Train passing time prediction method and system based on big data

Abstract

The invention relates to the technical field of intelligent traffic, and discloses a train passing time prediction method and system based on big data, and the method comprises the steps: obtaining a historical passing data record, determining a historical advanced passing time sequence and a historical delayed passing time sequence; and calculating to obtain an advanced transit time gentle coefficient, an advanced transit time hopping coefficient, a delayed transit time gentle coefficient and a delayed transit time hopping coefficient, and calculating a historical transit time change factor. Historical driving speeds are collected, and historical driving speed change factors are calculated; and determining a train passing time prediction factor according to the historical passing time change factor and the historical driving speed change factor. The real-time expected passing time is predicted according to the train passing time prediction factor, the train passing prediction time is obtained, historical passing data records are intelligently analyzed, prediction precision and prediction reliability are improved, prediction errors are eliminated, conflicts between vehicles and trains are effectively avoided, and the passing safety and efficiency of harbor district roads are greatly improved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent transportation technology, and in particular to a method and system for predicting train travel time based on big data. Background Art

[0002] There are many ways to transport cargo in the port area, and train transportation is an extremely important part of it. As an important cargo distribution center, the port area undertakes a large amount of cargo handling tasks. Trains play a key role in cargo transportation with their advantages of large capacity and long-distance transportation. Their efficient operation has a decisive impact on the overall operational efficiency of the port area.

[0003] In the existing technology, the train travel time prediction is a conventional traffic prediction model built on limited traffic flow data and simple road information. There are not only frequent trains in the port area, but also a large number of trucks and loading and unloading equipment shuttling through the complex road network. The road layout, train running routes, cargo loading and unloading operations and other situations are very different from conventional scenarios, making it difficult to adapt to the complex and unique environment of the port area. In addition, the conventional traffic prediction model ignores the rich information contained in the historical train travel data. The historical travel data can reflect some long-term laws and potential trends in the process of train travel, and has an undeniable value for more comprehensive and accurate prediction of train travel time. These special circumstances make it impossible for existing methods to accurately predict the travel time of trains, and it is even more difficult to reasonably plan the traffic in the port area based on the train travel time. Summary of the invention

[0004] The embodiments of the present invention provide a train travel time prediction method and system based on big data. The present invention can perform intelligent analysis on historical train travel data and real-time train travel data, improve the prediction accuracy and reliability of train travel time, eliminate prediction errors, effectively avoid conflicts between vehicles and trains, and greatly improve the travel safety and efficiency of port area roads.

[0005] In order to achieve the above object, the present invention provides a train travel time prediction method based on big data, comprising: Determine a train to be predicted, obtain multiple historical travel data records of the train to be predicted, analyze each historical travel data record, and determine a historical advance travel time sequence and a historical delay travel time sequence; Performing smoothing calculation and wave skipping calculation on the historical advance passage time sequence to obtain the advance passage time smoothing coefficient and the advance passage time wave skipping coefficient corresponding to the historical advance passage time sequence, and performing smoothing calculation and wave skipping calculation on the historical delayed passage time sequence to obtain the delayed passage time smoothing coefficient and the delayed passage time wave skipping coefficient corresponding to the historical delayed passage time sequence; Calculate the historical travel time variation factor of the train to be predicted based on the advance travel time smoothing coefficient, the advance travel time skipping coefficient, the delayed travel time smoothing coefficient and the delayed travel time skipping coefficient; Collecting the historical travel speed corresponding to each historical travel data record, and calculating the historical travel speed change factor of the train to be predicted based on all the historical travel speeds; Determine a train travel time prediction factor of the train to be predicted according to the historical travel time change factor and the historical travel speed change factor; Predeploy information collection equipment to collect real-time travel information of the train to be predicted, determine the real-time expected travel time based on the real-time travel information, and predict and adjust the real-time expected travel time according to the train travel time prediction factor to obtain the train travel prediction time of the train to be predicted.

[0006] Furthermore, when obtaining a plurality of historical travel data records of the train to be predicted, analyzing each historical travel data record, and determining a historical advance travel time sequence and a historical delay travel time sequence, the method includes: Obtain the historical expected travel time corresponding to each historical travel data record; Analyze each historical travel data record to determine the corresponding historical actual travel time, and generate a travel advance mark, a travel expectation mark, and a travel delay mark for the historical travel data record according to the historical actual travel time and the historical expected travel time; When the historical actual travel time is less than the historical expected travel time, the advance travel mark is generated for the corresponding historical travel data record; When the historical actual travel time is equal to the historical expected travel time, the travel expectation mark is generated for the corresponding historical travel data record; When the historical actual travel time is greater than the historical expected travel time, the travel delay mark is generated for the corresponding historical travel data record; Extracting the historical actual travel time corresponding to each advance travel mark, calculating the advance travel time difference between the historical actual travel time and the historical expected travel time, and determining the historical advance travel time sequence according to all the advance travel time differences; The historical actual travel time corresponding to each delayed travel mark is extracted, the delayed travel time difference between the historical actual travel time and the historical expected travel time is calculated, and the historical delayed travel time sequence is determined according to all the delayed travel time differences.

