CN115472003B - Urban traffic supervision system and method based on multi-source information - Google Patents
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Abstract
The invention discloses an urban traffic supervision system and method based on multi-source information.A traffic data analysis module obtains an alternative road section of a road section corresponding to a first prediction result according to the first prediction result and an urban road model, obtains a correlation coefficient between the alternative road section and the road section corresponding to the first prediction result in the urban road model, combines the prediction results of the number of traffic vehicles corresponding to different road sections in different time periods, calibrates the prediction results of the number of traffic vehicles in different time periods corresponding to the alternative road section, and predicts traffic pressure coefficients of all the alternative road sections. The invention relates to the technical field of traffic supervision, which is used for analyzing and predicting traffic conditions of different road sections of a city from the aspects of urban topography, road conditions, weather conditions and drainage system facility conditions, predicting traffic pressure conditions of each road section, further allocating personnel in advance, and evacuating congested vehicles in time to ensure that the road is smooth.
Description
Technical Field
The invention relates to the technical field of traffic supervision, in particular to an urban traffic supervision system and method based on multi-source information.
Background
With the improvement of the living standard of people, the number of urban vehicles rises year by year, so that greater pressure is brought to urban traffic; meanwhile, the traffic pressure of the city is also closely related to the soundness of the rest of the infrastructure of the city, such as: the road condition and the drainage system, the road broad degree and the drainage system's drainage speed to the rainwater all can influence urban traffic, especially drainage system, when the drainage system of road breaks down, can cause the traffic jam and corresponding highway section can't pass the vehicle.
The existing urban traffic supervision system only monitors the passing vehicles on the urban road simply, but cannot predict the traffic conditions of different sections of the city by combining the urban topography, road conditions, weather conditions and drainage system facility conditions, so that the road congestion sections cannot be dredged quickly and effectively, and the timeliness is poor.
Disclosure of Invention
The invention aims to provide an urban traffic supervision system and method based on multi-source information, which are used for solving the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme: an urban traffic supervision method based on multi-source information, the method comprising the following steps:
S1, measuring the heights and the diameters of drainage pipelines at different positions in each road through a sensor, constructing an urban drainage pipeline model, and marking the positions of drainage ports;
s2, obtaining information data of each road, and constructing an urban road model;
s3, obtaining urban weather information in real time, predicting a road section affected by water drainage in an urban road model in a first unit time, and marking the road section as a first prediction result, wherein the first unit time is a prefabricated constant in a database;
s4, obtaining the number of vehicles passing through each road section in each road in different time periods in the historical data, predicting the number of vehicles passing through corresponding to different road sections in different time periods, wherein the same road comprises one or more road sections, and marking the road between two adjacent road junctions on the same road as one road section;
s5, obtaining an alternative road section of a road section corresponding to the first prediction result according to the first prediction result and the urban road model, obtaining a correlation coefficient between the alternative road section and the road section corresponding to the first prediction result in the urban road model, and calibrating the prediction results of the number of vehicles passing in different time periods corresponding to the alternative road section by combining the prediction results of the number of vehicles passing in different time periods corresponding to the alternative road section in the S4, so as to predict the traffic pressure coefficient of each alternative road section;
And S6, personnel allocation is carried out on the road sections corresponding to the first prediction result and the alternative road sections with the traffic pressure coefficient larger than a first preset value, the congested vehicles are evacuated, and the first preset value is a preset constant in a database.
According to the invention, the urban topography, road conditions, weather conditions and drainage system facility conditions are combined, the traffic conditions of different road sections of the city are analyzed and predicted, the traffic pressure conditions of each road section are predicted, personnel are further allocated in advance, and the congested vehicles are evacuated in time, so that the road is ensured to be normal.
Further, the method for constructing the urban drainage pipeline model in the step S1 comprises the following steps:
s1.1, acquiring the height and the diameter of a drainage pipeline at different positions in each road measured by a sensor, wherein the height of the drainage pipeline is the height difference between the highest point and the original point of the drainage pipeline, and the height difference is a real number;
s1.2, taking the earth surface position of the urban central point as an origin, taking the direction from east to west as an x-axis positive direction, taking the direction from south to north as a y-axis positive direction, and taking the direction from bottom to top as a z-axis positive direction, so as to construct a space rectangular coordinate system;
s1.3, determining coordinate points (a 1, a2 and a 3) corresponding to the circle centers of the cross sections of the drainage pipelines at different positions in a space rectangular coordinate system, wherein the values of a1 and a2 and the coordinates of the projection points of the circle centers of the cross sections of the drainage pipelines at the horizontal plane are obtained, and a3 is equal to the sum of the height of the drainage pipelines and the radius of the drainage pipelines;
S1.4, corresponding coordinate points of the cross-section circle centers of the drainage pipelines at different positions on the same drainage pipeline in a space rectangular coordinate system form a drainage pipeline axis, each coordinate point corresponding to the drainage pipeline region in the space rectangular coordinate system is obtained according to the drainage pipeline axis and the corresponding diameters of the drainage pipelines at different positions, and then the urban drainage pipeline model is obtained,
the position of the water outlet is the coordinate of the point with the minimum distance between the axis of the water outlet and the center point of the water outlet.
The position of the water outlet is marked by the invention, and considering that the accumulated water on the road surface is usually discharged through the water outlet, under the condition that the accumulated water on the road surface cannot be timely discharged, the accumulated water is often gathered at the water outlet, thereby submerging the corresponding road section and obstructing the traffic of vehicles; the urban drainage pipeline model is constructed, data reference is provided for determining the connection condition of the drainage pipeline in the subsequent process, and the first prediction result is convenient to obtain.
