A railway passenger transport timetable generation method and system based on collaborative scheduling

Abstract

The present application provides a railway passenger timetable generation method and system based on collaborative scheduling, which constructs a local heat matrix according to the passenger flow heat of other stations corresponding to the local area where the current station is located; and generates an initial passenger timetable for the current station through the heat learning characteristics of the local heat matrix; performs train operation scheduling based on the initial passenger timetable, and determines the intra-station scheduling coupling degree of the current station through the passenger flow heat and the passenger adjustment ratio of the current station, and determines the inter-station scheduling coupling degree based on the local heat matrix and the heat contribution ratio; determines the dynamic scheduling sensitivity factor corresponding to the current station according to the intra-station scheduling coupling degree and the inter-station scheduling coupling degree, and dynamically feedback updates the initial passenger timetable through the dynamic scheduling sensitivity factor, so that the train timetable can be dynamically fed back and updated according to the passenger flow coupling situation in the local area where the station is located after scheduling, thereby improving the generation efficiency of the passenger adjustment plan and the passenger occupancy rate matching degree.

Description

Technical Field

[0001] The present application relates to the field of passenger transportation and scheduling technology, and more specifically, to a method and system for generating a railway passenger timetable based on collaborative scheduling. Background Art

[0002] Passenger train capacity deployment must adapt to changes in railway market demand. During peak seasons like the Spring Festival, Summer Travel, and holidays, market demand is high. Railway companies deploy capacity within constraints such as line capacity and rolling stock resources to meet passenger demand. During off-seasons, railway companies must develop accurate capacity deployment strategies based on future passenger flow to ensure timely, rapid, and convenient capacity adjustments. These strategies typically include adding / stopping trains and increasing / reducing train numbers to accommodate off-season market demand and reduce operating costs. The continuous expansion of my country's high-speed rail network places higher demands on the efficiency and effectiveness of daily passenger transport adjustment plans.

[0003] Among the existing passenger transport adjustment plans, daily capacity plan adjustments mainly rely on manual experience, lack scientificity and innovation, lack intelligent decision-making support, and cannot pre-evaluate and evaluate multiple possible plans; ticket quota adjustments and capacity plans are adjusted separately at different stages, and the two lack mutual feedback and linkage. In actual operations, plans are mostly transmitted in electronic documents and paper documents. A complete system has not yet been established to support the efficient implementation of daily operational organization adjustment business. Therefore, how to improve the generation efficiency of passenger transport adjustment plans and the passenger load factor matching has become a problem that needs to be solved urgently. Summary of the Invention

[0004] The present application provides a railway passenger timetable generation method and system based on collaborative scheduling, which can dynamically feedback and update the train timetable according to the passenger flow coupling situation in the local area where the station is located after scheduling, thereby improving the generation efficiency of passenger adjustment plans and the passenger occupancy rate matching.

[0005] In a first aspect, the present application provides a method for generating a railway passenger timetable based on collaborative scheduling. The method can be executed by a network device, or can also be executed by a chip configured in the network device, and the present application does not limit this.

[0006] Specifically, the method includes:

[0007] Obtain the passenger flow information of the current station, perform passenger flow heat learning based on the passenger flow information of the current station, obtain the passenger flow heat of the current station, determine the local area where the current station is located, and construct a local heat matrix based on the passenger flow heat of other stations corresponding to the local area where the current station is located;

[0008] Performing eigenvalue decomposition on the local heat matrix to obtain heat learning features corresponding to the local area where the current station is located, and generating an initial passenger timetable for the current station based on the heat learning features;

[0009] Performing train operation scheduling based on the initial passenger timetable, determining the intra-station scheduling coupling degree of the current station based on the passenger flow heat and passenger adjustment ratio of the current station, and determining the inter-station scheduling coupling degree based on the local heat matrix and heat contribution ratio corresponding to the local area where the current station is located;

[0010] The dynamic scheduling sensitivity factor corresponding to the current station is determined according to the intra-station scheduling coupling degree and the inter-station scheduling coupling degree, and the initial passenger transport timetable is dynamically fed back and updated using the dynamic scheduling sensitivity factor.

[0011] In conjunction with the first aspect, in certain implementations of the first aspect, generating an initial passenger timetable for the current station using the heat learning feature specifically includes:

[0012] Acquire multiple heat learning feature samples and their corresponding passenger transport schedule adjustment table templates, wherein the passenger transport schedule adjustment table templates are formulated through manual experience;

[0013] Mean clustering is performed on multiple heat learning feature samples and the heat learning features, and an initial passenger timetable for the current station is generated based on the passenger timetable adjustment table template corresponding to each heat learning feature in the clustering result.

[0014] In conjunction with the first aspect, in certain implementations of the first aspect, determining the inter-station scheduling coupling degree based on the local heat matrix and heat contribution ratio corresponding to the local area where the current station is located specifically includes:

[0015] Obtain a preset windowing period, extract the local heat matrices corresponding to the local area where the current station is located in different windowing periods after train operation scheduling, and perform coupling feature extraction on the local heat matrices corresponding to each windowing period to obtain the heat coupling features corresponding to each windowing period;

[0016] Obtain the heat contribution ratio corresponding to the current station in different windowing periods, and determine the inter-station scheduling coupling degree corresponding to the current station based on the heat contribution ratio and heat coupling characteristics of the current station in different windowing periods.

