CN117351747B - Traffic jam control method and device and computer readable storage medium - Google Patents

Abstract

The present invention relates to a traffic congestion control method, device and computer-readable storage medium, and belongs to the field of traffic technology. The method comprises: planning n paths that do not pass through the target area for the traffic flow to be passed through the target area in the k-period and performing random path allocation, so that the traffic flow to be passed through the target area in the k-period is 0; constructing a conservation function of the cumulative number of vehicles in the target area in the k+1 period; calculating the maximum cumulative number of vehicles in the target area in the k+1 period and the minimum cumulative number of vehicles in the target area in the k+1 period based on the MFD curve and the MSD curve; calculating the maximum value of the inflow traffic flow in the target area in the k-period and the minimum value of the inflow traffic flow in the target area in the k-period; calculating the green light time of the signal light entering the target area in the k-period, thereby controlling the inflow traffic flow in the target area in the k-period. The present application takes into account both boundary control and path induction, alleviates traffic congestion, and improves the efficiency of road network use and regional traffic safety.

Description

Traffic congestion control method, device and computer readable storage medium

Technical Field

The present invention relates to the field of traffic technology, and in particular to a traffic congestion control method, device and computer-readable storage medium.

Background Art

With the acceleration of urbanization, traffic congestion on urban roads is becoming more and more serious, and the impact on residents' travel is also increasing. Therefore, how to alleviate traffic congestion is an issue that needs to be urgently addressed.

Among the modern methods for alleviating traffic congestion, vehicle path guidance and regional boundary control based on Macroscopic Fundamental Diagram (MFD) have become a hot topic. Based on MFD, the vehicle number interval with the highest regional traffic efficiency is obtained and the regional vehicles are maintained within the interval, thereby maximizing the concentration and distribution of regional traffic. The existing methods of vehicle path guidance and regional boundary control based on MFD can be roughly divided into two types: the first is a traffic control method that only focuses on boundary control. This method can effectively reduce the number of vehicles entering the congested area, but ignores the path induction of vehicles, so there is a problem of not being able to effectively utilize road network resources and not being able to provide vehicles with the optimal traffic flow path selection; the second is a traffic control method that only focuses on path induction, which can effectively guide vehicles to choose a smoother path, but ignores the boundary control of vehicles, resulting in vehicles wandering on the road outside the congested area, wasting road resources and failing to effectively utilize road network capacity; therefore, the existing methods for alleviating traffic congestion do not consider the coordinated optimization of boundary control and path induction, resulting in the inability of vehicles to simultaneously consider congestion and utilize road network resources when selecting a path.

In addition, existing methods to alleviate traffic congestion only focus on traffic efficiency and lack consideration of regional traffic safety. However, alleviating regional traffic congestion and reducing traffic accidents will have a significant impact on residents' travel and urban development.

In summary, how to design a traffic congestion relief method that takes into account both boundary control and path guidance while ensuring regional traffic safety is a problem that needs to be solved at present.

Summary of the invention

To this end, the technical problem to be solved by the present invention is to overcome the problem that the traffic congestion control method in the prior art cannot take into account both boundary control and path guidance, and does not consider the problem of regional traffic safety.

In order to solve the above technical problems, the present invention provides a traffic congestion control method, comprising:

Obtain the traffic flow to be traversed in the target area during k periods, plan n paths that do not pass through the target area for each traffic flow to be traversed, and randomly assign paths to each traffic flow to be traversed, so that the traffic flow to be traversed in the target area during k periods is 0;

Based on the non-driving vehicles in the target area during k period, the internal traffic flow of the target area during k period, the inflow traffic flow of the target area during k period, and the outflow traffic flow of the target area during k period, a conservation function of the cumulative number of vehicles in the target area during k+1 period is constructed;

Based on the MFD curve, a first vehicle cumulative value corresponding to when the traffic efficiency of the target area road network is the maximum is obtained; based on the MSD curve, a second vehicle cumulative value corresponding to when the traffic safety of the target area road network is the lowest is obtained; and based on the first vehicle cumulative value and the second vehicle cumulative value, a maximum cumulative number of vehicles in the target area during the k+1 period and a minimum cumulative number of vehicles in the target area during the k+1 period are calculated;

Calculate the maximum value of the inflow traffic flow in the target area during the k period and the minimum value of the inflow traffic flow in the target area during the k period based on the maximum value of the cumulative number of vehicles in the target area during the k+1 period, the minimum value of the cumulative number of vehicles in the target area during the k+1 period, and the cumulative number of vehicles in the target area during the k+1 period.

The green light time of the signal light entering the target area in k period is calculated based on the maximum value of the inflow traffic flow in the target area in k period and the minimum value of the inflow traffic flow in the target area in k period, and the inflow traffic flow in the target area in k period is controlled by controlling the green light time of the signal light entering the target area in k period.

In one embodiment of the present invention, the conservation function of the cumulative number of vehicles in the target area during the k+1 period is:

N(k+1)=N(k)+T[q(k)+q _in (k)-q ^′ (k)-q _out (k)],

Among them, N(k+1) represents the cumulative number of vehicles in the target area during period k+1, N(k) represents the number of non-driving vehicles in the target area during period k, T represents the time interval between period k and period k+1, q(k) represents the internal traffic flow generated in the target area during period k, q _in (k) represents the inflow traffic flow in the target area during period k, q ^′ (k) represents the internal traffic flow completed in the target area during period k, and q _out (k) represents the outflow traffic flow in the target area during period k.

In one embodiment of the present invention, the calculation formula for the maximum cumulative number of vehicles in the target area during the k+1 period is:

N _max (k+1)=N ^MFD +α (N ^MSD -N ^MFD ),

Wherein, N _max (k+1) represents the maximum cumulative number of vehicles in the target area during the k+1 period, N ^MFD represents the first cumulative value of vehicles corresponding to the maximum traffic efficiency of the road network obtained based on the MFD curve, N ^MSD represents the second cumulative value of vehicles corresponding to the lowest traffic safety of the road network obtained based on the MSD curve, and α is a preset parameter;

The calculation formula for the minimum cumulative number of vehicles in the target area during the k+1 period is:

N _min (k+1)=N ^MFD -α (N ^MSD -N ^MFD ),

Wherein, N _min (k+1) represents the minimum cumulative number of vehicles in the target area during the k+1 period.

