Pedestrian flow composition proportion prediction method based on time sequence fluctuation

Abstract

The invention discloses a pedestrian flow composition proportion prediction method based on time sequence fluctuation, which comprises the steps of 1, collecting and processing real scene data, 2, simulating a pedestrian flow by combining the actually collected data to obtain a simulated pedestrian flow sequence corresponding to different types of pedestrians, 3, extracting the characteristics of the simulated pedestrian flow sequence and determining a judgment threshold value thereof, and 4, predicting the pedestrian flow composition proportion of different types of pedestrians according to the judgment threshold value. And 4, acquiring a fluctuation sequence of the pedestrian flow and predicting the pedestrian flow composition proportion according to the judgment threshold. The method can predict the composition proportion of the pedestrian flow according to the fluctuation sequence of the actual pedestrian flow in combination with the characteristics of the simulated pedestrian flow sequence, and intelligently manages and controls traffic signals, road resources and the like according to the types and behavior characteristics of pedestrians, thereby improving the traffic flow quality. Therefore, a new solution thought can be provided for optimizing traffic management, dispersion, emergency response and the like.

Description

Technical Field

[0001] The invention belongs to the field of intelligent transportation, and specifically is a pedestrian flow composition ratio prediction method based on time series fluctuation. Background Art

[0002] With the rapid development of intelligent transportation, traditional transportation is undergoing a profound transformation, and modern transportation is accelerating towards intelligence. In this context, the reasonable design of traffic scenes and the scientific setting of pedestrian crossing facilities are of great significance for quickly guiding pedestrians, reducing accident risks, and improving traffic safety and efficiency, especially in emergency situations.

[0003] To address the above problems, many scholars have optimized and modified the social force model by analyzing the behavior of pedestrians in different scenarios, making it closer to actual traffic scenarios. Based on the simulation results, not only can the layout of pedestrian crossing facilities be optimized, but also certain guidance suggestions can be provided in emergency situations, thereby improving traffic management and public safety capabilities. However, there are still obvious deficiencies in existing research: on the one hand, the research mainly focuses on the optimization of static facilities, and there is little exploration of intelligent management of dynamic adjustment of traffic signals and allocation of road resources; on the other hand, the actual scenarios in emergency situations are often complex and changeable, and existing technologies are difficult to provide efficient real-time simulation and targeted guidance solutions in a timely manner, and cannot fully meet the dynamic management needs of intelligent transportation. Summary of the invention

[0004] The present invention aims to solve the deficiencies of the above-mentioned prior art and proposes a method for predicting the pedestrian flow composition ratio based on time series fluctuations, in order to predict the pedestrian flow composition ratio according to the fluctuation sequence of actual pedestrian flow combined with the characteristics of simulated pedestrian flow sequence, and to intelligently manage and control traffic signals, road resources, etc. according to the types and behavioral characteristics of pedestrians, thereby providing a new solution for optimizing traffic management, diversion and emergency response.

[0005] In order to achieve the above-mentioned purpose, the present invention adopts the following technical scheme:

[0006] The pedestrian flow composition ratio prediction method based on time series fluctuation of the present invention is characterized in that it is performed according to the following steps:

[0007] Step 1: Collect and process real scene data;

[0008] Step 1.1: Get the Pedestrian videos of the same one-way straight channel in the same time period under the same cycle are collected and a reference line is set at the exit of the channel in each frame of the pedestrian video, so as to Pedestrian videos under a certain period of time Identify pedestrians passing through the reference line and obtain the number of pedestrians passing through the reference line to form the first Pedestrian flow sequence under a period , thus obtaining Pedestrian flow sequence under a period ,in, Indicates The next cycle Pedestrian flow of time, Indicates the total amount of time;

[0009] Step 1.2: After taking the average, median filtering is performed to obtain the overall trend sequence of pedestrian flow. ,in, Indicates The overall trend of pedestrian flow during the period;

[0010] Step 1.3: The pedestrian flow volume trend of each duration in is taken as the random arrival rate of Poisson distribution, thus generating a total of After taking the Poisson random number and reversing it, we can get the number of pedestrians arriving at the entrance of the one-way straight channel at each moment. ;in, Indicates The number of pedestrians arriving at the entrance of the one-way straight passage at a certain moment;

[0011] Step 1.4: According to Construct a pedestrian type array ,in, Indicates The type identification sequence of all pedestrians arriving at the entrance of the one-way straight passage at the moment, and ; Indicates The first one that arrives at the entrance of the one-way straight passage at the pedestrian types; and , Indicates aggregation type, Represents distributed type; initialization = ;

[0012] Step 2: Simulate pedestrian flow;

[0013] Step 2.0: Initialization ;

[0014] Take a vertex at the entrance of the channel in the pedestrian video as the origin and the horizontal direction of the channel as Axis direction, with the vertical direction of the channel as Axis direction, establish the first plane coordinate system;

[0015] Get the channel in Length in the axial direction and Width in the axial direction ;

[0016] Calibrate the coordinates of the first point in the first plane coordinate system using the four-point calibration method , coordinates of the second point , coordinates of the third point , the fourth point coordinates ;

[0017] Definition from arrive The line segments between arrive The line segments between the two sides of the channel represent the edges of the walls ;in, Indicates the channel Side wall edges;

[0018] Definition from arrive The line segment is the reference line of the simulation scene ;