Further, the method for constructing the urban road model in S2 includes the following steps:
s2.1, acquiring positions of all points on a road axis relative to an origin, and obtaining coordinates of each point on the road axis corresponding to the points in a space rectangular coordinate system;
S2.2, obtaining the road width corresponding to each point on the central axis of the road and the straight line where each point points, wherein the information data of the road comprises the central axis position of the road, the road width corresponding to each point on the central axis and the straight line where each point points,
when the connecting line of the adjacent three points on the central axis is a straight line, the straight line where the midpoint of the three points is the straight line where the connecting line of the three points is located,
when the connecting line of the adjacent three points on the central axis is a curve, the straight line at which the middle point of the three points is a tangent line passing through the middle point of the three points;
s2.3, obtaining road ranges corresponding to different points on a road axis in a space rectangular coordinate system, wherein the road range corresponding to each point on the axis is a line segment, the length of the line segment is the road width corresponding to the corresponding point on the axis, the road ranges are vertical to the corresponding point on the axis and symmetrical with respect to the corresponding point on the axis, and the z-axis coordinate values of all coordinates in the road range corresponding to each point on the axis are the same;
and S2.4, obtaining a coordinate range corresponding to each road in the space rectangular coordinate system, and further obtaining the urban road model.
The invention constructs an urban road model to intuitively acquire the position relation among different road sections in the urban road, and is convenient for obtaining the corresponding alternative road sections of the road sections corresponding to the first prediction result in the subsequent process.
Further, the method for obtaining the first prediction result in S3 includes the following steps:
s3.1, obtaining urban weather information, wherein the weather information comprises precipitation in different time periods of different road sections;
s3.2, numbering road sections according to the sequence from small to large, wherein the corresponding numbers of different road sections are different, one road section corresponds to one number, and a default water outlet is arranged at the lowest position of the road section to which the water outlet belongs;
s3.3, calculating precipitation convergence speed Vhit of the road section with the number i at a time point t, the sum VPsit of drainage speeds of the road sections which are connected with the road section with the number i and the water outlet position of which is higher than the water outlet position of the road section with the number i, the drainage speed VPit of the drainage pipeline in the road section with the number i at the time point t and the maximum drainage speed VPzi of the drainage pipeline in the road section with the number i,
the product of the obtained ratio and the area of the road section with the number i is Vhit, and the area of the road section with the number i is obtained through road data information;
the VPzi is acquired through a database and is positively correlated with the diameter of the drainage pipeline;
when a road section connected with the road section with the number i or the water outlet position not higher than the water outlet position of the road section with the number i does not exist in the urban drainage pipeline model, vpsit=0, otherwise, whether precipitation exists in each road section is further judged, if precipitation exists, VPsit is not equal to 0, and if precipitation does not exist, vpsit=0;
When Vhit + VPsit-VPzi is equal to or greater than 0, then VPit = VPzi,
when vhit+vpsit-VPzi < 0, then vpit=vhit+vpsit;
s3.4, predicting precipitation retention speeds Vt corresponding to different time points in the first unit time of the section with the number i, wherein the Vt is equal to Vhit+VPsit-VPit corresponding to the time point t,
predicting precipitation reserve Q for a subsequent first unit of time, said Q being equal to an integrated value of Vt for the subsequent first unit of time,
counting the duration corresponding to vpit=vpzi in the subsequent first unit time, and marking the ratio of the obtained duration to the first unit time as beta;
s3.5, predicting whether the road section with the number i in the first unit time has the road section ponding condition,
when Q is larger than the first threshold value Q1 and beta is larger than the second threshold value beta 1, judging that the road section with the number i in the first unit time has the road section ponding condition, otherwise, judging that the road section with the number i in the first unit time does not have the road section ponding condition,
the first threshold Q1 is equal to the average value of precipitation in each corresponding first unit time under the condition that the road section with the number i in the historical data has accumulated water and blocks the vehicle from passing,
the second threshold value beta 1 is equal to the average value of the ratio of the duration of vpit=vpzi to the first unit time in each corresponding first unit time under the condition that the road section with the number i in the historical data has accumulated water and blocks the vehicle from passing;
And S3.6, summarizing road sections with road section ponding conditions in the urban road model in the first unit time to obtain a first prediction result.
In the process of acquiring the first prediction result, the urban weather information is acquired to accurately predict the precipitation conditions corresponding to different time of different road sections; the road sections are numbered, so that different road sections in the city can be conveniently distinguished; the precipitation convergence speed Vhit of the road section with the number i at the time point t, the sum VPsit of drainage speeds of the road sections which are connected with the road section with the number i and the water outlet position of which is higher than the water outlet position of the road section with the number i, and the maximum drainage speed VPzi of the drainage pipeline in the road section with the number i are calculated to determine the value corresponding to the drainage speed VPit of the drainage pipeline in the road section with the number i at the time point t, so that the precipitation retention speeds Vt corresponding to different time points in the first unit time of the road section with the number i are predicted in the follow-up process, whether road section ponding occurs in the road section with the number i in the first unit time is predicted, and data reference is provided for the follow-up obtained first prediction result.