In one embodiment of the present invention, the calculation formula for the maximum value of the inflow traffic flow in the target area in the k time period is:

Where q _c (k) represents the traffic flow to be passed through the target area in time period k;

The calculation formula for the minimum value of the inflow traffic flow in the target area during the k-time period is:

In one embodiment of the present invention, calculating the green light time of a traffic light entering the target area in k time period based on the maximum value of the inflow traffic flow in the target area in k time period and the minimum value of the outflow traffic flow in the target area in k time period includes:

When q _in,min (k)≤q _min ,

Wherein, q _in,min (k) represents the minimum value of the inflow traffic flow in the target area during period k, q _min represents the traffic flow that can enter the target area when the traffic lights at all intersections entering the target area have the shortest green light time, gi _,j (k) represents the green light time of the jth traffic light at the i-th intersection entering the target area during period k, g _min represents the shortest green light time of the traffic light, m represents the number of intersections entering the target area, and x represents the number of traffic lights at each intersection entering the target area;

When q _in,max (k) ≥ q _max ,

Among them, _qin,max (k) represents the maximum value of the inflow traffic flow in the target area during the k-time period, _qmax represents the traffic flow that can enter the target area when the traffic lights at all intersections entering the target area have the longest green light time, and _gmax represents the longest green light time of the traffic light.

In one embodiment of the present invention, when _qin,min (k) _≥qmin , and qin _,max (k) _≤qmax ,

Based on the minimum value of the inflow traffic flow in the target area in k periods, the minimum value of the traffic flow that can pass through the jth traffic light at the i-th intersection entering the target area in k periods is calculated;

Calculate the shortest green light time of the jth signal light at the i-th intersection entering the target area during the k-time period based on the minimum value of the traffic flow that can pass through the jth signal light at the i-th intersection entering the target area during the k-time period;

Based on the maximum value of the inflow traffic flow in the target area in k period, the maximum value of the traffic flow that can pass through the jth signal light of the i-th intersection entering the target area in k period is calculated;

The longest green light time of the jth signal light at the i-th intersection entering the target area during the k-time period is calculated based on the maximum value of the traffic flow that can pass through the jth signal light at the i-th intersection entering the target area during the k-time period.

In one embodiment of the present invention, the calculation formula for the minimum value of the traffic flow that can pass through the jth traffic light at the i-th intersection entering the target area during the k-time period is:

Wherein, fi _{,j_min} (k) represents the minimum value of the traffic flow that can pass through the jth signal light at the i-th intersection entering the target area in the k-time period, and h _i,j (k) represents the traffic flow controlled by the jth signal light at the i-th intersection entering the target area in the k-time period;

The calculation formula for the shortest green light time of the jth traffic light at the i-th intersection entering the target area during the k-time period is:

g _{i,j_min} (k)＝t _loss +f _{i,j_min} (k)h _t ,

Wherein, g _{i,j_min} (k) represents the shortest green light time of the jth signal light at the i-th intersection entering the target area in time period k, t _loss represents the green light loss time, and h _t represents the saturated headway time;

The calculation formula for the maximum value of the traffic flow that can pass through the jth traffic light at the i-th intersection entering the target area during the k-time period is:

Wherein, _{fi,j_max} (k) represents the maximum value of the traffic flow that can pass through the jth traffic light at the i-th intersection entering the target area during the k-time period;

The calculation formula for the longest green light time of the jth traffic light at the i-th intersection entering the target area during the k-time period is:

g _{i,j_max} (k)＝t _loss +f _{i,j_max} (k)h _t ,

Wherein, _{gi,j_max} (k) represents the longest green light time of the jth traffic light at the i-th intersection entering the target area during the k-time period.

In one embodiment of the present invention, obtaining a traffic flow to be traversed in a target area in k time periods, planning n paths that do not pass through the target area for each traffic flow to be traversed, and performing random path allocation for each traffic flow to be traversed includes:

Based on obtaining the traffic flow to be traversed in the target area in k time periods, n paths that do not pass through the target area are planned for each traffic flow to be traversed, and the probability of each path being selected is calculated based on the impedance of each path. The probability calculation formula for each path being selected is:

in, represents the probability of selecting the rth path that does not pass through the target area from the starting point O to the end point D in the k-time period, _cr represents the impedance of the rth path that does not pass through the target area, n represents the number of paths that do not pass through the target area from the starting point O to the end point D, and θ is a constant;

Based on the probabilities of n paths being selected, n probability intervals between [0,1] are constructed, and the n probability intervals are expressed as:

A number greater than 0 and less than 1 is randomly generated for the traffic flow to be traversed in the target area. If the number falls in the dth probability interval among the n probability intervals, the dth path that does not traverse the target area is allocated to the traffic flow to be traversed in the target area.

The present invention also provides a traffic congestion control device, comprising:

The path guidance module is used to obtain the traffic flow to be traversed in the target area during the k-period period, plan n paths that do not pass through the target area for each traffic flow to be traversed, and randomly assign paths to each traffic flow to be traversed, so that the traffic flow to be traversed in the target area during the k-period period is 0;

A flow conservation function construction module is used to construct a flow conservation function for the cumulative number of vehicles in the target area of k+1 period based on the non-traveled vehicles in the target area of k period, the internal traffic flow of the target area of k period, the incoming traffic flow of the target area of k period, and the outgoing traffic flow of the target area of k period;

A first calculation module is used to obtain a first vehicle cumulative value corresponding to the maximum traffic efficiency of the target area road network based on the MFD curve, obtain a second vehicle cumulative value corresponding to the minimum traffic safety of the target area road network based on the MSD curve, and calculate the maximum cumulative number of vehicles in the target area during the k+1 period and the minimum cumulative number of vehicles in the target area during the k+1 period based on the first vehicle cumulative value and the second vehicle cumulative value;

The second calculation module is used to calculate the maximum value of the inflow traffic flow in the target area of k period and the minimum value of the inflow traffic flow in the target area of k period based on the maximum value of the cumulative number of vehicles in the target area of k+1 period, the minimum value of the cumulative number of vehicles in the target area of k+1 period and the cumulative number of vehicles in the target area of k+1 period. Flow conservation function;

The third calculation module is used to calculate the green light time of the signal light entering the target area in k period based on the maximum value of the inflow traffic flow in the target area in k period and the minimum value of the inflow traffic flow in the target area in k period, and control the inflow traffic flow in the target area in k period by controlling the green light time of the signal light entering the target area in k period.

The present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned traffic congestion control method are implemented.

