A Method for Modeling Mixed Passenger and Freight Traffic Flow on Highways Based on METANET Model Optimization

Abstract

This invention discloses a method for modeling mixed passenger and freight traffic flow on highways based on the METANET model. The method includes: constructing a dynamic critical density correction function based on the proportion of freight vehicles; establishing a differentiated lane-changing behavior decision mechanism, setting lane-changing probabilities based on the Logit utility function and inhibition coefficient for both passenger cars and freight vehicles to enhance the dynamic simulation capability of lane lateral flow distribution; and constructing a ramp interaction model coupled with the proportion of freight vehicles to analyze the capacity decay mechanism of merging areas. This method maintains the macroscopic modeling advantages of the METANET model and possesses good structural compatibility and generalizability. In mixed traffic scenarios with multiple vehicle types, this method outperforms traditional models in predicting flow, speed, and density, and is suitable for modeling, simulation, and control of intelligent highway systems.

Description

Technical Field

[0001] This invention relates to a method for modeling mixed passenger and freight traffic flow on highways, and more particularly to a method for modeling mixed passenger and freight traffic flow on highways based on METANET model optimization. Background Technology

[0002] Traditional highway traffic flow modeling is largely based on homogeneity assumptions, with the METANET model being a typical example, widely used for traffic state estimation and control strategy optimization. However, with the continuous increase in freight volume, highways have exhibited obvious mixed passenger and freight characteristics. Freight trucks, due to their larger safe headway, lower acceleration, and limited lane-changing ability, significantly impact system stability and capacity. Existing models suffer from the following problems: the critical density parameter is statically set, failing to dynamically reflect the nonlinear compression effect of freight volume changes on the phase transition threshold; lane-changing rules do not differentiate between vehicle types, easily leading to freight truck congestion in fast lanes; and ramp interaction mechanisms do not consider vehicle type heterogeneity, resulting in large errors in merging zone capacity estimation.

[0003] To address the aforementioned issues, this invention proposes a novel traffic flow modeling method that integrates dynamic critical density correction, differentiated lane-changing decision-making, and a truck ratio-coupled ramp mechanism. Summary of the Invention

[0004] Purpose of the invention: The purpose of this invention is to provide a method for modeling mixed passenger and freight traffic flow on highways based on the METANET model. This method improves lane utilization efficiency, enhances the prediction accuracy of highway congestion timing, maintains the macro-modeling advantages of the METANET model, and has good structural compatibility and scalability.

[0005] Technical solution: The present invention provides a method for modeling mixed passenger and freight traffic flow on highways based on METANET model optimization, comprising:

[0006] (1) Construct a dynamic critical density correction function based on the truck ratio;

[0007] (2) Establish a differentiated decision-making mechanism for lane-changing behavior;

[0008] (3) Construct a ramp interaction model with coupled truck ratio and analyze the capacity decay mechanism of merging area.

[0009] Preferably, step (1) includes defining a saturated critical density and constructing a saturated exponential critical density function, the formula of which is as follows:

[0010] ρ crit (p T )=ρ crit0 ·[1-β·(1-e-k p T )]

[0011] Where, ρ crit0 p is the critical density without trucks. T β is the truck ratio, β is the compression amplitude coefficient, and k is the decay rate parameter.

[0012] Preferably, the differentiated lane-changing behavior decision mechanism specifically involves setting lane-changing probabilities based on the Logit utility function and the inhibition coefficient for cars and trucks respectively, thereby enhancing the dynamic simulation capability of lane lateral traffic distribution.

[0013] Preferably, in the differentiated lane-changing decision-making mechanism, a decay coefficient related to the truck ratio is superimposed on the Logit model, and the lane-changing probability formula is:

[0014] p truck =δ·p car ·(1-γ·p T )

[0015] Where γ is the lane change suppression coefficient and δ is the lane change directionality correction factor.

[0016] Preferably, the ramp interaction model with coupled truck ratios uses a nonlinear efficiency function to adjust the actual merging flow of the ramps.

[0017]

[0018] Where, p T 'This represents the proportion of trucks on the ramps.' η is the merging efficiency attenuation coefficient, η is the nonlinear merging efficiency coefficient, and Q is the merging efficiency coefficient. merge This represents the actual merging flow rate of the ramp.