Further, the method for predicting the number of the passing vehicles corresponding to different road segments in different time segments in the S4 includes the following steps:
S4.1, acquiring the number of vehicles passing through each road section in each road in different time periods in the historical data;
s4.2, acquiring historical weather conditions, screening the number of vehicles passing through different road sections in a non-rainfall state in the historical data to obtain the vehicle flow corresponding to different time points of different road sections in the non-rainfall state in the historical data, and recording the vehicle flow corresponding to a number i road section at a time point t1 in the non-rainfall state in the historical data as Wit1, wherein Wit1 is equal to the day of the date corresponding to t1 and the absolute value of the difference value between the Wit1 is smaller than or equal to the time period of a second unit time, and the ratio of the number of vehicles passing through the number i road section to the duration of the corresponding time period is the constant prefabricated in the database;
s4.3, calculating average values of the vehicle flow corresponding to time points t1 of the road sections with the number i in different days under the condition of no rainfall in the historical data, and marking the average values as WPit1, wherein the predicted value of the number of vehicles passing through the road sections with the number i in the time periods [ td1, td2] is
Wherein td1 is the start time of the predicted time period, and td2 is the end time of the predicted time period;
s4.4, predicting the number of corresponding passing vehicles in different road segments in different time segments.
In the process of predicting the number of the vehicles passing through the different road sections in different time periods, the data of the vehicles passing through the different road sections in different time periods in the historical data are referred to for analysis, so that the corresponding traffic flow conditions of the different road sections at different time points in the day are accurately predicted, a more accurate data basis is provided for predicting the number of the vehicles passing through the different road sections in different time periods with follow-up, meanwhile, the follow-up acquisition of different alternative road sections is facilitated, and the calibration value of the number prediction result of the vehicles passing through the different road sections in the follow-up first unit time based on the current time is also provided.
Further, the method for obtaining the association coefficient between the candidate road section and the road section corresponding to the first prediction result in the urban road model in S5 includes the following steps:
s5.1, two endpoints of a road section with the number i in the urban road model are obtained and respectively marked as a first endpoint D1i and a second endpoint D2i;
s5.2, obtaining all routes from the first endpoint D1i to the second endpoint D2i in the urban road model except for the road section with the number i, wherein all routes do not pass through the road section corresponding to the first prediction result, each route corresponds to one or more road sections, and the number of the road sections corresponding to each route is defaulted to be not more than a third threshold, wherein the third threshold is a prefabricated constant in a database;
s5.3, obtaining the number Gi corresponding to all routes in S5.2 and road section numbers corresponding to all routes respectively, adding the number Gi and the road section numbers to a blank array, and marking the blank array as a first array;
s5.4, when Gi is equal to 0, judging that the number i road segment does not have an alternative road segment and the association coefficients between the rest road segments and the number i road segment are all 0;
when Gi is not equal to 0, determining that an alternative road section exists in the road section with the number i, counting the corresponding road section number types and the number of each road section number type in the first array, taking the quotient of the corresponding number of each road section number type in the first array and Gi as the association coefficient of the corresponding road section number relative to the road section with the number i, wherein each road section number type in the first array corresponds to one alternative road section.
Further, the method for predicting the traffic pressure coefficient of each alternative road section in S5 includes the following steps:
s5-1, acquiring an association coefficient between an alternative road section of a road section corresponding to a j-th element in the first prediction result and the road section corresponding to the element;
s5-2, obtaining a section corresponding to a j element in the first prediction result, and recording a predicted value of the number of vehicles passing in a follow-up first unit time based on the current time as WRj;
s5-3, acquiring a kth alternative road section of the road section corresponding to a jth element in the first prediction result, and marking the predicted value of the number of vehicles passing in a first unit time based on the current time as WYjk;
s5-4, obtaining the traffic pressure coefficient of the kth alternative road section of the road section corresponding to the jth element in the first prediction result
(WRj*Ujk+WYjk)/W0 jk
Wherein Ujk represents a correlation coefficient between a kth candidate segment of the segment corresponding to the jth element in the first prediction result and the segment corresponding to the element;
WRj, ujk +wyjk represents a k-th candidate segment of the segments corresponding to the j-th element in the first prediction result, and a calibration value of the prediction result of the number of vehicles passing in a first unit time based on the current time;
W0 jk a kth alternative road section corresponding to the jth element in the first prediction result is represented, and the vehicle passing threshold value in the corresponding first unit time in the database is represented, wherein the W0 jk The vehicle passing threshold value is the same in the corresponding first unit time in the database for different road sections with the same width, W0 jk ＞0。
Further, in the step S6, in the process of personnel allocation of the road section corresponding to the first prediction result and the alternative road section with the traffic pressure coefficient larger than the first preset value,
personnel allocation priorities of all road sections corresponding to the first prediction results are the same;
the personnel allocation priority of the road section corresponding to the first prediction result is higher than that of the candidate road section;
and the personnel allocation priority corresponding to the alternative road sections with larger traffic pressure coefficients in the alternative road sections is higher.
An urban traffic supervision system based on multi-source information, the system comprising the following modules:
the urban drainage pipeline model building module is used for measuring the heights and the diameters of drainage pipelines at different positions in each road through sensors, building an urban drainage pipeline model and marking the positions of the drainage ports;
the urban road model building module acquires information data of each road and builds an urban road model;
the system comprises a congestion road section analysis module, a first prediction result and a second prediction result, wherein the congestion road section analysis module acquires urban weather information in real time, predicts a road section affected by water drainage in an urban road model in a first unit time, and the first unit time is a constant prefabricated in a database;
The road data processing module is used for obtaining the number of vehicles passing through each road section in each road in different time periods in the historical data, predicting the number of vehicles passing through corresponding to different road sections in different time periods, wherein the same road comprises one or more road sections, and the road between two adjacent road junctions on the same road is marked as one road section;
the traffic data analysis module is used for acquiring an alternative road section of a road section corresponding to the first prediction result according to the first prediction result and the urban road model, acquiring a correlation coefficient between the alternative road section and the road section corresponding to the first prediction result in the urban road model, combining the prediction results of the number of the traffic vehicles corresponding to different road sections in different time periods, calibrating the prediction results of the number of the traffic vehicles in different time periods corresponding to the alternative road section, and predicting the traffic pressure coefficient of each alternative road section;
and the personnel scheduling module is used for personnel allocation of the road sections corresponding to the first prediction result and the alternative road sections with the traffic pressure coefficient larger than a first preset value, and evacuating the jammed vehicles, wherein the first preset value is a constant prefabricated in the database.