The traffic congestion control method provided by the present invention first guides the path of the traffic flow to be passed through the target area before controlling the cumulative number of vehicles in the target area, so that the traffic flow can reach the destination without passing through the target area, thereby reducing road congestion in the target area and improving the overall utilization efficiency of the road network; in addition, the present application calculates the maximum and minimum cumulative number of vehicles in the target area based on the first vehicle cumulative value corresponding to the maximum road network traffic efficiency and the second vehicle cumulative value corresponding to the lowest road network traffic safety, and obtains the maximum and minimum inflow traffic flow in the target area based on the maximum and minimum cumulative number of vehicles, and controls the green light time of the traffic light entering the target area so that the inflow traffic flow in the target area is between the maximum and minimum values, thereby controlling the cumulative number of vehicles in the target area, taking into account both the traffic efficiency and the safety of the road network; therefore, the traffic congestion control method provided by the present application takes into account both boundary control and path guidance, alleviates traffic congestion, and improves the overall utilization efficiency of the road network. In addition, while ensuring the traffic efficiency of the road network, it also improves traffic safety.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to make the content of the present invention more clearly understood, the present invention is further described in detail below according to specific embodiments of the present invention in conjunction with the accompanying drawings, wherein

FIG1 is a flow chart of a traffic congestion control method provided by the present invention;

FIG2 is a schematic diagram of a target area path guidance provided by the present invention;

FIG3 is a schematic diagram of a target area traffic flow interaction process provided by the present invention;

FIG4 is a schematic diagram of an MFD curve provided by the present invention;

FIG5 is a schematic diagram of an MSD curve provided by the present invention;

FIG6 is a schematic diagram of an MFD and MSD curve based on a target area provided by the present invention;

FIG7 is a schematic diagram of a traffic congestion control principle provided by the present invention;

FIG8 is a schematic structural diagram of a traffic congestion control device provided by the present invention;

FIG9 is a schematic diagram of a SUMO road network model provided by an embodiment of the present invention;

FIG10 is a schematic diagram of MFD curves under different CAV penetration rates provided by an embodiment of the present invention;

FIG11 is a schematic diagram of MSD curves under different CAV permeabilities provided by an embodiment of the present invention;

FIG12 is a schematic diagram of the evaluation indicators of the method provided by the present application when applied to different CAV penetration scenarios; wherein (a) in FIG12 is a schematic diagram of the cumulative number of arriving vehicles curve when the method of the present application is applied to different CAV penetration scenarios, (b) in FIG12 is a schematic diagram of the weighted average flow curve when the method of the present application is applied to different CAV penetration scenarios, and (c) in FIG12 is a schematic diagram of the total delay curve when the method of the present application is applied to different CAV penetration scenarios;

FIG13 is a schematic diagram showing the comparison of MFD curves before and after control in a scenario with 80% CAV penetration rate;

FIG14 is a schematic diagram showing the comparison of evaluation indicators before and after control in a scenario with 80% CAV penetration rate; wherein (a) in FIG14 is a schematic diagram showing the comparison of total delay curves before and after control in a scenario with 80% penetration rate, and (b) in FIG14 is a schematic diagram showing the comparison of the cumulative number of arriving vehicles curves before and after control in a scenario with 80% penetration rate;

FIG15 is a schematic diagram showing the comparison of MSD curves before and after control at 80% CAV penetration;

Figure 16 is a schematic diagram of the number of road vehicles on the road network shown in Figure 9 before and after the control using the method of the present application; wherein, (a) in Figure 16 is a schematic diagram of the number of road vehicles before the control using the method of the present application, and (b) in Figure 16 is a schematic diagram of the number of road vehicles after the control using the method of the present application.

DETAILED DESCRIPTION

The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments so that those skilled in the art can better understand the present invention and implement it, but the embodiments are not intended to limit the present invention.

Please refer to FIG1 , which is a traffic congestion control method provided by the present application, which specifically includes:

S10: Obtain the traffic flow to be traversed in the target area during k time periods, plan n paths that do not pass through the target area for each traffic flow to be traversed, and randomly assign paths to each traffic flow to be traversed, so that the traffic flow to be traversed in the target area during k time periods is 0;

S20: constructing a vehicle cumulative number flow conservation function for the target area in the k+1 period based on the non-traveling vehicles in the target area in the k period, the internal traffic flow in the target area in the k period, the incoming traffic flow in the target area in the k period, and the outgoing traffic flow in the target area in the k period;

S30: obtaining a first vehicle cumulative value corresponding to a time when the target area road network traffic efficiency is maximum based on the MFD curve, obtaining a second vehicle cumulative value corresponding to a time when the target area road network traffic safety is lowest based on the MSD curve, and calculating a maximum cumulative number of vehicles in the target area during the k+1 period and a minimum cumulative number of vehicles in the target area during the k+1 period based on the first vehicle cumulative value and the second vehicle cumulative value;

S40: Calculate the maximum value of the inflow traffic flow in the target area during the k period and the minimum value of the inflow traffic flow in the target area during the k period based on the maximum value of the cumulative number of vehicles in the target area during the k+1 period, the minimum value of the cumulative number of vehicles in the target area during the k+1 period, and the cumulative number of vehicles in the target area during the k+1 period.

S50: Calculate the green light time of the traffic light entering the target area in k period based on the maximum value of the inflow traffic flow in the target area in k period and the minimum value of the inflow traffic flow in the target area in k period, and control the inflow traffic flow in the target area in k period by controlling the green light time of the traffic light entering the target area in k period.

The traffic congestion control method provided by the present application first guides the traffic flow to be passed through the target area before controlling the number of vehicles in the target area, so that it can reach the end point without passing through the target area, which not only alleviates the road congestion in the target area, but also improves the road utilization rate of other areas; when controlling the number of vehicles in the target area, the upper and lower limits of the cumulative number of vehicles in the target area are obtained by combining the first vehicle cumulative value corresponding to the maximum road network traffic efficiency and the second vehicle cumulative value corresponding to the highest road network traffic safety. Since the cumulative number of vehicles in the target area is closely related to the inflow traffic flow in the target area, the upper and lower limits of the inflow traffic flow in the target area are calculated based on the upper and lower limits, and finally the green light time of the signal light entering the target area is calculated based on the upper and lower limits of the inflow traffic flow in the target area. The inflow traffic flow in the target area is controlled by controlling the green light time, so that the cumulative number of vehicles in the target area is within the number interval that takes into account both efficiency and traffic safety. The method provided by the present application combines path guidance and boundary control, and also comprehensively considers the road network traffic efficiency and road network traffic safety when performing boundary control, effectively alleviating traffic congestion while ensuring regional traffic safety.

