Intersection signal control scheme evaluation method under mixed traffic environment based on information entropy

Abstract

The invention discloses an intersection signal control scheme evaluation method in a mixed traffic environment based on information entropy, and relates to the technical field of traffic. According to the method, by quantifying the inherent complexity and uncertainty in a traffic control strategy, the information entropy promotes more effective management of the intersection and smoother traffic flow. The information entropy enables the traffic control strategies to be customized according to different traffic environments, the traffic control strategies are ensured to be most suitable for local conditions, and the method can recognize the bottleneck and imbalance in the traffic flow and guide targeted improvement by analyzing the entropy change of different time periods and road sections. A traffic management strategy can be continuously monitored and adjusted by using the information entropy so as to respond to continuously changing traffic demands and support a sustainable traffic mode.

Description

Technical Field

[0001] The invention relates to the field of traffic technology, and in particular to an evaluation method for intersection signal control schemes in a mixed traffic environment based on information entropy. Background Art

[0002] As car ownership expands in developing countries, traffic congestion and emissions are steadily increasing.

[0003] Addressing this problem requires intervention, either through infrastructure enhancements such as adding lanes or improving geometric design, or through traffic management systems.

[0004] However, traditional approaches to expanding and improving road infrastructure are often hampered by high construction costs, statutory restrictions, limited rights of way, and environmental considerations.

[0005] In this context, finding cost-effective traffic management solutions to alleviate congestion and emissions has become a pressing challenge facing the transportation sector, and a new solution to the above problems is needed. Summary of the invention

[0006] The purpose of the present invention is to provide an evaluation method for intersection signal control schemes under mixed traffic environments based on information entropy. By introducing the concept of information entropy (a key indicator for measuring the degree of system disorder), after fully describing and analyzing the dynamic characteristics of signalized intersections under mixed traffic conditions, a quantitative relationship between information entropy and the operating state of traffic flow is established. At the same time, we have constructed an evaluation model for signal control strategies of signalized intersections under mixed traffic environments, and the evaluation system proposed in the present invention can be directly applied to existing road infrastructure. The system can more clearly understand the most effective signal control strategies for signalized intersections under mixed traffic environments while maintaining current road traffic resources. This study provides a basic framework for traffic management authorities to solve traffic congestion and chaos, improve the utilization of traffic resources, and promote the sustainable development of traffic systems, so as to solve the problems raised in the background technology.

[0007] To achieve the above object, the present invention provides the following technical solution: an evaluation method for intersection signal control scheme in a mixed traffic environment based on information entropy, comprising at least the following steps:

[0008] Step 1: Describe the dynamic characteristics of traffic operation at signalized intersections in mixed traffic environments;

[0009] Step 2: Determine the quantitative correlation between information entropy and traffic flow operation status;

[0010] Step 3: Select evaluation indicators based on the dynamic characteristics of traffic operation, and divide the indicators into positive indicators and negative indicators based on their impact on traffic operation;

[0011] Step 4: Establish a signal control strategy evaluation model for signalized intersections in mixed traffic environments based on information entropy;

[0012] Step 5: Obtain the actual operation data of the signalized intersection under different signal control strategies in a mixed traffic environment, select n signal cycles and m evaluation indicators;

[0013] Step 6: Construct the initial matrix C of n cycles and m evaluation indicators = (c ij ) n×m , initialize the parameters and get the final matrix Z = (z ij ) n×m ;

[0014] Step 7: Calculate the information entropy S of the evaluation index j j ;

[0015] Step 8: Calculate the entropy weight w of evaluation index j oj ;

[0016] Step 9: Assume that the index space of factors affecting the stability of road traffic intersection system performance is X = (x j ), the corresponding probability space is P = (p j ), the indicator weight W = (wj) corresponding to each factor, and the probability space is constructed:

[0017]

[0018] Step 10: Define the weighted entropy expression of the system operation using the mathematical expectation of the self-information of the factors that affect the system operation status.

