73 results about "Travel behavior" patented technology

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 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 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.

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.

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.

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.

Provided herein is a computing system that includes a processor in communication with a memory. The processor is configured to (1) receive a plurality of data records associated with a plurality of users that include historical user data; (2) generate a model based upon the plurality of data records, wherein the model (i) predicts travel behavior of a user, and / or (ii) outputs an insurance policy and associated premium for the user based upon the predicted travel behavior; (3) retrieve current user data associated with the candidate user; (4) apply the model to (i) determine a user trial travel behavior, and / or (ii) output a trial insurance policy and associated premium for the candidate user; and / or (5) transmit a notification to the user computing device that includes a prompt for the user to register for the insurance policy.

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.

The present invention proposes an individual travel pattern analysis method based on semi-supervised hypergraph clustering, which is used to solve the problems that the existing technologies do not consider the complex correlation relationships between passengers' trips and the difficulty in labeling traffic travel data in practical applications, and thus cannot accurately mine individuals with similar travel behaviors. This method first extracts passengers' travel behavior characteristics from multiple dimensions according to public transportation travel chain data, and constructs a hypergraph based on the travel behavior characteristics to represent the complex high-order correlation relationships between passengers. Secondly, a convolutional fusion module is proposed to adaptively fuse the travel information of passengers themselves and the correlation structure information between passengers. Then, the fused feature representation is clustered to identify travel patterns. Finally, in order to improve the performance of clustering, semi-supervised learning is adopted to achieve a better clustering effect through a small amount of supervision information. This method can mine passengers with similar travel patterns from the public transportation data in Beijing.

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 work management system for a flying work machine (FW) and a ground work machine (GW) in a work site comprises: a work flight information management unit (32) that manages work flight information including work flight characteristics of a flying work machine (FW) that performs ground work on a work site; a work travel information management unit (33) that manages work travel information including work travel characteristics of a ground work machine (GW) in the work site; and a work management unit that manages work flight behavior of the flying work machine (FW) and work travel behavior of the ground work machine (GW) on the basis of the work flight information and the work travel information.

The invention relates to a travel control device, a travel control method, and a program product. The travel control device is configured to calculate a change in a travel behavior of a subsequent vehicle in a time period before and after a point in time at which a right turn direction indicator light of a host vehicle is turned on, based on a detection result of a surrounding situation sensor that detects a surrounding situation of the host vehicle passing on the right side, the surrounding condition of the vehicle comprises a subsequent vehicle located within a certain distance behind the vehicle; and predicting the traveling behavior of the following vehicle before or during the right turn of the host vehicle on the basis of a change in the traveling behavior of the following vehicle in a time period before and after the point in time at which the right turn direction indicator light is set to the on state.

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

The 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..