[0007] Furthermore, when the smoothing calculation and the wave-skipping calculation are performed on the historical advance passage time sequence to obtain the advance passage time smoothing coefficient and the advance passage time wave-skipping coefficient corresponding to the historical advance passage time sequence, it includes: The advance travel time smoothing coefficient corresponding to the historical advance travel time series is calculated according to the following formula: ; Among them, s1 is the advance travel time smoothing coefficient corresponding to the historical advance travel time series, a1 is the number of advance travel time differences in the historical advance travel time series, and f d+1 is the d+1th advance travel time difference in the historical advance travel time series, f d is the dth advance passage time difference in the historical advance passage time series; The advance travel time jump coefficient corresponding to the historical advance travel time series is calculated according to the following formula: ; Among them, s2 is the advance passage time jump coefficient corresponding to the historical advance passage time series.

[0008] Furthermore, when the smoothing calculation and the wave-skipping calculation are performed on the historical delayed passage time series to obtain the delayed passage time smoothing coefficient and the delayed passage time wave-skipping coefficient corresponding to the historical delayed passage time series, it includes: The delayed travel time smoothing coefficient corresponding to the historical delayed travel time series is calculated according to the following formula: ; Among them, s3 is the delayed travel time smoothing coefficient corresponding to the historical delayed travel time series, a2 is the number of delayed travel time differences in the historical delayed travel time series, and h g+1 is the g+1th delayed travel time difference in the historical delayed travel time series, h g is the g-th delayed passage time difference in the historical delayed passage time series; The delayed travel time jump coefficient corresponding to the historical delayed travel time series is calculated according to the following formula: ; Among them, s4 is the delayed passage time jump coefficient corresponding to the historical delayed passage time series.

[0009] Furthermore, when calculating the historical travel time variation factor of the train to be predicted based on the advance travel time smoothing coefficient, the advance travel time skipping coefficient, the delayed travel time smoothing coefficient and the delayed travel time skipping coefficient, it includes: Determine the number of advance pass marks of the advance pass mark, determine the number of expected pass marks of the expected pass mark, and determine the number of delayed pass marks of the delayed pass mark; The historical travel time variation factor of the train to be predicted is calculated according to the following formula: ; Among them, k is the historical travel time change factor of the train to be predicted, w1 is the number of advance travel marks, w2 is the number of expected travel marks, and w3 is the number of delayed travel marks.

[0010] Furthermore, when collecting the historical travel speed corresponding to each historical travel data record and calculating the historical travel speed change factor of the train to be predicted according to all the historical travel speeds, it includes: All historical driving speeds are randomly assigned to the blank point map template, and the location of the maximum historical driving speed is determined; Determine an initial historical driving speed and a terminal historical driving speed on the blank point map template, and count a first number of historical driving speeds between the initial historical driving speed and the maximum historical driving speed; Counting a second number of historical driving speeds between the terminal historical driving speed and the maximum historical driving speed; determining whether both the first number and the second number are greater than a preset number, and if so, dividing the historical driving speeds between the initial historical driving speed and the maximum historical driving speed into a first historical driving speed sequence, wherein the first historical driving speed sequence does not include the maximum historical driving speed; dividing the historical driving speeds between the terminal historical driving speed and the maximum historical driving speed into a second historical driving speed sequence, wherein the second historical driving speed sequence does not include the maximum historical driving speed; If not, all historical driving speeds are randomly redistributed until both the first number and the second number obtained are greater than the preset number; The historical running speed change factor of the train to be predicted is calculated according to the first historical running speed sequence and the second historical running speed sequence.

[0011] Further, when calculating the historical travel speed change factor of the train to be predicted according to the first historical travel speed sequence and the second historical travel speed sequence, it includes: The historical speed change factor of the train to be predicted is calculated according to the following formula: ; Wherein, e is the historical speed change factor of the train to be predicted, r1 is the initial historical speed, r2 is the maximum historical speed, r3 is the terminal historical speed, t1 is the first calculation coefficient, t2 is the second calculation coefficient, t3 is the third calculation coefficient, t1+t2+t3=1, t1>0, t2>0, t3>0, y is the number of historical speeds, u1 is the variance of the historical speeds other than the initial historical speed in the first historical speed sequence, u2 is the variance of the historical speeds other than the terminal historical speed in the second historical speed sequence, and u is the variance of all historical speeds other than the maximum historical speed.

[0012] Further, when determining the train transit time prediction factor of the train to be predicted according to the historical transit time variation factor and the historical travel speed variation factor, it includes: configuring a first calculation weight for the historical travel time change factor, and calculating a first product value of the historical travel time change factor and the first calculation weight; configuring a second calculation weight for the historical driving speed change factor, and calculating a second product value of the historical driving speed change factor and the second calculation weight, wherein the first calculation weight is greater than the second calculation weight; A sum of the first product value and the second product value is determined and used as a train travel time prediction factor of the train to be predicted.

[0013] Further, when the real-time expected travel time is predicted and adjusted according to the train travel time prediction factor to obtain the train travel prediction time of the train to be predicted, it includes: Presetting a plurality of preset train travel time prediction factors; Presetting a plurality of preset travel time prediction adjustment coefficients; According to the relationship between the train travel time prediction factor and the preset train travel time prediction factor, a corresponding preset travel time prediction adjustment coefficient is selected, wherein the train travel time prediction factor and the preset travel time prediction adjustment coefficient are in a positive proportional relationship; The product value of the selected preset travel time prediction adjustment coefficient and the real-time expected travel time is calculated as the train travel prediction time of the train to be predicted.