[0019] Furthermore, the step 1 at least includes the following steps:

[0020] Based on the results of literature research and actual observation, the main dynamic characteristics of signalized intersections in mixed traffic environments are summarized;

[0021] Representative cases are selected for detailed analysis to gain a deeper understanding of traffic operation characteristics under specific conditions.

[0022] Furthermore, the step 2 at least includes the following steps:

[0023] The concept of information entropy in information theory is used to measure the uncertainty of traffic flow state, that is, the more uniform the distribution of traffic flow state, the higher the information entropy corresponding to different traffic flow and speed distribution;

[0024] When the traffic flow state is evenly distributed, it means that the probability of various states appearing in the traffic flow is similar, and no state is dominant. Since the appearance of various states has high uncertainty, the overall information entropy of the traffic flow system will be high.

[0025] On the contrary, the lower it is, the quantitative relationship between information entropy and traffic flow status is established by calculating the value of information entropy under different traffic flow status.

[0026] Furthermore, the step 3 at least includes the following steps:

[0027] Select appropriate evaluation indicators based on the key features identified in step 1, wherein the evaluation indicators at least include average delay time, traffic capacity, average number of stops and average queue length;

[0028] According to the impact of indicators on traffic operation, they are divided into positive indicators and negative indicators;

[0029] The larger the positive indicator value, the better, while the opposite is true for negative indicators.

[0030] Furthermore, the step 4 at least includes the following steps:

[0031] Combining information entropy with selected evaluation indicators, a comprehensive evaluation model is constructed, which can evaluate the effects of different signal control strategies;

[0032] Different signal control strategies are input into the model, and the advantages and disadvantages of these strategies are compared through the output evaluation results, so as to select the most effective signal control solution.

[0033] Furthermore, the formula of step 6 is as follows:

[0034]

[0035] Where X ij is the value of the jth evaluation index in the i-th time interval, max(x j ) is the maximum value of the jth index, min(x j ) is the minimum value of the j-th indicator.

[0036] Furthermore, the formula of step 7 is as follows:

[0037]

[0038] Where: K = -(1 / lnn);

[0039] Furthermore, the formula of step 8 is as follows:

[0040]

[0041] In the formula

[0042] Furthermore, the formula of step 10 is as follows:

[0043]

[0044] Where P j =p(x j )≥0,j=1,2,3,.....,n;

[0045] Where p j It represents the probability of mutation of factor index, and the variable “O” represents the weighted entropy of the road traffic intersection system, which is used to measure the stability of the system.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] 1. This invention promotes more efficient management and smoother traffic flow at intersections by quantifying the complexity and uncertainty inherent in traffic control strategies. Information entropy allows traffic control strategies to be customized according to different traffic environments, ensuring that they are best suited to local conditions.

[0048] 2. By analyzing the changes in entropy values ​​in different time periods and sections, the present invention can identify bottlenecks and imbalances in traffic flow and guide targeted improvements. Using information entropy can continuously monitor and adjust traffic management strategies to respond to changing traffic needs and support sustainable traffic patterns.

[0049] 3. The results of this study provide a practical framework for evaluating and optimizing the operation of signalized intersections in other mixed traffic scenarios, contributing to broader urban traffic management strategies. By maximizing the use of existing traffic resources, this research supports the goals of sustainable transportation and promotes the implementation of green transportation initiatives. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0051] Figure 1 It is a schematic diagram of the method flow of the present invention;

[0052] Figure 2 Schematic diagram of the study of entropy flow mechanism at signalized intersections under different traffic control strategies;

[0053] Figure 3 It is the dynamic entropy flow diagram of the traffic operation of the whole intersection of Renmin East Road and Baita Road, the east-west and north-south entrances and exits;

[0054] Figure 4 Traffic operation efficiency index and traffic pollutant emission map for the east-west and southwest entrances and exits of the intersection of Renmin East Road and Baita Road;

