Traffic flow prediction method, device, equipment, medium and computer program product

Abstract

The application provides a vehicle flow prediction method, device, equipment, medium and computer program product. The method comprises the following steps: collecting vehicle operation information based on historical train arrival and departure reports; the vehicle operation information comprises goods categories, a starting station, a terminal station, passing stations, and arrival and departure times of each passing station; performing statistical calculation based on the vehicle operation information, and constructing a track table; each track data of the track table comprises goods categories, a starting station, a terminal station, a current station, a next station, an average stay time of the current station, an average interval operation time from the current station to the next station, and a probability; and predicting the operation of a to-be-predicted vehicle based on a starting station, a current station, a terminal station, goods categories of the to-be-predicted vehicle, and the track table to obtain a prediction result. Through the constructed track table, the future operation path and the arrival and departure time sequence of the vehicle are cyclically predicted based on the current station and the current time of the vehicle, and a more accurate vehicle flow prediction scheme is provided.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of traffic flow prediction, and in particular to a traffic flow prediction method, device, equipment, medium and computer program product. BACKGROUND

[0002] Traffic flows continuously and unbalancedly through each section to reach each station in a road network. When the number of traffic flows exceeds the limited capacity of stations and sections, congestion phenomenon will be formed. By establishing a more accurate traffic flow prediction method, the road network capacity bottleneck can be predicted in advance, which can provide data support for formulating effective traffic flow adjustment measures. SUMMARY

[0003] The present application provides a traffic flow prediction method, device, equipment, medium and computer program product to solve the defect of inaccurate prediction in the existing traffic flow prediction technology and realize accurate traffic flow prediction.

[0004] The present application provides a traffic flow prediction method, which comprises the following steps:

[0005] Collecting vehicle operation information based on historical train arrival and departure reports; the vehicle operation information comprises freight categories, starting stations, terminal stations, passing stations, and arrival and departure times at each passing station;

[0006] Statistically calculating based on the vehicle operation information to construct a trajectory table; each trajectory data of the trajectory table comprises freight categories, starting stations, terminal stations, current stations, next stations, average stay times at current stations, average section operation times from current stations to next stations, and probabilities;

[0007] Based on the starting station, current station, terminal station, freight categories of the vehicle to be predicted and the trajectory table, the operation of the vehicle to be predicted is predicted to obtain a prediction result.

[0008] According to the traffic flow prediction method provided by the present application, the operation of the vehicle to be predicted is predicted based on the starting station, current station, terminal station, freight categories of the vehicle to be predicted and the trajectory table to obtain a prediction result, which comprises:

[0009] Obtaining the starting station, terminal station and freight categories of the vehicle to be predicted;

[0010] Obtaining the current time and the current station of the vehicle to be predicted;

[0011] In the case that the current station is not the terminal station of the vehicle to be predicted, determining the next station of the current station, the stay time of the current station, and the section operation time from the current station to the next station based on the starting station, current station, terminal station, freight categories of the vehicle to be predicted and the trajectory table;

[0012] the current station departure time is the sum of the current time and the dwell time of the current station, and the next station arrival time is the sum of the current station departure time and the interval running time;

[0013] the next station of the current station is added to the predicted route station set, the dwell time of the current station and the interval running time are added to the predicted running time sequence, and the current station departure time and the next station arrival time are added to the predicted arrival and departure time sequence;

[0014] the next station of the current station is taken as the current station, the next station arrival time is taken as the current time, and the steps of obtaining the current time and the current station of the vehicle to be predicted are returned until the current station is the terminal station of the vehicle to be predicted, thereby obtaining the vehicle predicted route and the vehicle predicted arrival and departure time sequence.

[0015] According to the vehicle flow prediction method provided by the application, the next station of the current station, the dwell time of the current station, and the interval running time from the current station to the next station are determined based on the origin station, the current station, the terminal station, the cargo category of the vehicle to be predicted, and the trajectory table.

[0016] The next station with the maximum probability of the current station in the trajectory table is determined as the next station of the current station.

[0017] The average dwell time of the current station is determined as the dwell time of the current station based on the trajectory table.

[0018] The average interval running time from the current station to the next station is determined as the interval running time from the current station to the next station based on the trajectory table.