Specifically, in the present application, the traffic flow to be passed through the target area in the k-time period refers to the traffic flow that is expected to flow into the target area in the k-time period and whose end point is not within the target area. This part of the traffic flow flows into the target area in the k-time period, but because its end point is not within the target area, it will flow out of the target area in the k-time period; the internal traffic flow in the target area in the k-time period refers to the traffic flow whose starting point and end point are both within the target area, and this part of the traffic flow refers to the traffic flow that is within the target area within the k-time period; the traffic flow flowing into the target area in the k-time period refers to the traffic flow that is expected to flow into the target area in the k-time period and whose end point is within the target area; the traffic flow flowing out of the target area in the k-time period refers to the traffic flow that is expected to flow out of the target area in the k-time period and whose end point is not within the target area.

Please refer to Figure 2, which is a schematic diagram of path guidance for traffic flow to be passed through a target area in a k-time period provided by an embodiment of the present application. In the figure, area 1 is the target area, O represents the starting point of the traffic flow _qc (k) to be passed through the target area in the k-time period, and D represents the end point of the traffic flow _qc (k) to be passed through the target area in the k-time period. Since the starting point and end point of _qc (k) are both in area 2, most vehicles will choose the path with the least impedance, that is, passing through the target area 1, which will cause congestion on specific roads in the road network and reduce the overall efficiency of the road network. Therefore, it is necessary to perform path guidance for this part of the traffic flow so that it can reach the end point without passing through the target area 1.

Specifically, the step of allocating the path of the traffic flow to be traversed in the target area in k time periods in step S10 includes:

S100: Based on obtaining the traffic flow to be traversed in the target area during the k time periods, n paths that do not pass through the target area are planned for each traffic flow to be traversed, and the probability of each path being selected is calculated based on the impedance of each path. The probability calculation formula for each path being selected is:

in, represents the probability of selecting the rth path that does not pass through the target area from the starting point O to the end point D in the k-time period, _cr represents the impedance of the rth path that does not pass through the target area, n represents the number of paths that do not pass through the target area from the starting point O to the end point D, and θ is a constant;

S102: construct n probability intervals between [0, 1] based on the probabilities of n paths being selected, and the n probability intervals are expressed as:

S103: For the traffic flow to be traversed in the target area, a number greater than 0 and less than 1 is randomly generated. If the number falls in the dth probability interval among the n probability intervals, the dth path that does not traverse the target area is allocated to the traffic flow to be traversed in the target area.

For example, when there are three paths from the starting point O to the end point D that do not pass through the target area, and the probability of each path being selected is 1/3, the corresponding three probability intervals between [0,1] are: [0,1/3], [1/3,2/3], [2/3,1]. If the randomly generated number for the traffic flow to pass through the target area from the starting point O to the end point D is 1/5, which falls in the first probability interval, the first path is assigned to the traffic flow to pass through.

Please refer to FIG3 , which is a schematic diagram of traffic flow interaction in a target area provided by the present application;

Specifically, the vehicle cumulative number flow conservation function of the target area in the k+1 period constructed in step S20 is:

N(k+1)=N(k)+T[q(k)+q _in (k)-q′(k)-q _out (k)],

Among them, N(k+1) represents the cumulative number of vehicles in the target area during the k+1 period, N(k) represents the number of non-driving vehicles in the target area during the k period, T represents the duration of the k period, q(k) represents the internal traffic flow generated in the target area during the k period, q _in (k) represents the inflow traffic flow in the target area during the k period, q′(k) represents the internal traffic flow completed in the target area during the k period, and q _out (k) represents the outflow traffic flow in the target area during the k period.

Since the inflow traffic flow of the target area may be divided into controlled traffic flow and uncontrolled traffic flow, in some embodiments of the present application, the control of the inflow traffic flow of the target area is mainly for the controlled traffic flow, that is, q _in (k) in the embodiments of the present application is the controlled traffic flow. Specifically, the calculation formula of the uncontrolled traffic flow is:

_{qin_u} (k)＝ _βQin (k),

Where _Qin (k) represents all incoming traffic flows in the target area in time period k, including controlled traffic flows and uncontrolled traffic flows, and β is the proportionality coefficient.

Therefore, in some preferred embodiments, the calculation formula for the cumulative number of vehicles in the target area in the k+1 time period is: N(k+1)=N(k)+T[q(k)+q _in (k)+βQ _in (k)-q′(k)-q _out (k)]. Furthermore, when calculating the maximum and minimum values of the incoming traffic flow q _in (k) in the target area in the k time period in step S40, the calculation can also be based on this formula, so as to more accurately control the cumulative number of vehicles in the target area.

Please refer to Figure 4, which is a schematic diagram of an MFD curve provided in an embodiment of the present application. For a road network with uniform density, the MFD curve describes the unimodal relationship between the cumulative number of vehicles N and the weighted average flow ^qw , which can measure the traffic efficiency of the road network; it can be seen from the figure that when the cumulative traffic volume is less than N ^MFD , the larger the cumulative number of vehicles in the road network, the larger its weighted flow; when the cumulative traffic volume is greater than N ^MFD , the larger the cumulative number of vehicles in the road network, the smaller its weighted flow, until the cumulative traffic volume reaches N _max , the entire road network is completely blocked and the weighted flow is 0. Therefore, when the cumulative traffic volume is near N ^MFD , the traffic efficiency of the road network is maintained at a high value, which can effectively alleviate regional congestion.

Please refer to Figure 5, which is a schematic diagram of an MSD curve provided in an embodiment of the present application, which describes the unimodal relationship between the cumulative number of vehicles N and the accident rate S. When the cumulative traffic volume is near N ^MSD , the road network conflicts are the most and the safety is the lowest. Therefore, it is necessary to control the cumulative traffic volume within a range away from N ^MSD to improve the road network traffic safety.

Please refer to Figure 6, which is a schematic diagram of the MFD and MSD curves of a target area provided in an embodiment of the present application. In the figure, N ^lb and N ^ub respectively represent the maximum and minimum cumulative numbers of vehicles when taking into account both the traffic efficiency and the safety of the road network. The present application calculates the cumulative number interval of vehicles in the target area in the k+1 period based on the first vehicle cumulative value corresponding to the highest road network traffic efficiency and the second vehicle cumulative value corresponding to the lowest road network traffic safety. By controlling the cumulative number of vehicles in the target area within the interval, it is possible to achieve both high traffic efficiency and regional traffic safety within the control interval.