[0055] Figure 5 A schematic diagram of the optimization scheme for lane allocation at a signalized intersection;

[0056] Figure 6 A schematic diagram of the signal timing optimization scheme for a signalized intersection;

[0057] Figure 7 It is the traffic operation dynamic entropy flow diagram of different signal control strategies in mixed traffic environment;

[0058] Figure 8 Schematic diagram of the signalized intersection described in Table 1. DETAILED DESCRIPTION

[0059] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0060] Embodiment 1:

[0061] See also Figure 1 - Figure 7 The evaluation method of intersection signal control scheme under mixed traffic environment based on information entropy at least includes the following steps:

[0062] Step 1: Describe the dynamic characteristics of traffic operation at signalized intersections in mixed traffic environments: Based on the results of literature research and actual observations, summarize the main dynamic characteristics of signalized intersections in mixed traffic environments. Select representative cases for detailed analysis to gain a deeper understanding of the traffic operation characteristics under specific conditions.

[0063] Step 2: Determine the quantitative relationship between information entropy and traffic flow operation status: Use the concept of information entropy in information theory to measure the uncertainty of traffic flow status, that is, the more uniform the distribution of traffic flow status, the higher the information entropy (in the traffic flow system, different traffic flow states (such as congestion, smooth flow, slow movement, etc.) correspond to different traffic flow and speed distributions. When the traffic flow state is evenly distributed, it means that the probability of various states appearing in the traffic flow is similar, and no state occupies a dominant position. In this case, since the appearance of various states has a high uncertainty, the overall information entropy of the traffic flow system will be higher); otherwise, it will be lower. By calculating the value of information entropy under different traffic flow states, a quantitative relationship between information entropy and traffic flow status is established;

[0064] Step 3: Select evaluation indicators based on the dynamic characteristics of traffic operation, and divide the indicators into positive indicators and negative indicators based on the impact of the evaluation indicators on traffic operation: Select appropriate evaluation indicators based on the key characteristics identified in step 1, such as average delay time, capacity, average number of stops, and average queue length. According to the impact of the indicators on traffic operation, they are divided into positive indicators (such as capacity) and negative indicators (such as average delay time). The larger the value of the positive indicator, the better, while the opposite is true for the negative indicator;

[0065] Step 4: Establish an evaluation model for signal control strategies at signal intersections in mixed traffic environments based on information entropy: Combine information entropy with the selected evaluation indicators to build a comprehensive evaluation model that can evaluate the effects of different signal control strategies. Input different signal control strategies into the model, compare the advantages and disadvantages of these strategies through the output evaluation results, and select the most effective signal control solution;

[0066] Step 5: Obtain the actual operation data of the signalized intersection under different signal control strategies in a mixed traffic environment, select n signal cycles and m evaluation indicators;

[0067] Step 6: Construct the initial matrix C of n cycles and m evaluation indicators = (c ij ) n×m , initialize the parameters and get the final matrix Z = (z ij ) n×m ;

[0068]

[0069] Where X ij is the value of the jth evaluation index in the i-th time interval, max(x j ) is the maximum value of the jth index, min(x j ) is the minimum value of the j-th indicator.

[0070] Step 7: Calculate the information entropy S of the evaluation index j j :

[0071]

[0072] Where: K = -(1 / lnn);

[0073] Step 8: Calculate the entropy weight w of evaluation index j j :

[0074]

[0075] In the formula

[0076] Step 10: Assume that the index space of factors affecting the stability of road traffic intersection system performance is X = (x j ), the corresponding probability space is P = (p j ), the indicator weight W = (wj) corresponding to each factor, and the probability space is constructed:

[0077]

[0078] Step 9: Define the weighted entropy expression of system operation using the mathematical expectation of the self-information of the factors that affect the system operation status, where P j =p(x j )≥0,j=1,2,3,.....,n.