[0017] In conjunction with the first aspect, in certain implementations of the first aspect, determining the dynamic scheduling sensitivity factor corresponding to the current station according to the intra-station scheduling coupling degree and the inter-station scheduling coupling degree specifically includes:

[0018] Get the dynamic sensitivity prediction interval, intra-station prediction weight and inter-station prediction weight;

[0019] Based on the intra-station prediction weight and the inter-station prediction weight, a weighted fusion is performed on the intra-station scheduling coupling degree and the inter-station scheduling coupling degree within each dynamic sensitivity prediction interval to obtain the station passenger flow coupling degree corresponding to different interval times;

[0020] The forward correlation prediction of the passenger flow coupling degree of stations corresponding to different interval times is carried out, and the passenger flow coupling degree of the station in the next interval time is used as the dynamic scheduling sensitivity factor corresponding to the current station.

[0021] In combination with the first aspect, in certain implementations of the first aspect, the dynamic feedback update of the initial passenger timetable through the dynamic scheduling sensitivity factor specifically includes: determining the corresponding dynamic feedback cycle based on the threshold interval of the dynamic scheduling sensitivity factor, and using the dynamic feedback cycle to collect the heat learning features corresponding to the local area where the current station is located in real time, and generating an updated passenger timetable based on the heat learning features and the dynamic scheduling sensitivity factor.

[0022] In combination with the first aspect, in certain implementations of the first aspect, the passenger flow information of the current station specifically includes: historical ticket sales data, entry and exit gate records, and train operation records.

[0023] In combination with the first aspect, in certain implementation methods of the first aspect, passenger flow heat learning is performed through the passenger flow information of the current station to obtain the passenger flow heat of the current station, which specifically includes: using a single hidden layer neural network to learn and cluster the passenger flow information of the current station, and using the heat value corresponding to the clustering result as the passenger flow heat corresponding to the current station.

[0024] In a second aspect, the present application provides a railway passenger timetable generation system based on collaborative scheduling, which includes a timetable generation unit, and the timetable generation unit includes:

[0025] The station data recording unit is used to obtain the passenger flow information of the current station, perform passenger flow heat learning based on the passenger flow information of the current station, obtain the passenger flow heat of the current station, determine the local area where the current station is located, and construct a local heat matrix based on the passenger flow heat of other stations corresponding to the local area where the current station is located;

[0026] A first timetable generation module is configured to perform eigenvalue decomposition on the local heat matrix to obtain heat learning features corresponding to the local area where the current station is located, and generate an initial passenger timetable for the current station based on the heat learning features;

[0027] A second timetable generation module is configured to perform train operation scheduling based on the initial passenger timetable, determine the intra-station scheduling coupling degree of the current station based on the passenger flow heat and passenger adjustment ratio of the current station, and determine the inter-station scheduling coupling degree based on the local heat matrix and heat contribution ratio corresponding to the local area where the current station is located;

[0028] The second timetable generation module is used to determine the dynamic scheduling sensitivity factor corresponding to the current station according to the intra-station scheduling coupling degree and the inter-station scheduling coupling degree, and dynamically feedback and update the initial passenger timetable through the dynamic scheduling sensitivity factor.

[0029] In a third aspect, the present application provides a computer terminal device, which includes a memory and a processor, wherein the memory stores code, and the processor is configured to obtain the code and execute the above-mentioned method for generating a railway passenger timetable based on collaborative scheduling.

[0030] In a fourth aspect, the present application provides a computer-readable storage medium, which stores at least one computer program, and the computer program is loaded and executed by a processor to implement the operations performed by the above-mentioned method for generating a railway passenger timetable based on collaborative scheduling.

[0031] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects:

[0032] The present application provides a railway passenger timetable generation method and system based on collaborative scheduling, which first obtains passenger flow information of the current station, performs passenger flow heat learning based on the passenger flow information of the current station to obtain the passenger flow heat of the current station, determines the local area where the current station is located, and constructs a local heat matrix based on the passenger flow heat of other stations corresponding to the local area where the current station is located; performs eigenvalue decomposition on the local heat matrix to obtain heat learning features corresponding to the local area where the current station is located, and generates an initial passenger timetable for the current station based on the heat learning features; performs train operation scheduling based on the initial passenger timetable, and determines the intra-station scheduling coupling degree of the current station based on the passenger flow heat and passenger adjustment ratio of the current station, and determines the inter-station scheduling coupling degree based on the local heat matrix and heat contribution ratio corresponding to the local area where the current station is located; determines the dynamic scheduling sensitivity factor corresponding to the current station based on the intra-station scheduling coupling degree and the inter-station scheduling coupling degree, and dynamically feedback updates the initial passenger timetable based on the dynamic scheduling sensitivity factor.

[0033] Therefore, it can be seen that this application constructs a heat matrix of the local area where the current station is located, performs eigenvalue decomposition, and extracts the main component pattern of passenger flow changes in the area as a heat learning feature, so that the initial timetable generation stage has regional characteristics, avoiding the separation of scheduling between stations, and measures the impact of scheduling on the passenger flow heat of the station itself through the intra-station scheduling coupling degree, and measures the linkage impact of station scheduling on other stations in the region through the inter-station scheduling coupling degree, realizing a real-time closed loop of scheduling behavior → passenger flow change → model perception, improving the local and global response effects of the dynamic perception scheduling strategy, and being able to generate optimizable plans faster than the static adjustment method based on manual experience, thereby improving the generation efficiency of passenger adjustment plans.