Compared with the prior art, the invention has the following beneficial effects: according to the invention, from the aspects of urban topography, road conditions, weather conditions and drainage system facility conditions, traffic conditions of different road sections of a city are analyzed and predicted, traffic pressure conditions of each road section are predicted, personnel are further allocated in advance, and congested vehicles are evacuated in time, so that the road is ensured to be smooth.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a schematic diagram of a system for urban traffic supervision based on multi-source information according to the present invention;
fig. 2 is a schematic flow chart of an urban traffic supervision method based on multi-source information.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1-2, the present invention provides the following technical solutions: an urban traffic supervision method based on multi-source information, the method comprising the following steps:
s1, measuring the heights and the diameters of drainage pipelines at different positions in each road through a sensor, constructing an urban drainage pipeline model, and marking the positions of drainage ports;
s2, obtaining information data of each road, and constructing an urban road model;
s3, obtaining urban weather information in real time, predicting a road section affected by water drainage in an urban road model in a first unit time, and marking the road section as a first prediction result, wherein the first unit time is a prefabricated constant in a database;
the first unit time in this example is 30 minutes.
S4, obtaining the number of vehicles passing through each road section in each road in different time periods in the historical data, predicting the number of vehicles passing through corresponding to different road sections in different time periods, wherein the same road comprises one or more road sections, and marking the road between two adjacent road junctions on the same road as one road section;
s5, obtaining an alternative road section of a road section corresponding to the first prediction result according to the first prediction result and the urban road model, obtaining a correlation coefficient between the alternative road section and the road section corresponding to the first prediction result in the urban road model, and calibrating the prediction results of the number of vehicles passing in different time periods corresponding to the alternative road section by combining the prediction results of the number of vehicles passing in different time periods corresponding to the alternative road section in the S4, so as to predict the traffic pressure coefficient of each alternative road section;
And S6, personnel allocation is carried out on the road sections corresponding to the first prediction result and the alternative road sections with the traffic pressure coefficient larger than a first preset value, the congested vehicles are evacuated, and the first preset value is a preset constant in a database.
The method for constructing the urban drainage pipeline model in the S1 comprises the following steps of:
s1.1, acquiring the height and the diameter of a drainage pipeline at different positions in each road measured by a sensor, wherein the height of the drainage pipeline is the height difference between the highest point and the original point of the drainage pipeline, and the height difference is a real number;
s1.2, taking the earth surface position of the urban central point as an origin, taking the direction from east to west as an x-axis positive direction, taking the direction from south to north as a y-axis positive direction, and taking the direction from bottom to top as a z-axis positive direction, so as to construct a space rectangular coordinate system;
s1.3, determining coordinate points (a 1, a2 and a 3) corresponding to the circle centers of the cross sections of the drainage pipelines at different positions in a space rectangular coordinate system, wherein the values of a1 and a2 and the coordinates of the projection points of the circle centers of the cross sections of the drainage pipelines at the horizontal plane are obtained, and a3 is equal to the sum of the height of the drainage pipelines and the radius of the drainage pipelines;
s1.4, corresponding coordinate points of the cross-section circle centers of the drainage pipelines at different positions on the same drainage pipeline in a space rectangular coordinate system form a drainage pipeline axis, each coordinate point corresponding to the drainage pipeline region in the space rectangular coordinate system is obtained according to the drainage pipeline axis and the corresponding diameters of the drainage pipelines at different positions, and then the urban drainage pipeline model is obtained,
The position of the water outlet is the coordinate of the point with the minimum distance between the axis of the water outlet and the center point of the water outlet.
The method for constructing the urban road model in the S2 comprises the following steps:
s2.1, acquiring positions of all points on a road axis relative to an origin, and obtaining coordinates of each point on the road axis corresponding to the points in a space rectangular coordinate system;
s2.2, obtaining the road width corresponding to each point on the central axis of the road and the straight line where each point points, wherein the information data of the road comprises the central axis position of the road, the road width corresponding to each point on the central axis and the straight line where each point points,
when the connecting line of the adjacent three points on the central axis is a straight line, the straight line where the midpoint of the three points is the straight line where the connecting line of the three points is located,
when the connecting line of the adjacent three points on the central axis is a curve, the straight line at which the middle point of the three points is a tangent line passing through the middle point of the three points;
s2.3, obtaining road ranges corresponding to different points on a road axis in a space rectangular coordinate system, wherein the road range corresponding to each point on the axis is a line segment, the length of the line segment is the road width corresponding to the corresponding point on the axis, the road ranges are vertical to the corresponding point on the axis and symmetrical with respect to the corresponding point on the axis, and the z-axis coordinate values of all coordinates in the road range corresponding to each point on the axis are the same;
And S2.4, obtaining a coordinate range corresponding to each road in the space rectangular coordinate system, and further obtaining the urban road model.
