weibo image and text datasets, this travel behavior recognition method can accurately identify whether Weibo users are engaging in travel activities, promptly release weather warning information to users, and improve the user's travel experience.

The present disclosure provides a travel scheme recommendation method and device, a model training method and device, equipment and a storage medium, relates to the technical field of computers, and particularly relates to the technical field of intelligent transportation. The specific implementation scheme is as follows: obtaining a travel behavior feature determined according to a historical travel scheme of a target user; obtaining a scheme feature of a candidate travel scheme from a starting point to a destination of the target user; determining a recommendation degree of each candidate travel scheme according to the obtained travel behavior feature and the scheme feature; and recommending each candidate travel scheme to the target user according to the recommendation degree of each candidate travel scheme. The travel scheme recommendation method and device provided by the present disclosure can improve the accuracy of the recommendation.

The application belongs to the technical field of social and human science resource allocation, and discloses a community facility configuration optimization method based on low-carbon travel behavior of residents and related equipment; wherein the community facility configuration optimization method comprises obtaining travel data of residents in a target area, the travel data comprising travel mode, travel purpose, travel frequency, travel time and travel distance, inputting the travel data into a configuration optimization model, and outputting a community facility configuration optimization scheme; the application can realize dynamic quantitative evaluation and optimization of community facility configuration, and support objective quantitative calculation of demands of different types of facilities, so that the configuration scheme is more in line with the behavior rules of residents.

The invention provides a business travel reimbursement optimization method and system based on cloud computing and computing equipment. The method comprises the following steps: constructing a business travel behavior portrait for each employee; obtaining a reimbursement approval application initiated by an employee, wherein the reimbursement approval application comprises event time, an event type and a reimbursement amount; according to the reimbursement approval application initiated by the employee and the business travel behavior portrait corresponding to the employee, obtaining a processing priority category of the approval authority of the employee through a pre-trained reinforcement learning model; and performing classification processing on the reimbursement approval application according to the processing priority category. According to the technical scheme of the invention, the examination and approval efficiency can be improved, the human subjective influence is reduced according to the comprehensive and accurate examination and approval decision basis, and the compliance and transparency of the examination and approval process are enhanced.

The invention discloses a rail station reachability and service range evaluation method based on multi-mode connection. The method comprises the following steps: determining a rail station travel mode selection set; obtaining track station trip behavior survey data; preprocessing travel behavior survey data and taking the preprocessed travel behavior survey data as input of the trained improved XGBoost classification model to obtain a predicted selection probability of each travel mode of the rail station; the SHAP value of each feature is calculated, and the contribution degree of each feature to the travel mode selection probability of different rail stations is quantified; comparing the contribution degrees of the features to obtain the features with higher contribution degrees of the travel distances; and fitting a curvilinear relationship between the travel distance and the travel mode selection probability of different rail stations, and determining the reachable distance of a certain travel mode according to the obtained curvilinear relationship. According to the method, the reachable distance of the rail station is evaluated more practically, the service range is further evaluated on the basis of an actual road network, and the method has more practical significance.

The invention provides a passenger demand driven train operation adjustment method and system, and belongs to the technical field of train operation scheduling. A train operation adjustment model is constructed, and a micro train timetable is generated; generating a passenger path selection set by adopting a shortest path algorithm: constructing a passenger discrete selection model, obtaining passenger travel behavior change characteristics caused by train timetable changes, and describing distribution of passengers on train services; integrating the train operation adjustment model and the passenger discrete selection model into a mixed integer nonlinear model; and a transformation and linearization method is introduced to linearize nonlinear terms of the mixed integer nonlinear model, a mixed integer linear programming model is formed and solved, and a final adjusted train operation scheme is obtained. The method is used for adjusting the train operation plan under the interference condition, when the original train operation plan cannot be executed, the train operation plan influenced by interval blocking can be rapidly and automatically adjusted, and meanwhile the passenger satisfaction degree and the enterprise operation benefit can be maximized.