102 results about "Trip chain" patented technology

The invention discloses a traveler home-work location identification and trip chain depicting method based on mobile phone signaling data, and belongs to the field of analysis of transportation planning data. The method comprises the steps of: triggering an information database by communication network records provided by a mobile network operator; after cleaning, integrating and calculating by using a position conversion algorithm, converting the data of the operator into a reference dataset; aiming at each user, converting the dataset into a mobile time-space trajectory, identifying stopover points, high frequency points and long time points in the mobile time-space trajectory, and on the basis, identifying home-work locations, activity target locations and insignificant stopover points; and finally depicting a closed trip chain of the user by being combined with the mobile time-space trajectory. The traveler home-work location identification and trip chain depicting method based on mobile phone signaling data has the advantages of being conductive to reducing trip survey costs of residents, obtaining more precision trip characteristics of crowds with different identities, and thereby allowing the traffic demand forecasting to be more accurate; and 2, being conductive to improving scientificity of planning layout construction, such as transport corridors, public transit networks and traffic facilities, in urban traffic planning.

The invention relates to a resident trip information obtaining method based on an intelligent mobile phone. The method comprises the following steps: (1), a mobile phone client detecting whether a GPS and a Network are opened; (2), the mobile phone client uploading user registration information and home / work place coordinate information to a web server; (3), the web server receiving the registration information and the coordinate information; (4), the mobile phone client acquiring tracing point latitude and longitude information and time information of a user in accordance with specific time intervals and uploading the information to a local place; (5), the mobile phone client automatically uploading the tracing point information stored in the local place at user specific time or uploading the information when the user clicks a hand-operated transmission button; (6), the web server receiving tracing point data and storing the data in a traveler locus table in a database; and (7), the web server determining an algorithm according to information compiled in advance, and through combination with POI information, analyzing to obtain a trip chain of a resident. Compared to the prior art, the method provided by the invention has the advantages of wide application scope, low cost, simple process, high information accuracy and the like.

The invention relates to a mobile phone information processing method, in particular to a mobile phone signaling trip chain-based OD matrix acquisition method. The method includes the following steps that: mobile phone signaling trip chain data of a specified date are acquired according to sampling requirements; an origin point (O point) and a destination point (D point) of a trip are determined according to the mobile phone signaling trip chain data; a sample is expanded according to a sampling coefficient; coarse granularity information OD matrix data are obtained; mode divisional OD matrix data are obtained; a multiple-trip OD matrix data are obtained; and a trip occurrence attraction histogram and a corresponding OD expectation graph are outputted. According to the mobile phone signaling trip chain-based OD matrix acquisition method of the invention, the trip chain data which are depicted by mobile phone signaling information and are simple in acquisition mode, low in acquisition cost, large in sample information and flexible in sampling time are utilized to construct a coarse granularity information OD matrix, a trip mode divisional OD matrix and a multi-destination and multi-trip OD matrix. The mobile phone signaling trip chain-based OD matrix acquisition method has the advantages of high real-time performance and high precision.

The invention provides an estimation method of a passenger trip spatial-temporal trajectory of an urban rail transit network. The method comprises the following steps: according to trip state characteristics of passengers in the urban rail transit network, constructing a rail transit super network based on a trip chain, including a network layer, a path layer and a station layer; using WIFI signal data within the scope of the urban rail transit network, and adopting a positioning algorithm based on a received signal strength indication to convert the WIFI signal data into position coordinates of the passengers in the station which are changed over time; based on the correspondence between the position coordinates of the passengers and a station plan, realizing the reduction of walking trajectories in the station; and determining passing stations by identifying the changes in line codes and station codes in passenger position information, and after checking an effective path set in the path layer, realizing the inference of the passenger network travel spatial-temporal trajectory. The estimation method of the passenger trip spatial-temporal trajectory of the urban rail transit network provided by the invention can meet the requirement of positioning accuracy and effectively reduce the walking trajectories of the passengers, and provide a theoretical and technical support for passenger flow analysis, ticket clearing and trip guidance and so on of the urban rail transit network.