The method for obtaining the first prediction result in the S3 comprises the following steps:
s3.1, obtaining urban weather information, wherein the weather information comprises precipitation in different time periods of different road sections;
s3.2, numbering road sections according to the sequence from small to large, wherein the corresponding numbers of different road sections are different, one road section corresponds to one number, and a default water outlet is arranged at the lowest position of the road section to which the water outlet belongs;
s3.3, calculating precipitation convergence speed Vhit of the road section with the number i at a time point t, the sum VPsit of drainage speeds of the road sections which are connected with the road section with the number i and the water outlet position of which is higher than the water outlet position of the road section with the number i, the drainage speed VPit of the drainage pipeline in the road section with the number i at the time point t and the maximum drainage speed VPzi of the drainage pipeline in the road section with the number i,
the product of the obtained ratio and the area of the road section with the number i is Vhit, and the area of the road section with the number i is obtained through road data information;
the VPzi is acquired through a database and is positively correlated with the diameter of the drainage pipeline;
When a road section connected with the road section with the number i or the water outlet position not higher than the water outlet position of the road section with the number i does not exist in the urban drainage pipeline model, vpsit=0, otherwise, whether precipitation exists in each road section is further judged, if precipitation exists, VPsit is not equal to 0, and if precipitation does not exist, vpsit=0;
when Vhit + VPsit-VPzi is equal to or greater than 0, then VPit = VPzi,
when vhit+vpsit-VPzi < 0, then vpit=vhit+vpsit;
s3.4, predicting precipitation retention speeds Vt corresponding to different time points in the first unit time of the section with the number i, wherein the Vt is equal to Vhit+VPsit-VPit corresponding to the time point t,
predicting precipitation reserve Q for a subsequent first unit of time, said Q being equal to an integrated value of Vt for the subsequent first unit of time,
counting the duration corresponding to vpit=vpzi in the subsequent first unit time, and marking the ratio of the obtained duration to the first unit time as beta;
s3.5, predicting whether the road section with the number i in the first unit time has the road section ponding condition,
when Q is larger than the first threshold value Q1 and beta is larger than the second threshold value beta 1, judging that the road section with the number i in the first unit time has the road section ponding condition, otherwise, judging that the road section with the number i in the first unit time does not have the road section ponding condition,
The first threshold Q1 is equal to the average value of precipitation in each corresponding first unit time under the condition that the road section with the number i in the historical data has accumulated water and blocks the vehicle from passing,
the second threshold value beta 1 is equal to the average value of the ratio of the duration of vpit=vpzi to the first unit time in each corresponding first unit time under the condition that the road section with the number i in the historical data has accumulated water and blocks the vehicle from passing;
and S3.6, summarizing road sections with road section ponding conditions in the urban road model in the first unit time to obtain a first prediction result.
The method for predicting the number of the passing vehicles corresponding to different road segments in different time segments in S4 comprises the following steps:
s4.1, acquiring the number of vehicles passing through each road section in each road in different time periods in the historical data;
s4.2, acquiring historical weather conditions, screening the number of vehicles passing through different road sections in a non-rainfall state in the historical data to obtain the vehicle flow corresponding to different time points of different road sections in the non-rainfall state in the historical data, and recording the vehicle flow corresponding to a number i road section at a time point t1 in the non-rainfall state in the historical data as Wit1, wherein Wit1 is equal to the day of the date corresponding to t1 and the absolute value of the difference value between the Wit1 is smaller than or equal to the time period of a second unit time, and the ratio of the number of vehicles passing through the number i road section to the duration of the corresponding time period is the constant prefabricated in the database;
The second unit time in this example is 20 minutes.
S4.3, calculating average values of the vehicle flow corresponding to time points t1 of the road sections with the number i in different days under the condition of no rainfall in the historical data, and marking the average values as WPit1, wherein the predicted value of the number of vehicles passing through the road sections with the number i in the time periods [ td1, td2] is
Wherein td1 is the start time of the predicted time period, and td2 is the end time of the predicted time period;
s4.4, predicting the number of corresponding passing vehicles in different road segments in different time segments.
The method for acquiring the association coefficient between the candidate road section and the road section corresponding to the first prediction result in the urban road model in the S5 comprises the following steps:
s5.1, two endpoints of a road section with the number i in the urban road model are obtained and respectively marked as a first endpoint D1i and a second endpoint D2i;
s5.2, obtaining all routes from the first endpoint D1i to the second endpoint D2i in the urban road model except for the road section with the number i, wherein all routes do not pass through the road section corresponding to the first prediction result, each route corresponds to one or more road sections, and the number of the road sections corresponding to each route is defaulted to be not more than a third threshold, wherein the third threshold is a prefabricated constant in a database;
the third threshold is 6 in this embodiment.
S5.3, obtaining the number Gi corresponding to all routes in S5.2 and road section numbers corresponding to all routes respectively, adding the number Gi and the road section numbers to a blank array, and marking the blank array as a first array;
S5.4, when Gi is equal to 0, judging that the number i road segment does not have an alternative road segment and the association coefficients between the rest road segments and the number i road segment are all 0;
when Gi is not equal to 0, determining that an alternative road section exists in the road section with the number i, counting the corresponding road section number types and the number of each road section number type in the first array, taking the quotient of the corresponding number of each road section number type in the first array and Gi as the association coefficient of the corresponding road section number relative to the road section with the number i, wherein each road section number type in the first array corresponds to one alternative road section.