[0019] According to the vehicle flow prediction method provided by the application, the running of the vehicle to be predicted is predicted based on the origin station, the current station, the terminal station, the cargo category of the vehicle to be predicted, and the trajectory table, and then the following steps are included.

[0020] The arrival or departure information of the vehicle to be predicted is received, and the arrival or departure information includes a station and the arrival or departure time of the station.

[0021] In the case where the station is the terminal station, the vehicle predicted route and the vehicle predicted arrival and departure time sequence are the prediction results.

[0022] In the case where the station is not the terminal station, it is determined whether the station is in the predicted route station set.

[0023] In the case that the station is in the set of stations in the predicted route, the predicted arrival and departure time sequence is updated based on the arrival or departure time of the station and the predicted running time sequence.

[0024] According to the train flow prediction method provided in the present application, in the case that the station is not the terminal station, it is determined whether the station is in the set of stations in the predicted route, and then the following steps are included:

[0025] In the case that the station is not in the set of stations in the predicted route, the station is taken as the current station of the train to be predicted, the arrival or departure time of the station is taken as the current time, the set of stations in the predicted route, the predicted running time sequence and the predicted arrival and departure time sequence are reset, and the step of obtaining the current time and the current station of the train to be predicted is returned.

[0026] According to the train flow prediction method provided in the present application, the statistical calculation based on the train operation information includes the following steps:

[0027] Based on the abnormal data elimination algorithm, the arrival and departure time in the train operation information is subjected to an abnormal data elimination operation, and the average stay time of the current station, the average interval running time from the current station to the next station and the probability included in the track table are calculated.

[0028] The present application also provides a train flow prediction device, which comprises the following modules:

[0029] The train operation information collection module is used for collecting train operation information based on historical train arrival and departure reports; the train operation information includes freight categories, originating stations, terminal stations, passing stations and arrival and departure times of each passing station.

[0030] The track table construction module is used for performing statistical calculation based on the train operation information to construct a track table; each piece of track data of the track table includes freight categories, originating stations, terminal stations, current stations, next stations, average stay times of current stations, average interval running times from current stations to next stations and probabilities.

[0031] The train flow prediction module is used for predicting the operation of the train to be predicted based on the originating station, the current station, the terminal station, the freight categories of the train to be predicted and the track table to obtain a prediction result.

[0032] The present application also provides an electronic device, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor; when the processor executes the computer program, the train flow prediction method according to any one of the above embodiments is realized.

[0033] The application further provides a non-transitory computer-readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the vehicle flow prediction method.

[0034] The application further provides a computer program product, which comprises a computer program, and the computer program is executed by a processor to implement the vehicle flow prediction method.

[0035] The vehicle flow prediction method, device, equipment, medium and computer program product provided by the application are based on historical train arrival and departure reports, a track table containing path and time information is constructed, when the vehicle flow prediction is performed, the current station of the vehicle is determined, the stations possibly passed by the vehicle are determined in the track table containing path and time information, the predicted path of the vehicle is composed, and the arrival and departure time of the vehicle at the stations, the stay time at each station and the interval running time between the stations are determined to compose the predicted arrival and departure time sequence and the predicted running time sequence of the vehicle. During the running of the vehicle, the predicted path of the vehicle and the predicted arrival and departure time sequence of the vehicle are cyclically updated. The track table is constructed, the future running path and the arrival and departure time sequence of the vehicle are cyclically predicted based on the current station and the current time of the vehicle, and a more accurate vehicle flow prediction scheme is provided. BRIEF DESCRIPTION OF DRAWINGS

[0036] In order to more clearly illustrate the technical solutions in the application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are some embodiments of the application, and other drawings can be obtained by those skilled in the art without any creative effort on the basis of these drawings.

[0037] Figure 1 is one of the flow schematic diagrams of the vehicle flow prediction method provided by the application.

[0038] Figure 2 is the second flow schematic diagram of the vehicle flow prediction method provided by the application.

[0039] Figure 3 is the third flow schematic diagram of the vehicle flow prediction method provided by the application.

[0040] Figure 4 is the fourth flow schematic diagram of the vehicle flow prediction method provided by the application.