[0019] Preferably, when the main line flow is low, the merging capacity is dominated by the effective flow of the ramps; when the main line is close to saturation, the merging capacity is limited by the main line capacity, and the ramp flow is suppressed.

[0020] Preferably, in step (2), in the traffic flow model, based on the density conservation equation, a lane-changing mechanism is introduced. The transfer of vehicles between different lanes will change the density of each lane. For the j-th lane, its density conservation equation is modified as follows:

[0021]

[0022] Where, ρ i,j (k) is the vehicle density of lane j at point i on road segment k at time k, Q j’→j Q is the traffic flow from lane j' to lane j. j→j’ΔU is the traffic flow from lane j to lane j', and ΔU is the difference in utility between the target lane and the current lane.

[0023] Preferably, the merging zone has a traffic capacity C merge Mainline passability C main and the effective inflow rate of the ramp η(p) T )·Q ramp Joint decision:

[0024] C merge =min(C main Q main +Q merge )

[0025] Among them, Q main The current traffic for the main line, Q merge This represents the actual merging flow rate of the ramp.

[0026] A computer device includes one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the programs, when executed by the processors, implement the steps of the method for modeling mixed passenger and freight traffic flow on highways based on METANET model optimization.

[0027] A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method for modeling mixed passenger and freight traffic flow on highways based on METANET model optimization.

[0028] Beneficial effects: Compared with the prior art, the present invention has the following significant advantages:

[0029] (1) A dynamic critical density function based on truck ratio is proposed to characterize the phase transition compression phenomenon caused by poor truck acceleration performance and large safety distance, which significantly improves the prediction accuracy of congestion formation timing; (2) Differentiated lane-changing rules are established to finely describe special behaviors such as truck lingering and returning, thereby improving lane utilization efficiency; (3) Ramp merging efficiency is coupled with vehicle type ratio to solve the problem of overestimation of bottleneck capacity under high truck ratio in traditional models; (4) Through simulation experiments based on the US-101 dataset of NGSIM, the improved model described in this invention outperforms the traditional METANET model by more than 15% in terms of RMSE prediction of flow, speed and density, and has good adaptability and reliability in actual deployment. Attached Figure Description

[0030] Figure 1 This is a schematic diagram of the METANET road segment division described in this invention.

[0031] Figure 2 This is the model design framework described in this invention.

[0032] Figure 3 This is a logical relationship diagram of the three mechanisms described in this invention.

[0033] Figure 4 This is the critical density-truck ratio curve.

[0034] Figure 5 A three-dimensional graph showing the flow rate, density, and truck ratio.

[0035] Figure 6 This is a diagram showing the attenuation effect of ramp merging efficiency.

[0036] Figure 7 This is a validation diagram for a traffic flow model based on the NGSIM dataset. Detailed Implementation

[0037] The technical solution of the present invention will be further described below with reference to the accompanying drawings.

[0038] This invention discloses a method for modeling mixed passenger and freight traffic flow on highways based on METANET model optimization, comprising:

[0039] (1) Construct a dynamic critical density correction function based on the truck ratio;

[0040] A dynamic critical density correction function is established, and the dynamic compression effect of vehicle heterogeneity on the traffic phase transition point is quantified by the time-varying parameter β(pT) which is dependent on the truck proportion, thus solving the modeling inaccuracy problem of left shift of critical density.

[0041] Set the ratio of cars to 1-p T The proportion of trucks is p T Then the average headway h e =(1-p T )h c +p T h T , where h c The headway of the car, h T This represents the headway of the trucks. According to the Greenshields model, the critical density is inversely proportional to the average headway. Substituting the average headway into the critical density formula:

[0042]

[0043] Because of h T h c truck ratio p T An increase in density leads to a decrease in critical density, indicating a linear negative correlation between critical density and the proportion of trucks, thus establishing a linear relationship:

[0044]

[0045] Where, ρ crit0 This is the critical density when there are no trucks.

[0046] However, relevant NGSIM data show that the rate of leftward shift of critical density is exponentially decreasing with the proportion of trucks, that is, it decreases rapidly in the low proportion stage and slows down in the high proportion stage. Traditional linear models cannot capture this characteristic of diminishing marginal effect.