[0014] In order to achieve the above object, the present invention also provides a train travel time prediction system based on big data, comprising: A sequence determination module is used to determine a train to be predicted, obtain multiple historical travel data records of the train to be predicted, analyze each historical travel data record, and determine a historical advance travel time sequence and a historical delay travel time sequence; A coefficient calculation module is used to perform smoothing calculation and wave skipping calculation on the historical advance passage time sequence to obtain the advance passage time smoothing coefficient and the advance passage time wave skipping coefficient corresponding to the historical advance passage time sequence, and perform smoothing calculation and wave skipping calculation on the historical delayed passage time sequence to obtain the delayed passage time smoothing coefficient and the delayed passage time wave skipping coefficient corresponding to the historical delayed passage time sequence; A first calculation module is used to calculate the historical travel time variation factor of the train to be predicted based on the advance travel time smoothing coefficient, the advance travel time skipping coefficient, the delayed travel time smoothing coefficient and the delayed travel time skipping coefficient; The second calculation module is used to collect the historical travel speed corresponding to each historical travel data record, and calculate the historical travel speed change factor of the train to be predicted according to all the historical travel speeds; A factor determination module, used to determine the train travel time prediction factor of the train to be predicted according to the historical travel time change factor and the historical travel speed change factor; The travel prediction module is used to pre-deploy information collection equipment to collect real-time travel information of the train to be predicted, determine the real-time expected travel time based on the real-time travel information, and predict and adjust the real-time expected travel time according to the train travel time prediction factor to obtain the train travel prediction time of the train to be predicted.

[0015] Compared with the prior art, the present invention has the following beneficial effects: The present invention discloses a train travel time prediction method and system based on big data, which obtains historical travel data records, determines the historical advance travel time series and the historical delayed travel time series, calculates the advance travel time smoothing coefficient, the advance travel time jump wave coefficient, the delayed travel time smoothing coefficient and the delayed travel time jump wave coefficient, and calculates the historical travel time change factor. The historical driving speed is collected and the historical driving speed change factor is calculated. The train travel time prediction factor is determined according to the historical travel time change factor and the historical driving speed change factor. The real-time expected travel time is predicted according to the train travel time prediction factor to obtain the train travel prediction time, and the historical travel data records are intelligently analyzed to improve the prediction accuracy and prediction reliability, eliminate prediction errors, effectively avoid conflicts between vehicles and trains, and greatly improve the travel safety and efficiency of port area roads. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings: Figure 1 A schematic diagram of the flow of a train travel time prediction method based on big data in an embodiment of the present invention is shown; Figure 2 A structural schematic diagram of a train travel time prediction system based on big data in an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0017] The specific implementation of the present invention is further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0018] In the description of the present application, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0019] The terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0020] In the description of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0021] The following is a description of preferred embodiments of the present invention with reference to the accompanying drawings.

[0022] like Figure 1 As shown, an embodiment of the present invention discloses a method for predicting train travel time based on big data, comprising: S110: Determine a train to be predicted, obtain multiple historical travel data records of the train to be predicted, analyze each historical travel data record, and determine a historical advance travel time sequence and a historical delay travel time sequence; In this embodiment, the historical travel data record refers to the historical travel behavior of the train to be predicted, including the passing time, travel speed, position change, stop sites, etc.

[0023] In this embodiment, the number of historical traffic data records obtained is preferably 20, which can be adjusted according to actual conditions.

[0024] In some embodiments of the present application, when obtaining a plurality of historical travel data records of the train to be predicted, analyzing each historical travel data record, and determining a historical advance travel time sequence and a historical delay travel time sequence, the process includes: Obtain the historical expected travel time corresponding to each historical travel data record; Analyze each historical travel data record to determine the corresponding historical actual travel time, and generate a travel advance mark, a travel expectation mark, and a travel delay mark for the historical travel data record according to the historical actual travel time and the historical expected travel time; When the historical actual travel time is less than the historical expected travel time, the advance travel mark is generated for the corresponding historical travel data record; When the historical actual travel time is equal to the historical expected travel time, the travel expectation mark is generated for the corresponding historical travel data record; When the historical actual travel time is greater than the historical expected travel time, the travel delay mark is generated for the corresponding historical travel data record; Extracting the historical actual travel time corresponding to each advance travel mark, calculating the advance travel time difference between the historical actual travel time and the historical expected travel time, and determining the historical advance travel time sequence according to all the advance travel time differences; The historical actual travel time corresponding to each delayed travel mark is extracted, the delayed travel time difference between the historical actual travel time and the historical expected travel time is calculated, and the historical delayed travel time sequence is determined according to all the delayed travel time differences.

[0025] In this embodiment, the historical expected travel time is a time obtained according to the train travel information when the train passes, which is a theoretical value, such as the historical expected travel time is 15 minutes. The historical actual travel time refers to the time taken by the train from the beginning to the end of the passage, and the historical actual travel time may be equal to or different from the historical expected travel time, such as the historical actual travel time is 12 minutes.