[0041] Figure 5 is the structural schematic diagram of the vehicle flow prediction device provided by the application.

[0042] Figure 6 is the structural schematic diagram of the electronic equipment provided by the application. DETAILED DESCRIPTION

[0043] In order to make the objects, technical solutions and advantages of the present application clearer, the technical solutions in the present application will be described clearly and completely below in conjunction with the accompanying drawings in the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0044] The present application provides a vehicle flow prediction method, device, equipment, medium and computer program product. Figures 1-6 The present application provides a vehicle flow prediction method, device, equipment, medium and computer program product.

[0045] Figure 1 is one of the flow diagrams of the vehicle flow prediction method provided by the present application, as shown in Figure 1 The method comprises the following steps:

[0046] Step 100, collecting vehicle operation information based on historical train arrival and departure reports; the vehicle operation information comprises freight categories, starting stations, terminal stations, passing stations, and arrival and departure times of each passing station;

[0047] The train arrival and departure report refers to a report reported by a station or a vehicle when a train arrives at or departs from a station, wherein the train arrival report contains information such as the arrival station, the arrival time, the train number, the train type, the starting station and the terminal station of the train marshalling vehicle, and the freight categories; the train departure report contains information such as the departure station, the departure time, the train number, the train type, the starting station and the terminal station of the train marshalling vehicle, and the freight categories. By collecting a certain number of train arrival and departure reports in history, correlating all train arrival and departure reports of the vehicle starting station, the terminal station and the passing station, the vehicle operation information is obtained, which comprises freight categories, starting stations, terminal stations, passing stations, and arrival and departure times of each passing station.

[0048] Step 200, statistically calculating based on the vehicle operation information to construct a track table; each track data of the track table comprises freight categories, starting stations, terminal stations, the current station, the next station, the average stay time of the current station, the average interval operation time from the current station to the next station, and the probability.

[0049] Based on the vehicle operation information obtained by analyzing the historical train arrival and departure reports, the vehicle operation information is statistically calculated to construct a track table, which contains a certain number of track data, wherein each track data contains freight categories, starting stations, terminal stations, the current station, the next station of the current station, the average stay time of the current station, the average interval operation time from the current station to the next station, and the probability of the track occurring in the historical data.

[0050] Step 300, based on the origin station, current station, terminal station, and cargo category of the vehicle to be predicted, and the track table, the operation of the vehicle to be predicted is predicted to obtain a prediction result.

[0051] The origin station, current station, terminal station, and cargo category of the vehicle to be predicted are acquired, and on the basis of the track table, the next station (of the current station) and the corresponding operation time (including the stay time of the current station and the interval operation time from the current station to the next station) are predicted. Through cyclic judgment, the future stations after the current station are predicted, and finally the future operation path of the vehicle and the departure time sequence are obtained, so as to realize accurate prediction of the train flow.

[0052] On the basis of the historical train departure report, a track table containing path and time information is constructed. When the train flow is predicted, the current station of the vehicle is determined, the stations possibly passed by the vehicle are determined in the track table containing path and time information, the prediction path of the vehicle is composed, the departure time of the vehicle passing through the stations and the stay time of each station and the interval operation time between the stations are determined, and the prediction departure time sequence and the prediction operation time sequence of the vehicle are composed. In the process of operation of the vehicle, the prediction path of the vehicle and the prediction departure time sequence of the vehicle are cyclically updated, and finally the prediction result of the train flow is obtained. Through the constructed track table, on the basis of the current station and the current time of the vehicle, the future operation path of the vehicle and the departure time sequence are cyclically predicted, and a more accurate train flow prediction scheme is provided.

[0053] In one embodiment, the train flow prediction method provided by the embodiment of the present application can further include:

[0054] Step 210, based on an abnormal data elimination algorithm, the departure time in the vehicle operation information is subjected to abnormal data elimination operation, and the average stay time of the current station, the average interval operation time from the current station to the next station, and the probability contained in the track table are calculated. The abnormal data elimination algorithm includes a clustering algorithm.