The method for predicting the traffic pressure coefficient of each alternative road section in the S5 comprises the following steps:
s5-1, acquiring an association coefficient between an alternative road section of a road section corresponding to a j-th element in the first prediction result and the road section corresponding to the element;
s5-2, obtaining a section corresponding to a j element in the first prediction result, and recording a predicted value of the number of vehicles passing in a follow-up first unit time based on the current time as WRj;
s5-3, acquiring a kth alternative road section of the road section corresponding to a jth element in the first prediction result, and marking the predicted value of the number of vehicles passing in a first unit time based on the current time as WYjk;
S5-4, obtaining the traffic pressure coefficient of the kth alternative road section of the road section corresponding to the jth element in the first prediction result
(WRj*Ujk+WYjk)/W0 jk
Wherein Ujk represents a correlation coefficient between a kth candidate segment of the segment corresponding to the jth element in the first prediction result and the segment corresponding to the element;
WRj, ujk +wyjk represents a k-th candidate segment of the segments corresponding to the j-th element in the first prediction result, and a calibration value of the prediction result of the number of vehicles passing in a first unit time based on the current time;
W0 jk a kth alternative road section corresponding to the jth element in the first prediction result is represented, and the vehicle passing threshold value in the corresponding first unit time in the database is represented, wherein the W0 jk The vehicle passing threshold value is the same in the corresponding first unit time in the database for different road sections with the same width, W0 jk ＞0。
In the step S6, in the process of personnel allocation of the road section corresponding to the first prediction result and the alternative road section with the traffic pressure coefficient larger than the first preset value,
personnel allocation priorities of all road sections corresponding to the first prediction results are the same;
the personnel allocation priority of the road section corresponding to the first prediction result is higher than that of the candidate road section;
And the personnel allocation priority corresponding to the alternative road sections with larger traffic pressure coefficients in the alternative road sections is higher.
In this embodiment, the first preset value is 2.
An urban traffic supervision system based on multi-source information, the system comprising the following modules:
the urban drainage pipeline model building module is used for measuring the heights and the diameters of drainage pipelines at different positions in each road through sensors, building an urban drainage pipeline model and marking the positions of the drainage ports;
the urban road model building module acquires information data of each road and builds an urban road model;
the system comprises a congestion road section analysis module, a first prediction result and a second prediction result, wherein the congestion road section analysis module acquires urban weather information in real time, predicts a road section affected by water drainage in an urban road model in a first unit time, and the first unit time is a constant prefabricated in a database;
the road data processing module is used for obtaining the number of vehicles passing through each road section in each road in different time periods in the historical data, predicting the number of vehicles passing through corresponding to different road sections in different time periods, wherein the same road comprises one or more road sections, and the road between two adjacent road junctions on the same road is marked as one road section;
The traffic data analysis module is used for acquiring an alternative road section of a road section corresponding to the first prediction result according to the first prediction result and the urban road model, acquiring a correlation coefficient between the alternative road section and the road section corresponding to the first prediction result in the urban road model, combining the prediction results of the number of the traffic vehicles corresponding to different road sections in different time periods, calibrating the prediction results of the number of the traffic vehicles in different time periods corresponding to the alternative road section, and predicting the traffic pressure coefficient of each alternative road section;
and the personnel scheduling module is used for personnel allocation of the road sections corresponding to the first prediction result and the alternative road sections with the traffic pressure coefficient larger than a first preset value, and evacuating the jammed vehicles, wherein the first preset value is a constant prefabricated in the database.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present invention, and the present invention is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present invention has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (8)
1. An urban traffic supervision method based on multi-source information, which is characterized by comprising the following steps:
s1, measuring the heights and the diameters of drainage pipelines at different positions in each road through a sensor, constructing an urban drainage pipeline model, and marking the positions of drainage ports;
s2, obtaining information data of each road, and constructing an urban road model;
s3, obtaining urban weather information in real time, predicting a road section affected by water drainage in an urban road model in a first unit time, and marking the road section as a first prediction result, wherein the first unit time is a prefabricated constant in a database;
s4, obtaining the number of vehicles passing through each road section in each road in different time periods in the historical data, predicting the number of vehicles passing through corresponding to different road sections in different time periods, wherein the same road comprises one or more road sections, and marking the road between two adjacent road junctions on the same road as one road section;
S5, obtaining an alternative road section of a road section corresponding to the first prediction result according to the first prediction result and the urban road model, obtaining a correlation coefficient between the alternative road section and the road section corresponding to the first prediction result in the urban road model, and calibrating the prediction results of the number of vehicles passing in different time periods corresponding to the alternative road section by combining the prediction results of the number of vehicles passing in different time periods corresponding to the alternative road section in the S4, so as to predict the traffic pressure coefficient of each alternative road section;
s6, personnel allocation is carried out on the road sections corresponding to the first prediction result and the alternative road sections with the traffic pressure coefficient larger than a first preset value, and the congested vehicles are evacuated, wherein the first preset value is a preset constant in a database;
the method for acquiring the association coefficient between the candidate road section and the road section corresponding to the first prediction result in the urban road model in the S5 comprises the following steps:
s5.1, two endpoints of a road section with the number i in the urban road model are obtained and respectively marked as a first endpoint D1i and a second endpoint D2i;
s5.2, obtaining all routes from the first endpoint D1i to the second endpoint D2i in the urban road model except for the road section with the number i, wherein all routes do not pass through the road section corresponding to the first prediction result, each route corresponds to one or more road sections, and the number of the road sections corresponding to each route is defaulted to be not more than a third threshold, wherein the third threshold is a prefabricated constant in a database;
S5.3, obtaining the number Gi corresponding to all routes in S5.2 and road section numbers corresponding to all routes respectively, adding the number Gi and the road section numbers to a blank array, and marking the blank array as a first array;
s5.4, when Gi is equal to 0, judging that the number i road segment does not have an alternative road segment and the association coefficients between the rest road segments and the number i road segment are all 0;
when Gi is not equal to 0, determining that an alternative road section exists in the road section with the number i, counting the corresponding road section number types and the number of each road section number type in the first array, taking the quotient of the corresponding number of each road section number type in the first array and Gi as the association coefficient of the corresponding road section number relative to the road section with the number i, wherein each road section number type in the first array corresponds to one alternative road section.