[0034] This enables the train schedule to be updated dynamically based on the passenger flow coupling situation in the local area after the station is dispatched, thereby improving the efficiency of generating passenger adjustment plans and the matching degree of passenger occupancy rates. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 is an exemplary flow chart of a method for generating a railway passenger timetable based on collaborative scheduling according to some embodiments of the present application;

[0036] Figure 2 is a schematic structural diagram of a timetable generating unit according to some embodiments of the present application;

[0037] Figure 3 It is a structural diagram of a computer terminal device for implementing a method for generating a railway passenger timetable based on collaborative scheduling according to some embodiments of the present application. DETAILED DESCRIPTION

[0038] The present application obtains passenger flow information of the current station, performs passenger flow heat learning based on the passenger flow information of the current station, obtains the passenger flow heat of the current station, determines the local area where the current station is located, and constructs a local heat matrix based on the passenger flow heat of other stations corresponding to the local area where the current station is located; performs eigenvalue decomposition on the local heat matrix to obtain heat learning features corresponding to the local area where the current station is located, and generates an initial passenger timetable for the current station based on the heat learning features; performs train operation scheduling based on the initial passenger timetable, and determines the intra-station scheduling coupling degree of the current station based on the passenger flow heat and passenger adjustment ratio of the current station, and determines the inter-station scheduling coupling degree based on the local heat matrix and heat contribution ratio corresponding to the local area where the current station is located; determines the dynamic scheduling sensitivity factor corresponding to the current station based on the intra-station scheduling coupling degree and the inter-station scheduling coupling degree, and dynamically feedback updates the initial passenger timetable based on the dynamic scheduling sensitivity factor, so that the train timetable can be dynamically fed back and updated based on the passenger flow coupling situation of the local area where the station is located after scheduling, thereby improving the generation efficiency of the passenger adjustment plan and the passenger occupancy rate matching degree.

[0039] In order to better understand the above technical solution, the following will be combined with the accompanying drawings and specific implementation methods to describe the above technical solution in detail. Figure 1 , which is an exemplary flow chart of a method for generating a railway passenger timetable based on collaborative scheduling according to some embodiments of the present application. The method 100 for generating a railway passenger timetable based on collaborative scheduling mainly includes the following steps:

[0040] In step S101, the passenger flow information of the current station is obtained, and the passenger flow heat is learned through the passenger flow information of the current station to obtain the passenger flow heat of the current station, determine the local area where the current station is located, and construct a local heat matrix based on the passenger flow heat of other stations corresponding to the local area where the current station is located.

[0041] Optionally, in some embodiments, the passenger flow information of the current station specifically includes: historical ticket sales data, entry and exit gate records, train operation records, holidays, weather, event tags, and authorized facial recognition real-time monitoring data.

[0042] It should be noted that the passenger flow heat described in this application is used to quantify the density of the total number of people entering and exiting the station per unit time and the intensity of the density change trend relative to the same type of riding environment, and is used to reflect the overall passenger flow busyness of the station. Preferably, in some embodiments, passenger flow heat learning is performed through the passenger flow information of the current station, and the passenger flow heat of the current station is obtained specifically including: using a single hidden layer neural network to learn and cluster the passenger flow information of the current station, and using the heat value corresponding to the clustering result as the passenger flow heat corresponding to the current station.

[0043] In the specific implementation, in the process of learning and clustering the passenger flow information of the current station using a single hidden layer neural network, the passenger flow information of the current station can be input in the form of a data vector, the hidden layer of the single hidden layer neural network contains multiple activation function nodes for classification training, and the classification results are output through the output layer of the single hidden layer neural network, and the passenger flow heat score mapping is performed according to the clustering results corresponding to the passenger flow information of the current station, and the corresponding heat value is obtained as the passenger flow heat corresponding to the current station, wherein the different dimensions of the data vector include: the number of people entering the station on the same day, the number of people leaving the station on the same day, the current time period, the number of trains, special event signs, weather information and historical comparison ratios for the same period, wherein multiple passenger flow information prepared in advance are used. The samples and the corresponding manual scoring results of passenger flow heat are used as training sets, and multiple passenger flow information samples are input into the single hidden layer neural network for classification training. The hidden layer of the single hidden layer neural network includes multiple activation function nodes for classification training of the input samples, and the classification results are output through the output layer of the single hidden layer neural network, and then the classification results are compared with the corresponding manual scoring results of passenger flow heat. When the correlation between the clustering results and the manual scoring results of passenger flow heat is lower than the preset threshold, the activation parameters in the activation function in the hidden layer of the single hidden layer neural network are adjusted until the correlation between the clustering results and the manual scoring results of passenger flow heat reaches the preset standard, and the mapping relationship between the clustering results and the manual scoring of passenger flow heat is obtained.

[0044] It should be noted that, in the railway passenger dispatching optimization system, the local area where the current station is located usually refers to a set of adjacent stations selected within a certain range with the current station as the core, which is used for passenger flow heat analysis, resource coordination and dispatching optimization within the area. Preferably, in some embodiments, the provincial area where the current station is located can be used as the local area where the current station is located. In the process of constructing a local heat matrix based on the passenger flow heat of other stations corresponding to the local area where the current station is located, the passenger flow heat of each station corresponding to the local area where the current station is located is organized into a column vector according to the label order, and the column vectors corresponding to different time periods in the preset historical period are combined into the local heat matrix.

[0045] In step S102, the local heat matrix is ​​subjected to eigenvalue decomposition to obtain heat learning features corresponding to the local area where the current station is located, and the initial passenger timetable of the current station is generated by the heat learning features.

[0046] Optionally, in some embodiments, the principal component analysis method can be used to perform eigenvalue decomposition on the local heat matrix to obtain a eigenvector composed of the principal component eigenvalues ​​corresponding to the local heat matrix as a heat learning feature corresponding to the local area where the current station is located.

[0047] Optionally, in some embodiments, the heat learning features can be input into a pre-trained pressure scoring model, and the passenger flow pressure score can be output according to the scoring model. The number of daily train runs at the current station is determined based on the passenger flow pressure score, and a plurality of operable time periods are divided into equal intervals according to the daily train run number, and an initial passenger timetable is generated based on all operable time periods. In the process of generating the initial passenger timetable according to the daily train run number, the train running time must meet the principle of non-conflict of train runs.