[0019] The radius interval of pedestrians is defined as ,in, is the minimum radius of pedestrians, is the maximum radius of pedestrians;

[0020] The weight range of pedestrians is defined as ,in, is the minimum weight of the pedestrian, is the maximum weight of the pedestrian;

[0021] The expected speed range of pedestrians is defined as ,in, is the minimum expected speed of pedestrians, is the maximum expected speed of the pedestrian;

[0022] Define the initial vertical coordinate interval of the pedestrian as , where 0 is the minimum value of the pedestrian’s initial ordinate. is the maximum value of the pedestrian’s initial ordinate;

[0023] Define the coordinates of the target point for each pedestrian to move to as ,in, , is the horizontal coordinate of a point in front of the channel exit, ;

[0024] Step 2.1: Randomly generate Radius sequence of pedestrians at each moment ,in, Indicates Arrive at the entrance of the one-way straight passage at the moment The radius of pedestrians, Indicates The number of pedestrians arriving at the entrance of the one-way straight passage at a certain moment, and ;

[0025] exist Randomly generate The weight sequence of pedestrians at each moment ,in, Indicates Arrive at the entrance of the one-way straight passage at the moment The weight of the pedestrian;

[0026] exist Randomly generate The expected speed sequence of pedestrians at each moment ,in, Indicates Arrive at the entrance of the one-way straight passage at the moment The expected speed of each pedestrian;

[0027] Definition The vector velocity sequence of pedestrians at each moment ,in, Indicates Arrive at the entrance of the one-way straight passage at the moment The vector velocity of pedestrians and initialize is 0;

[0028] exist Randomly generate The initial ordinate sequence of the pedestrian at time ;in, Indicates Arrive at the entrance of the one-way straight passage at the time The initial vertical coordinate of the pedestrian is generated Pedestrian position sequence at each moment , Indicates Arrive at the entrance of the one-way straight passage at the time The initial position of each pedestrian;

[0029] Step 2.2: Initialization ;

[0030] Step 2.3: Calculate the first Pedestrian position sequence at each moment Middle Pedestrian self-driving force ;

[0031] (1)

[0032] In formula (1), Indicates Moment The time it takes for a pedestrian's walking speed to return to the expected speed; For the Moment The position of the pedestrian points to the target point coordinates The direction vector of

[0033] Step 2.4: Calculate the Pedestrian position sequence at each moment Middle Pedestrians and The interaction forces between pedestrians ;

[0034] (2)

[0035] In formula (2), For the The repulsive force parameter of each pedestrian is The minimum range for generating repulsive force; For the Moment Pedestrians and The sum of the radii of the pedestrians; For the Moment Pedestrians and The distance between the centroids of pedestrians; For the Moment A pedestrian points to The unit vector of each pedestrian; is the extrusion pressure parameter; is the friction parameter; For the Moment Pedestrians and The unit vector of the tangential direction between pedestrians; represents the contact force discriminant function, when season ,when season ; For the Moment Pedestrians and The tangential velocity difference between pedestrians;

[0036] Step 2.5: Calculate the first Pedestrian position sequence at each moment Middle Pedestrians and Side wall edge The force between ;

[0037] (3)

[0038] In formula (3), For the The first pedestrians to the edge of the wall The distance For the The first Pedestrians and the edge of the wall The unit vector in the perpendicular direction, Indicates The first Pedestrians walking parallel The unit vector of the direction;

[0039] Step 2.6: If = , then using formula (4) we can get Pedestrian position sequence at each moment Middle The driving force behind pedestrians' tendency to speed up or slow down when faced with congestion ,like = , then using formula (5) we can get Pedestrian position sequence at each moment Middle The driving force behind pedestrians' tendency to speed up or slow down when faced with congestion ;

[0040] (4)

[0041] (5)

[0042] In formula (4) and formula (5), It indicates the driving force of any pedestrian to speed up or slow down when facing congestion. For the The first position in the pedestrian position sequence at the moment The pedestrian density in front of the pedestrian, and , For the The first position in the pedestrian position sequence at the moment The length in front of the pedestrian is The total number of pedestrians within the range; Indicates the threshold for judging the congestion ahead. Indicates the threshold for judging that the front is not crowded;

[0043] Step 2.7: Update the first Pedestrian position sequence at each moment Middle The vector velocity of each pedestrian ;

[0044] (6)

[0045] Step 2.8: Update the first Pedestrian position sequence at each moment Middle The positions of pedestrians after the adjacent time interval ;

[0046] (7)

[0047] Step 2.9: Assign to After that, if , then return to step 2.2 to execute sequentially, otherwise, it means that the The position sequence of all pedestrians after adjacent time intervals at the moment ; and record the Pedestrian position sequence at each moment Through the reference line Number of pedestrians ;

[0048] Step 2.10: Delete Pedestrian position sequence at each moment The horizontal coordinate is in the interval The coordinate information of pedestrians outside the radius sequence is deleted at the same time. , weight sequence , expected speed sequence , vector velocity sequence and identification information sequence The information in the Moment , , , , and ; and update the number of pedestrians ;

[0049] Step 2.11: Assign to ,like , then follow step 2.1 to obtain Moment , , , , and as well as , and respectively and , and , and , and , and , and Merge Assign to After that, return to step 2.2 and execute sequentially; otherwise, it means that the number of pedestrians sequence is obtained ;

