A traffic flow model simulation method that considers driver reaction time

Abstract

This invention discloses a traffic flow model simulation method that considers driver reaction time, comprising: collecting driver-related information and environmental information related to reaction time, and determining the indices affecting driver reaction time; using bijective fuzzy soft set analysis to determine the degree of influence of the indices on reaction time; establishing a mapping relationship between the indices and driver reaction time based on the degree of influence; and constructing a vehicle car-following model and simulating traffic flow based on the mapping relationship. The method provided by this invention can simulate traffic flow more accurately.

Description

Technical Field

[0001] This invention belongs to the field of intelligent transportation information technology and relates to a traffic flow model simulation method that takes into account driver reaction time. Background Technology

[0002] With the rapid development of road transportation, residents' travel has become more convenient and comfortable. However, this has also brought about traffic safety hazards. Traffic accidents can cause serious injuries and property damage. Therefore, traffic safety has become a global problem that urgently needs to be solved. Drivers are a major component of the road traffic system, and the incidence of traffic accidents is mainly caused by driver-related factors. Vehicle following models are important models for describing vehicle behavior. More than half a century has passed since the introduction of vehicle following models. Many vehicle following models have been developed in the past few decades. However, further research is needed to develop and improve their accuracy and reliability. These models comprise the main part of traffic simulation tools that attempt to simulate driver behavior in the real world. This is a multidisciplinary research field that includes expensive, time-consuming, and calibrated parametric models. Any change in the model output, even a small one, can lead to serious interference with the model output.

[0003] Driver reaction time is a crucial parameter in driving behavior research. Different types of drivers may have significantly different reaction times. Currently, both domestic and international studies use fixed values ​​for the reaction times of different driver types. Research on driver reaction time mainly focuses on a single, specific factor. However, many factors influence driver reaction time, and research on the differences between these factors is limited. Statistical records show that drivers' reaction time on highways is set to a fixed value of 1.2 seconds. Furthermore, domestic and international researchers also use fixed values ​​for driver reaction time when simulating vehicles; and in the foreseeable future, in the process of mixed autonomous and manual driving, due to the significant differences in following characteristics (such as reaction time) among different types of drivers, there will be substantial differences in following distance and other aspects. Summary of the Invention

[0004] In view of this, in order to solve the above problems, this invention analyzes the influence of some key factors on driver reaction time, studies driver reaction time under different conditions, and constructs a traffic flow model based on soft sets, so that the traffic flow modeling is more in line with the driving characteristics of drivers.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solution:

[0006] This invention discloses a traffic flow model simulation method that considers driver reaction time, characterized by comprising:

[0007] Collect driver-related information and environmental information related to reaction time, and determine the impact indicators on reaction time;

[0008] Use a bijective fuzzy soft set to analyze the degree of influence of the impact indicators on reaction time;

[0009] Establish a mapping relationship between fuzzy factors and driver reaction time according to the degree of influence;

[0010] Construct a vehicle following model according to the mapping relationship and simulate traffic flow.

[0011] Further, the impact indicators include: driver driving experience, fatigue level, adaptability, traffic conditions, weather conditions, and reaction time.

[0012] Further, the use of a bijective fuzzy soft set to analyze the degree of influence of different impact indicators on reaction time includes:

[0013] Use the universe U = {x1, x2, …, x r} to represent the set of drivers, r represents the number of drivers, E i represents the impact indicators affecting driver reaction time, m + 1 represents the total number of impact indicators, and the impact indicator "reaction time" and other impact indicators form a bijective decision soft set decision system on the universe U as the conditional soft set, as the decision soft set, specifically including:

[0014] Step1: Construct a membership function to determine the membership degree of each parameter value of each impact indicator;

[0015] Step2: According to the definition of the fuzzy bijective soft set, construct a soft set of impact indicators and their corresponding parameter values;

[0016] Step3: Determine the optimal cut-off level λ;

[0017] Step4: Construct a λ-level fuzzy bijective soft set decision system

[0018] Step5: Calculate the intersection of each conditional soft set and the decision soft set to obtain the sub-dependency degree k λ , where 0 < j ≤ m, and it is expressed by the formula:

[0019]

[0020] where the symbol |A| represents the cardinality of set A, and the sub-dependency degree reflects the degree of division of one fuzzy bijective soft set by another. Assuming k = 0, it means that the set Completely independent of collections If k λ =1, which means the set Completely dependent on sets

[0021] Step 6: Calculate the λ-level fuzzy bijective soft set decision system The parameter dependence κ is expressed by the formula:

[0022]

[0023] Step 7: Calculate the decision system of bijective soft sets Various influencing indicators E j Importance ω j This can be expressed as a formula:

[0024]

[0025] Step 8: Obtain the decision rules and the mapping relationship between each influencing indicator and the reaction time τ.