[0048] Optionally, in some embodiments, the initial passenger transport schedule for the current station generated by using the heat learning feature may also be determined in the following manner:

[0049] Acquire multiple heat learning feature samples and their corresponding passenger transport schedule adjustment table templates, wherein the passenger transport schedule adjustment table templates are formulated through manual experience;

[0050] Mean clustering is performed on multiple heat learning feature samples and the heat learning features, and an initial passenger timetable for the current station is generated based on the passenger timetable adjustment table template corresponding to each heat learning feature in the clustering result.

[0051] It should be noted that, through the study and analysis of historical capacity deployment and actual passenger departure and arrival data, it was found that during the capacity adjustment process during the study period, especially during the off-season, there was a certain deviation between capacity deployment and passenger flow growth expectations. It is urgent to analyze and study the capacity deployment strategies and implementation effects in different time periods. By statistically analyzing indicators such as actual deployed capacity, EMU utilization, passenger occupancy rate, and operation efficiency, the capacity deployment strategies, deployment effects, and existing problems adopted by different railway bureaus are analyzed and summarized. The analysis results show that the current capacity adjustment strategy mainly focuses on the capacity allocation strategy at the train level and the batch allocation strategy at the ticket quota level. The present application identifies similar heat features through clustering, and can automatically match the corresponding manual experience adjustment timetable template to generate the initial passenger timetable of the current station, thereby improving the degree of automation and retaining the experience of scheduling experts. Optionally, in some embodiments, K-means mean clustering can be used to perform mean clustering on multiple heat learning feature samples and the heat learning features. Each passenger timetable adjustment table template corresponds to a plurality of heat learning feature samples, and then the passenger timetable adjustment table template with the largest number of corresponding heat learning feature samples in the cluster center where the heat learning feature is located is used as the initial passenger timetable.

[0052] In step S103, train operation scheduling is performed based on the initial passenger timetable, and the intra-station scheduling coupling degree of the current station is determined by the passenger flow heat and passenger adjustment ratio of the current station, and the inter-station scheduling coupling degree is determined based on the local heat matrix and heat contribution ratio corresponding to the local area where the current station is located.

[0053] It should be noted that the intra-station scheduling coupling degree described in this application is used to quantify the sensitivity of passenger flow heat within the station to changes in passenger transport scheme scheduling. The intra-station scheduling coupling degree is determined based on the changing trend of the passenger adjustment ratio and the changing trend of passenger flow heat after train operation scheduling. Optionally, in some embodiments, determining the intra-station scheduling coupling degree of the current station based on the passenger flow heat and passenger adjustment ratio of the current station specifically includes:

[0054] After the train operation is scheduled, obtain the passenger flow trend of the current station;

[0055] Obtain the daily passenger traffic adjustment ratio, and extract the passenger traffic adjustment trend of the current station based on the daily passenger traffic adjustment ratio;

[0056] The trend coupling degree is extracted based on the passenger flow heat change trend and the passenger transport adjustment trend to determine the station scheduling coupling degree of the current station.

[0057] It should be noted that the passenger adjustment ratio is the ratio between the total number of passenger trains departing today and the total number of passenger trains departing yesterday at the current station.

[0058] Optionally, in some embodiments, obtaining the passenger flow popularity change trend of the current station specifically includes:

[0059] Record the passenger flow heat of the current station on different dates and compose a passenger flow heat sequence based on the time sequence;

[0060] Trend extraction is performed on the passenger flow heat sequence to obtain the passenger flow heat change trend corresponding to the current station; in specific implementation, the empirical mode decomposition method can be used to extract multiple eigenmode functions corresponding to the passenger flow heat sequence, and the mean of the eigenmode function is used as the passenger flow heat change trend corresponding to the current station.

[0061] Optionally, in some embodiments, extracting the passenger traffic adjustment trend of the current station based on the daily passenger traffic adjustment ratio specifically includes:

[0062] Record the daily passenger adjustment ratio of the current station on different dates and compose a passenger adjustment ratio sequence based on the time sequence;

[0063] The passenger adjustment ratio sequence is trend extracted to obtain the passenger adjustment trend corresponding to the current station. In specific implementation, the process of trend extraction of the passenger adjustment ratio sequence can adopt the same extraction method as the passenger flow heat change trend, which will not be elaborated in this application.

[0064] Preferably, in some embodiments, extracting the trend coupling degree from the passenger flow popularity change trend and the passenger transport adjustment trend, and determining the station scheduling coupling degree of the current station specifically includes:

[0065] Based on a preset coupling degree extraction cycle, trend values ​​of the passenger flow heat change trend and the passenger transport adjustment trend are collected respectively to obtain a passenger flow heat trend value sequence and a passenger transport adjustment trend value sequence;

[0066] After normalizing the passenger flow heat trend value sequence and the passenger transport adjustment trend value sequence, a coupling degree analysis is performed based on the passenger flow heat trend value sequence and the passenger transport adjustment trend value sequence to obtain the station scheduling coupling degree of the current station.

[0067] In specific implementation, the coupling degree extraction period can be mapped based on the current passenger flow density. In some embodiments, the coupling degree extraction period can be fixed to a constant value of 6h, and then the Pearson correlation coefficient between the passenger flow heat trend value sequence and the passenger adjustment trend value sequence can be used as the intra-station scheduling coupling degree.

[0068] It should be noted that in the process of determining the inter-station scheduling coupling degree based on the local heat matrix and heat contribution ratio corresponding to the local area where the current station is located, the local area heat is the sum of the passenger flow heat of all stations in the local area where the current station is located, and the heat contribution ratio corresponding to the current station is the ratio between the passenger flow heat of the current station and the local area heat.