[0050] Step 2.12: In each The sum of the number of pedestrians is used as the simulated pedestrian flow sequence The elements in , thus obtaining the simulated clustered pedestrian flow sequence ; Indicates The simulated aggregated pedestrian flow of time duration;

[0051] Step 2.13: Initialization = ,initialization Then, follow the process from step 2.1 to step 2.12 to obtain the simulated dispersed pedestrian flow sequence. ;in, Indicates simulated dispersed pedestrian flow for a certain period of time;

[0052] Step 3: Sequence feature extraction and judgment threshold determination;

[0053] Step 3.1: Sequence , Subtract , and obtain the fluctuation sequence of simulated aggregated pedestrian flow sequence and the wave sequence simulating the dispersed pedestrian flow sequence ;in, It means that the simulated clustered pedestrian flow sequence is higher than the overall trend sequence in the The fluctuation value of the time length, Indicates The simulated dispersed pedestrian flow of duration is compared with the overall trend sequence in Fluctuation value over a period of time;

[0054] Step 3.2: Use equation (8) and set the moving window length to Calculate separately The moving standard deviation series and The moving standard deviation series ,in, Indicates that the moving window length is Time Series exist The moving standard deviation at time, Indicates that the moving window length is Time Series exist The moving standard deviation of the moment;

[0055] (8)

[0056] In formula (8), Indicates that the moving window length is Time Series exist The moving average of time, and ; Indicates that the moving window length is Time Series exist The moving average of time, and ;

[0057] Step 3.3: Calculation The mean , The mean ;

[0058] Step 3.4: Calculate the upper threshold of the moving standard deviation , the lower threshold of the moving standard deviation ;

[0059] Step 4: Pedestrian flow composition prediction;

[0060] Step 4.1: Obtained in step 1.1 Pedestrian flow sequence under a period minus After that, get The volatility sequence under the period ,in, Indicates The pedestrian flow sequence in the period is compared with the overall trend sequence in Fluctuation value over a period of time;

[0061] Step 4.2: Calculate according to formula (8) The moving standard deviation series ,in, Indicates that the moving window length is At that time, Pedestrian flow fluctuation sequence under a period exist The moving standard deviation of the moment;

[0062] Step 4.3: When , indicating the In the cycle The proportion of clustered pedestrians in the pedestrian flow composition after the time is high;

[0063] when , then it means In the cycle After the time, the proportion of dispersed pedestrians in the pedestrian flow is relatively high.

[0064] The pedestrian flow composition ratio prediction method based on time series fluctuations of the present invention is also characterized in that steps 3.2 to 4.3 are replaced by the following steps:

[0065] Step 1: Use equation (9) and set the moving window length as Calculate separately The moving root mean square sequence and The moving root mean square sequence ,in, Indicates that the moving window length is Time Series exist The moving root mean square at time, Indicates that the moving window length is Time Series exist Moving RMS at time;

[0066] (9)

[0067] Step 2: Calculation The mean , The mean ;

[0068] Step 3: Calculate the upper threshold of the moving mean square , the lower threshold of the moving root mean square ;

[0069] Step 4: Get the first Pedestrian flow sequence under a period minus After that, get The volatility sequence under the period ,in, Indicates The pedestrian flow sequence in the period is compared with the overall trend sequence in Fluctuation value over a period of time;

[0070] Step 5: Calculate according to formula (9) The moving standard deviation series ,in, It means that the moving window length is At that time, Pedestrian flow fluctuation sequence under a period exist The moving standard deviation of the moment;

[0071] Step 6: When , then it means In the cycle The proportion of clustered pedestrians in the pedestrian flow composition after the time is high;

[0072] when , then it means In the cycle After the time, the proportion of dispersed pedestrians in the pedestrian flow is relatively high.

[0073] An electronic device of the present invention includes a memory and a processor, wherein the memory is used to store a program that supports the processor to execute the pedestrian flow composition ratio prediction method, and the processor is configured to execute the program stored in the memory.

[0074] The present invention provides a computer-readable storage medium, wherein a computer program is stored on the computer-readable storage medium, and the computer program executes the steps of the pedestrian flow composition ratio prediction method when the computer program is executed by a processor.

[0075] Compared with the prior art, the present invention has the following beneficial effects:

[0076] 1. The present invention provides a pedestrian flow composition ratio prediction method based on time series fluctuations, which can effectively make up for the shortcomings of existing research in dynamically adjusting traffic signals and road resource allocation. By analyzing the types and behavioral characteristics of pedestrians through prediction results, intelligent dynamic management and optimization control of traffic signals, road resources, etc. can be achieved, thereby improving the adaptability and intelligence level of the traffic system and meeting the high requirements of modern intelligent transportation for real-time regulation.