[0026] Furthermore, the vehicle car-following model is as follows:

[0027]

[0028] in, v represents the acceleration of vehicle n at time t; b is the driver's reaction sensitivity coefficient; v n (t) represents the velocity of vehicle n at time t; Δv(t) = v n+1 (t)-v n (t) represents the speed difference between the vehicles before and after time t; s = l n+1 (t)-l n (t) represents the distance between the vehicles at time t; n (t) represents the position of vehicle n at time t; This is the reciprocal of the reaction time τ of the driver associated with vehicle n at time t under the corresponding influence index, and V(s) is the optimized speed function of the vehicle spacing s, expressed by the formula:

[0029]

[0030] Among them, v max The set maximum vehicle speed; h c The set safe distance between vehicles.

[0031] The beneficial effects of this invention are:

[0032] This invention makes traffic flow simulation more accurate by utilizing soft sets and taking into account the reaction time of different drivers under different conditions. Attached Figure Description

[0033] Figure 1 This is a schematic diagram of the overall process of the present invention. Detailed Implementation

[0034] To make the technical solutions, advantages, and objectives of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the described embodiments of the present invention without creative effort are within the protection scope of this application.

[0035] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0036] Figure 1 This is a traffic flow model simulation method that takes into account driver reaction time, according to an embodiment of this application.

[0037] Step 1: Collect driver-related information and environmental information related to reaction time to determine the indices affecting the driver's reaction time. Driver-related information may include the driver's age, driving experience, gender, personality, occupation, reaction ability, fatigue level, etc. Environmental information related to reaction time may include weather conditions, traffic conditions, etc. Influencing indicators may include driver's driving experience, fatigue level, adaptability, traffic conditions, weather conditions, and reaction time. In some embodiments, the established indices affecting reaction time are shown in Table 1.

[0038] Table 1 Influencing Indicators

[0039]

[0040] As shown in Table 1, this embodiment selects four influencing indicators: driving experience, weather conditions, responsiveness, and traffic conditions. These are all important factors that cause differences in driver reaction time. Each influencing indicator is assigned three parameter values, as shown in the index parameter layer of Table 1.

[0041] Table 2. Parameter Descriptions of the Indicators

[0042]

[0043] Table 2 shows the parameter values ​​for each influencing factor. For E1, driving experience, the parameter values ​​include "sufficient experience," "average experience," and "insufficient experience"; for E2, weather conditions, the parameter values ​​include "rainy," "cloudy," and "sunny"; for E3, responsiveness, three corresponding values ​​are also provided; and for E4, traffic conditions, the parameter values ​​include "congested," "average," and "smooth." Table 2 also includes the influencing factor "reaction time," with parameter values ​​including "short reaction time," "average reaction time," and "long reaction time."

[0044] Step 2: Analyze the influence of the influencing indicators on reaction time using bijective fuzzy soft set analysis. In some embodiments, the global domain U = {x1, x2, ..., x...} can be used. r Let} represent the set of drivers, r represent the number of drivers, and E represent the number of drivers. i Let m represent the influencing index on the driver's reaction time, and m+1 represent the total number of influencing indices. The influencing index "reaction time" and other influencing indices constitute a bijective decision soft set decision system on the universe of discourse U. For conditional soft sets, This is a decision soft set. In some embodiments, this step may specifically include the following sub-steps:

[0045] Step 1: Construct membership functions to determine the membership degree of each parameter value of each influencing indicator;

[0046] Step 2: Based on the definition of fuzzy bijective soft sets, construct soft sets of influencing indicators and their corresponding parameter values;

[0047] Step 3: Determine the optimal cut-off level λ;

[0048] λ is the optimal cutoff level for the transformation from a fuzzy bijective soft set to a bijective soft set. The calculation method for λ is as follows:

[0049] Assumption Let x∈U, e∈E be a fuzzy soft set on the universe of discourse U. δ(e) (x) represents the membership degree of element x with respect to parameter e.