[0069] It should be noted that the inter-station scheduling coupling degree described in this application is used to reflect the degree of coupling effect of the current station scheduling change on the passenger flow heat of the local area where the station is located. The inter-station scheduling coupling degree is determined based on the local heat matrix and heat contribution ratio in the local area after the train operation scheduling. Optionally, in some embodiments, determining the inter-station scheduling coupling degree based on the local heat matrix and heat contribution ratio corresponding to the local area where the current station is located specifically includes:

[0070] Obtain a preset windowing period, extract the local heat matrices corresponding to the local area where the current station is located in different windowing periods after train operation scheduling, and perform coupling feature extraction on the local heat matrices corresponding to each windowing period to obtain the heat coupling features corresponding to each windowing period;

[0071] Obtain the heat contribution ratio corresponding to the current station in different windowing periods, and determine the inter-station scheduling coupling degree corresponding to the current station based on the heat contribution ratio and heat coupling characteristics of the current station in different windowing periods.

[0072] In specific implementation, the windowing period is preset to 24 hours, and then the local heat matrices corresponding to the local areas where the current station is located in different windowing periods can be subjected to singular value decomposition respectively, and the mean values ​​of the matrix eigenvalues ​​corresponding to the local heat matrices corresponding to the local areas where the current station is located in different windowing periods are obtained as the heat coupling characteristics corresponding to the windowing periods. Preferably, in some embodiments, after normalizing the heat contribution ratio and the heat coupling characteristics of the current station in different windowing periods, the standard deviation of the heat contribution ratio and the heat coupling characteristics is used as the inter-station scheduling coupling degree.

[0073] In step S104, a dynamic scheduling sensitivity factor corresponding to the current station is determined according to the intra-station scheduling coupling degree and the inter-station scheduling coupling degree, and the initial passenger transport timetable is dynamically fed back and updated using the dynamic scheduling sensitivity factor.

[0074] It should be noted that the dynamic scheduling sensitivity factor described in this application is used to quantify the sensitivity of passenger flow in stations and local areas to scheduling changes. When the dynamic scheduling sensitivity factor is large, due to the large changes in scheduling, the feedback for dynamic feedback updates of the train schedule can be appropriately reduced, that is, the update speed of the train schedule is reduced, thereby avoiding passenger flow disorder caused by frequent updates of the train schedule and improving the safety and comfort of train passengers. Preferably, in some embodiments, determining the dynamic scheduling sensitivity factor corresponding to the current station based on the intra-station scheduling coupling degree and the inter-station scheduling coupling degree specifically includes:

[0075] Get the dynamic sensitivity prediction interval, intra-station prediction weight and inter-station prediction weight;

[0076] Based on the intra-station prediction weight and the inter-station prediction weight, a weighted fusion is performed on the intra-station scheduling coupling degree and the inter-station scheduling coupling degree within each dynamic sensitivity prediction interval to obtain the station passenger flow coupling degree corresponding to different interval times;

[0077] The forward correlation prediction of the passenger flow coupling degree of stations corresponding to different interval times is carried out, and the passenger flow coupling degree of the station in the next interval time is used as the dynamic scheduling sensitivity factor corresponding to the current station.

[0078] In specific implementation, the intra-station prediction weight and the inter-station prediction weight are calibrated as constants based on experience, and the station passenger flow coupling degree = the intra-station prediction weight × the intra-station scheduling coupling degree + the inter-station prediction weight × the inter-station scheduling coupling degree. In some embodiments, a moving average autoregressive model can be used to perform forward correlation prediction on the station passenger flow coupling degrees corresponding to different interval times, and the station passenger flow coupling degree in the next interval time is used as the dynamic scheduling sensitivity factor corresponding to the current station. Preferably, a preferred embodiment of obtaining the dynamic scheduling sensitivity factor in this application is given below: First, the prediction sample period is preset to 15 days, and the dynamic sensitivity prediction interval is 1 hour. At this time, the station passenger flow coupling degrees of the station in the past 360 hours can be recorded separately to obtain a station passenger flow coupling degree sequence. In other embodiments, the station passenger flow coupling degrees can also be preset to other time lengths; and then a time series graph of the station passenger flow coupling degree sequence can be drawn, and the horizontal axis of the time series graph corresponds to different time moments. Then, the time series graph of the station passenger flow coupling degree sequence set can be exponentially converted to eliminate the trend of variance changing over time in the time series graph.

[0079] Next, based on the time series graph of the station passenger flow coupling degree sequence, an autocorrelation coefficient graph of the station passenger flow coupling degree is plotted, where the horizontal axis of the autocorrelation coefficient graph represents the number of lag periods and the vertical axis represents the value of the autocorrelation coefficient. A partial autocorrelation coefficient graph of the station passenger flow coupling degree is plotted, where the horizontal axis of the partial autocorrelation coefficient graph represents the number of lag periods and the vertical axis represents the value of the partial autocorrelation coefficient.

[0080] According to the characteristics of the autocorrelation coefficient graph and the partial autocorrelation coefficient graph, the order of the model and the range of coefficient values ​​can be preliminarily determined. For example, the autocorrelation coefficient graph can be drawn to observe whether the autocorrelation coefficient shows a truncation characteristic after a certain order. If the autocorrelation coefficient drops sharply after a certain order and remains near 0, the order of the autoregressive model can be preliminarily determined; the partial autocorrelation coefficient graph can be drawn to observe whether the partial autocorrelation coefficient shows a truncation characteristic after a certain order. If the partial autocorrelation coefficient drops sharply after a certain order and remains near 0, the order of the moving average model can be preliminarily determined.