Specifically, the calculation formula for the maximum cumulative number of vehicles in the target area during the k+1 period in step S30 is:

N _max (k+1)=N ^MFD +α (N ^MSD -N ^MFD ),

Wherein, N _max (k+1) represents the maximum cumulative number of vehicles in the target area during the k+1 period, N ^MFD represents the first cumulative value of vehicles corresponding to the maximum traffic efficiency of the road network obtained based on the MFD curve, N ^MSD represents the second cumulative value of vehicles corresponding to the lowest traffic safety of the road network obtained based on the MSD curve, and α is a preset parameter;

The calculation formula for the minimum cumulative number of vehicles in the target area during the k+1 period is:

N _min (k+1)=N ^MFD -α (N ^MSD -N ^MFD ),

Wherein, N _min (k+1) represents the minimum cumulative number of vehicles in the target area during the k+1 period.

Furthermore, the embodiment of the present application calculates the maximum and minimum values of the inflow traffic flow in the k-period target area based on the maximum and minimum values of the cumulative number of vehicles in the k+1-period target area and the cumulative number of vehicles in the k+1-period target area in step S20, and controls the cumulative number of vehicles in the k+1-period target area by controlling the inflow traffic flow in the k-period target area.

Specifically, the calculation formula for the maximum value of the inflow traffic flow in the target area in the k-time period in step S40 is:

Where q _c (k) represents the traffic flow to be passed through the target area in time period k;

The calculation formula for the minimum value of the inflow traffic flow in the target area during the k-time period is:

Because the traffic flow to be passed through the target area is first path-guided before the number of vehicles in the target area is controlled, that is, the traffic flow that originally passed through the target area in the k-period will not pass through the target area, the traffic flow flowing into the target area in the k-period also includes the traffic flow to be passed through the original target area.

Furthermore, in the embodiment of the present application, the traffic flow entering the target area is controlled by controlling the green light time of the traffic light entering the target area.

Specifically, in step S50, the green light time of the signal light entering the target area in the k period is calculated based on the maximum value of the inflow traffic flow in the target area in the k period and the minimum value of the inflow traffic flow in the target area in the k period, and the inflow traffic flow in the target area in the k period is controlled by controlling the green light time of the signal light entering the target area in the k period, which includes:

When q _in,min (k)≤q _min ,

Wherein, q _in,min (k) represents the minimum value of the inflow traffic flow in the target area during period k, q _min represents the traffic flow that can enter the target area when the traffic lights at all intersections entering the target area have the shortest green light time, gi _,j (k) represents the green light time of the jth traffic light at the i-th intersection entering the target area during period k, g _min represents the shortest green light time of the traffic light, m represents the number of intersections entering the target area, and x represents the number of traffic lights at each intersection entering the target area;

For example, when the traffic lights entering the target area all meet the shortest green light time g _min for pedestrian crossings, the traffic flow that can enter the target area is 50, and the minimum inflow traffic flow in the target area during the k-period period is 30. At this time, the green light time of all traffic lights entering the target area takes the shortest green light time.

When q _in,max (k) ≥ q _max ,

Among them, q _in,max (k) represents the maximum value of the inflow traffic flow in the target area during the k-time period, q _max represents the traffic flow that can enter the target area when the traffic lights at all intersections entering the target area are all green for the longest time, and g _max represents the longest green time of the traffic light;

For example, when the traffic lights entering the target area all have the longest green light time g _max that can be set, the traffic flow that can enter the target area is 200, and the maximum value of the inflow traffic flow in the target area in k time period is 300. At this time, the green light time of all traffic lights entering the target area takes the longest green light time.

When _qin,min (k) _≥qmin and _qin,max (k) _≤qmax , the green light time of each traffic light entering the target area needs to be calculated based on the maximum and minimum values of the inflow traffic flow in the target area during the k-time period, which specifically includes:

Based on the minimum value of the inflow traffic flow in the target area in k periods, the minimum value of the traffic flow that can pass through the jth traffic light at the i-th intersection entering the target area in k periods is calculated;

Specifically, the calculation formula for the minimum value of the traffic flow that can pass through the jth traffic light at the i-th intersection entering the target area in the k-time period is:

Wherein, fi _{,j_min} (k) represents the minimum value of the traffic flow that can pass through the jth signal light at the i-th intersection entering the target area in the k-time period, and h _i,j (k) represents the traffic flow controlled by the jth signal light at the i-th intersection entering the target area in the k-time period;

Calculate the shortest green light time of the jth signal light at the i-th intersection entering the target area during the k-time period based on the minimum value of the traffic flow that can pass through the jth signal light at the i-th intersection entering the target area during the k-time period;

Specifically, the calculation formula for the shortest green light time of the jth traffic light at the i-th intersection entering the target area in the k-time period is:

g _{i,j_min} (k)＝t _loss +f _{i,j_min} (k)h _t ,

Wherein, g _{i,j_min} (k) represents the shortest green light time of the jth signal light at the i-th intersection entering the target area in time period k, t _loss represents the green light loss time, and h _t represents the saturated headway time;

Based on the maximum value of the traffic flow that can enter the target area in the k-time period, the maximum value of the traffic flow that can pass through the j-th signal light of the i-th intersection entering the target area in the k-time period is calculated;

Specifically, the calculation formula for the maximum value of the traffic flow that can pass through the jth traffic light at the i-th intersection entering the target area in the k-time period is:

Wherein, _{fi,j_max} (k) represents the maximum value of the traffic flow that can pass through the jth traffic light at the i-th intersection entering the target area during the k-time period;

The longest green light time of the jth signal light at the i-th intersection entering the target area during the k-period is calculated based on the maximum value of the traffic flow that can pass through the jth signal light at the i-th intersection entering the target area during the k-period;

Specifically, the longest green light time of the jth traffic light at the i-th intersection entering the target area in the k-time period is calculated as follows:

g _{i,j_max} (k)＝t _loss +f _{i,j_max} (k)h _t ,

Wherein, _{gi,j_max} (k) represents the longest green light time of the jth traffic light at the i-th intersection entering the target area during the k-time period.

Optionally, in some embodiments of the present application, the Dijkstra algorithm can be used to calculate the shortest path and perform path allocation in real time for the incoming traffic flow in the target area of k time periods, so as to further alleviate road network traffic congestion and improve the overall efficiency of the road network.

