Expressway reconstruction and extension construction area path guiding method in hybrid network connection environment

Abstract

The invention discloses a path guiding method for a highway reconstruction and extension construction area in a hybrid network connection environment, and the method comprises the steps: analyzing the flow time distribution characteristics of the construction area, and analyzing the congestion mechanism of a construction road section; analyzing a corresponding time headway according to the hybrid driving characteristics of the networked vehicle and the non-networked vehicle, selecting a following mode by combining the permeability of the networked vehicle, and calculating the traffic capacity of a single lane; considering the influence of the hybrid network connection environment and reconstruction and extension road characteristics, establishing an impedance function of time and cost, and integrating the impedance function into comprehensive impedance; the influence of traffic information on path selection is studied, a road network total expected travel time model under information guidance is established, a multi-target guidance model is constructed and optimized and solved in combination with minimization of flow in a congestion area, travel cost and travel expected time, and a guidance implementation scheme is obtained.

Description

Technical Field

[0001] The present invention belongs to the technical field of path guidance during highway construction in intelligent transportation, and in particular provides a path guidance method for highway reconstruction and expansion construction areas in a hybrid network environment. Background Art

[0002] During highway expansion and reconstruction construction, traffic must remain uninterrupted. However, construction zones experience reduced capacity, increased safety hazards, and the potential for traffic congestion and even accidents. Ensuring safety while simultaneously improving capacity in construction zones has been a persistent pain point. Current static traffic organization and dynamic traffic control methods have proven limited in their effectiveness in alleviating congestion in construction zones. There is a need to explore new route guidance methods in hybrid connected environments to alleviate congestion and improve overall traffic efficiency in these zones. Intelligent connected vehicles can reduce accident rates and improve road capacity. Their superior perception and following capabilities can effectively help drivers make quick driving decisions and reduce fuel consumption.

[0003] A hybrid connected environment can provide powerful data perception and proactive control to ensure traffic flow in highway construction zones undergoing expansion and reconstruction. During the expansion and reconstruction process, the hybrid connected environment can assist in optimizing the routing of traffic around the construction section, thereby alleviating vehicle congestion at traffic diversion points and improving the efficiency and safety of traffic in the mainline construction zone and the road network. Therefore, during the expansion and reconstruction of highways, connected vehicles can provide data perception for uninterrupted traffic and transform static and inefficient guidance methods into dynamic and efficient guidance methods based on the actual conditions of the road network. Existing technologies have not yet proposed a path guidance method that uses intelligent connected vehicles during highway expansion and reconstruction. In other words, the hybrid connected environment has not yet been applied to uninterrupted traffic during highway expansion and reconstruction. Summary of the Invention

[0004] Purpose of the invention: In response to the above technical problems, the present invention proposes a path guidance method for highway reconstruction and expansion construction areas under a hybrid networked environment. The present invention introduces a hybrid networked environment and technology, which can provide data perception and optimized guidance solutions for safe driving during the reconstruction and expansion period.

[0005] Technical solution: In order to solve the above problems and achieve the above invention objectives, the present invention proposes a method for guiding the path of a highway reconstruction and expansion construction area in a hybrid network environment, which includes the following steps:

[0006] S1. Calculate the time impedance under different penetration rates of connected vehicles in the highway reconstruction and expansion;

[0007] S2. Calculate the road cost impedance under different connected vehicle penetration rates;

[0008] S3. Combine time impedance and cost impedance to determine comprehensive impedance in hybrid network environment;

[0009] S4. Calculate the overall travel time of the road network under the guidance of traffic information based on the route selection of the inertial and non-inertial drivers after receiving guidance;

[0010] S5. Convert the two objectives of congested area flow and overall road network travel time into constraints, and solve the route guidance plan during the highway construction period with the goal of minimizing the travel cost calculated based on the comprehensive impedance.

[0011] Furthermore, the S1 includes the following steps:

[0012] S11. Conduct hybrid connected vehicle simulation experiments based on traffic characteristics of the expanded and renovated expressways;

[0013] S12. According to the penetration characteristics of connected vehicles, the time impedance model is improved based on the Davidson function;

[0014] S13. Conduct simulation experiments with different connected vehicle occupancy rates and, combined with road saturation, obtain the time impedance under different connected vehicle penetration rates.