[0055] Since the historical train departure report is reported when the vehicle arrives at or leaves a station, there may be abnormal data. The abnormal data may be the stay time of the vehicle at the station or the interval operation time between adjacent stations. The reasons for the abnormal data may be vehicle scheduling problems at the station or maintenance problems of the interval line, or may be accident problems of a small number of vehicles. In summary, the occurrence of these abnormal conditions will lead to abnormal data, which is reflected in the abnormally long stay time of the vehicle at the station and the interval operation time between adjacent stations. Therefore, the abnormal data elimination algorithm (such as a clustering algorithm and a discrete algorithm) is used to eliminate the abnormal data in the departure time of the vehicle operation information, and then the vehicle operation information after the abnormal data is eliminated is calculated to obtain the average stay time of the current station, the average interval operation time from the current station to the next station, and the probability contained in the track table.

[0056] This embodiment eliminates the influence of abnormal data on the accurate calculation of time data and probability data through an abnormal data elimination algorithm.

[0057] Figure 2 This is the second flow chart of the traffic flow prediction method provided by the present invention. Figure 2 As shown, the method may further include:

[0058] Step 310: Obtain the departure station, final destination station, and cargo category of the vehicle to be predicted;

[0059] Step 320: Obtain the current time and the current station of the vehicle to be predicted;

[0060] Step 330: If the current station is not the final destination of the vehicle to be predicted, determine the next station of the current station, the dwell time at the current station, and the travel time between the current station and the next station based on the departure station, current station, final destination, cargo type, and trajectory table of the vehicle to be predicted.

[0061] Step 340: Calculate the departure time of the current station as the sum of the current time and the stay time at the current station, and the arrival time at the next station as the sum of the departure time of the current station and the interval running time;

[0062] Step 350: Add the next station of the current station to the predicted route station set, add the stay time of the current station and the interval running time to the predicted running time sequence; add the departure time of the current station and the arrival time of the next station to the predicted arrival and departure time sequence;

[0063] Step 360: Set the next station of the current station as the current station, and the arrival time of the next station as the current time, and return to the step of obtaining the current time and the current station of the vehicle to be predicted, until the current station is the final destination of the vehicle to be predicted, and obtain the vehicle predicted route and vehicle predicted arrival and departure time series.

[0064] The vehicle to be predicted in this embodiment is the vehicle The departure station of the vehicle to be predicted is The final destination of the vehicle to be predicted is ; The current time is , the current station is .exist In the case of, according to the trajectory table, determine the next station with the maximum probability of the current station ,Will Add to predicted route station collection In, that is Then the historical average stay time of vehicles at the current station is used as the current station the dwell time of the current station , so as to predict the departure time of the current station Then, the dwell time is added to the predicted running time sequence (initially empty), i.e. The departure time of the current station is added to the predicted departure time sequence , i.e. .

[0065] The is determined, the time at which the vehicle arrives at the next station , is the current time plus the running time between the stations , i.e. The running time between the current station and its next station may be the historical average running time of the vehicle for this section. Then is added to the predicted running time sequence , i.e. is added to the predicted departure time sequence , i.e. . Then ; , the above-mentioned loop continues. Finally, in the case that the current station is the terminal station of the vehicle to be predicted , the vehicle flow prediction result, i.e. the predicted route of the vehicle and the predicted departure time sequence , is output. The embodiment provides a more accurate vehicle flow prediction scheme by predicting the future running route of the vehicle and the departure time sequence on the basis of the current station and the current time of the vehicle.

[0066] Fig. 3 is a flowchart of the third embodiment of the vehicle flow prediction method provided by the present application, as shown in the figure, the method can further include the following steps:

[0067] Figure 3 Step 331, determining the next station with the maximum probability of the current station in the trajectory table as the next station of the current station. Figure 3 Step 332, determining the average dwell time of the current station based on the trajectory table as the dwell time of the current station.

[0068]

[0069] Step 332, determining the average dwell time of the current station based on the trajectory table as the dwell time of the current station.

[0070] ​​​​Step 333, determining, based on the trajectory table, that the average interval running time of the current station to its next station is the interval running time of the current station to its next station.