[0077] 2. The present invention can predict the proportion of pedestrian flow in real time by recording pedestrian flow fluctuation data and combining it with the characteristics of simulated pedestrian flow. By analyzing the types and behaviors of pedestrians in the scene through the prediction results, a more accurate and efficient diversion plan can be provided according to the complexity of the actual scene. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] Figure 1 It is the overall flow chart of the present invention;

[0079] Figure 2 A schematic diagram of video recognition in an embodiment of the present invention;

[0080] Figure 3 This is a data graph obtained by splicing pedestrian flow sequences under three cycles in an embodiment of the present invention;

[0081] Figure 4 This is a general trend diagram of pedestrian flow in an embodiment of the present invention;

[0082] Figure 5 A first plane coordinate system constructed with a vertex at the entrance of a channel in a pedestrian video as the origin of the present invention;

[0083] Figure 6a It is a sequence diagram of simulated aggregated pedestrian flow in an embodiment of the present invention;

[0084] Figure 6b It is a sequence diagram of simulated dispersed pedestrian flow in an embodiment of the present invention;

[0085] Figure 7a It is a simulated aggregated pedestrian flow fluctuation sequence and its moving standard deviation and moving root mean square diagram in an embodiment of the present invention;

[0086] Figure 7b The simulated dispersed pedestrian flow fluctuation sequence and its moving standard deviation and moving root mean square diagram in the embodiment of the present invention;

[0087] Figure 8a This is a moving standard deviation prediction result diagram in an embodiment of the present invention;

[0088] Figure 8b This is a moving root mean square prediction result diagram in an embodiment of the present invention. DETAILED DESCRIPTION

[0089] The present invention is further described in detail below with reference to the accompanying drawings and embodiments.

[0090] In this embodiment, a pedestrian flow composition ratio prediction method based on time series fluctuations is provided. Figure 1 As shown, the steps are as follows:

[0091] Step 1: Collect and process real scene data;

[0092] Step 1.1: Get the Pedestrian videos of the same one-way straight channel in the same time period under the same cycle are collected and a reference line is set at the exit of the channel in each frame of the pedestrian video, so as to Pedestrian videos under a certain period of time Pedestrians in the video are identified by reference lines. Figure 2 As shown in the figure, pedestrians will be identified in the green box and assigned a unique ID. The red dot on the green box is used to determine whether the pedestrian has passed the reference line. The yellow and blue lines are reference lines, which are used to record the number of pedestrians passing through. The number of pedestrians passing through the reference line is obtained and constitutes the first Pedestrian flow sequence under a period , thus obtaining Pedestrian flow sequence under a period ,in, Indicates The next cycle Pedestrian flow of time, Indicates the total duration, When is 5, the pedestrian flow sequence of 3 cycles is as follows Figure 3 As shown, the sequences under each period are separated by dotted lines.

[0093] Step 1.2: After taking the average, median filtering is performed to obtain the overall trend sequence of pedestrian flow. ,in, Indicates The overall trend of pedestrian flow over a certain period of time, such as Figure 4 As shown;

[0094] Step 1.3: The pedestrian flow volume trend of each duration in is taken as the random arrival rate of Poisson distribution, thus generating a total of After taking the Poisson random number and reversing it, we get the sequence of the number of pedestrians arriving at the entrance of the one-way straight channel at each moment. ;in, Indicates The number of pedestrians arriving at the entrance of the one-way straight passage at a certain moment.

[0095] Step 1.4: According to Construct a pedestrian type array ,in, Indicates The type identification sequence of all pedestrians arriving at the entrance of the one-way straight passage at the moment, and ; Indicates The first one that arrives at the entrance of the one-way straight passage at the pedestrian types; and , Indicates aggregation type, Represents distributed type; initialization = ;

[0096] Step 2: Simulate pedestrian flow;

[0097] Step 2.0: Initialization ;

[0098] Take a vertex at the entrance of the channel in the pedestrian video as the origin and the horizontal direction of the channel as Axis direction, with the vertical direction of the channel as Axis direction, establish the first plane coordinate system, such as Figure 5 As shown;

[0099] Get channel in Length in the axial direction and Width in the axial direction ;

[0100] Use the four-point calibration method to calibrate the coordinates of the first point in the first plane coordinate system , coordinates of the second point , coordinates of the third point , the fourth point coordinates ;

[0101] Definition from arrive The line segments between arrive The line segments between represent the edges of the walls on both sides of the channel ;in, Indicates the channel Side wall edges;

[0102] Definition from arrive The line segment is the reference line of the simulation scene ;

[0103] The radius interval of pedestrians is defined as ,in, is the minimum radius of pedestrians, is the maximum radius of pedestrians;

[0104] The weight range of pedestrians is defined as ,in, is the minimum weight of the pedestrian, is the maximum weight of the pedestrian;

[0105] The expected speed range of pedestrians is defined as ,in, is the minimum expected speed of pedestrians, is the maximum expected speed of the pedestrian;

[0106] Define the initial vertical coordinate interval of the pedestrian as , where 0 is the minimum value of the pedestrian’s initial ordinate. is the maximum value of the pedestrian’s initial ordinate;

[0107] Define the coordinates of the target point for each pedestrian to move to as ,in, , is the horizontal coordinate of a point in front of the channel exit, .

[0108] Step 2.1: Randomly generate Radius sequence of pedestrians at each moment ,in, Indicates Arrive at the entrance of the one-way straight passage at the moment The radius of pedestrians, Indicates The number of pedestrians arriving at the entrance of the one-way straight passage at a certain moment, and ;

[0109] exist Randomly generate The weight sequence of pedestrians at each moment ,in, Indicates Arrive at the entrance of the one-way straight passage at the moment The weight of the pedestrian;

[0110] exist Randomly generate The expected speed sequence of pedestrians at each moment ,in, Indicates Arrive at the entrance of the one-way straight passage at the moment The expected speed of each pedestrian;

[0111] Definition The vector velocity sequence of pedestrians at each moment ,in, Indicates Arrive at the entrance of the one-way straight passage at the moment The vector velocity of pedestrians and initialize is 0;

[0112] exist Randomly generate The initial ordinate sequence of the pedestrian at time ;in, Indicates Arrive at the entrance of the one-way straight passage at the time The initial vertical coordinate of the pedestrian is generated Pedestrian position sequence at each moment , Indicates Arrive at the entrance of the one-way straight passage at the time The initial position of a pedestrian.