2. The urban traffic supervision method based on multi-source information according to claim 1, wherein: the method for constructing the urban drainage pipeline model in the S1 comprises the following steps of:
s1.1, acquiring the height and the diameter of a drainage pipeline at different positions in each road measured by a sensor, wherein the height of the drainage pipeline is the height difference between the highest point and the original point of the drainage pipeline, and the height difference is a real number;
s1.2, taking the earth surface position of the urban central point as an origin, taking the direction from east to west as an x-axis positive direction, taking the direction from south to north as a y-axis positive direction, and taking the direction from bottom to top as a z-axis positive direction, so as to construct a space rectangular coordinate system;
S1.3, determining coordinate points (a 1, a2 and a 3) corresponding to the circle centers of the cross sections of the drainage pipelines at different positions in a space rectangular coordinate system, wherein the values of a1 and a2 and the coordinates of the projection points of the circle centers of the cross sections of the drainage pipelines at the horizontal plane are obtained, and a3 is equal to the sum of the height of the drainage pipelines and the radius of the drainage pipelines;
s1.4, corresponding coordinate points of the cross-section circle centers of the drainage pipelines at different positions on the same drainage pipeline in a space rectangular coordinate system form a drainage pipeline axis, each coordinate point corresponding to the drainage pipeline region in the space rectangular coordinate system is obtained according to the drainage pipeline axis and the corresponding diameters of the drainage pipelines at different positions, and then the urban drainage pipeline model is obtained,
the position of the water outlet is the coordinate of the point with the minimum distance between the axis of the water outlet and the center point of the water outlet.
3. The urban traffic supervision method based on multi-source information according to claim 2, wherein: the method for constructing the urban road model in the S2 comprises the following steps:
s2.1, acquiring positions of all points on a road axis relative to an origin, and obtaining coordinates of each point on the road axis corresponding to the points in a space rectangular coordinate system;
s2.2, obtaining the road width corresponding to each point on the central axis of the road and the straight line where each point points, wherein the information data of the road comprises the central axis position of the road, the road width corresponding to each point on the central axis and the straight line where each point points,
When the connecting line of the adjacent three points on the central axis is a straight line, the straight line where the midpoint of the three points is the straight line where the connecting line of the three points is located,
when the connecting line of the adjacent three points on the central axis is a curve, the straight line at which the middle point of the three points is a tangent line passing through the middle point of the three points;
s2.3, obtaining road ranges corresponding to different points on a road axis in a space rectangular coordinate system, wherein the road range corresponding to each point on the axis is a line segment, the length of the line segment is the road width corresponding to the corresponding point on the axis, the road ranges are vertical to the corresponding point on the axis and symmetrical with respect to the corresponding point on the axis, and the z-axis coordinate values of all coordinates in the road range corresponding to each point on the axis are the same;
and S2.4, obtaining a coordinate range corresponding to each road in the space rectangular coordinate system, and further obtaining the urban road model.
4. The urban traffic supervision method based on multi-source information according to claim 1, wherein: the method for obtaining the first prediction result in the S3 comprises the following steps:
s3.1, obtaining urban weather information, wherein the weather information comprises precipitation in different time periods of different road sections;
s3.2, numbering road sections according to the sequence from small to large, wherein the corresponding numbers of different road sections are different, one road section corresponds to one number, and a default water outlet is arranged at the lowest position of the road section to which the water outlet belongs;
S3.3, calculating precipitation convergence speed Vhit of the road section with the number i at a time point t, the sum VPsit of drainage speeds of the road sections which are connected with the road section with the number i and the water outlet position of which is higher than the water outlet position of the road section with the number i, the drainage speed VPit of the drainage pipeline in the road section with the number i at the time point t and the maximum drainage speed VPzi of the drainage pipeline in the road section with the number i,
the product of the obtained ratio and the area of the road section with the number i is Vhit, and the area of the road section with the number i is obtained through road data information;
the VPzi is acquired through a database and is positively correlated with the diameter of the drainage pipeline;
when a road section connected with the road section with the number i or the water outlet position not higher than the water outlet position of the road section with the number i does not exist in the urban drainage pipeline model, vpsit=0, otherwise, whether precipitation exists in each road section is further judged, if precipitation exists, VPsit is not equal to 0, and if precipitation does not exist, vpsit=0;
when Vhit + VPsit-VPzi is equal to or greater than 0, then VPit = VPzi,
when vhit+vpsit-VPzi < 0, then vpit=vhit+vpsit;
s3.4, predicting precipitation retention speeds Vt corresponding to different time points in the first unit time of the section with the number i, wherein the Vt is equal to Vhit+VPsit-VPit corresponding to the time point t,
Predicting precipitation reserve Q for a subsequent first unit of time, said Q being equal to an integrated value of Vt for the subsequent first unit of time,
counting the duration corresponding to vpit=vpzi in the subsequent first unit time, and marking the ratio of the obtained duration to the first unit time as beta;
s3.5, predicting whether the road section with the number i in the first unit time has the road section ponding condition,
when Q is larger than the first threshold value Q1 and beta is larger than the second threshold value beta 1, judging that the road section with the number i in the first unit time has the road section ponding condition, otherwise, judging that the road section with the number i in the first unit time does not have the road section ponding condition,
the first threshold Q1 is equal to the average value of precipitation in each corresponding first unit time under the condition that the road section with the number i in the historical data has accumulated water and blocks the vehicle from passing,
the second threshold value beta 1 is equal to the average value of the ratio of the duration of vpit=vpzi to the first unit time in each corresponding first unit time under the condition that the road section with the number i in the historical data has accumulated water and blocks the vehicle from passing;
and S3.6, summarizing road sections with road section ponding conditions in the urban road model in the first unit time to obtain a first prediction result.