[0071] In the above embodiment, the basis for determining the next station of the current station can be based on probability, that is, determining the next station with the maximum probability of the current station as the next station of the current station; the basis for determining the stay time of the current station can be based on average value calculation, that is, determining the historical average stay time of the current station as the stay time of the current station; and the basis for determining the interval running time of the current station to its next station can also be based on average value calculation, that is, the historical average interval running time of the current station to its next station is the interval running time of the current station to its next station. The train flow prediction method provided in the application reserves other calculation methods of the next station, the stay time of the current station and the interval running time of the current station to its next station through the trajectory table.

[0072] The embodiment determines the next station of the current station, the stay time of the current station and the interval running time of the current station to its next station accurately through analysis and calculation on the trajectory table.

[0073] Figure 4 Fig. 4 is a flowchart of the train flow prediction method provided in the application, as shown in the figure, the method can further include: Figure 4

[0074] Step 400, receiving the arrival or departure information of the to-be-predicted vehicle; the arrival or departure information includes a station and the arrival or departure time of the station;

[0075] Step 500, in the case that the station is the terminal station, the vehicle prediction route and the vehicle prediction departure time sequence are the prediction results;

[0076] Step 600, in the case that the station is not the terminal station, determining whether the station is in the prediction route station set;

[0077] Step 700, in the case that the station is in the prediction route station set, updating the prediction departure time sequence based on the arrival or departure time of the station and the prediction running time sequence.

[0078] The train flow prediction method provided in the embodiment can further include:

[0079] Step 800, in the case that the station is not in the prediction route station set, taking the station as the current station of the to-be-predicted vehicle, taking the arrival or departure time of the station as the current time, resetting the prediction route station set, the prediction running time sequence and the prediction departure time sequence, and returning to the step of acquiring the current time and the current station of the to-be-predicted vehicle.​

[0080] After the vehicle predicted path and the vehicle predicted arrival and departure time sequence are predicted, in the actual operation of the vehicle to be predicted, the arrival or departure information reported by the station on the path of the vehicle to be predicted is received, the arrival or departure information including the station and the arrival or departure time of the station. Then the station is judged, and the reported station is not the terminal station of the vehicle to be predicted, it is determined that the station is not in the predicted path station set , if the station is in the predicted path station set , based on the arrival or departure time of the vehicle to be predicted at the station , the predicted arrival and departure time sequence is updated based on the predicted operation time sequence.

[0081] In the case that the reported station is the terminal station of the vehicle to be predicted, it means that the vehicle to be predicted has arrived at the terminal station, and the prediction is terminated.

[0082] In the case that the station is not in the predicted path station set, it means that the predicted path station set obtained by the above prediction is wrong, the station is taken as the current station of the vehicle to be predicted, that is , and the execution step of the above loop prediction is returned.

[0083] The embodiment updates the predicted result obtained above by the arrival and departure report of the train in which the vehicle to be predicted is formed, and realizes the real-time and accuracy of the vehicle flow prediction.

[0084] The vehicle flow prediction device provided by the present application is described below, and the vehicle flow prediction device described below can be referred to each other corresponding to the vehicle flow prediction method described above.

[0085] Please refer to Figure 5 , the present application also provides a vehicle flow prediction device, comprising:

[0086] a vehicle operation information collection module 501 for collecting vehicle operation information based on historical train arrival and departure reports; the vehicle operation information includes freight categories, starting stations, terminal stations, stations on the way, arrival and departure times at each station on the way;

[0087] a trajectory table construction module 502 for statistical calculation based on the vehicle operation information to construct a trajectory table; each piece of trajectory data of the trajectory table includes freight categories, starting stations, terminal stations, current stations, next stations, average stay times at current stations, average interval operation times from current stations to next stations, and probabilities; ​​

[0088] The vehicle flow prediction module 503 is configured to predict the operation of the vehicle to be predicted based on the origin station, the current station, the destination station, the cargo category of the vehicle to be predicted, and the trajectory table, and obtain a prediction result.