[0113] Step 2.2: Initialization ;

[0114] Step 2.3: Calculate the first Pedestrian position sequence at each moment Middle Pedestrian self-driving force ;

[0115] (1)

[0116] In formula (1), Indicates Moment The time it takes for a pedestrian's walking speed to return to the expected speed; For the Moment The position of the pedestrian points to the target point coordinates The direction vector of .

[0117] Step 2.4: Calculate the Pedestrian position sequence at each moment Middle Pedestrians and The interaction forces between pedestrians ;

[0118] (2)

[0119] In formula (2), For the The repulsive force parameter of each pedestrian is The minimum range for generating repulsive force; For the Moment Pedestrians and The sum of the radii of the pedestrians; For the Moment Pedestrians and The distance between the centroids of pedestrians; For the Moment A pedestrian points to The unit vector of each pedestrian; is the extrusion pressure parameter; is the friction parameter; For the Moment Pedestrians and The unit vector in the tangential direction between pedestrians; represents the contact force discriminant function, when season ,when season ; For the Moment Pedestrians and The tangential velocity difference between pedestrians.

[0120] Step 2.5: Calculate the first Pedestrian position sequence at each moment Middle Pedestrians and Side wall edge The force between ;

[0121] (3)

[0122] In formula (3), For the The first pedestrians to the edge of the wall The distance For the The first Pedestrians and wall edge The unit vector in the perpendicular direction, Indicates The first Pedestrians walking parallel The unit vector of the direction.

[0123] Step 2.6: If = , then using formula (4) we can get Pedestrian position sequence at each moment Middle The driving force behind pedestrians' tendency to speed up or slow down when faced with congestion ,like = , then using formula (5) we can get Pedestrian position sequence at each moment Middle The driving force behind pedestrians' tendency to speed up or slow down when faced with congestion , It is a modification of the social force model to reflect the characteristics of clustered and dispersed pedestrians;

[0124] (4)

[0125] (5)

[0126] In formula (4) and formula (5), It indicates the driving force of any pedestrian to speed up or slow down when facing congestion. For the The first position in the pedestrian position sequence at the moment The pedestrian density in front of the pedestrian, and , For the The first position in the pedestrian position sequence at the moment The length in front of the pedestrian is The total number of pedestrians within the range; Indicates the threshold for judging the congestion ahead. represents the threshold for judging that the road ahead is not crowded. Formula (4) can reflect the characteristics of clustered pedestrians, who tend to accelerate when the road ahead is crowded and decelerate when it is not crowded. Formula (5) can reflect the characteristics of dispersed pedestrians, who tend to decelerate when the road ahead is crowded and accelerate when it is not crowded.

[0127] Step 2.7: Update the first Pedestrian position sequence at each moment Middle The vector velocity of each pedestrian ;

[0128] (6)

[0129] Step 2.8: Update the first Pedestrian position sequence at each moment Middle The positions of pedestrians after the adjacent time interval ;

[0130] (7)

[0131] Step 2.9: Assign to After that, if , then return to step 2.2 to execute sequentially, otherwise, it means that the The position sequence of all pedestrians after adjacent time intervals at the moment ; and record the Pedestrian position sequence at each moment Through the reference line Number of pedestrians .

[0132] Step 2.10: Delete Pedestrian position sequence at each moment The horizontal coordinate is in the interval The coordinate information of pedestrians outside the radius sequence is deleted at the same time. , weight sequence , expected speed sequence , vector velocity sequence and identification information sequence The information in the Moment , , , , and ; and update the number of pedestrians ;

[0133] Step 2.11: Assign to ,like , then follow step 2.1 to obtain Moment , , , , and as well as , and respectively and , and , and , and , and , and Merge Assign to After that, return to step 2.2 and execute sequentially; otherwise, it means that the number of pedestrians sequence is obtained .

[0134] Step 2.12: In each The sum of the number of pedestrians is used as the simulated pedestrian flow sequence The elements in , thus obtaining the simulated clustered pedestrian flow sequence ,The simulated crowd flow sequence can reflect the sequence characteristics corresponding to the crowd flow.,When the scene is full of crowd flow pedestrians, the amplitude of the data curve will be stretched, and the fluctuation of the curve will be more obvious, such as Figure 6a As shown; Indicates The simulated aggregated pedestrian flow of a certain duration.

[0135] Step 2.13: Initialization = ,initialization Then, follow the process from step 2.1 to step 2.12 to obtain the simulated dispersed pedestrian flow sequence. ,The simulated dispersed pedestrian flow sequence can reflect the sequence characteristics corresponding to dispersed pedestrians.,When the scene is full of dispersed pedestrians, the amplitude of the data curve will be compressed, and the curve will become smooth, such as Figure 6b As shown; Indicates The simulated dispersed pedestrian flow of duration, the sequence values ​​in Figure 6 are simulated by the model parameters in Table I;

[0136] Table I Model parameters

[0137]

[0138] Step 3: Sequence feature extraction and judgment threshold determination;

[0139] Step 3.1: Sequence , Subtract , and obtain the fluctuation sequence of simulated aggregated pedestrian flow sequence and the wave sequence simulating the dispersed pedestrian flow sequence , thus reflecting the fluctuation characteristics corresponding to the aggregation type and the dispersion type; It means that the simulated clustered pedestrian flow sequence is higher than the overall trend sequence in the The fluctuation value of the time length, Indicates The simulated dispersed pedestrian flow of duration is compared with the overall trend sequence in The fluctuation value over a period of time.