[0047] Therefore, an exponential function is introduced for nonlinear correction. Based on Kerner's three-phase traffic flow theory, the denominator of the linear model is replaced with an exponential form. This formula is in p T When the density approaches 1, the critical density approaches 0. The critical density when trucks account for 100% should be... This is inconsistent. Therefore, the formula needs to be further adjusted to ensure physical validity.

[0048] First, define the saturation critical density: the critical density when the truck ratio is 100%. (0<γ<1), where, The saturated compressibility coefficient of the truck with respect to the critical density;

[0049] Constructing a saturated exponential function: Introducing a saturated exponential function, the formula is as follows:

[0050]

[0051] When p T When ρ = 0, crit =ρ crit0 When p T When ρ = 1, crit =ρ crit0 ·(1-β), where 1-β=γ, and β is the maximum compression amplitude of the truck ratio with respect to the critical density.

[0052] (2) Establish a differentiated decision-making mechanism for lane-changing behavior;

[0053] A differentiated lane-changing decision-making approach is proposed, which integrates the Logit decision-making model and the forced return mechanism for trucks. While maintaining the continuity of lane-changing decisions for ordinary vehicles, the approach uses the probability decay coefficient γ to constrain the lingering behavior of trucks in the fast lane, thus compensating for the homogeneity of lane-changing rules.

[0054] The utility difference between the target lane and the current lane is defined as:

[0055] ΔU=U target -U current =-α lane ·(ρ target -ρ current)

[0056] Among them, U target and U current Let α be the lane utility value. lane Let ρ be the lane attractiveness coefficient. In a certain region's traffic regulations in China, the left lane is defined as the "overtaking lane" or "fast lane," with higher traffic priority. Drivers generally perceive the left lane as more efficient, thus assigning it a higher attractiveness coefficient. According to traffic flow assignment theory, lane attractiveness is positively correlated with lane speed, density, and safety. The left lane, due to its higher average speed and lower density, is more attractive. The recommended capacity correction coefficient for the left lane in this region is 1.2, and for the right lane, it is 0.8. ρ represents lane density. The negative correlation between utility difference and density difference indicates that drivers tend to choose the lower-density lane to improve traffic efficiency.

[0057] According to the Logit model, the lane-changing probability is:

[0058]

[0059] Where θ is the lane-changing sensitivity coefficient, preferably 0.15, to balance model accuracy and computational efficiency, reflecting the driver's sensitivity to lane density differences. This formula indicates that ΔU is the difference in utility between the target lane and the current lane. When ΔU > 0, the target lane has higher utility, and the lane-changing probability increases exponentially with the increase in utility difference. For example, when Δρ = 5veh / km, P... 2→1 =0.68, meaning that there is a 68% probability that vehicles in high-density lanes will switch to low-density lanes.

[0060] The truck lane-changing decision is based on the Logit model, with an added attenuation coefficient related to the truck ratio, as shown in the formula.

[0061] P truck =P logit ·(1-γp T )

[0062] Among them, P logit The lane-changing probability calculated for the model, where γ is the attenuation coefficient, and p T This represents the current proportion of trucks in the lane. The lane-changing probability is dynamically adjusted based on the truck proportion, reflecting that the higher the proportion of trucks, the stronger the system's constraint on their lane-changing behavior. Simultaneously, trucks returning from the fast lane to the slow lane should be encouraged, requiring differentiated processing based on lane-changing direction. The final formula for calculating the lane-changing probability is shown below:

[0063] p truck =δ·p car ·(1-γ·p T )

[0064] Among them, δ is the lane-changing direction correction factor, which is used to enhance the behavioral tendency of trucks to return from the fast lane to the slow lane.

[0065] The Logit utility function considers the impact of lane density differences on vehicle lane-changing decisions at a macroscopic level. Vehicles typically tend to switch from high-density lanes to low-density lanes to improve driving efficiency, and the Logit utility function quantifies this tendency. Secondly, the truck return rule addresses the situation where trucks are traveling slowly in the fast lane by stipulating a decay in their lane-changing probability, prompting trucks to return to the slow lane. The combination of these two lane-changing decisions simulates the impact of truck lane-changing behavior on traffic flow, maintaining traffic flow stability and preventing congestion in the fast lane due to slow-moving trucks.