[0089] Optionally, the vehicle flow prediction module comprises:

[0090] a first obtaining unit configured to obtain the origin station, the destination station, and the cargo category of the vehicle to be predicted;

[0091] a second obtaining unit configured to obtain the current time and the current station of the vehicle to be predicted;

[0092] a first determining unit configured to, in a case where the current station is not the destination station of the vehicle to be predicted, determine, based on the origin station, the current station, the destination station, the cargo category of the vehicle to be predicted, and the trajectory table, the next station of the current station, the stay time of the current station, and the interval operation time from the current station to the next station of the current station;

[0093] a time calculating unit configured to calculate the departure time of the current station as the sum of the current time and the stay time of the current station, and the arrival time of the next station as the sum of the departure time of the current station and the interval operation time;

[0094] a data set adding unit configured to add the next station of the current station to a predicted route station set, add the stay time of the current station and the interval operation time to a predicted operation time sequence, and add the departure time of the current station and the arrival time of the next station to a predicted arrival and departure time sequence;

[0095] a loop prediction unit configured to take the next station of the current station as the current station, take the arrival time of the next station as the current time, and return to the step of obtaining the current time and the current station of the vehicle to be predicted, until the current station is the destination station of the vehicle to be predicted, to obtain a vehicle predicted route and a vehicle predicted arrival and departure time sequence.

[0096] Optionally, the first determining unit comprises:

[0097] a next station determining unit configured to determine the next station of the current station with the maximum probability in the trajectory table as the next station of the current station;

[0098] a stay time determining unit configured to determine, based on the trajectory table, the average stay time of the current station as the stay time of the current station;

[0099] an interval operation time determining unit configured to determine, based on the trajectory table, the average interval operation time from the current station to the next station of the current station as the interval operation time from the current station to the next station of the current station.

[0100] Optionally, the train flow prediction device further comprises:

[0101] an arrival or departure information receiving module, configured to receive arrival or departure information of the to-be-predicted vehicle; the arrival or departure information comprises a station and an arrival or departure time of the station;

[0102] a prediction result determining module, configured to, in a case where the station is the terminal station, determine that the vehicle prediction route and the vehicle prediction arrival or departure time sequence are the prediction result;

[0103] a station determining module, configured to, in a case where the station is not the terminal station, determine whether the station is in the predicted route station set;

[0104] a prediction arrival or departure time sequence updating module, configured to, in a case where the station is in the predicted route station set, update the prediction arrival or departure time sequence based on the arrival or departure time of the station and the prediction running time sequence.

[0105] Optionally, the train flow prediction device further comprises:

[0106] a loop prediction module, configured to, in a case where the station is not in the predicted route station set, reset the predicted route station set, the prediction running time sequence and the prediction arrival or departure time sequence, and return to the step of obtaining the current time and the current station of the to-be-predicted vehicle, with the station as the current station of the to-be-predicted vehicle and the arrival or departure time of the station as the current time.

[0107] Optionally, the trajectory table constructing module comprises:

[0108] an abnormal data elimination unit, configured to perform an abnormal data elimination operation on the arrival or departure time in the vehicle running information based on an abnormal data elimination algorithm to calculate the average stay time of the station, the average interval running time from the station to the next station and the probability included in the trajectory table; the abnormal data elimination algorithm comprises a clustering algorithm.

[0109] Figure 6 An example of an entity structure diagram of an electronic device is shown in FIG. 1. Figure 6As shown, the electronic device can include a processor 610, a communications interface 620, a memory 630, and a communications bus 640, wherein the processor 610, the communications interface 620, and the memory 630 complete communications with each other through the communications bus 640. The processor 610 can invoke a logic instruction in the memory 630 to execute a train flow prediction method, which includes collecting vehicle operation information based on historical train arrival and departure reports; the vehicle operation information includes a cargo category, a departure station, a terminal station, a passing station, and a departure time at each passing station; performing statistical calculation based on the vehicle operation information to construct a trajectory table; each piece of trajectory data of the trajectory table includes a cargo category, a departure station, a terminal station, a current station, a next station, an average stay time at the current station, an average interval operation time from the current station to the next station, and a probability; and predicting operation of a to-be-predicted vehicle based on a departure station, a current station, a terminal station, a cargo category of the to-be-predicted vehicle, and the trajectory table to obtain a prediction result.

[0110] In addition, the logic instruction in the memory 630 described above can be implemented in the form of a software functional unit and sold or used as an independent product, and can be stored in a computer-readable storage medium. Based on such understanding, the technical solutions of the present application essentially or the part that contributes to the prior art or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes several instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various program code storage media.