[0081] In specific implementation, we can first find the last significant autocorrelation coefficient according to the autocorrelation coefficient graph, which is the order of the autocorrelation model. For example, if the last significant autocorrelation coefficient in the autocorrelation coefficient graph is at the 3rd order, then the order of the autocorrelation model is 3; then, we can find the last significant partial autocorrelation coefficient according to the partial autocorrelation coefficient graph, which is the order of the moving average model. For example, if the last significant partial autocorrelation coefficient in the partial autocorrelation coefficient graph is at the 2nd order, then the order of the moving average model is 2; finally, we can determine the order of the autocorrelation model according to the autocorrelation coefficient graph and the partial autocorrelation coefficient graph. The order of the autoregressive moving average model is (p, q). For example, if the autocorrelation coefficient graph and the partial autocorrelation coefficient graph both decay to zero after the third order, the order of the autoregressive moving average model is (3, 3). Then, according to the order of the autoregressive moving average model, appropriate parameters are selected to establish an autoregressive moving average model of the station passenger flow coupling degree sequence. The station passenger flow coupling degree sequence is brought into the autoregressive moving average model, and the subsequent station passenger flow coupling degree can be predicted, wherein the station passenger flow coupling degree at the end of the next maintenance time period is used as the dynamic scheduling sensitive factor.

[0082] In specific implementation, for example, the least squares method can be used to perform parameter estimation and significance test on the model autoregressive and moving average processes. In some embodiments, the test level of the significance test is 0.04. Finally, the most suitable autoregressive moving average model parameters are selected according to the Schwarz Bayes criterion to determine the final autoregressive moving average model of the station passenger flow coupling degree.

[0083] It should be noted that the present application adaptively adjusts the schedule update frequency and adjustment range according to the station's sensitivity to scheduling changes (i.e., the dynamic scheduling sensitivity factor), thereby realizing dynamic closed-loop optimization of the passenger schedule. Optionally, in some embodiments, the dynamic feedback update of the initial passenger schedule through the dynamic scheduling sensitivity factor specifically includes: determining the corresponding dynamic feedback cycle based on the threshold interval of the dynamic scheduling sensitivity factor, and using the dynamic feedback cycle to collect the heat learning features corresponding to the local area where the current station is located in real time, and generating an updated passenger schedule based on the heat learning features and the dynamic scheduling sensitivity factor.

[0084] In specific implementation, the dynamic scheduling sensitivity factor can be interval-mapped according to the threshold interval of the dynamic scheduling sensitivity factor and the preset linear mapping table to obtain the corresponding dynamic feedback cycle, and the passenger timetable can be periodically updated based on the dynamic feedback cycle. In other words, the larger the dynamic scheduling sensitivity factor, the longer the dynamic feedback cycle, so that when the coupling degree of passenger train scheduling is high, the frequency of changes in the train timetable can be reduced, thereby avoiding frequent changes in the train timetable that cause chaos in station operations, and improving the comfort and safety of passengers in the train process.

[0085] Preferably, in some embodiments, generating an updated passenger transport timetable according to the heat learning feature and the dynamic scheduling sensitivity factor specifically includes:

[0086] Acquire multiple heat learning feature samples and their corresponding passenger transport schedule adjustment table templates, wherein the passenger transport schedule adjustment table templates are formulated through manual experience;

[0087] The dynamic scheduling sensitivity factor is obtained, the clustering threshold of the initial clustering is corrected according to the dynamic scheduling sensitivity factor, multiple thermal learning feature samples and the thermal learning features are mean clustered according to the corrected clustering threshold, and an updated passenger timetable is generated based on the passenger timetable adjustment table template corresponding to each thermal learning feature in the clustering result.

[0088] Among them, K-means mean clustering is used to perform mean clustering on multiple heat learning feature samples and the heat learning features. K-means mean clustering forms a cluster center by assigning each data point to the centroid closest to it. The condition for satisfying the clustering result is that the distance difference between each cluster center and the centroid is lower than the preset clustering threshold, wherein the corrected clustering threshold = initial clustering threshold / the dynamic scheduling sensitivity factor.

[0089] It should be noted that the collaborative technology for preparing daily passenger adjustment plans and generating full-route passenger train timetables provided in this application belongs to the field of railway passenger operation optimization. To address the problems of insufficient dynamic response, difficulty in multi-disciplinary collaboration, and low computational efficiency caused by the independent operation of passenger flow prediction, train adjustment, seat allocation, and timetable compilation in traditional methods, this technology proposes a phased collaborative optimization and closed-loop feedback mechanism. By constructing a modular collaborative system for passenger transportation, transportation, dispatching, and vehicle operations, a closed-loop process of "adjustment plan generation → dynamic timetable generation → operation evaluation → feedback optimization" is established, and standardized interfaces are used to achieve lightweight data interaction. Core technologies include: a dynamic hierarchical response mechanism to achieve coordinated adjustment of seat control and train operation; a six-stage operating line generation algorithm for the transportation system based on spatiotemporal conflict detection; a three-dimensional conflict detection system for the dispatching profession that integrates vehicle turnover, spatiotemporal resources, and station capacity; and a four-stage collaborative execution process to achieve cross-stage parallel call of vehicle resources and closed-loop feedback of full-link data. This technology significantly improves the efficiency of adjustment plan generation, passenger seat matching, and vehicle turnover efficiency, effectively solving the problem of dynamic adaptation of daily railway transportation capacity in large-scale road networks.