[0026] In this embodiment, the difference between the historical actual travel time and the historical expected travel time is calculated as the advance travel time difference or the delay travel time difference.

[0027] The beneficial effect of the above technical solution is: the present invention determines the historical advance travel time sequence according to all the advance travel time differences, and determines the historical delayed travel time sequence according to all the delayed travel time differences, thereby realizing the fine division of the historical actual travel time and providing a prerequisite for the calculation of the historical travel time change factor.

[0028] S120: performing a smoothing calculation and a wave skipping calculation on the historical advance travel time sequence to obtain an advance travel time smoothing coefficient and an advance travel time wave skipping coefficient corresponding to the historical advance travel time sequence; performing a smoothing calculation and a wave skipping calculation on the historical delayed travel time sequence to obtain a delayed travel time smoothing coefficient and a delayed travel time wave skipping coefficient corresponding to the historical delayed travel time sequence; In some embodiments of the present application, when performing smoothing calculation and wave skipping calculation on the historical advance passage time sequence to obtain the advance passage time smoothing coefficient and advance passage time wave skipping coefficient corresponding to the historical advance passage time sequence, it includes: The advance travel time smoothing coefficient corresponding to the historical advance travel time series is calculated according to the following formula: ; Among them, s1 is the advance travel time smoothing coefficient corresponding to the historical advance travel time series, a1 is the number of advance travel time differences in the historical advance travel time series, and f d+1 is the d+1th advance travel time difference in the historical advance travel time series, f d is the dth advance passage time difference in the historical advance passage time series; The advance travel time jump coefficient corresponding to the historical advance travel time series is calculated according to the following formula: ; Among them, s2 is the advance passage time jump coefficient corresponding to the historical advance passage time series.

[0029] The beneficial effect of the above technical solution is: the present invention obtains the advance passage time smoothing coefficient and the advance passage time jumping coefficient according to the historical advance passage time sequence, thereby ensuring the calculation accuracy of the advance passage time smoothing coefficient and the advance passage time jumping coefficient, and through two calculation methods, ensuring the comprehensiveness of the calculation, which can realize the smooth calculation of the historical advance passage time sequence, that is, sequential calculation, reflecting the law of historical actual passage time, and can also realize the jumping calculation of the historical advance passage time sequence, that is, holistic calculation, further reflecting the law of historical actual passage time.

[0030] In some embodiments of the present application, when performing smoothing calculation and wave skipping calculation on the historical delayed passage time sequence to obtain a delayed passage time smoothing coefficient and a delayed passage time wave skipping coefficient corresponding to the historical delayed passage time sequence, it includes: The delayed travel time smoothing coefficient corresponding to the historical delayed travel time series is calculated according to the following formula: ; Among them, s3 is the delayed travel time smoothing coefficient corresponding to the historical delayed travel time series, a2 is the number of delayed travel time differences in the historical delayed travel time series, and h g+1 is the g+1th delayed travel time difference in the historical delayed travel time series, h g is the g-th delayed passage time difference in the historical delayed passage time series; The delayed travel time jump coefficient corresponding to the historical delayed travel time series is calculated according to the following formula: ; Among them, s4 is the delayed passage time jump coefficient corresponding to the historical delayed passage time series.

[0031] S130: Calculating the historical travel time variation factor of the train to be predicted based on the advance travel time smoothing coefficient, the advance travel time skipping coefficient, the delayed travel time smoothing coefficient and the delayed travel time skipping coefficient; In some embodiments of the present application, when calculating the historical travel time variation factor of the train to be predicted based on the advance travel time smoothing coefficient, the advance travel time skipping coefficient, the delayed travel time smoothing coefficient and the delayed travel time skipping coefficient, it includes: Determine the number of advance pass marks of the advance pass mark, determine the number of expected pass marks of the expected pass mark, and determine the number of delayed pass marks of the delayed pass mark; The historical travel time variation factor of the train to be predicted is calculated according to the following formula: ; Among them, k is the historical travel time change factor of the train to be predicted, w1 is the number of advance travel marks, w2 is the number of expected travel marks, and w3 is the number of delayed travel marks.

[0032] The beneficial effect of the above technical solution is: the present invention calculates the historical travel time variation factor of the train to be predicted according to the advance travel time smoothing coefficient, the advance travel time skipping coefficient, the delayed travel time smoothing coefficient and the delayed travel time skipping coefficient, thereby ensuring the calculation accuracy and efficiency of the historical travel time variation factor, and comprehensively reflects the changing law and trend of the historical travel time through the historical travel time variation factor. At the same time, the historical travel time variation factor is constrained for calculation through the number of advance travel marks, the number of expected travel marks and the number of delayed travel marks, thereby further ensuring the accuracy of the analysis and eliminating errors.

[0033] S140: collecting the historical travel speed corresponding to each historical travel data record, and calculating the historical travel speed change factor of the train to be predicted according to all the historical travel speeds; In this embodiment, the travel speed of the train changes during its travel, and therefore, the average of the historical travel speeds corresponding to each historical travel data record is taken as the historical travel speed corresponding to each historical travel data record.