The invention discloses a method for extracting public transport passenger trip space-time tracks. Data resources of full-sample passenger bus-taking records and bus running records are fused; based on the continuous trip chain method, starting from similarity analysis of everyday bus station tracks of each passenger, similar trip days are extracted; a statistical sample space is constructed to mine passenger activity rules; by means of the Bayesian estimation method, getting-off stations of one-ticket bus passenger card-swiping bus-taking records are extracted more reasonably and accurately, and trip tracks of the passengers are reduced from missing information. According to the method, implicit passenger individual activity rules in full-sample card-swiping data can be fully utilized for reasonably deducing the card-swiping getting-off stations of the passengers, and follow-up public transit network section flow statistics and data mining are promoted.

The invention discloses a bus passenger travel station prediction method, a storage medium and a server. The bus passenger travel station prediction method comprises the steps that bus GPS track data,passenger bus IC card swiping record data and bus station and route vector data of a certain urban area are acquired; based on the bus GPS track data, the passenger bus IC card swiping record data and the bus station and route vector data, historical bus card swiping get-on stations of passengers are matched, and passenger historical get-on station travel links are constructed; spatial clusteringis carried out on the bus stations, and a passenger historical get-on clustering travel link is constructed; bus get-on clustering is predicated based on a variable-order Markov model and the passenger historical get-on clustering travel link, and a specific get-on station is predicated based on a probability model and based on the analysis of the bus and subway mixed travel chain, bus get-off clusters and get-off stations are predicated. According to the method, the bus get-on and get-off stations are well predicted, and the prediction accuracy of the travel stations is obviously improved.

The invention provides a method for judging the getting-off stations of public transportation IC card passengers based on historical trip patterns. Intelligent public transportation system data are acquired; public transportation IC card data are associated with intelligent dispatching system data, so that the getting-on station information of the public transportation passengers is obtained; commuting passengers in the public transportation IC card passengers, as well as the residence places and working sites of the commuting passengers are identified through a commuting OD estimation method; getting-off stations of the commuting trips of the current day are judged through using obtained commuting trip patterns; the getting-off station information of a part of the trips of the public transportation IC card passengers in the previous days is obtained through using a trip chain method; and the trips of the current day are matched with the associated trips of the previous days, so that the getting-off stations of the trips of the current day can be judged. The method provided by the invention is implemented through algorithms; and testing and accuracy analysis are performed on the algorithms through using a large number of practical data. The proportion of judged getting-off stations of the passengers in workdays is increased by 12.13% on the basis of an existing method, and an accuracy rate reaches 87.89%.

The invention provides a vehicle travel trajectory identification method based on bayonet data, which adopts a spectral clustering method to realize automatic separation of travel chain from the time-space characteristic of bayonet data, thereby identifying the starting point, the end point and the passing point of a single travel trajectory. The method particualy includes generating a complete travel chain of the vehicle on the day; establishing a similarity matrix S; calculating an adjacency matrix W and a degree matrix D; constructing a Laplace matrix L and further determining a characteristic matrix F; using the DBSCASN clustering algorithm to perform cluster analysis, and obtaining the clustering results; determining the cluster partition result of trajectory points to generate sub-trajectories. This method takes into account both the time and space attributes of vehicles, and is more comprehensive than the method based on travel time to partition the trajectory. No preset threshold is needed, and the vehicle trajectory recognition is completely driven by the measured data, which improves the accuracy of trajectory recognition. The analysis results can be used in vehicle travel behavior recognition, travel characteristics analysis, traffic flow OD estimation and so on.

A bus IC card passenger get-off station derivation method based on multi-source data has accuracy, effectiveness and operability, can be widely applied to bus OD derivation, and provides theoretical and method bases for analyzing line passenger flow distribution, determining departure shifts and the like. The method comprises the following steps: (1) making basic data preparation: preprocessing original data to obtain effective bus GPS data and IC card swiping data, calculating a bus arrival time table, and counting people getting on a bus at each station; (2) deriving get-off stations based on a commuting travel chain: identifying the commuting travel chain, deriving commuting crowd get-off stations, and counting people getting off at each station; (3) deriving get-off stations based on acommon trip chain: identifying the common trip chain, deriving get-off stations according to the characteristics of the trip chain, and counting people getting off at each station; and (4) deriving get-off stations based on the station attraction weight: defining a station attraction weight calculation method, and calculating the get-off probability of each station from the perspective of gathercounting, thereby obtaining the number of people getting off at each station.