[0090] In addition, in another aspect of the present application, in some embodiments, the present application provides a railway passenger timetable generation system based on collaborative scheduling, the device includes a timetable generation unit, reference Figure 2, which is a schematic diagram of the structure of exemplary hardware and / or software of a timetable generation unit according to some embodiments of the present application. The timetable generation unit 200 includes a station data recording unit 201, a first timetable generation module 202, and a second timetable generation module 203, which are described as follows:

[0091] The station data recording unit 201 is used to obtain the passenger flow information of the current station, perform passenger flow heat learning based on the passenger flow information of the current station, obtain the passenger flow heat of the current station, determine the local area where the current station is located, and construct a local heat matrix based on the passenger flow heat of other stations corresponding to the local area where the current station is located;

[0092] The first timetable generation module 202 is configured to perform eigenvalue decomposition on the local heat matrix to obtain heat learning features corresponding to the local area where the current station is located, and generate an initial passenger timetable for the current station based on the heat learning features;

[0093] The second timetable generation module 203 is configured to perform train operation scheduling based on the initial passenger timetable, determine the intra-station scheduling coupling degree of the current station based on the passenger flow heat and passenger adjustment ratio of the current station, and determine the inter-station scheduling coupling degree based on the local heat matrix and heat contribution ratio corresponding to the local area where the current station is located;

[0094] The second timetable generating module 203 is further configured to determine a dynamic scheduling sensitivity factor corresponding to the current station according to the intra-station scheduling coupling degree and the inter-station scheduling coupling degree, and dynamically feedback update the initial passenger timetable using the dynamic scheduling sensitivity factor.

[0095] The above describes in detail an example of a railway passenger timetable generation method and system based on collaborative scheduling provided in an embodiment of the present application. It can be understood that in order to achieve the above functions, the corresponding device includes hardware structures and / or software modules corresponding to executing each function.

[0096] Those skilled in the art should easily realize that, in combination with the units and algorithm steps of each example described in the embodiments disclosed herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a certain function in the application is executed in hardware or in a computer software-driven hardware manner depends on the specific application and design constraints of the technical solution. Therefore, professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0097] In addition, the present application also provides a computer terminal device, which includes a memory and a processor, the memory stores code, and the processor is configured to obtain the code and execute the above-mentioned method for generating a railway passenger timetable based on collaborative scheduling.

[0098] In some embodiments, reference Figure 3 , which is a schematic diagram of the structure of a computer terminal device for implementing a method for generating a railway passenger timetable based on collaborative scheduling according to some embodiments of the present application. Figure 3 The computer terminal device 300 shown in FIG. 1 is implemented as shown in FIG. 1 , and the computer terminal device 300 includes at least one communication bus 301 , a communication interface 302 , a processor 303 and a memory 304 .

[0099] The processor 303 can be a general-purpose central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more processors for controlling the execution of a method for generating a railway passenger timetable based on collaborative scheduling in this application.

[0100] The communication bus 301 may include a path for transmitting information between the aforementioned components.

[0101] Memory 304 may be, but is not limited to, a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, a random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disk storage, an optical disk storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), a magnetic disk or other magnetic storage device, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer. Memory 304 may be independent and connected to processor 303 via communication bus 301. Memory 304 may also be integrated with processor 303.

[0102] The memory 304 is used to store the program code for executing the solution of the present application, and the execution is controlled by the processor 303. The processor 303 is used to execute the program code stored in the memory 304. The program code may include one or more software modules. The determination of the heat learning feature in the above embodiment can be implemented by the processor 303 and one or more software modules in the program code in the memory 304.

[0103] The communication interface 302 uses any transceiver or other device for communicating with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area network (WLAN), etc.

[0104] Optionally, the computer terminal device 300 may further include a power supply 305 for providing power to various devices or circuits in the real-time computer terminal device.

[0105] In a specific implementation, as an example, a computer terminal device may include multiple processors, each of which may be a single-core (single-CPU) processor or a multi-core (multi-CPU) processor. A processor herein may refer to one or more devices, circuits, and / or processing cores for processing data (e.g., computer program instructions).

[0106] The aforementioned computer terminal device can be a general-purpose computer terminal device or a dedicated computer terminal device. In a specific implementation, the computer terminal device can be a desktop computer, a portable computer, a network server, a personal digital assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device, or an embedded device. The embodiments of this application do not limit the type of computer terminal device.

[0107] In addition, in other aspects of the present application, a computer-readable storage medium is provided, which stores at least one computer program, and the computer program is loaded and executed by a processor to implement the operations performed by the above-mentioned method for generating a railway passenger timetable based on collaborative scheduling.

[0108] In summary, the embodiment of the present application discloses a method and system for generating a railway passenger timetable based on collaborative scheduling, which first obtains the passenger flow information of the current station, performs passenger flow heat learning based on the passenger flow information of the current station, obtains the passenger flow heat of the current station, determines the local area where the current station is located, and constructs a local heat matrix based on the passenger flow heat of other stations corresponding to the local area where the current station is located; performs eigenvalue decomposition on the local heat matrix to obtain the heat learning features corresponding to the local area where the current station is located, and generates the initial passenger timetable of the current station based on the heat learning features; performs Train operation scheduling is carried out, and the intra-station scheduling coupling degree of the current station is determined by the passenger flow heat and passenger adjustment ratio of the current station, and the inter-station scheduling coupling degree is determined based on the local heat matrix and heat contribution ratio corresponding to the local area where the current station is located; the dynamic scheduling sensitivity factor corresponding to the current station is determined according to the intra-station scheduling coupling degree and the inter-station scheduling coupling degree, and the initial passenger timetable is dynamically fed back and updated through the dynamic scheduling sensitivity factor, so that the train timetable can be dynamically fed back and updated according to the passenger flow coupling situation of the local area where the station is located after scheduling, thereby improving the generation efficiency of the passenger adjustment plan and the passenger occupancy rate matching degree.

[0109] The above description is merely an embodiment of the present application. Common knowledge such as the specific technical solutions or features of the solutions is not described in detail herein. It should be noted that those skilled in the art may make various modifications and improvements without departing from the technical solution of the present application, and these modifications and improvements should also be considered within the scope of protection of the present application. These modifications and improvements will not affect the effectiveness of the implementation of the present application or the practical application of the patent.