[0034] In some embodiments of the present application, when collecting the historical travel speed corresponding to each historical travel data record and calculating the historical travel speed change factor of the train to be predicted according to all the historical travel speeds, it includes: All historical driving speeds are randomly assigned to the blank point map template, and the location of the maximum historical driving speed is determined; Determine an initial historical driving speed and a terminal historical driving speed on the blank point map template, and count a first number of historical driving speeds between the initial historical driving speed and the maximum historical driving speed; Counting a second number of historical driving speeds between the terminal historical driving speed and the maximum historical driving speed; determining whether both the first number and the second number are greater than a preset number, and if so, dividing the historical driving speeds between the initial historical driving speed and the maximum historical driving speed into a first historical driving speed sequence, wherein the first historical driving speed sequence does not include the maximum historical driving speed; dividing the historical driving speeds between the terminal historical driving speed and the maximum historical driving speed into a second historical driving speed sequence, wherein the second historical driving speed sequence does not include the maximum historical driving speed; If not, all historical driving speeds are randomly redistributed until both the first number and the second number obtained are greater than the preset number; The historical running speed change factor of the train to be predicted is calculated according to the first historical running speed sequence and the second historical running speed sequence.

[0035] In this embodiment, the blank point chart template is a pre-designed scatter chart framework that does not contain any data series but has a basic chart structure and format settings. There are horizontal axis (X axis) and vertical axis (Y axis), and there are scales on the axis to determine the location of the data point. The vertical axis has no specific value label in the blank template, and the value of the horizontal axis presents an arithmetic progression, such as 1, 2, 3, 4, 5, etc., which is convenient for the corresponding labeling of historical driving speed.

[0036] In this embodiment, all historical driving speeds are randomly assigned to the blank point graph template, and a scatter plot of historical driving speeds can be obtained.

[0037] In this embodiment, the initial historical driving speed refers to the first randomly assigned historical driving speed on the scatter plot, and the terminal historical driving speed refers to the last randomly assigned historical driving speed on the scatter plot.

[0038] In this embodiment, if the number of maximum historical driving speeds is greater than or equal to 2, a maximum historical driving speed is randomly selected and the position is determined.

[0039] In this embodiment, the preset number is preferably 7, and can be adjusted according to actual conditions.

[0040] In this embodiment, when counting the first number, the number of initial historical driving speeds is also counted, but the number of maximum historical driving speeds is not counted, and when counting the second number, the number of terminal historical driving speeds is also counted, but the number of maximum historical driving speeds is not counted.

[0041] The beneficial effect of the above technical solution is: the present invention calculates the historical speed change factor of the train to be predicted based on the first historical speed sequence and the second historical speed sequence, thereby ensuring the calculation accuracy of the historical speed change factor, eliminating the need for manual participation in the calculation, and eliminating subjective calculation errors. At the same time, the historical speed change factor can reflect the historical speed law of the train to be predicted, providing a basis for calculating the train travel time prediction factor.

[0042] In some embodiments of the present application, when calculating the historical travel speed change factor of the train to be predicted according to the first historical travel speed sequence and the second historical travel speed sequence, it includes: The historical speed change factor of the train to be predicted is calculated according to the following formula: ; Wherein, e is the historical speed change factor of the train to be predicted, r1 is the initial historical speed, r2 is the maximum historical speed, r3 is the terminal historical speed, t1 is the first calculation coefficient, t2 is the second calculation coefficient, t3 is the third calculation coefficient, t1+t2+t3=1, t1>0, t2>0, t3>0, y is the number of historical speeds, u1 is the variance of the historical speeds other than the initial historical speed in the first historical speed sequence, u2 is the variance of the historical speeds other than the terminal historical speed in the second historical speed sequence, and u is the variance of all historical speeds other than the maximum historical speed.

[0043] S150: Determine a train travel time prediction factor of the train to be predicted according to the historical travel time change factor and the historical travel speed change factor; In some embodiments of the present application, when determining the train transit time prediction factor of the train to be predicted according to the historical transit time change factor and the historical travel speed change factor, it includes: configuring a first calculation weight for the historical travel time change factor, and calculating a first product value of the historical travel time change factor and the first calculation weight; configuring a second calculation weight for the historical driving speed change factor, and calculating a second product value of the historical driving speed change factor and the second calculation weight, wherein the first calculation weight is greater than the second calculation weight; A sum of the first product value and the second product value is determined and used as a train travel time prediction factor of the train to be predicted.

[0044] In this embodiment, the first calculation weight is preferably 0.7, and the second calculation weight is preferably 0.3, which can be adaptively adjusted according to actual conditions.

[0045] The beneficial effect of the above technical solution is: the present invention determines the train transit time prediction factor of the train to be predicted based on the historical transit time change factor and the historical travel speed change factor, realizes the comprehensive analysis of the historical transit data records, and lays the foundation for the train transit time prediction of the train to be predicted.

[0046] S160: Pre-deploy information collection equipment to collect real-time travel information of the train to be predicted, determine the real-time expected travel time based on the real-time travel information, and predict and adjust the real-time expected travel time according to the train travel time prediction factor to obtain the train travel prediction time of the train to be predicted.

[0047] In this embodiment, the information collection equipment includes a camera, a geomagnetic sensor, an RFID device, etc.

[0048] In this embodiment, the real-time travel information includes train images, travel speed, location, identity information, etc.