[0050] a. Let M = ∪ x∈U,e∈E μ δ(ee) Delete values ​​less than 0.5 from M.

[0051] b. When M is not empty, use maxM to find the cutoff of the fuzzy soft set to obtain the soft set (F,E). If (F,E) satisfies the criterion for a bijective soft set, then λ = maxM; otherwise, M = M - maxM.

[0052] If M is an empty set and λ is not obtained, output an exception; otherwise, output λ.

[0053] Step 4: Construct the λ-level fuzzy bijective soft set decision-making system

[0054] Step 5: Calculate the intersection of each conditional soft set and the decision-making soft set of the sub-dependency degree k λ , where 0 < j ≤ m, which is expressed by the formula:

[0055]

[0056] Among them, the symbol |A| represents the cardinality of the set A. The sub-dependency degree reflects the partitioning degree of one fuzzy bijective soft set to another fuzzy bijective soft set. Assuming k = 0, it means that the set completely does not depend on the set If k λ = 1, it means that the set completely depends on the set

[0057] Step 6: Calculate the parameter dependency degree κ of the λ-level fuzzy bijective soft set decision-making system , which is expressed by the formula as:

[0058]

[0059] Step 7: Calculate the importance ω of each influencing index E[[ID=X]] j in the decision-making system of the bijective soft set j , which is expressed by the formula as:

[0060]

[0061] Step 8: Obtain the decision-making rules and get the mapping relationship between each influencing index and the reaction time τ.

[0062] The above process is illustrated below with reference to Tables 3 - 5. Table 3 shows the membership degrees of the parameter values of each index in the bijective decision-making soft set decision-making system. The following selects U = {x1, x2,..., x 10Ten drivers are used as examples. The membership degree of each parameter value in the table can be determined manually or set by the system based on the driver's situation. For manual setting, it can be determined based on the comprehensive evaluation results of driving behavior research experts and experienced drivers who have driven a certain distance. To make the results more reasonable, initial weights θ1 and θ2 can be assigned to the two types of survey subjects respectively. The weights can be the same or different. Among them, driving behavior research experts can refer to experts engaged in research on the basic reaction characteristics of drivers.

[0063] In this embodiment, the optimal cutoff level λ = 0.75 is selected. Based on expert experience and collected results, the fuzzy soft set form in Table 4 is obtained, and the fuzzy soft set form is transformed into a bijective soft set form, as shown in Table 5.

[0064] Table 3 Membership Degrees of Each Subset

[0065]

[0066] Table 4. Fuzzy soft set form of indicators

[0067]

[0068] Table 5 shows the bijective soft set form of the indicators.

[0069]

[0070]

[0071] According to Step 5, calculate... With decision bijective soft sets The degree of all sub-dependencies k between them λ ,

[0072]

[0073] Where, γ λ () indicates the calculation of sub-dependency. Some calculation results are shown below:

[0074]

[0075]

[0076]

[0077]

[0078]

[0079]

[0080]

[0081]

[0082] Based on the above calculations, it can be concluded that... This indicates that driving experience, weather conditions, responsiveness, and traffic conditions are all important factors affecting a driver's reaction time.

[0083] Next, the importance of each influencing indicator is calculated, and the importance is represented by δ.

[0084] The importance of driving experience E1 is calculated as follows:

[0085]

[0086] The importance of weather condition E2 is calculated as follows:

[0087]

[0088]

[0089] The importance of strain capacity E3 is calculated as follows:

[0090]

[0091] The importance of traffic condition E4 is calculated as follows:

[0092]

[0093] The higher the calculated importance, the greater the impact of that indicator on the system. Based on the above calculations, driving experience is the most important factor, followed by weather conditions, responsiveness, and traffic conditions. Therefore, the following rules can be used to evaluate the factors influencing reaction time. The specific decision-making rules can be:

[0094] The probability of a short reaction time is a1 when the driver is inexperienced, the weather is sunny, the driver has poor adaptability, or the traffic is congested.

[0095] Given that the driver has average driving experience, sunny weather, average responsiveness, and average traffic conditions, the probability of a long reaction time is a2.