[0140] Step 3.2: Use equation (8) and set the moving window length to Calculate separately The moving standard deviation series and The moving standard deviation series ,in, Indicates that the moving window length is Time Series exist The moving standard deviation at time, Indicates that the moving window length is Time Series exist The moving standard deviation of the moment;

[0141] (8)

[0142] In formula (8), Indicates that the moving window length is Time Series exist The moving average of time, and ; Indicates that the moving window length is Time Series exist The moving average of time, and ;

[0143] Step 3.3: Calculation The mean , The mean , , ,The characteristics of clustering and dispersion have obvious differences in the value of the moving standard deviation. Therefore, the moving standard deviation can be used to predict the pedestrian flow composition;

[0144] Step 3.4: Calculate the upper threshold of the moving standard deviation , the lower threshold of the moving standard deviation In order to make the selection of the judgment threshold more general, the floating value is adopted based on the middle value. % to determine the final threshold, The value of is 5, When the value of is 10, , .

[0145] Step 4: Pedestrian flow composition prediction;

[0146] Step 4.1: Obtained in step 1.1 Pedestrian flow sequence under a period minus Then, we get its fluctuation sequence ,in, Indicates The pedestrian flow sequence in the period is compared with the overall trend sequence in Fluctuation value over a period of time;

[0147] Step 4.2: Calculate according to formula (8) The moving standard deviation series ,in, It means that the moving window length is At that time, Pedestrian flow fluctuation sequence under a period exist The moving standard deviation at each moment.

[0148] Step 4.3: When , indicating the In the cycle The proportion of clustered pedestrians in the pedestrian flow composition after the time is high;

[0149] when , then it means In the cycle After the time, the proportion of dispersed pedestrians in the pedestrian flow is high. The prediction result of the moving standard deviation of the pedestrian flow composition of the downward pedestrian flow fluctuation sequence is shown in Figure 8(a). The red upper triangle in Figure 8(a) indicates that the moving standard deviation has just exceeded the upper threshold of the moving standard deviation. It is predicted that the proportion of clustered pedestrians is high at this moment. Therefore, in the time period from the red upper triangle to the black lower triangle, the proportion of clustered pedestrians is high. On the contrary, in the time period from the black lower triangle to the red upper triangle, the proportion of dispersed pedestrians is high.

[0150] In this embodiment, steps 3.2 to 4.3 are replaced by the following steps:

[0151] Step 1: Use equation (9) and set the moving window length as Calculate separately The moving root mean square sequence and The moving root mean square sequence ,in, Indicates that the moving window length is Time Series exist The moving root mean square at time, Indicates that the moving window length is Time Series exist The moving root mean square at time, When it is 5, the sequence and its moving standard deviation series and the moving RMS series like Figure 7a As shown, the sequence and its moving standard deviation series and the moving RMS series like Figure 7b As shown;

[0152] (9)

[0153] Step 2: Calculation The mean , The mean , , ,The characteristics of clustering and dispersion have obvious differences in the values ​​of the moving mean square ,values. Therefore, the moving mean square can be used to predict the composition of pedestrian flow;

[0154] Step 3: Calculate the upper threshold of the moving mean square , the lower threshold of the moving root mean square In order to make the selection of the judgment threshold more general, the floating value is adopted based on the middle value. % to determine the final threshold, The value of is 5, When the value of is 10, , ;

[0155] Step 4: Get the first Pedestrian flow sequence under a period minus Then, we get its fluctuation sequence ,in, Indicates The pedestrian flow sequence in the period is compared with the overall trend sequence in Fluctuation value over a period of time;

[0156] Step 5: Calculate according to formula (9) The moving standard deviation series ,in, It means that the moving window length is At that time, Pedestrian flow fluctuation sequence under a period exist The moving standard deviation of the moment;

[0157] Step 6: When , then it means In the cycle The proportion of clustered pedestrians in the pedestrian flow composition after the time is high;

[0158] when , then it means In the cycle After the time, the proportion of dispersed pedestrians in the pedestrian flow is high. The prediction results of the moving mean square root of the pedestrian flow composition of the periodic downlink pedestrian flow fluctuation sequence are as follows: Figure 8b shown. Figure 8b The red upper triangle indicates that the moving mean square has just exceeded the upper threshold limit of the moving mean square. It is predicted that the proportion of clustered pedestrians is high at this moment. Therefore, during the time period from the red upper triangle to the black lower triangle, the proportion of clustered pedestrians is high. On the contrary, during the time period from the black lower triangle to the red upper triangle, the proportion of dispersed pedestrians is high.

[0159] In this embodiment, an electronic device includes a memory and a processor, wherein the memory is used to store a program that supports the processor to execute the above method, and the processor is configured to execute the program stored in the memory.

[0160] In this embodiment, a computer-readable storage medium stores a computer program on the computer-readable storage medium, and the computer program executes the steps of the above method when executed by a processor.