The invention discloses a public electric bicycle leasing point address-selecting method based on a trip chain. The method comprises steps of: 1) acquiring the planning basic information of a public electric bicycle system to be established; 2) establishing the generalized trip cost function; 3) establishing an upper model and a lower model by considering the planner and the users of the public electric bicycle system respectively so as to construct a double-layer planning model to describe the public electric bicycle leasing point address-selecting problem; and 4) solving the double-layer planning model formed by the upper planning model and the lower planning model to obtain a public electric bicycle leasing point address-selecting scheme. The public electric bicycle leasing point address-selecting method may enhance the accuracy and reliability of the public electric bicycle leasing points, gives an impetus to the construction of associated city public electric bicycle system, and has real popularization value.

The invention provides a bus get-on / off site identification and examination method based on multi-source data mining. The method comprises steps that based on IC card swiping and operating vehicle data of conventional buses, IC card swiping passenger get-on site identification based on clustering and correlation analysis is performed; according to the IC card swiping and operating vehicle data ofconventional buses, BRT and subways, IC card swiping passenger travel chain get-off site identification based on the conventional buses, the BRT and the subways are performed; data of unidentified get-off sites is subjected to IC card swiping passenger get-off site identification based on historical ride records; the data of the re-unidentified get-off sites is subjected to IC card swiping passenger get-off site identification based on Bayesian posterior maximum likelihood estimation; the data of the matched get-on site is subjected to IC card swiping passenger get-on site identification basedon the paired sample t test; the data of the matched get-off site is subjected to IC card swiping passenger get-off site identification based on transfer behavior identification. The method has advantages of wide application scope and high site identification accuracy.

The invention provides a feature recognition method for different travel activities based on mobile phone positioning data. The method comprises the steps of carrying out the job-house and life travelpurpose time-space kernel clustering analysis and time-space correlation feature analysis of OD residence points of the mobile phone positioning data, forming different travel activity purpose travelchains with time-space correlation features, and carrying out the pairing of the travel chains; performing verification and algorithm optimization on the obtained time-space stationary point set, performing analysis on crowds with different travel activity target features, and finally forming an OD matrix travel table based on a traffic cell, thereby providing big data support meeting the features of a space-time travel activity chain for traffic planning and traffic demand management. According to the invention, the respective advantages of the time-space activity correlation analysis and the OD matrix generation table are combined to finally form the stationary point sequence with time-space activity target features, so that the rationality and accuracy of OD matrixes of different travel targets of traffic are further improved, and the cost is effectively reduced.

The invention provides an airport passenger multi-traffic-mode transfer travel chain construction device. The system comprises a travel preference model construction device which is based on passengerportraits and used for predicting airport passenger travel behaviors; a travel full-coverage segmentation time consumption estimation device which is used for estimating travel time inside and outside a port arrival passenger land side traffic transfer room; a passenger flow gathering and dispersing rule and multi-mode traffic capacity coupling module which is used for analyzing the passenger gathering and dispersing rule in the land-side traffic hub and predicting waiting time of different traffic modes; a passenger travel chain multi-target construction method device which takes passenger travel preferences, travel whole-course time, passenger flow gathering and scattering distribution, traffic mode transport capacity and the like are taken as influence factors, and generates a passenger travel chain through comprehensive optimization. The method can predict passenger group travel preferences with different travel purposes, the number of people traveling together, age structures andthe like, accurately estimate the walking time of passengers in the terminal, the travel time outside the terminal and the waiting time, and recommend an efficient and comfortable travel scheme for airport arrival passengers.

The invention discloses an individual behavior analysis method based on mobile phone signaling data and POI (Point of Interest) data, which is used for solving the problem of individual travel behavior identification of special social crowds. The method comprises two parts of personal travel feature extraction and travel behavior prediction: (1) personal travel feature extraction: completing personal travel chain extraction by using mobile phone signaling data; based on the personal travel chain data, designing travel features and calculating the personal travel features in combination with POI interest point data a;nd (2) personal travel behavior prediction: based on personal travel characteristics, predicting personal travel behaviors by adopting a decision tree method.