[0066] Based on the traffic flow density conservation equation, a lane-changing mechanism is introduced, where vehicle movement between different lanes alters the density of each lane. For the j-th lane, its density conservation equation is modified as follows:

[0067]

[0068] Where, ρ i,j (k) is the vehicle density of lane j at point i on road segment k at time k, q i,j (k) is the traffic flow of lane j at point i on road segment k, Q j’→j Q is the traffic flow from lane j' to lane j. j→j’ This represents the traffic flow from lane j to lane j'. The lane-changing probability determines the magnitude of the lane-changing flow, such as Q. j→j’ =P j →j'·Q j When the utility difference ΔU between the target lane and the current lane is large, the lane-changing probability p increases, thus affecting the density changes of each lane. Simultaneously, the traditional speed-density relationship needs to be modified after introducing the lane-changing mechanism. Lane-changing behavior alters the lane density distribution, thereby affecting vehicle speed. Considering the lane-changing mechanism, the speed-density relationship can be expressed as:

[0069]

[0070] Among them, v i,j v is the vehicle speed in the j-th lane of road segment i. f,i,j It is the free-flow velocity of the lane, ρ i,j It is the original density of the lane, Δρ i,j ρ is the change in lane density caused by lane-changing behavior. j,i,j This refers to the congestion density of that lane. For example, in road segment i, if a truck returns from the fast lane to the slow lane, increasing the slow lane density, then the speed of vehicles in the slow lane will decrease.

[0071] (3) Construct a ramp interaction model with coupled truck ratio and analyze the capacity decay mechanism of merging area.

[0072] A ramp interaction model coupled with the truck ratio is constructed to analyze the capacity attenuation mechanism in the merging zone. A nonlinear merging efficiency coefficient η is introduced to establish a quantitative mapping relationship between the truck ratio and the intensity of local bottlenecks. Merging efficiency, as a core indicator for measuring the smoothness of vehicle merging on ramps, is defined as the ratio of the actual effective merging flow to the maximum flow on the ramp, reflecting the interference intensity of the truck ratio on the merging process. The merging efficiency function is shown below:

[0073]

[0074] p' is the merging efficiency attenuation coefficient. T For the proportion of trucks on the ramp, when p' T =0 indicates that when there are no trucks, η=1, and the efficiency is the highest. T =1 indicates that the truck is fully loaded. Lowest efficiency.

[0075] Merging area capacity C merge This refers to the maximum number of vehicles that can safely pass through the merging zone per unit time, determined by the mainline capacity C. main and the effective inflow rate of the ramp η(p) T )·Q ramp The joint decision reflects the dynamic balance between the "mainline carrying capacity" and the "ramp merging demand".

[0076] C merge =min(C main Q main +Q merge )

[0077] Q main The current traffic for the main line, Q merge This represents the actual merging flow rate of the ramp;

[0078]

[0079] Among them, Q ramp,max This represents the maximum flow rate entering the ramp. When the mainline flow rate is low, the merging capacity is dominated by the effective flow rate of the ramp. When the mainline is close to saturation, the merging capacity is limited by the mainline capacity, and the ramp flow rate is suppressed.

[0080] The density conservation equation of the traditional METANET model only describes the longitudinal flow conservation along the mainline and does not include the lateral effects of ramp merging. By introducing a vehicle-type merging term based on merging efficiency modulation, it is modified as follows:

[0081]

[0082] Where, ρ merge,j (t) represents the change in lane j density caused by ramp merging. The following steps are used to achieve a refined model of the merging process.

[0083] The efficiency impact of merging flow and vehicle type breakdown:

[0084] Firstly, due to the merging efficiency, the actual total flow rate merging into the main line from the ramp is calculated as follows, based on the ramp truck ratio p. T The merged traffic flow is broken down into passenger car and truck components:

[0085]

[0086] Among them, the proportion of trucks p T The higher the actual inflow Q, the greater the actual inflow. merge The lower.

[0087] Density increment calculation by vehicle type:

[0088] Based on the flow-density-velocity relationship, the merging density of cars and trucks at the merging zone entrance is:

[0089]

[0090] Among them, v merge,car and v merge,truck These are the design speeds for cars and trucks in the merging zone, respectively.

[0091] In the traffic flow model, the CFL condition is used to ensure the numerical stability of the density conservation equation after discretization. Considering the CFL stability condition, the density update in the merging zone is as follows:

[0092]

[0093] Where Δx is the road segment length, Δt is the time step, and ρ max This represents the maximum permissible density.