[0049] In some embodiments of the present application, when the real-time expected travel time is predicted and adjusted according to the train travel time prediction factor to obtain the train travel prediction time of the train to be predicted, it includes: Presetting a plurality of preset train travel time prediction factors; Presetting a plurality of preset travel time prediction adjustment coefficients; According to the relationship between the train travel time prediction factor and the preset train travel time prediction factor, a corresponding preset travel time prediction adjustment coefficient is selected, wherein the train travel time prediction factor and the preset travel time prediction adjustment coefficient are in a positive proportional relationship; The product value of the selected preset travel time prediction adjustment coefficient and the real-time expected travel time is calculated as the train travel prediction time of the train to be predicted.

[0050] In this embodiment, it is preferred to pre-set three preset train passage time prediction factors, including a first preset train passage time prediction factor, a second preset train passage time prediction factor and a third preset train passage time prediction factor, and the first preset train passage time prediction factor is preferably 5, the second preset train passage time prediction factor is preferably 8, and the third preset train passage time prediction factor is preferably 11, and the specific factors can also be adjusted according to actual conditions.

[0051] In this embodiment, four preset travel time prediction adjustment coefficients are preferably pre-set, including a first preset travel time prediction adjustment coefficient, a second preset travel time prediction adjustment coefficient, a third preset travel time prediction adjustment coefficient and a fourth preset travel time prediction adjustment coefficient, and the first preset travel time prediction adjustment coefficient is preferably 0.85, the second preset travel time prediction adjustment coefficient is preferably 0.95, the third preset travel time prediction adjustment coefficient is preferably 1.05, and the fourth preset travel time prediction adjustment coefficient is preferably 1.15, and the specific coefficients can also be adjusted according to actual conditions.

[0052] In this embodiment, when the train transit time prediction factor is less than the first preset train transit time prediction factor, the product value of the first preset transit time prediction adjustment coefficient and the real-time expected transit time is calculated as the train transit prediction time of the train to be predicted. For example, if the real-time expected transit time is 16 minutes, the train transit prediction time is 13.6 minutes.

[0053] In this embodiment, when the train transit time prediction factor is greater than or equal to the first preset train transit time prediction factor and less than the second preset train transit time prediction factor, the product value of the second preset transit time prediction adjustment coefficient and the real-time expected transit time is calculated as the train transit prediction time of the train to be predicted.

[0054] In this embodiment, when the train transit time prediction factor is greater than or equal to the second preset train transit time prediction factor and less than the third preset train transit time prediction factor, the product value of the third preset transit time prediction adjustment coefficient and the real-time expected transit time is calculated as the train transit prediction time of the train to be predicted.

[0055] In this embodiment, when the train travel time prediction factor is greater than or equal to the third preset train travel time prediction factor, the product value of the fourth preset travel time prediction adjustment coefficient and the real-time expected travel time is calculated as the train travel prediction time of the train to be predicted.

[0056] The beneficial effects of the above technical solution are: the present invention realizes the accurate determination of the predicted time of train passage, effectively avoids the conflict between vehicles and trains, greatly improves the traffic safety and efficiency of port roads, provides intuitive and accurate traffic prompts for drivers, accelerates the smoothness of port traffic, and improves the overall production efficiency of the terminal.

[0057] In order to further explain the technical idea of ​​the present invention, the technical solution of the present invention is now described in combination with specific application scenarios.

[0058] Correspondingly, such as Figure 2 As shown, the present application also provides a train travel time prediction system based on big data, including: A sequence determination module is used to determine a train to be predicted, obtain multiple historical travel data records of the train to be predicted, analyze each historical travel data record, and determine a historical advance travel time sequence and a historical delay travel time sequence; A coefficient calculation module is used to perform smoothing calculation and wave skipping calculation on the historical advance passage time sequence to obtain the advance passage time smoothing coefficient and the advance passage time wave skipping coefficient corresponding to the historical advance passage time sequence, and perform smoothing calculation and wave skipping calculation on the historical delayed passage time sequence to obtain the delayed passage time smoothing coefficient and the delayed passage time wave skipping coefficient corresponding to the historical delayed passage time sequence; A first calculation module is used to calculate the historical travel time variation factor of the train to be predicted based on the advance travel time smoothing coefficient, the advance travel time skipping coefficient, the delayed travel time smoothing coefficient and the delayed travel time skipping coefficient; The second calculation module is used to collect the historical travel speed corresponding to each historical travel data record, and calculate the historical travel speed change factor of the train to be predicted according to all the historical travel speeds; A factor determination module, used to determine the train travel time prediction factor of the train to be predicted according to the historical travel time change factor and the historical travel speed change factor; The travel prediction module is used to pre-deploy information collection equipment to collect real-time travel information of the train to be predicted, determine the real-time expected travel time based on the real-time travel information, and predict and adjust the real-time expected travel time according to the train travel time prediction factor to obtain the train travel prediction time of the train to be predicted.

[0059] In the description of the above embodiments, specific features, structures, materials or characteristics may be combined in a suitable manner in any one or more embodiments or examples.