[0096] The probability of a short reaction time is a3 when the driver is inexperienced, the weather is sunny, the driver has poor responsiveness, and there is no traffic congestion.

[0097] Step 3: Based on the degree of impact (i.e. importance), establish the mapping relationship between impact indicators and driver reaction time. Partial mapping is shown in Table 6.

[0098] Table 6. Mapping Relationship between Influencing Indicators and Driver Reaction Time

[0099]

[0100] Step 4: Based on the mapping relationship, construct a vehicle car-following model and simulate traffic flow. This means that drivers with the same driving experience and adaptability will have different reaction times under different weather and traffic conditions. Using different reaction times in the vehicle car-following model to simulate traffic flow allows for a more accurate simulation.

[0101] In some embodiments, the vehicle following model is:

[0102]

[0103] in, v represents the acceleration of vehicle n at time t; b is the driver's reaction sensitivity coefficient; v n (t) represents the velocity of vehicle n at time t; Δv(t) = v n+1( t)-v n (t) represents the speed difference between the vehicles before and after time t; s = l n+1 (t)-l n (t) represents the distance between the vehicles at time t; n (t) represents the position of vehicle n at time t; This is the reciprocal of the reaction time τ of the driver associated with vehicle n at time t under the corresponding influence index. The value of the reaction time τ under different conditions corresponds to the value of β under different conditions. V(s) is the optimized speed function of the vehicle spacing s, expressed by the formula:

[0104]

[0105] Among them, v max The set maximum vehicle speed; h c The set safe distance between vehicles.

Claims

1. A traffic flow model simulation method considering driver reaction time, characterized in that, include: Collect driver-related information and environmental information related to reaction time to determine the indicators that affect driver reaction time; The influencing indicators include: driver's driving experience, fatigue level, responsiveness, traffic conditions, weather conditions, and reaction time; The influence of the aforementioned influencing indicators on reaction time was analyzed using bijective fuzzy soft set analysis. Based on the degree of impact, establish a mapping relationship between the impact indicators and the driver's reaction time; Based on the mapping relationship, a vehicle following model is constructed and traffic flow is simulated; The analysis of the influence of different influencing indicators on reaction time using bijective fuzzy soft set theory includes: Use global Let r represent the set of drivers, and r represent the number of drivers. The term "reaction time" represents the influencing indicators affecting driver reaction time, and m+1 represents the total number of influencing indicators. The influencing indicator "reaction time" and other influencing indicators constitute the domain of discourse. Bijective decision soft set decision system , For conditional soft sets, This is a soft set for decision-making, specifically including: Step 1: Construct membership functions to determine the membership degree of the parameters that affect the index values; Step 2: Based on the definition of fuzzy bijective soft sets, construct soft sets of influencing indicators and their corresponding parameter values; Step 3: Determine the optimal cut-off level ; Step 4: Construction Fuzzy bijective soft set decision system ; Step 5: Calculate the intersection of the conditional soft sets. With decision soft set Sub-dependencies between ,in This can be expressed as a formula: ， Among them, symbols The sub-dependency represents the cardinality of set A. The sub-dependency reflects the degree to which one fuzzy bijective soft set partitions another fuzzy bijective soft set. Assuming... This indicates that the set Completely independent of collections ,like This indicates that the set Completely dependent on sets ; Step 6: Calculation -level fuzzy bijective soft set decision system parameter dependency This can be expressed as a formula: ， Step 7: Calculate the decision system of bijective soft sets Various influencing indicators Importance This can be expressed as a formula: ; Step 8: Obtain decision rules, and obtain various influencing indicators and reaction times. The mapping relationship between them.

2. The traffic flow model simulation method considering driver reaction time according to claim 1, characterized in that, The vehicle following model is as follows: , in, Indicates vehicle exist Acceleration at any moment; This is the driver's reaction sensitivity coefficient; Indicates vehicle exist The speed of time; express The speed difference between vehicles before and after a given moment; express The distance between vehicles in front and behind at any given time; Indicates vehicle exist The position at that moment; That is, with vehicles Associated drivers Reaction time under the corresponding influencing indicators at any given time The reciprocal, For vehicle spacing The optimized velocity function is expressed by the formula: ， in, The maximum speed of the vehicle is set. The set safe distance between vehicles.

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