The invention discloses a resident trip chain generation method and a ride-sharing query method based on multi-source data fusion. The resident trip chain generation method comprises the following steps: performing related processing on mobile phone signaling data to obtain trip information of a user; cutting a user travel based on the travel information; carrying out travel mode matching on each travel of the user; performing space-time matching on the bus travel route, the bus route track and the bus number information to generate a first matching result; performing space-time matching on the track traffic travel route, the track traffic line track and the track traffic train number information to generate a second matching result; performing space-time matching on the car travel journey and cars in a preset database to generate a third matching result; and according to a matching result, obtaining a riding train number of each travel of the user, and generating a travel chain of the user by combining the travel origin and destination of each travel of the user. According to the invention, during construction of the trip chain, joint modeling of people and public transport vehicles is carried out, and a more accurate trip analysis result is obtained.

The invention discloses multi-mode trip chain optimization method and system facing to integration trip. Selectable trip routes, containing multiple trip modes and combinations thereof, are divided until non-dividable units; and with consideration of real-time data, short-time prediction, user preference and the like, the utilization coefficient of each non-dividable unit is calculated; and then on the basis of different targets including convenience, reliability, economy, comfort and the like, generalized costs of the available trip routes are calculated so as to form a whole trip chain alternative route sequence, which satisfies user demands, and a recommended trip route is pushed to the user. The method and the system can integrate information of multiple trip modes and can recommend trip schemes, at trip chain level, including parking, bicycles and the like comprehensive trip modes; and meanwhile, trip modes and user intention are combined, so that a specific trip mode is supplied for the user; the trip route is individually pushed and recommendation of differentiation trip modes is supplied, so that individual trip server level is greatly improved.

The invention provides a comprehensive traffic service evaluation method and device based on individual travel chains. The comprehensive traffic service evaluation method based on individual travel chains mainly comprises the steps: acquiring travel data of the individual travel chains of a plurality of tested objects; grading each individual trip chain according to the trip data through a presetevaluation index; and calculating a traffic service index of the preset area according to the levels and the number of the individual travel chains in the preset area. By implementing the comprehensive traffic service evaluation method, the traffic service is evaluated based on the individual travel chain; starting from the perspective of travelers, the travel experience of the traveler is fully considered, and the travel chain of the traveler is composed of a plurality of travel modes; and by taking the individual travel chain as a research object, the cooperative operation and service capability of urban comprehensive passenger transport can be reflected, and the evaluation result of evaluating the traffic service quality in such a way can more truly reflect the travel condition of travelers and better meet the actual requirements of travelers.

The invention discloses an urban road network traffic flow OD estimation method based on automatic vehicle license plate identification data. The method comprises the following steps: researching and analyzing vehicle license plate monitoring data in a selected road network area, performing precision processing on the vehicle license plate data, performing trip chain division on the vehicle license plate data according to a space-time interval, and reconstructing all the travel trajectories with trajectory missing, and obtaining the OD flow of the reconstructed travel trajectories in the road network region. Compared with a traditional method based on manual investigation, the method has the advantages that complete vehicle travel trajectory information is extracted in an automatic mode, the road network dynamic OD flow is estimated on the basis, the manual workload is remarkably reduced, and the authenticity and reliability of an OD estimation result are effectively improved. Compared with a classical method for reversely deducing OD flow based on road section flow, the method can greatly reduce the complexity of modeling and solving, and has better implementability.

The invention relates to the technical field of bus population geographical distribution, in particular to a method for accurately analyzing bus population geographical distribution based on multi-source data. The method comprises the following steps: S1, acquiring mobile phone signaling data, and acquiring a personnel trip chain result including job-house analysis; S2, collecting IC card swipingdata, and acquiring a public transport OD result; S3, performing clustering coverage analysis on the two results in S1 and S2 on a map, and performing coincidence analysis on the two results and bus routes in an existing urban area range, so as to obtain population which does not adopt public transport and distribution positions of the population. According to the method, the uncovered area of a bus is obtained by combining mobile phone signaling and IC card swiping data, then the uncovered area gathered by users is obtained through clustering coverage analysis, and alternative routes and timeperiods needing to be started are obtained according to the main flow direction of the users and the number of the users; and coincidence analysis is carried out with an existing bus route, and passenger flow is combined, so the design of a tail-end bus connection route and the delivery capacity is facilitated.