[0111] In another aspect, the present application also provides a computer program product comprising a computer program, which can be stored on a non-transitory computer readable storage medium, and the computer program is executable by a processor to cause a computer to perform the train flow prediction method provided by any of the above methods, which comprises: collecting vehicle operation information based on historical train arrival and departure reports; the vehicle operation information comprises freight categories, originating stations, terminal stations, passing stations, and arrival and departure times at each passing station; performing statistical calculations based on the vehicle operation information to construct a track table; each track data of the track table comprises freight categories, originating stations, terminal stations, current stations, next stations, average stay times at current stations, average interval operation times from current stations to next stations, and probabilities; and predicting the operation of a vehicle to be predicted based on the originating station, current station, terminal station, freight category of the vehicle to be predicted, and the track table to obtain a prediction result.

[0112] In yet another aspect, the present application also provides a non-transitory computer readable storage medium having a computer program stored thereon, which is executable by a processor to implement a train flow prediction method provided by any of the above methods, which comprises: collecting vehicle operation information based on historical train arrival and departure reports; the vehicle operation information comprises freight categories, originating stations, terminal stations, passing stations, and arrival and departure times at each passing station; performing statistical calculations based on the vehicle operation information to construct a track table; each track data of the track table comprises freight categories, originating stations, terminal stations, current stations, next stations, average stay times at current stations, average interval operation times from current stations to next stations, and probabilities; and predicting the operation of a vehicle to be predicted based on the originating station, current station, terminal station, freight category of the vehicle to be predicted, and the track table to obtain a prediction result.

[0113] The device embodiments described above are merely illustrative, wherein the units described as separate components can or can not be physically separated, and the components displayed as units can or can not be physical units, i.e., they can be located in one place, or distributed on multiple network units. Part or all of the modules can be selected to achieve the purpose of the present embodiment scheme according to actual needs. Those skilled in the art can understand and implement it without creative labor.

[0114] Those skilled in the art can clearly understand the technical solutions of the various embodiments from the above description of the embodiments, and the various embodiments can be implemented by means of software with the necessary general hardware platforms, and of course, can also be implemented by hardware. Based on such understanding, the above technical solutions, essentially or in other words, the part of the prior art that makes a contribution, can be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as a ROM / RAM, a magnetic disk, an optical disk, and the like, and includes a number of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0115] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for some technical features therein; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A traffic flow prediction method, characterized in that: include: Collect vehicle operation information based on historical train arrival and departure reports; The vehicle operation information includes cargo type, departure station, final destination, en route stations, and departure and arrival time of each en route station; Perform statistical calculations based on the vehicle operation information to construct a trajectory table; each trajectory data in the trajectory table includes cargo category, departure station, final destination station, current station, next station, average stay time at current station, average interval travel time from current station to next station, and probability; Based on the departure station, current station, final station, cargo category and the trajectory table of the vehicle to be predicted, the operation of the vehicle to be predicted is predicted to obtain a prediction result; The step of predicting the operation of the vehicle to be predicted based on the departure station, current station, destination station, cargo category, and the trajectory table to obtain a prediction result includes: Obtain the origin, destination, and cargo category of the vehicle to be predicted; Obtaining the current time and the current station of the vehicle to be predicted; If the current station is not the final destination of the vehicle to be predicted, determining the next station of the current station, the stay time at the current station, and the interval travel time from the current station to the next station based on the departure station, current station, final destination, cargo type, and trajectory table of the vehicle to be predicted; The departure time at the current station is calculated as the sum of the current time and the stay time at the current station, and the arrival time at the next station is calculated as the sum of the departure time at the current station and the interval travel time; Add the next station of the current station to the predicted route station set, add the stay time of the current station and the interval running time to the predicted running time sequence; add the departure time of the current station and the arrival time of the next station to the predicted arrival and departure time sequence; The next station of the current station is used as the current station, the arrival time of the next station is used as the current time, and the step of obtaining the current time and the current station of the vehicle to be predicted is returned to, until the current station is the final destination of the vehicle to be predicted, and the vehicle predicted route and vehicle predicted arrival and departure time series are obtained.