[0060] Although the present invention has been described above with reference to the embodiments, various modifications may be made thereto and parts thereof may be replaced with equivalents without departing from the scope of the present invention. In particular, as long as there is no structural conflict, the various features in the embodiments disclosed by the present invention may be used in combination with each other in any manner, and the fact that these combinations are not fully described in this specification is only for the sake of omitting space and saving resources.

[0061] Those skilled in the art can understand that the above are only preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions recorded in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A train travel time prediction method based on big data, characterized in that: include: Determine a train to be predicted, obtain multiple historical travel data records of the train to be predicted, analyze each historical travel data record, and determine a historical advance travel time sequence and a historical delay travel time sequence; Performing smoothing calculation and wave skipping calculation on the historical advance passage time sequence to obtain the advance passage time smoothing coefficient and the advance passage time wave skipping coefficient corresponding to the historical advance passage time sequence, and performing smoothing calculation and wave skipping calculation on the historical delayed passage time sequence to obtain the delayed passage time smoothing coefficient and the delayed passage time wave skipping coefficient corresponding to the historical delayed passage time sequence; Calculate the historical travel time variation factor of the train to be predicted based on the advance travel time smoothing coefficient, the advance travel time skipping coefficient, the delayed travel time smoothing coefficient and the delayed travel time skipping coefficient; Collecting the historical travel speed corresponding to each historical travel data record, and calculating the historical travel speed change factor of the train to be predicted based on all the historical travel speeds; Determine a train travel time prediction factor of the train to be predicted according to the historical travel time change factor and the historical travel speed change factor; Predeploy information collection equipment to collect real-time travel information of the train to be predicted, determine the real-time expected travel time based on the real-time travel information, and predict and adjust the real-time expected travel time according to the train travel time prediction factor to obtain the train travel prediction time of the train to be predicted.

2. The train travel time prediction method based on big data according to claim 1 is characterized in that: When obtaining a plurality of historical travel data records of the train to be predicted, analyzing each historical travel data record, and determining a historical advance travel time sequence and a historical delay travel time sequence, the method includes: Obtain the historical expected travel time corresponding to each historical travel data record; Analyze each historical travel data record to determine the corresponding historical actual travel time, and generate a travel advance mark, a travel expectation mark, and a travel delay mark for the historical travel data record according to the historical actual travel time and the historical expected travel time; When the historical actual travel time is less than the historical expected travel time, the advance travel mark is generated for the corresponding historical travel data record; When the historical actual travel time is equal to the historical expected travel time, the travel expectation mark is generated for the corresponding historical travel data record; When the historical actual travel time is greater than the historical expected travel time, the travel delay mark is generated for the corresponding historical travel data record; Extracting the historical actual travel time corresponding to each advance travel mark, calculating the advance travel time difference between the historical actual travel time and the historical expected travel time, and determining the historical advance travel time sequence according to all the advance travel time differences; The historical actual travel time corresponding to each delayed travel mark is extracted, the delayed travel time difference between the historical actual travel time and the historical expected travel time is calculated, and the historical delayed travel time sequence is determined according to all the delayed travel time differences.

3. The train travel time prediction method based on big data according to claim 2 is characterized in that: When performing smoothing calculation and wave skipping calculation on the historical advance passage time sequence to obtain the advance passage time smoothing coefficient and the advance passage time wave skipping coefficient corresponding to the historical advance passage time sequence, it includes: The advance travel time smoothing coefficient corresponding to the historical advance travel time series is calculated according to the following formula: ； Among them, s1 is the advance travel time smoothing coefficient corresponding to the historical advance travel time series, a1 is the number of advance travel time differences in the historical advance travel time series, and f d+1 is the d+1th advance travel time difference in the historical advance travel time series, f d is the dth advance passage time difference in the historical advance passage time series; The advance travel time jump coefficient corresponding to the historical advance travel time series is calculated according to the following formula: ； Among them, s2 is the advance passage time jump coefficient corresponding to the historical advance passage time series.

4. The train travel time prediction method based on big data according to claim 3 is characterized in that: When performing smoothing calculation and wave skipping calculation on the historical delayed passage time series to obtain a delayed passage time smoothing coefficient and a delayed passage time wave skipping coefficient corresponding to the historical delayed passage time series, it includes: The delayed travel time smoothing coefficient corresponding to the historical delayed travel time series is calculated according to the following formula: ； Among them, s3 is the delayed travel time smoothing coefficient corresponding to the historical delayed travel time series, a2 is the number of delayed travel time differences in the historical delayed travel time series, and h g+1 is the g+1th delayed travel time difference in the historical delayed travel time series, h g is the g-th delayed passage time difference in the historical delayed passage time series; The delayed travel time jump coefficient corresponding to the historical delayed travel time series is calculated according to the following formula: ； Among them, s4 is the delayed passage time jump coefficient corresponding to the historical delayed passage time series.

5. The train travel time prediction method based on big data according to claim 4 is characterized in that: When calculating the historical travel time variation factor of the train to be predicted based on the advance travel time smoothing coefficient, the advance travel time skipping coefficient, the delayed travel time smoothing coefficient and the delayed travel time skipping coefficient, it includes: Determine the number of advance pass marks of the advance pass mark, determine the number of expected pass marks of the expected pass mark, and determine the number of delayed pass marks of the delayed pass mark; The historical travel time variation factor of the train to be predicted is calculated according to the following formula: ； Among them, k is the historical travel time change factor of the train to be predicted, w1 is the number of advance travel marks, w2 is the number of expected travel marks, and w3 is the number of delayed travel marks.