The invention discloses a space load prediction method considering electric vehicle charging demands, and the method comprises the following steps: 1, dividing electric vehicles into three types according to operation characteristics and charging modes; 2, fitting the probability distribution of the travel information according to the survey data to obtain a probability density function of the travel information; 3, constructing a travel chain model according to the type of the travel destination of the electric vehicle, and counting the probability proportion of the travel chain model; 4, according to the probability density function of the travel information and the probability proportion of the travel chain model, based on a Monte Carlo algorithm, simulating and calculating charging load variance coefficients of the three types of electric vehicles, carrying out the superposition, and completing the space load prediction. According to the method, the influence of large-scale electric vehicle access to the power grid on the uncertainty of the space load prediction result is solved, the space-time distribution of the electric vehicle charging demand is simulated by comprehensivelyconsidering multiple factors, and reference is provided for planning of a future urban power distribution network.

The invention discloses a trip chain extraction method based on motor vehicle electronic identification data, and the method comprises the following steps of S1: extracting a manned private car trackchain; S2: cleaning redundant data in the track chain; S3: generating a reader successor set Successor Set; S4: performing track completion based on a Markov decision process; and according to the method, redundant data are cleaned, missing data are supplemented, the track chain is finally divided into a plurality of trip chains, and trip characteristics of vehicles can be completely acquired based on analysis of the trip chains, so that the urban traffic state can be better mastered, and better service is provided for construction of smart cities.

The invention discloses a trip chain-oriented demand response bus service mode and optimization method. The method comprises the following steps: formulating a trip chain-oriented demand response bus'one ticket 'service mode; generating a constraint set and an objective function of a trip chain-oriented demand response bus 'one-ticket' service operation cost according to the pick-up and drop-off station network topology, the passenger trip chain information and the bus information; according to the constraint set and the target function, establishing a mixed integer linear programming model for travel chain-oriented demand response bus one-ticket service operation vehicle path optimization; and solving the mixed integer linear programming model, and obtaining an optimized service vehicle running path scheme through an effective inequality and a genetic algorithm based on an optimal time difference insertion method. According to the invention, the high-level customized public transportservice is provided, and the complexity that passengers need to select a travel chain combination scheme from a plurality of conventional fixed public transport modes is also reduced.

The invention discloses a traffic demand prediction method and device, and electronic equipment. The method comprises the steps of obtaining a trip chain of each target trip crowd in a target area; generating a model based on the trip chain and the trip data, and generating predicted trip data of each target trip crowd; determining a traffic distribution result of the target area based on the predicted travel data of each target travel crowd; if the traffic distribution result does not meet a preset convergence condition, calculating traffic comprehensive impedance according to the traffic distribution result, obtaining updated predicted travel data of each target travel crowd by combining the traffic comprehensive impedance, and obtaining an updated traffic distribution result based on the updated predicted travel data of each target travel crowd; and if the traffic distribution result or the updated traffic distribution result satisfies a convergence condition, outputting a traffic demand prediction result in the target area, the traffic demand prediction result including predicted travel data and the traffic distribution result. The accuracy of the travel demand prediction result can be improved.

The invention provides a low-carbon travel carbon emission reduction verification method and system based on travel chain big data. The method comprises the steps of 1) obtaining sample data of traveldata; 2) when the sample data is walking data, turning to the step 3); when the sample data is bicycle data, turning to the step 4); when the sample data is electric bicycle data, turning to the step5); 3) adopting a walking travel distance verification method to perform verification and deduplication on the sample data to obtain a final effective travel distance; calculating the carbon emissionof walking according to the final effective travel distance; 4) adopting a bicycle travel distance verification method to perform verification and deduplication on the sample data to obtain a final effective travel distance; calculating the carbon emission of the bicycle according to the final effective travel distance; 5) verifying and de-weighting the sample data by adopting a verification method of the travel distance of the electric bicycle to obtain a final effective travel distance; and calculating the carbon emission of the electric bicycle.