[0079]

[0080] Where p j It represents the probability of sudden change of factor index. The variable “O” represents the weighted entropy of the road traffic intersection system, which is used to measure the stability of the system.

[0081] Embodiment 2:

[0082] Based on the above embodiments, the following specific application examples are proposed;

[0083] In the embodiment, field survey data at the intersection of Renmin East Road and Baita Road in Kunming City, Yunnan Province were selected to verify the evaluation method.

[0084] For details, please refer to Figure 8 , Figure 8 Description of signalized intersection studied for Table 1;

[0085] Depend on Figure 3 It is the dynamic entropy flow diagram of the traffic operation of the whole intersection of Renmin East Road and Baita Road, the east-west and north-south entrance and exit lanes and Figure 4It is the traffic operation efficiency index and traffic pollutant emission diagram of the east-west and southwest entrances and exits of the Renmin East Road-Baita Road intersection; it can be inferred that in a mixed traffic environment, the traffic flow in the east-west direction is more chaotic than the traffic flow in the north-south direction.

[0086] Therefore, two targeted optimization schemes are proposed with the focus on enhancing the east-west traffic flow at the intersection.

[0087] Solution 1: Optimizing Lane Allocation at Signalized Intersections

[0088] The obvious conflict between vehicles turning left and vehicles going straight along the east-west direction poses a pressing challenge that requires immediate attention: vehicles turning left often have to endure long waits while vehicles going straight pass. This not only significantly prolongs the time vehicles spend at the intersection, reducing overall traffic flow and efficiency, but also leads to increasing lengths of vehicle queues, further exacerbating traffic congestion. In addition, this long wait and intermingling of vehicles undoubtedly increase the risk of traffic accidents, posing a serious threat to road safety. In order to effectively address this issue, a strategy of prohibiting left turns has been proposed.

[0089] Solution 2: Signal timing optimization at signalized intersections

[0090] Due to the lack of a protected left-turn phase, the queue time at the east and west entrance lanes of the intersection is significantly longer. During the green light phase for north-south traffic, these vehicles cannot pass through the intersection. This situation not only hinders traffic efficiency, but also exacerbates conflicts between vehicles, threatening overall traffic safety. In order to effectively solve this problem, the initial signal timing scheme of the signalized intersection is optimized.

[0091] The results show that the comparative analysis shows that Scheme 1 is outstanding in improving traffic capacity and road resource utilization, while Scheme 2 is outstanding in reducing vehicle delays and parking times. However, Scheme 2 will bring the disadvantage of reduced traffic capacity. Therefore, when choosing an optimization method, the specific conditions and traffic demand of the intersection must be considered, as shown in the table below.

[0092] Table 2 Intersection evaluation indicators before and after traffic control strategy optimization

[0093]

[0094] Through a series of optimization measures with signal control strategy as the core, the specific impact on intersection operation is studied in depth. It can be seen that even if the signal control strategy is adjusted moderately, the traffic operation efficiency can be significantly improved and the orderliness of traffic flow can be increased. In summary, optimizing signal control strategy is crucial to improving the efficiency of intersection traffic operation. By adjusting these strategies scientifically and rationally, seamless connection of traffic flow can be achieved and the overall operation level of urban transportation can be improved.

[0095] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. An evaluation method for intersection signal control schemes in mixed traffic environments based on information entropy, characterized by: At least the following steps are included: Step 1: Describe the dynamic characteristics of traffic operation at signalized intersections in mixed traffic environments; Step 2: Determine the quantitative correlation between information entropy and traffic flow operation status; Step 3: Select evaluation indicators based on the dynamic characteristics of traffic operation, and divide the indicators into positive indicators and negative indicators based on their impact on traffic operation; Step 4: Establish a signal control strategy evaluation model for signalized intersections in mixed traffic environments based on information entropy; Step 5: Obtain the actual operation data of the signalized intersection under different signal control strategies in a mixed traffic environment, select n signal cycles and m evaluation indicators; Step 6: Construct the initial matrix C of n cycles and m evaluation indicators = (c ij ) n×m , initialize the parameters and get the final matrix Z = (z ij ) n×m ; Step 7: Calculate the information entropy S of the evaluation index j j ; Step 8: Calculate the entropy weight w of evaluation index j oj ; Step 9: Assume that the index space of factors affecting the stability of road traffic intersection system performance is X = (x j ), the corresponding probability space is P = (p j ), the indicator weight W = (wj) corresponding to each factor, and the probability space is constructed: Step 10: Define the weighted entropy expression of the system operation using the mathematical expectation of the self-information of the factors that affect the system operation status.