5. The urban traffic supervision method based on multi-source information according to claim 1, wherein: the method for predicting the number of the passing vehicles corresponding to different road segments in different time segments in S4 comprises the following steps:
S4.1, acquiring the number of vehicles passing through each road section in each road in different time periods in the historical data;
s4.2, acquiring historical weather conditions, screening the number of vehicles passing through different road sections in a non-rainfall state in the historical data to obtain the vehicle flow corresponding to different time points of different road sections in the non-rainfall state in the historical data, and recording the vehicle flow corresponding to a number i road section at a time point t1 in the non-rainfall state in the historical data as Wit1, wherein Wit1 is equal to the day of the date corresponding to t1 and the absolute value of the difference value between the Wit1 is smaller than or equal to the time period of a second unit time, and the ratio of the number of vehicles passing through the number i road section to the duration of the corresponding time period is the constant prefabricated in the database;
s4.3, calculating average values of the vehicle flow corresponding to time points t1 of the road sections with the number i in different days under the condition of no rainfall in the historical data, and marking the average values as WPit1, wherein the predicted value of the number of vehicles passing through the road sections with the number i in the time periods [ td1, td2] is
Wherein td1 is the start time of the predicted time period, and td2 is the end time of the predicted time period;
s4.4, predicting the number of corresponding passing vehicles in different road segments in different time segments.
6. The urban traffic supervision method based on multi-source information according to claim 1, wherein: the method for predicting the traffic pressure coefficient of each alternative road section in the S5 comprises the following steps:
S5-1, acquiring an association coefficient between an alternative road section of a road section corresponding to a j-th element in the first prediction result and the road section corresponding to the element;
s5-2, obtaining a section corresponding to a j element in the first prediction result, and recording a predicted value of the number of vehicles passing in a follow-up first unit time based on the current time as WRj;
s5-3, acquiring a kth alternative road section of the road section corresponding to a jth element in the first prediction result, and marking the predicted value of the number of vehicles passing in a first unit time based on the current time as WYjk;
s5-4, obtaining the traffic pressure coefficient of the kth alternative road section of the road section corresponding to the jth element in the first prediction result
(WRj*Ujk+WYjk)/W0 jk
Wherein Ujk represents a correlation coefficient between a kth candidate segment of the segment corresponding to the jth element in the first prediction result and the segment corresponding to the element;
WRj, ujk +wyjk represents a k-th candidate segment of the segments corresponding to the j-th element in the first prediction result, and a calibration value of the prediction result of the number of vehicles passing in a first unit time based on the current time;
W0 jk a kth alternative road section corresponding to the jth element in the first prediction result is represented, and the vehicle passing threshold value in the corresponding first unit time in the database is represented, wherein the W0 jk The vehicle passing threshold value is the same in the corresponding first unit time in the database for different road sections with the same width, W0 jk ＞0。
7. The urban traffic supervision method based on multi-source information according to claim 1, wherein: in the step S6, in the process of personnel allocation of the road section corresponding to the first prediction result and the alternative road section with the traffic pressure coefficient larger than the first preset value,
personnel allocation priorities of all road sections corresponding to the first prediction results are the same;
the personnel allocation priority of the road section corresponding to the first prediction result is higher than that of the candidate road section;
and the personnel allocation priority corresponding to the alternative road sections with larger traffic pressure coefficients in the alternative road sections is higher.
8. Urban traffic supervision system based on multi-source information, the system being implemented using the urban traffic supervision method based on multi-source information according to any one of claims 1 to 7, characterized in that the system comprises the following modules:
the urban drainage pipeline model building module is used for measuring the heights and the diameters of drainage pipelines at different positions in each road through sensors, building an urban drainage pipeline model and marking the positions of the drainage ports;
The urban road model building module acquires information data of each road and builds an urban road model;
the system comprises a congestion road section analysis module, a first prediction result and a second prediction result, wherein the congestion road section analysis module acquires urban weather information in real time, predicts a road section affected by water drainage in an urban road model in a first unit time, and the first unit time is a constant prefabricated in a database;
the road data processing module is used for obtaining the number of vehicles passing through each road section in each road in different time periods in the historical data, predicting the number of vehicles passing through corresponding to different road sections in different time periods, wherein the same road comprises one or more road sections, and the road between two adjacent road junctions on the same road is marked as one road section;
the traffic data analysis module is used for acquiring an alternative road section of a road section corresponding to the first prediction result according to the first prediction result and the urban road model, acquiring a correlation coefficient between the alternative road section and the road section corresponding to the first prediction result in the urban road model, combining the prediction results of the number of the traffic vehicles corresponding to different road sections in different time periods, calibrating the prediction results of the number of the traffic vehicles in different time periods corresponding to the alternative road section, and predicting the traffic pressure coefficient of each alternative road section;
And the personnel scheduling module is used for personnel allocation of the road sections corresponding to the first prediction result and the alternative road sections with the traffic pressure coefficient larger than a first preset value, and evacuating the jammed vehicles, wherein the first preset value is a constant prefabricated in the database.
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