As shown in Figure 7, it is a schematic diagram of the control principle of the traffic congestion control method provided by the present application, which divides the entire control process into two layers. The upper layer calculates the cumulative interval of the number of vehicles with the best comprehensive traffic efficiency and safety based on the MFD curve and the MSD curve, and calculates the critical value of the traffic flow that can flow into the target area according to the cumulative interval of the number of vehicles and feeds this value back to the lower-level controller. The lower-level controller controls the cumulative value of the number of vehicles in the target area within the cumulative interval of the number of vehicles through boundary control and path induction.

Based on the traffic congestion control method provided in the above embodiment, the embodiment of the present application further provides a traffic congestion control device, as shown in Figure 8, the device includes a path induction module 10, a flow conservation function construction module 20, a first calculation module 30, a second calculation module 40 and a third calculation module 50; the traffic congestion control device of this embodiment is used to implement the aforementioned traffic congestion control method, so the specific implementation method of the traffic congestion control device can be seen in the embodiment part of the traffic congestion control method in the previous text, for example, the path induction module 10 is used to implement step S10 in the above traffic congestion control method; the flow conservation function construction module 20 is used to implement step S20 in the above traffic congestion control method; the first calculation module 30 is used to implement step S30 in the above traffic congestion control method; the second calculation module 40 is used to implement step S40 in the above traffic congestion control method; the third calculation module 50 is used to implement step S50 in the above traffic congestion control method, so its specific implementation method can refer to the description of the corresponding various parts of the embodiment, which will not be repeated here.

An embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned traffic congestion control method are implemented.

In order to verify the effectiveness of the method provided by the present application, the present application embodiment also uses a road network in a certain city for testing:

This application uses SUMO software to build various mixed traffic flow simulation environments. Simulation of Urban Mobility (SUMO) is a highly portable, microscopic and continuous traffic simulation package designed to handle large road networks. It can simulate a road network with given traffic needs consisting of single or multiple vehicles, where each vehicle has a clear trajectory and moves individually through the network. In addition, SUMO software also provides all the applications required to prepare and execute traffic simulations, as well as a large number of tools and development kits for users to carry out secondary development. This application integrates the basic simulation functions of traditional open source traffic micro-simulation software and carries out secondary development on it. The estimated path flow can be input into the road network for simulation, feedback and calibration.

Specifically, the schematic diagram of the SUMO road network model constructed in the present application is shown in FIG9 , where area 1 is the target area, representing a high-density urban center, and area 2 represents a traffic distribution area.

In this road network, the cycle length of each intersection is in the range of 80 to 120 seconds, and each intersection has 3 to 4 traffic lights. For controlled intersections, the initial green time of the traffic lights flowing into the target area is set to 28 seconds, the shortest green time is 10 seconds, and the longest green time is 42 seconds. When calculating the green time, the 4-second loss time t _loss and the 2-second saturated headway h _t are considered. There is a 3-second yellow time after each traffic light, and there is no full red time. The cumulative value of the number of vehicles in the target area is controlled once every 100 seconds, and the entire simulation time is 30 minutes.

In order to study the control effect of the control method provided by the present application in various mixed traffic flow environments, the embodiments of the present application were simulated in 6 scenarios with different levels of CAV penetration (0%, 20%, 40%, 60%, 80%, 100%), where CAV penetration refers to the ratio of the number of vehicles using connected and autonomous vehicle (Connected and Autonomous Vehicle) technology to the total number of vehicles on urban roads.

Due to the different penetration rates of CAV in a mixed traffic flow environment, it will have different MFD curves and MSD curves. FIG10 is a schematic diagram of the MFD curves for six different levels of CAV penetration rates. It can be seen that the cumulative value of the number of vehicles corresponding to the maximum traffic efficiency of the road network in the MFD curve gradually increases with the increase of the CAV penetration rate; FIG11 is a schematic diagram of the MSD curves for six different levels of CAV penetration rates. The cumulative value of the number of vehicles corresponding to the lowest traffic safety of the road network in the MSD curve gradually decreases with the increase of the CAV penetration rate. For different CAV penetration rates, the average hazard rates of TTC≤1.5 are 5.16% (ρ＝80%), 7.31% (ρ＝60%), 8.86% (ρ＝40%), 9.68% (ρ＝20%), and 10.76% (ρ＝0%), respectively. Therefore, the cumulative intervals of the number of vehicles in the target area under different levels of CAV penetration rate scenarios are also different.

This embodiment adopts the CACC model to describe the following and relaxing behavior of CAV, adopts the IDM model to describe the following and relaxing behavior of HV (manually driven vehicle), and uses TTC to evaluate the safety of the road network.

Among them, the Cooperative Adaptive Cruise Control (CACC) model is a following model based on vehicle-to-vehicle communication, which aims to achieve cooperative driving between vehicles in a convoy. The model takes into account the relative speed, distance and acceleration between vehicles, and combines vehicle-to-vehicle communication to achieve efficient operation of the convoy. Specifically, each vehicle obtains the involved information, including speed and acceleration, through wireless communication, and then adjusts its own speed and distance according to certain algorithms and control strategies to achieve stable following and avoid collisions.

The Intelligent Driver Model (IDM) is a following model based on driver behavior modeling, which is used to describe the acceleration and speed changes of vehicles. The model takes into account the distance between vehicles, relative speed, expected speed and driver's comfort preference. Based on these factors, the IDM model can control the movement of the vehicle by calculating the optimal acceleration. Specifically, when the distance between the vehicle and the vehicle in front is too close, the IDM model will reduce the speed of the vehicle to maintain a safe distance. When the distance is far, the IDM model will increase the speed of the vehicle to maintain smooth traffic.

TTC (Time-to-Collision) is an indicator to measure the risk of collision between two vehicles. The expected collision time is calculated based on the relative speed and distance between the two vehicles. The smaller the TTC value, the higher the probability of a collision. When the TTC value is lower than a certain threshold, if the driver's response is delayed or the vehicle's braking efficiency is poor, it is considered that a traffic accident may occur. Therefore, the frequency with which TTC is recorded can be used as a measure of road network safety. When the TTC value is less than 1.5s, it is considered dangerous.

Please refer to Figure 12, which is a schematic diagram of the evaluation indicators obtained after using the method provided in the present application for control in scenarios with different levels of CAV penetration rates. It can be seen from (a) in the figure that the cumulative number of arriving vehicles under the six CAV penetration rates are: 3095 (ρ=0%), 3277 (ρ=20%), 3612 (ρ=40%), 4077 (ρ=60%), 4505 (ρ=80%), and 5170 (ρ=100%); it can be seen from (b) in the figure that the weighted average flow rate under the six CAV penetration rates increases with the increase of penetration rate; it can be seen from (c) in the figure that the total delay time under the six CAV penetration rates decreases with the increase of penetration rate.