The invention discloses a pseudo code signaling data preprocessing and trip chain identification method. The pseudo code signaling data preprocessing and trip chain identification method includes thefollowing steps: (1) merging redundant signaling records and culling abnormal records; (2) according to parameter characteristics of ping-pong effect records, identifying the ping-pong records and deleting the ping-pong records; (3) calculating the user residence centroid, setting the time and space thresholds when the trip occurs, and identifying the start record point; and (4) setting the user residence time and space threshold, and identifying the record point for ending of the trip, thus completing the trip chain identification. The pseudo code signaling data preprocessing and trip chain identification method effectively cleans and filters the data, improves the data precision, extracts the complicated data as valuable trip OD information, and improves the availability of data in various fields such as transportation. The pseudo code signaling data preprocessing and trip chain identification method is wide in the application range, and is simple and practicable.

The invention provides a bus passenger getting-off station speculating method based on AFC data. A bus taking behavior of a passenger is analyzed according to two adjacent bus card swiping records ina day, and thus on the premise that getting-off station information lacks, the getting-off station corresponding to a getting-on card swiping record each time is speculated; according to the bus passenger getting-off station speculating method based on AFC data, by temporal thresholds which comprise average bus taking time, the longest waiting time and the longest transfer duration, transferring behaviors of bus passengers are recognized, and thus a whole bus trip chain is analyzed. By a space analyzing method, based on walking time, the most possible getting-off station is recognized in a radiating area of a station, and reliability of a getting-off station speculating result is ensured.

The invention relates to the technical field of intelligent traffic control, in particular to a vehicle real-time travel track acquisition method based on vehicle passing video data. The problems thatthe vehicle cannot obtain the travel track, the dynamic update of the vehicle travel chain cannot be realized and the consumption of system resources for calculating the travel chain is too large dueto the lack of GPS equipment can be solved to a certain extent. The method comprises the following steps: constructing an optimal adjacency matrix of a road network according to road network topologyinformation of a city; analyzing the vehicle passing video data from the detection point location to obtain vehicle driving information, wherein the vehicle driving information comprises a snapshot moment, a snapshot point location and a license plate number; and based on the optimal adjacency matrix, comparing and judging the vehicle driving information and a historical travel chain of the vehicle, and constructing a real-time travel track of the vehicle.

The invention discloses an electric vehicle charging demand analysis method considering limited rationality of a user. The method comprises the following steps: S1, judging the occurrence probabilityof an activity under a current condition through the analysis of the attributes of the activity, and building a transfer relation between the activity of a traveler and a travel chain; S2, establishing a travel selection probability model on an activity-travel chain of the traveler; S3, acquiring a transfer rule of the traveler on the activity-travel chain and a charging rule of an electric vehicle on the activity-travel chain; and S4, carrying out analyzing to obtain the daily travel frequency of the traveler, and combining the daily travel frequency of the traveler with the charging rule ofthe electric vehicle on each activity-travel chain to obtain the daily charging demand of the electric vehicle. Compared with a statistical analysis method, the method provided by the invention not only can obtain a conclusion with higher general applicability, but also can reveal internal reasons influencing the change of the charging demand of the electric vehicle; and compared with an electricvehicle behavior and charging demand analysis method based on complete rational hypothesis, the method is more reasonable.

The invention provides a scenic spot tourist identification method and device, electronic equipment and a storage medium, is applied to the technical field of big data application, and can identify tourists in a scenic spot. The method comprises the following steps: acquiring mobile phone signaling data of at least one day of a base station located in a preset range of a target scenic area; according to the mobile phone signaling data, determining a trip chain used for representing a trip track of a to-be-identified person and the residence time of the to-be-identified person in a preset range of the target scenic area; and if the trip chain and the stay time meet a first preset condition, determining that the to-be-identified person is a tourist of the target scenic area.

The invention provides an inter-provincial travel flow estimation method based on highway toll collection data. The method comprises the following steps of extracting data field data related to trans-provincial travel in highway toll collection data, carrying out data preprocessing on the extracted highway toll collection data, and extracting an inter-provincial travel chain set according to the highway toll collection data after data preprocessing, and calculating highway inter-provincial travel O-D and O-D traffic based on the extracted inter-provincial travel chain set. According to the method, based on national highway toll collection data, in combination with national highway network topology data, national toll station basic information data and the like, an extraction algorithm of a highway inter-provincial trip chain is given, highway inter-provincial trip flow is calculated and is compared with actual section flow obtained through statistics, and the practicability of inter-provincial section flow calculation is effectively improved.