Claims

1. A pedestrian flow composition ratio prediction method based on time series fluctuations, characterized in that: The steps are as follows: Step 1: Collect and process real scene data; Step 1.1: Get the Pedestrian videos of the same one-way straight channel in the same time period under the same cycle are collected and a reference line is set at the exit of the channel in each frame of the pedestrian video, so as to Pedestrian videos under a certain period of time Identify pedestrians passing through the reference line and obtain the number of pedestrians passing through the reference line to form the first Pedestrian flow sequence under a period , thus obtaining Pedestrian flow sequence under a period ,in, Indicates The next cycle Pedestrian flow of time, Indicates the total amount of time; Step 1.2: After taking the average, median filtering is performed to obtain the overall trend sequence of pedestrian flow. ,in, Indicates The overall trend of pedestrian flow during the period; Step 1.3: The pedestrian flow volume trend of each duration in is taken as the random arrival rate of Poisson distribution, thus generating a total of After taking the Poisson random number and reversing it, we can get the number of pedestrians arriving at the entrance of the one-way straight channel at each moment. ;in, Indicates The number of pedestrians arriving at the entrance of the one-way straight passage at a certain moment; Step 1.4: According to Construct a pedestrian type array ,in, Indicates The type identification sequence of all pedestrians arriving at the entrance of the one-way straight passage at the moment, and ; Indicates The first one that arrives at the entrance of the one-way straight passage at the pedestrian types; and , Indicates aggregation type, Represents distributed type; initialization = ; Step 2: Simulate pedestrian flow; Step 2.0: Initialization ; Take a vertex at the entrance of the channel in the pedestrian video as the origin and the horizontal direction of the channel as Axis direction, with the vertical direction of the channel as Axis direction, establish the first plane coordinate system; Get the channel in Length in the axial direction and Width in the axial direction ; Calibrate the coordinates of the first point in the first plane coordinate system using the four-point calibration method , coordinates of the second point , the third point coordinates , the fourth point coordinates ; Definition from arrive The line segments between arrive The line segments between the two sides of the channel represent the edges of the walls ;in, Indicates the channel Side wall edges; Definition from arrive The line segment is the reference line of the simulation scene ; The radius interval of pedestrians is defined as ,in, is the minimum radius of pedestrians, is the maximum radius of pedestrians; The weight range of pedestrians is defined as ,in, is the minimum weight of the pedestrian, is the maximum weight of the pedestrian; The expected speed range of pedestrians is defined as ,in, is the minimum expected speed of pedestrians, is the maximum expected speed of the pedestrian; Define the initial vertical coordinate interval of the pedestrian as , where 0 is the minimum value of the pedestrian’s initial ordinate. is the maximum value of the pedestrian’s initial ordinate; Define the coordinates of the target point for each pedestrian to move to as ,in, , is the horizontal coordinate of a point in front of the channel exit, ; Step 2.1: Randomly generate Radius sequence of pedestrians at each moment ,in, Indicates Arrive at the entrance of the one-way straight passage at the moment The radius of pedestrians, Indicates The number of pedestrians arriving at the entrance of the one-way straight passage at a certain moment, and ; exist Randomly generate The weight sequence of pedestrians at each moment ,in, Indicates Arrive at the entrance of the one-way straight passage at the moment The weight of the pedestrian; exist Randomly generate The expected speed sequence of pedestrians at each moment ,in, Indicates Arrive at the entrance of the one-way straight passage at the moment The expected speed of each pedestrian; Definition The vector velocity sequence of pedestrians at each moment ,in, Indicates Arrive at the entrance of the one-way straight passage at the moment The vector velocity of pedestrians and initialize is 0; exist Randomly generate The initial ordinate sequence of the pedestrian at time ;in, Indicates Arrive at the entrance of the one-way straight passage at the time The initial vertical coordinate of the pedestrian is generated Pedestrian position sequence at each moment , Indicates Arrive at the entrance of the one-way straight passage at the time The initial position of each pedestrian; Step 2.2: Initialization ; Step 2.3: Calculate the first Pedestrian position sequence at each moment Middle Pedestrian self-driving force ; (1) In formula (1), Indicates Moment The time it takes for a pedestrian's walking speed to return to the expected speed; For the Moment The position of the pedestrian points to the target point coordinates The direction vector of Step 2.4: Calculate the Pedestrian position sequence at each moment Middle Pedestrians and The interaction forces between pedestrians ; (2) In formula (2), For the The repulsive force parameter of each pedestrian is The minimum range for generating repulsive force; For the Moment Pedestrians and The sum of the radii of the pedestrians; For the Moment Pedestrians and The distance between the centroids of pedestrians; For the Moment A pedestrian points to The unit vector of each pedestrian; is the extrusion pressure parameter; is the friction parameter; For the Moment Pedestrians and The unit vector in the tangential direction between pedestrians; represents the contact force discriminant function, when season ,when season ; For the Moment Pedestrians and The tangential velocity difference between pedestrians; Step 2.5: Calculate the first Pedestrian position sequence at each moment Middle Pedestrians and Side wall edge The force between ; (3) In formula (3), For the The first pedestrians to the edge of the wall The distance For the The first Pedestrians and the edge of the wall The unit vector in the perpendicular direction, Indicates The first Pedestrians walking parallel The unit vector of the direction; Step 2.6: If = , then using formula (4) we can get Pedestrian position sequence at each moment Middle The driving force behind pedestrians' tendency to speed up or slow down when faced with congestion ,like = , then using formula (5) we can get Pedestrian position sequence at each moment Middle The driving force behind pedestrians' tendency to speed up or slow down when faced with congestion ; (4) (5) In formula (4) and formula (5), It indicates the driving force of any pedestrian to speed up or slow down when facing congestion. For the The first position in the pedestrian position sequence at the moment The pedestrian density in front of the pedestrian, and , For the The first position in the pedestrian position sequence at the moment The length in front of the pedestrian is The total number of pedestrians within the range; It indicates the threshold for judging the congestion ahead. Indicates the threshold for judging that the front is not crowded; Step 2.7: Update the first Pedestrian position sequence at each moment Middle The vector velocity of each pedestrian ; (6) Step 2.8: Update the first Pedestrian position sequence at each moment Middle The positions of pedestrians after the adjacent time interval ; (7) Step 2.9: Assign to After that, if , then return to step 2.2 to execute sequentially, otherwise, it means that the The position sequence of all pedestrians after adjacent time intervals at the moment ; and record the Pedestrian position sequence at each moment Through the reference line Number of pedestrians ; Step 2.10: Delete Pedestrian position sequence at each moment The horizontal coordinate is in the interval The coordinate information of pedestrians outside the radius sequence is deleted at the same time. , weight sequence , expected speed sequence , vector velocity sequence and identification information sequence The information in the Moment , , , , and ; and update the number of pedestrians ; Step 2.11: Assign to ,like , then follow step 2.1 to obtain Moment , , , , and as well as , and respectively and , and , and , and , and , and Merge Assign to After that, return to step 2.2 and execute sequentially; otherwise, it means that the number of pedestrians sequence is obtained ; Step 2.12: Zhongmei The sum of the number of pedestrians is used as the simulated pedestrian flow sequence The elements in , thus obtaining the simulated clustered pedestrian flow sequence ; Indicates The simulated aggregated pedestrian flow of time duration; Step 2.13: Initialization = ,initialization Then, follow the process from step 2.1 to step 2.12 to obtain the simulated dispersed pedestrian flow sequence. ;in, Indicates simulated dispersed pedestrian flow for a certain period of time; Step 3: Sequence feature extraction and judgment threshold determination; Step 3.1: Sequence , Subtract , and obtain the fluctuation sequence of simulated aggregated pedestrian flow sequence and the wave sequence simulating the dispersed pedestrian flow sequence ;in, It means that the simulated clustered pedestrian flow sequence is higher than the overall trend sequence in the The fluctuation value of the time length, Indicates The simulated dispersed pedestrian flow of duration is compared with the overall trend sequence in Fluctuation value over a period of time; Step 3.2: Use equation (8) and set the moving window length to Calculate separately The moving standard deviation series and The moving standard deviation series ,in, Indicates that the moving window length is Time Series exist The moving standard deviation at time, Indicates that the moving window length is Time Series exist The moving standard deviation of the moment; (8) In formula (8), Indicates that the moving window length is Time Series exist The moving average of time, and ; Indicates that the moving window length is Time Series exist The moving average of time, and ; Step 3.3: Calculation The mean , The mean ; Step 3.4: Calculate the upper threshold of the moving standard deviation , the lower threshold of the moving standard deviation ; Step 4: Pedestrian flow composition prediction; Step 4.1: Obtained in step 1.1 Pedestrian flow sequence under a period minus After that, get The volatility sequence under the period ,in, Indicates The pedestrian flow sequence in the cycle is compared with the overall trend sequence in Fluctuation value over a period of time; Step 4.2: Calculate according to formula (8) The moving standard deviation series ,in, Indicates that the moving window length is At that time, Pedestrian flow fluctuation sequence under a period exist The moving standard deviation of the moment; Step 4.3: When , indicating the In the cycle The proportion of clustered pedestrians in the pedestrian flow composition after the time is high; when , then it means In the cycle After the time, the proportion of dispersed pedestrians in the pedestrian flow is relatively high.