Table 1 shows the comparison data of various indicators under 6 different levels of CAV penetration scenarios provided by the embodiment of the present application:

Table 1

It can be seen from the data in Table 1 that the method provided by this application can effectively control traffic congestion, improve the overall efficiency of the road network, and ensure regional traffic safety.

The present application also uses 80% CAV penetration rate as an example to compare various indicators before and after the control method provided by the present application is used. The comparison results are as follows:

Please refer to Figure 13, which is a schematic diagram comparing the MFD curves before and after control. It can be seen from the figure that the data points on the MFD curve after control are more concentrated than those before control. This is because the purpose of path induction and boundary control is to control the traffic volume within a given range, which makes the MFD curve more convergent; in addition, the MFD curve after control is obviously shifted to the upper left, indicating that the cumulative traffic volume in the target area is small and the average weighted flow is high, proving that the road network achieves better traffic efficiency under this control method.

Please refer to Figure 14, which is a schematic diagram of the comparison curve of the total delay time and the cumulative number of arriving vehicles before and after control. It can be seen from (a) in the figure that after the road network is controlled, the average weighted flow of the road network increases by 4.48%, and the total delay time of the vehicles gradually decreases; it can be seen from (b) in the figure that after the path is controlled, the cumulative number of arriving vehicles increases from 4505 to 4835 within 30 minutes, an increase of 7.33%. This is because the path guidance directs the vehicles from the congested area to the non-congested area, so the vehicles in the congested area can evacuate quickly, and the guided vehicles can also reach the destination faster.

Please refer to FIG. 15 , which is a schematic diagram showing the comparison of the MSD curves before and after the control. It can be seen from the figure that when the cumulative number of vehicles is between 0 and 800, the safety of the road network is improved under the control.

Please refer to Figure 16, which is a schematic diagram of the road network traffic conditions before and after the road network model shown in Figure 9 is controlled, (a) in the figure is a schematic diagram of the road network traffic conditions before control, and (b) in the figure is a schematic diagram of the road network traffic conditions after control. The dark area indicates that there are more vehicles on the path, and the light area indicates that there are fewer vehicles on the path. It can be seen from the figure that before the control, there are more vehicles on most paths in the target area, while there are fewer vehicles on paths outside the target area. After the control, the number of vehicles on some roads in the target area decreases, and the number of vehicles on some roads outside the target area increases, making the traffic flow distribution of the entire road network more uniform.

Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

The present application is described with reference to the flowchart and/or block diagram of the method, device (system) and computer program product according to the embodiment of the present application. It should be understood that each process and/or box in the flowchart and/or block diagram, and the combination of the process and/or box in the flowchart and/or block diagram can be realized by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a device for realizing the function specified in one process or multiple processes in the flowchart and/or one box or multiple boxes in the block diagram.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device that implements the functions specified in one or more processes in the flowchart and/or one or more boxes in the block diagram.

These computer program instructions may also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more processes in the flowchart and/or one or more boxes in the block diagram.

Obviously, the above embodiments are merely examples for the purpose of clear explanation and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived therefrom are still within the scope of protection of the invention.

Claims (4)

1. A traffic congestion control method, characterized by comprising:

Obtaining traffic flows to be traversed in a k-period target area, planning n paths which do not traverse the target area for each traffic flow to be traversed, and carrying out random path allocation on each traffic flow to be traversed so that the traffic flow to be traversed in the k-period target area is 0;

Constructing a vehicle accumulated quantity flow conservation function of the k+1 time period target area based on the non-running vehicles in the k time period target area, the internal traffic flow of the k time period target area, the inflow traffic flow of the k time period target area and the outflow traffic flow of the k time period target area; the vehicle accumulated quantity flow conservation function of the k+1 period target area is as follows:

N(k+1)＝N(k)+T[q(k)+q_in(k)-q^′(k)-q_out(k)]，

Wherein N (k+1) represents the vehicle accumulation number of the k+1 period target area, N (k) represents the number of non-traveling vehicles in the k period target area, T represents the duration of the k period, q (k) represents the internal traffic flow generated by the k period target area, q _in (k) represents the inflow traffic flow of the k period target area, q ^′ (k) represents the internal traffic flow completed by the k period target area, and q _out (k) represents the outflow traffic flow of the k period target area;

acquiring a first vehicle accumulated value corresponding to the maximum traffic efficiency of the road network of the target area based on the MFD curve, acquiring a second vehicle accumulated value corresponding to the minimum traffic safety of the road network of the target area based on the MSD curve, and calculating the maximum value of the vehicle accumulated number of the target area in the k+1 period and the minimum value of the vehicle accumulated number of the target area in the k+1 period based on the first vehicle accumulated value and the second vehicle accumulated value; the MFD curve describes a unimodal relation between the accumulated number of vehicles and the weighted average flow, and is used for measuring the traffic efficiency of the road network, and when the accumulated traffic quantity is equal to N ^MFD, the traffic efficiency of the road network is maximum; the MSD curve describes a unimodal relationship between the cumulative number of vehicles and the accident rate, with minimum safety when the cumulative traffic is equal to N ^MSD; the calculation formula of the maximum value of the accumulated number of vehicles in the k+1 period target area is as follows:

N_max(k+1)＝N^MFD+α(N^MSD-N^MFD)，

Wherein N _max (k+1) represents the maximum value of the vehicle accumulation number in the target area in the k+1 period, N ^MFD represents the first vehicle accumulation value corresponding to the maximum road network traffic efficiency obtained based on the MFD curve, N ^MSD represents the second vehicle accumulation value corresponding to the minimum road network traffic safety obtained based on the MSD curve, and α is a preset parameter;

The calculation formula of the minimum value of the vehicle accumulation number of the k+1 period target area is as follows:

N_min(k+1)＝N^MFD-α(N^MSD-N^MFD)，

wherein N _min (k+1) represents the minimum value of the vehicle cumulative number of the target area in the k+1 period;

Calculating the maximum value of the inflow traffic flow of the k-period target area and the minimum value of the inflow traffic flow of the k-period target area based on the maximum value of the vehicle accumulation number of the k+1-period target area, the minimum value of the vehicle accumulation number of the k+1-period target area and the vehicle accumulation number flow conservation function of the k+1-period target area; the calculation formula of the maximum value of the inflow traffic flow of the k-period target area is as follows:

Wherein q _c (k) represents the traffic flow to be traversed by the k-period target region;

The calculation formula of the minimum value of the inflow traffic flow of the k-period target area is as follows:

