56 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 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 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.

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 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 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.

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.

The application discloses a kind of public transport passenger travel behavior space-time semantic similarity measurement method, the implementation steps of this method include: (1) based on the card data of passenger extraction departure place, departure time, travel mode, activity type and destination 5 kinds of travel attributes, further construct public transport passenger individual travel sequence;(2) the travel attribute in passenger travel sequence is expressed as discrete variable;(3) the travel behavior attribute and travel sequence are analogized as word and sentence respectively, and the travel attribute is converted into word vector by applying Word2vec model, so as to realize the space-time semantic embedding representation of travel sequence;(4) the space-time semantic similarity of passenger multi-day travel behavior is measured by using improved word shift distance.The application solves the defect that traditional travel behavior similarity measurement model cannot consider its space-time semantic correlation, can provide support for passenger market segmentation, individual travel demand modeling and public transport policy making etc..

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 present invention relates to a method, a computing device and a storage medium for calculating the accessibility of an integrated transportation multimodal network. The method comprises: constructing a network structure of an integrated transportation network, wherein the transportation network comprises a road network, a railway network and an aviation network; recording the latitude and longitude coordinates of the starting point and the end point; improving the heuristic function of the A* algorithm; a hierarchical search algorithm comprehensively considering the travel paths in the transportation network, including the intra-city travel paths of the cities where the starting point and the end point are located, and the inter-city travel paths connecting the cities where the starting point and the end point are located; calculating the total travel time; calculating the accessibility and diagnosing accessibility depressions. The present invention improves the path search algorithm with the goal of improving efficiency, and constructs a path search algorithm suitable for a multimodal transportation network from the two aspects of optimizing the search path and improving the search strategy. By introducing a hierarchical search strategy, unnecessary repeated searches are effectively reduced, and the search efficiency of the algorithm is improved. At the same time, the algorithm integrates the influence of the number of transfers and the train connection relationship on the travel path selection, and more accurately simulates the actual travel behavior of travelers.

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 present invention provides a railway passenger identification method, device, electronic device, and computer-readable storage medium, relating to the field of big data technology. This method utilizes the signaling set within a target area within a specified timeframe and the signaling interaction range of a railway station to initially identify potential users who have stopped at the railway station and generated signaling interactions. Based on the potential users' stay information and the travel behavior characteristics of railway passengers, non-railway passengers who have only stopped at a single railway station, such as station attendants or railway staff, are then excluded. Finally, various misjudgment scenarios are comprehensively considered to exclude the itineraries of non-railway travelers, thereby accurately identifying each railway passenger's journey within the specified timeframe.

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.