2. The traffic flow prediction method according to claim 1, characterized in that: The step of determining the next station of the current station, the dwell time of the current station, and the interval travel time from the current station to the next station based on the departure station, current station, destination station, cargo category, and the trajectory table of the vehicle to be predicted includes: Determine the next station with the highest probability of the current station in the trajectory table as the next station of the current station; Based on the trajectory table, determining the average stay time of the current station as the stay time of the current station; Based on the trajectory table, the average interval running time from the current station to the next station is determined as the interval running time from the current station to the next station.

3. The traffic flow prediction method according to claim 1, characterized in that: The method further includes: predicting the operation of the vehicle to be predicted based on the departure station, current station, destination station, cargo category and trajectory table of the vehicle to be predicted to obtain a prediction result, and then including: Receiving arrival or departure information of the vehicle to be predicted; the arrival or departure information includes a station and the arrival or departure time of the station; In the case where the station is the final destination, the predicted vehicle route and the predicted vehicle arrival and departure time series are the prediction results; In the case that the station is not the final destination station, determining whether the station is in the predicted route station set; In a case where the station is in the predicted route station set, the predicted arrival and departure time series is updated based on the arrival or departure time of the station and the predicted operation time series.

4. The traffic flow prediction method according to claim 3, characterized in that: In the case where the station is not the final station, determining whether the station is in the predicted path station set, then comprising: In the case that the station is not in the predicted route station set, the station is used as the current station of the vehicle to be predicted, the arrival or departure time of the station is used as the current time, the predicted route station set, the predicted operation time series and the predicted arrival and departure time series are reset, and the step of obtaining the current time and the current station of the vehicle to be predicted is returned.

5. The traffic flow prediction method according to claim 1, characterized in that: The performing statistical calculation based on the vehicle operation information to construct a trajectory table includes: Based on the abnormal data elimination algorithm, abnormal data elimination operations are performed on the arrival and departure times in the vehicle operation information, and the average stay time at the station, the average interval running time from the station to the next station, and the probability contained in the trajectory table are calculated; the abnormal data elimination algorithm includes a clustering algorithm.

6. A traffic flow prediction device, characterized in that: include: The vehicle operation information collection module is used to collect vehicle operation information based on historical train arrival and departure reports; The vehicle operation information includes cargo type, departure station, final destination, en route stations, and departure and arrival time of each en route station; A trajectory table construction module is used to perform statistical calculations based on the vehicle operation information to construct a trajectory table; each trajectory data in the trajectory table includes cargo category, departure station, final destination station, current station, next station, average stay time at current station, average interval travel time from current station to next station, and probability; A traffic flow prediction module is used to predict the operation of the vehicle to be predicted based on the departure station, current station, final station, cargo category and the trajectory table to obtain a prediction result; The step of predicting the operation of the vehicle to be predicted based on the departure station, current station, destination station, cargo category, and the trajectory table to obtain a prediction result includes: Obtain the origin, destination, and cargo category of the vehicle to be predicted; Obtaining the current time and the current station of the vehicle to be predicted; If the current station is not the final destination of the vehicle to be predicted, determining the next station of the current station, the stay time at the current station, and the interval travel time from the current station to the next station based on the departure station, current station, final destination, cargo type, and trajectory table of the vehicle to be predicted; The departure time at the current station is calculated as the sum of the current time and the stay time at the current station, and the arrival time at the next station is calculated as the sum of the departure time at the current station and the interval travel time; Add the next station of the current station to the predicted route station set, add the stay time of the current station and the interval running time to the predicted running time sequence; add the departure time of the current station and the arrival time of the next station to the predicted arrival and departure time sequence; The next station of the current station is used as the current station, the arrival time of the next station is used as the current time, and the step of obtaining the current time and the current station of the vehicle to be predicted is returned to, until the current station is the final destination of the vehicle to be predicted, and the vehicle predicted route and vehicle predicted arrival and departure time series are obtained.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the traffic flow prediction method according to any one of claims 1 to 5 is implemented.

8. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the traffic flow prediction method according to any one of claims 1 to 5 is implemented.

9. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the traffic flow prediction method according to any one of claims 1 to 5 is implemented.

Images