Calculating the green time of a signal lamp of which the k time period enters the target area based on the maximum value of the inflow traffic flow of the k time period target area and the minimum value of the inflow traffic flow of the k time period target area, and controlling the inflow traffic flow of the k time period target area by controlling the green time of the signal lamp of which the k time period enters the target area; the method specifically comprises the following steps:

When q _in,min(k)≤q_min is used in the process of the present invention, Wherein q _in,min (k) represents the minimum value of the inflow traffic flow of the target area in the k period, q _min represents the traffic flow which can enter the target area when the signal lamps of all the intersections entering the target area are the shortest green lamp time, g _i,j (k) represents the green lamp time of the j signal lamp of the i-th intersection entering the target area in the k period, g _min represents the shortest green lamp time of the signal lamp, m represents the number of the intersections entering the target area, and x represents the number of the signal lamps at each intersection entering the target area;

When q _in,max(k)≥q_max is used in the process of the present invention, Wherein q _in,max (k) represents the maximum value of the inflow traffic flow of the target area in the k period, q _max represents the traffic flow which can enter the target area when the traffic lights entering all intersections of the target area are the longest green time, and g _max represents the longest green time of the traffic lights;

When q _in,min(k)≥q_min, and q _in,max(k)≤q_max,

Calculating the minimum value of the traffic flow which can pass through the jth signal lamp of the ith intersection of the k time period entering the target area based on the minimum value of the traffic flow flowing into the target area of the k time period; the calculation formula of the minimum value of the traffic flow which can pass through the jth signal lamp of the ith intersection of the k period entering target area is as follows:

Wherein f _{i,j_min} (k) represents the minimum value of the traffic flow which can pass through the j-th signal lamp of the i-th intersection entering the target area in the k period, and h _i,j (k) represents the traffic flow of which the k period is controlled by the j-th signal lamp of the i-th intersection entering the target area;

Calculating the shortest green light time of the jth signal lamp of the ith intersection of the k period entering the target area based on the minimum value of the traffic flow which can pass through the jth signal lamp of the ith intersection of the k period entering the target area; the calculation formula of the shortest green lamp time of the jth signal lamp of the kth intersection entering the target area in the k period is as follows:

g_{i,j_min}(k)＝t_loss+f_{i,j_min}(k)h_t，

Wherein g _{i,j_min} (k) represents the shortest green light time of the jth signal light of the ith intersection of the k period entering the target area, t _loss represents the green light loss time, and h _t represents the saturated headway;

Calculating the maximum value of the traffic flow which can pass through at the jth signal lamp of the ith intersection of the k period entering the target area based on the maximum value of the traffic flow flowing into the target area of the k period; the calculation formula of the maximum value of the traffic flow which can pass through the jth signal lamp of the ith intersection of the k period entering target area is as follows:

wherein f _{i,j_max} (k) represents the maximum value of the traffic flow which can pass through the jth signal lamp of the ith intersection where the k period enters the target area;

Calculating the longest green time of the jth signal lamp of the ith intersection of the k period entering the target area based on the maximum value of the traffic flow which can pass through the jth signal lamp of the ith intersection of the k period entering the target area; the calculation formula of the longest green time of the jth signal lamp of the ith intersection of the k period entering the target area is as follows:

g_{i,j_max}(k)＝t_loss+f_{i,j_max}(k)h_t，

Where g _{i,j_max} (k) represents the longest green time of the jth signal lamp of the ith intersection where the k period enters the target area.

2. The traffic congestion control method according to claim 1, wherein obtaining traffic flows to be traversed by the target area of the k period, planning n paths not traversing the target area for each traffic flow to be traversed, and performing random path allocation for each traffic flow to be traversed comprises:

Based on the traffic flow to be traversed of the target area of the k period, planning n paths which do not traverse the target area for each traffic flow to be traversed, and calculating the probability of each path being selected based on the impedance of each path, wherein the probability calculation formula of each path being selected is as follows:

Wherein, Representing the probability that the path of the (r) th period from the starting point O to the end point D does not pass through the target area is selected, c _r represents the path impedance of the (r) th path from the starting point O to the end point D, n represents the number of paths from the starting point O to the end point D which do not pass through the target area, and θ is a constant;

constructing n probability intervals between [0,1] based on probabilities that n paths are selected, the n probability intervals being expressed as:

And randomly generating a number which is more than 0 and less than 1 for the traffic flow to be passed through the target area, and if the number falls in the d probability interval in the n probability intervals, distributing the d path which does not pass through the target area for the traffic flow to be passed through the target area.

3. A traffic congestion control apparatus realized based on the traffic congestion control method according to claim 1, comprising:

The route guidance module is used for acquiring the traffic flow to be traversed in the k-period target area, planning n routes which do not traverse the target area for each traffic flow to be traversed, and carrying out random route distribution on each traffic flow to be traversed so that the traffic flow to be traversed in the k-period target area is 0;

The flow conservation function construction module is used for constructing a vehicle accumulation quantity flow conservation function of the k+1 time period target area based on the non-running vehicles in the k time period target area, the internal traffic flow of the k time period target area, the inflow traffic flow of the k time period target area and the outflow traffic flow of the k time period target area;

The first calculation module is used for acquiring a first vehicle accumulation value corresponding to the maximum traffic efficiency of the road network of the target area based on the MFD curve, acquiring a second vehicle accumulation value corresponding to the minimum traffic safety of the road network of the target area based on the MSD curve, and calculating the maximum value of the vehicle accumulation number of the target area in the k+1 period and the minimum value of the vehicle accumulation number of the target area in the k+1 period based on the first vehicle accumulation value and the second vehicle accumulation value;

the second calculation module is used for calculating the maximum value of the inflow traffic flow of the k time period target area and the minimum value of the inflow traffic flow of the k time period target area based on the maximum value of the vehicle accumulation number of the k+1 time period target area, the minimum value of the vehicle accumulation number of the k+1 time period target area and the vehicle accumulation number flow conservation function of the k+1 time period target area;

And the third calculation module is used for calculating the green time of the signal lamp of which the k period enters the target area based on the maximum value of the inflow traffic flow of the k period target area and the minimum value of the inflow traffic flow of the k period target area, and controlling the inflow traffic flow of the k period target area by controlling the green time of the signal lamp of which the k period enters the target area.

4. A computer readable storage medium, characterized in that the computer readable storage medium has stored thereon a computer program which, when executed by a processor, implements the steps of the traffic congestion control method according to any one of claims 1-2.