[0094] In the specific implementation process, a merging ramp is located on a two-lane mainline of a highway. The mainline is divided into fast lanes and slow lanes, with the fast lanes only allowing cars and the slow lanes prioritizing trucks. The proportion of trucks (p) in the current lanes is statistically analyzed. T Dynamically update the critical density ρ of each segment crit And adjust the velocity-density relationship.

[0095] For lane changing between lanes, different strategies are adopted for cars and trucks. For cars, the lane changing probability is calculated based on the density difference Logit model, while for trucks, an additional attenuation weight γ·p is applied. T Control its lane-changing behavior.

[0096] In the merging zone, according to the ramp flow rate Q ramp and truck ratio p T 'Calculate the nonlinear merging efficiency η(p) T Then adjust the merging flow rate Q. merge The mainline density is updated. This method accurately predicts traffic flow, speed, and density in mixed traffic scenarios involving multiple vehicle types, and is suitable for modeling, simulation, and control of intelligent highway systems.

[0097] To examine the dynamic response characteristics and generalization ability of the METANET-DHR model in heterogeneous traffic flow scenarios, this invention selects the US-101 highway dataset from the NGSIM dataset for model effectiveness verification. The simulation results of the improved METANET-DHR model, the original METANET model, and the spatiotemporal evolution curves of macroscopic flow, speed, and density from the NGSIM US-101 measured data are compared side-by-side. Relative error, root mean square error (RMSE), and peak time synchronicity are used to quantify the model's prediction accuracy and spatiotemporal consistency. Figure 7 As shown, the differences in response of each model throughout the “formation-persistence-dissipation” process are demonstrated, highlighting the superiority of the improved model over the traditional METANET model in error control and phase transition capture, thus verifying the robustness and reliability of the METANET-DHR model.

Claims

1. A method for modeling mixed passenger and freight traffic flow on highways based on METANET model optimization, characterized in that, include: (1) Construct a dynamic critical density correction function based on the truck ratio, as shown in the following formula: Where, ρ crit0 The critical density without trucks, p T Where β is the truck ratio, k is the compression amplitude coefficient, and ρ is the decay rate parameter. crit It is the critical density; (2) Establish a differentiated lane-changing behavior decision-making mechanism; specifically, set lane-changing probabilities based on the Logit utility function and inhibition coefficient for cars and trucks respectively; the lane-changing probability of trucks is based on the Logit model with a decay coefficient related to the proportion of trucks added, as follows: Where γ is the lane-switching suppression coefficient. This is a lane-changing directional correction factor. The probability of a truck changing lanes. The probability of a car changing lanes; In the traffic flow model, based on the density conservation equation, a lane-changing mechanism is introduced. The movement of vehicles between different lanes will change the density of each lane. For the j-th lane, the vehicle density conservation equation is modified as follows: in, It is the vehicle density of lane j at point i on road segment k at time k. It refers to the traffic flow at point i where vehicles switch from lane j' to lane j. It refers to the traffic flow at point i where vehicles switch from lane j to lane j'. It is the traffic flow of lane j at point i on road segment k at time k. It is the traffic flow of lane j at point i-1 on road segment k at time k. For the length of the road segment, For time step; (3) Construct a ramp interaction model with coupled truck ratios and analyze the capacity decay mechanism of merging area; The ramp interaction model with coupled truck ratios uses a nonlinear efficiency function to adjust the actual merging flow rate of the ramps. Where, p T 'This represents the proportion of trucks on the ramp.' Here, η is the merging efficiency attenuation coefficient, and η is the nonlinear merging efficiency coefficient. This represents the actual merging flow of the ramp. This represents the maximum flow rate entering through the ramp; The merging zone's traffic capacity C merge Mainline passability C main and the effective inflow rate of the ramp η(p) T ’ )•Q ramp,max Joint decision: in, The current traffic is the main line. This represents the actual merging flow rate of the ramp.

2. A computer device, characterized in that, It includes one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the programs, when executed by the processors, implement the steps of the method for modeling mixed passenger and freight traffic flow on highways based on METANET model optimization as described in claim 1.

3. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the method for modeling mixed passenger and freight traffic flow on highways based on METANET model optimization as described in claim 1.

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