[0110] The scope of protection claimed by this application shall be determined by the content of the claims. The specific embodiments and other descriptions in the specification may be used to interpret the content of the claims. Obviously, those skilled in the art may make various changes and modifications to this application without departing from the spirit and scope of the invention. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application is intended to include such modifications and variations.

Claims

1. A method for generating a railway passenger timetable based on collaborative scheduling, characterized in that: include: Obtain the passenger flow information of the current station, perform passenger flow heat learning based on the passenger flow information of the current station, obtain the passenger flow heat of the current station, determine the local area where the current station is located, and construct a local heat matrix based on the passenger flow heat of other stations corresponding to the local area where the current station is located; Performing eigenvalue decomposition on the local heat matrix to obtain heat learning features corresponding to the local area where the current station is located, and generating an initial passenger timetable for the current station based on the heat learning features; Performing train operation scheduling based on the initial passenger timetable, determining the intra-station scheduling coupling degree of the current station based on the passenger flow heat and passenger adjustment ratio of the current station, and determining the inter-station scheduling coupling degree based on the local heat matrix and heat contribution ratio corresponding to the local area where the current station is located; The dynamic scheduling sensitivity factor corresponding to the current station is determined according to the intra-station scheduling coupling degree and the inter-station scheduling coupling degree, and the initial passenger transport timetable is dynamically fed back and updated using the dynamic scheduling sensitivity factor.

2. The method according to claim 1, wherein Generating the initial passenger transport schedule of the current station by using the heat learning feature specifically includes: Acquire multiple heat learning feature samples and their corresponding passenger transport schedule adjustment table templates, wherein the passenger transport schedule adjustment table templates are formulated through manual experience; Mean clustering is performed on multiple heat learning feature samples and the heat learning features, and an initial passenger timetable for the current station is generated based on the passenger timetable adjustment table template corresponding to each heat learning feature in the clustering result.

3. The method according to claim 1, wherein The inter-station scheduling coupling degree is determined based on the local heat matrix and heat contribution ratio corresponding to the local area where the current station is located. Specifically, the following are included: Obtain a preset windowing period, extract the local heat matrices corresponding to the local area where the current station is located in different windowing periods after train operation scheduling, and perform coupling feature extraction on the local heat matrices corresponding to each windowing period to obtain the heat coupling features corresponding to each windowing period; Obtain the heat contribution ratio corresponding to the current station in different windowing periods, and determine the inter-station scheduling coupling degree corresponding to the current station based on the heat contribution ratio and heat coupling characteristics of the current station in different windowing periods.

4. The method according to claim 1, wherein Determining the dynamic scheduling sensitivity factor corresponding to the current station according to the intra-station scheduling coupling degree and the inter-station scheduling coupling degree specifically includes: Get the dynamic sensitivity prediction interval, intra-station prediction weight and inter-station prediction weight; Based on the intra-station prediction weight and the inter-station prediction weight, a weighted fusion is performed on the intra-station scheduling coupling degree and the inter-station scheduling coupling degree within each dynamic sensitivity prediction interval to obtain the station passenger flow coupling degree corresponding to different interval times; The forward correlation prediction of the passenger flow coupling degree of stations corresponding to different interval times is carried out, and the passenger flow coupling degree of the station in the next interval time is used as the dynamic scheduling sensitivity factor corresponding to the current station.

5. The method according to claim 1, wherein The dynamic feedback update of the initial passenger timetable through the dynamic scheduling sensitivity factor specifically includes: determining a corresponding dynamic feedback cycle based on the threshold interval of the dynamic scheduling sensitivity factor, using the dynamic feedback cycle to collect the heat learning features corresponding to the local area where the current station is located in real time, and generating an updated passenger timetable based on the heat learning features and the dynamic scheduling sensitivity factor.

6. The method according to claim 1, wherein The passenger flow information of the current station specifically includes: historical ticket sales data, entry and exit gate records and train operation records.

7. The method according to claim 1, wherein Passenger flow heat learning is performed through the passenger flow information of the current station to obtain the passenger flow heat of the current station. Specifically, the passenger flow heat of the current station is obtained by using a single hidden layer neural network to learn and cluster the passenger flow information of the current station, and the heat value corresponding to the clustering result is used as the passenger flow heat corresponding to the current station.

8. A railway passenger timetable generation system based on collaborative scheduling, comprising a timetable generation unit, characterized in that: The timetable generating unit includes: The station data recording unit is used to obtain the passenger flow information of the current station, perform passenger flow heat learning based on the passenger flow information of the current station, obtain the passenger flow heat of the current station, determine the local area where the current station is located, and construct a local heat matrix based on the passenger flow heat of other stations corresponding to the local area where the current station is located; A first timetable generation module is configured to perform eigenvalue decomposition on the local heat matrix to obtain heat learning features corresponding to the local area where the current station is located, and generate an initial passenger timetable for the current station based on the heat learning features; A second timetable generation module is configured to perform train operation scheduling based on the initial passenger timetable, determine the intra-station scheduling coupling degree of the current station based on the passenger flow heat and passenger adjustment ratio of the current station, and determine the inter-station scheduling coupling degree based on the local heat matrix and heat contribution ratio corresponding to the local area where the current station is located; The second timetable generation module is further used to determine the dynamic scheduling sensitivity factor corresponding to the current station according to the intra-station scheduling coupling degree and the inter-station scheduling coupling degree, and dynamically feedback update the initial passenger timetable through the dynamic scheduling sensitivity factor.

9. A computer terminal device, characterized in that: The computer terminal device includes a memory and a processor, the memory stores code, and the processor is configured to obtain the code and execute the method for generating a railway passenger timetable based on collaborative scheduling as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing at least one computer program, characterized in that: The computer program is loaded and executed by a processor to implement the operations performed by the method for generating a railway passenger timetable based on collaborative scheduling as described in any one of claims 1 to 7.

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