2. The method for evaluating intersection signal control schemes under mixed traffic environment based on information entropy according to claim 1 is characterized by: The step 1 at least comprises the following steps: Based on the results of literature research and actual observation, the main dynamic characteristics of signalized intersections in mixed traffic environments are summarized; Representative cases are selected for detailed analysis to gain a deeper understanding of traffic operation characteristics under specific conditions.

3. The method for evaluating intersection signal control schemes under mixed traffic environment based on information entropy according to claim 2 is characterized by: The step 2 at least comprises the following steps: The concept of information entropy in information theory is used to measure the uncertainty of traffic flow state, that is, the more uniform the distribution of traffic flow state, the higher the information entropy corresponds to different traffic flow and speed distribution; When the traffic flow state is evenly distributed, it means that the probability of various states appearing in the traffic flow is similar, and no state is dominant. Since the appearance of various states has high uncertainty, the overall information entropy of the traffic flow system will be high. On the contrary, the lower it is, the quantitative relationship between information entropy and traffic flow status is established by calculating the value of information entropy under different traffic flow status.

4. The method for evaluating intersection signal control schemes under mixed traffic environment based on information entropy according to claim 3 is characterized by: The step 3 at least comprises the following steps: Select appropriate evaluation indicators based on the key features identified in step 1, wherein the evaluation indicators at least include average delay time, traffic capacity, average number of stops and average queue length; According to the impact of indicators on traffic operation, they are divided into positive indicators and negative indicators; The larger the positive indicator value, the better, while the opposite is true for negative indicators.

5. The method for evaluating intersection signal control schemes in a mixed traffic environment based on information entropy according to claim 4 is characterized by: The step 4 at least comprises the following steps: Combining information entropy with selected evaluation indicators, a comprehensive evaluation model is constructed, which can evaluate the effects of different signal control strategies; Different signal control strategies are input into the model, and the advantages and disadvantages of these strategies are compared through the output evaluation results, so as to select the most effective signal control solution.

6. The method for evaluating intersection signal control schemes in a mixed traffic environment based on information entropy according to claim 5 is characterized by: The formula for step 6 is as follows: Where X ij is the value of the jth evaluation index in the i-th time interval, max(x j ) is the maximum value of the jth index, min(x j ) is the minimum value of the j-th indicator.

7. The method for evaluating intersection signal control schemes in a mixed traffic environment based on information entropy according to claim 6 is characterized by: The formula of step 7 is as follows: Where: S j is the information entropy value of the indicator, K = -(1 / lnn); 8. The method for evaluating intersection signal control schemes in a mixed traffic environment based on information entropy according to claim 7, characterized in that: The formula of step 8 is as follows: In the formula S j is the information entropy value of the indicator, w j is the weight of the jth evaluation index of the signalized intersection system.

9. The method for evaluating intersection signal control schemes in a mixed traffic environment based on information entropy according to claim 8, characterized in that: The formula of step 10 is as follows: Where P j =p(x j )≥0,j=1,2,3,.....,n;where p j represents the probability of mutation of factor index, w j is the weight of the jth evaluation index of the signalized intersection system. The variable "O" represents the weighted entropy of the road traffic intersection system, which is used to measure the stability of the system.

Images