2. The pedestrian flow composition ratio prediction method based on time series fluctuation according to claim 1 is characterized in that: The steps 3.2 to 4.3 are replaced by the following steps: Step 1: Use equation (9) and set the moving window length as Calculate separately The moving root mean square sequence and The moving root mean square sequence ,in, Indicates that the moving window length is Time Series exist The moving root mean square at time, Indicates that the moving window length is Time Series exist Moving RMS at time; (9) Step 2: Calculation The mean , The mean ; Step 3: Calculate the upper threshold of the moving mean square , the lower threshold of the moving root mean square ; Step 4: Get the first Pedestrian flow sequence under a period minus After that, get The volatility sequence under the period ,in, Indicates The pedestrian flow sequence in the cycle is compared with the overall trend sequence in Fluctuation value over a period of time; Step 5: Calculate according to formula (9) The moving standard deviation series ,in, It means that the moving window length is At that time, Pedestrian flow fluctuation sequence under a period exist The moving standard deviation of the moment; Step 6: When , then it means In the cycle The proportion of clustered pedestrians in the pedestrian flow composition after the time is high; when , then it means In the cycle After the time, the proportion of dispersed pedestrians in the pedestrian flow is relatively high.

3. An electronic device, comprising a memory and a processor, characterized in that: The memory is used to store a program that supports the processor to execute the pedestrian flow composition ratio prediction method according to claim 1 or 2, and the processor is configured to execute the program stored in the memory.

4. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the pedestrian flow composition ratio prediction method according to claim 1 or 2 are executed.

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