6. The train travel time prediction method based on big data according to claim 1 is characterized in that: When collecting the historical travel speed corresponding to each historical travel data record and calculating the historical travel speed change factor of the train to be predicted according to all the historical travel speeds, it includes: All historical driving speeds are randomly assigned to the blank point map template, and the location of the maximum historical driving speed is determined; Determine an initial historical driving speed and a terminal historical driving speed on the blank point map template, and count a first number of historical driving speeds between the initial historical driving speed and the maximum historical driving speed; Counting a second number of historical driving speeds between the terminal historical driving speed and the maximum historical driving speed; determining whether both the first number and the second number are greater than a preset number, and if so, dividing the historical driving speeds between the initial historical driving speed and the maximum historical driving speed into a first historical driving speed sequence, wherein the first historical driving speed sequence does not include the maximum historical driving speed; dividing the historical driving speeds between the terminal historical driving speed and the maximum historical driving speed into a second historical driving speed sequence, wherein the second historical driving speed sequence does not include the maximum historical driving speed; If not, all historical driving speeds are randomly redistributed until both the first number and the second number obtained are greater than the preset number; The historical running speed change factor of the train to be predicted is calculated according to the first historical running speed sequence and the second historical running speed sequence.

7. The train travel time prediction method based on big data according to claim 6 is characterized in that: When calculating the historical travel speed change factor of the train to be predicted according to the first historical travel speed sequence and the second historical travel speed sequence, it includes: The historical speed change factor of the train to be predicted is calculated according to the following formula: ； Wherein, e is the historical speed change factor of the train to be predicted, r1 is the initial historical speed, r2 is the maximum historical speed, r3 is the terminal historical speed, t1 is the first calculation coefficient, t2 is the second calculation coefficient, t3 is the third calculation coefficient, t1+t2+t3=1, t1>0, t2>0, t3>0, y is the number of historical speeds, u1 is the variance of the historical speeds other than the initial historical speed in the first historical speed sequence, u2 is the variance of the historical speeds other than the terminal historical speed in the second historical speed sequence, and u is the variance of all historical speeds other than the maximum historical speed.

8. The train travel time prediction method based on big data according to claim 1 is characterized in that: When determining the train transit time prediction factor of the train to be predicted according to the historical transit time variation factor and the historical travel speed variation factor, it includes: configuring a first calculation weight for the historical travel time change factor, and calculating a first product value of the historical travel time change factor and the first calculation weight; configuring a second calculation weight for the historical driving speed change factor, and calculating a second product value of the historical driving speed change factor and the second calculation weight, wherein the first calculation weight is greater than the second calculation weight; A sum of the first product value and the second product value is determined and used as a train travel time prediction factor of the train to be predicted.

9. The train travel time prediction method based on big data according to claim 1, characterized in that: When the real-time expected travel time is predicted and adjusted according to the train travel time prediction factor to obtain the train travel prediction time of the train to be predicted, it includes: Presetting a plurality of preset train travel time prediction factors; Presetting a plurality of preset travel time prediction adjustment coefficients; According to the relationship between the train travel time prediction factor and the preset train travel time prediction factor, a corresponding preset travel time prediction adjustment coefficient is selected, wherein the train travel time prediction factor and the preset travel time prediction adjustment coefficient are in a positive proportional relationship; The product value of the selected preset travel time prediction adjustment coefficient and the real-time expected travel time is calculated as the train travel prediction time of the train to be predicted.

10. A train travel time prediction system based on big data, applied to the train travel time prediction method based on big data as claimed in any one of claims 1 to 9, characterized in that: include: A sequence determination module is used to determine a train to be predicted, obtain multiple historical travel data records of the train to be predicted, analyze each historical travel data record, and determine a historical advance travel time sequence and a historical delay travel time sequence; A coefficient calculation module is used to perform smoothing calculation and wave skipping calculation on the historical advance passage time sequence to obtain the advance passage time smoothing coefficient and the advance passage time wave skipping coefficient corresponding to the historical advance passage time sequence, and perform smoothing calculation and wave skipping calculation on the historical delayed passage time sequence to obtain the delayed passage time smoothing coefficient and the delayed passage time wave skipping coefficient corresponding to the historical delayed passage time sequence; A first calculation module is used to calculate the historical travel time variation factor of the train to be predicted based on the advance travel time smoothing coefficient, the advance travel time skipping coefficient, the delayed travel time smoothing coefficient and the delayed travel time skipping coefficient; The second calculation module is used to collect the historical travel speed corresponding to each historical travel data record, and calculate the historical travel speed change factor of the train to be predicted according to all the historical travel speeds; A factor determination module, used to determine the train travel time prediction factor of the train to be predicted according to the historical travel time change factor and the historical travel speed change factor; The travel prediction module is used to pre-deploy information collection equipment to collect real-time travel information of the train to be predicted, determine the real-time expected travel time based on the real-time travel information, and predict and adjust the real-time expected travel time according to the train travel time prediction factor to obtain the train travel prediction time of the train to be predicted.

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