[0015] Furthermore, the basic assumptions of S11 are as follows: in a connected environment, non-connected vehicles adopt the IDM following model, and connected vehicles adopt the CACC following model; due to the restricted and speed-limited characteristics of the highway reconstruction and expansion, its road grade is degraded to the national and provincial trunk road grade; the impact of intersection delays is ignored when calculating impedance; in a mixed connected environment, freight vehicles are all assumed to be non-connected vehicles, and each vehicle type is converted into a standard passenger car through the vehicle conversion coefficient.

[0016] Furthermore, the improved time impedance model of S12 is as follows:

[0017] Based on the Davidson function, the relationship between time impedance, the proportion of intelligent connected vehicles, and road network traffic flow is studied, and the time impedance is obtained as:

[0018] t n =t n0 [1+J·f n (p,μ)] (1)

[0019]

[0020] Where, t n0 is the unimproved road impedance; f n (p,μ) is the fitting function of p and μ; p is the proportion of connected vehicles; μ is the saturation; Q n is the road flow; C n is the single lane capacity; J is the preset coefficient.

[0021] Furthermore, the specific steps of S2 are:

[0022] Simplifying the cost of driving on the highway into fuel costs and road tolls, the cost impedance expression is:

[0023] E n =(K n +M n )L n +u (2)

[0024] Where K n Indicates the cost per unit mileage; M n Indicates the road section charging rate, unit: yuan / km. If it is a non-toll road, its value is 0; L n It represents the length of road section n; u represents the toll collection value of ordinary road bridge or open highway, and is 0 if there is no toll.

[0025] Furthermore, the specific steps of S3 are:

[0026] S31. Convert the cost impedance into time impedance using a certain coefficient. The conversion is as follows:

[0027] T n =γE n (3)

[0028] Among them, T n represents the time impedance on section n; E n represents the cost impedance on road section n; γ represents the time / cost conversion coefficient;

[0029] Taking the travel time of the road section as an influencing factor and taking the ratio of passenger and freight traffic into consideration, the calculation method of formula γ is obtained:

[0030] γ=γ1×a1+γ2×a2 (4)

[0031] Where a1 and a2 represent the traffic ratios of passenger cars and trucks, respectively; γ1 and γ2 represent the unit cost of passenger cars and trucks equivalent to time, respectively, in min / yuan;

[0032] S32. Based on the actual road conditions during the traveler's route selection process, taking into account the fuel costs and highway tolls incurred during vehicle travel, the time impedance and the cost impedance are integrated to construct a comprehensive road impedance;

[0033] F n =k1T n +k2E n (5)

[0034] Among them, F nrepresents the impedance value of road section n; k1 and k2 represent the weight coefficients of time and cost for the comprehensive impedance of the road.

[0035] Furthermore, the specific steps of S4 are:

[0036] S41. Define the groups of travelers affected by traffic guidance information;

[0037] Inertial travelers refer to those who, regardless of the information guidance process, believe that the travel time of the original route is less than or equal to other routes they can perceive, and thus do not change their original route selection; non-inertial travelers refer to those who, during the information guidance process, choose other guided routes with expected travel time less than or equal to the original route and continuously make judgments and choices during the guidance process;

[0038] S42, road network user equilibrium model guided by traffic information;

[0039]

[0040] Among them, E(T a ) represents the travel time of road segment a; represents the free flow time of section a; c a represents the capacity of road section a; T′ represents the reference formula for simplifying the model; x a and They represent the expectation and variance of the travel time of the road segment respectively;

[0041] The road network equilibrium model is shown in formula (7):

[0042]

[0043] Where, represents the expected travel time of g people in network w under the selected path r, g is an indicator variable, and its value is only o or z; z represents inertial travelers, o represents non-inertial travelers, Z is the set of all inertial travelers, and O is the set of all non-inertial travelers; where, is the association matrix between segments and paths. When path r includes segment a, is equal to 1, otherwise it is 0; η represents the transfer rate of non-inertial travelers guided by traffic information; ε g1 represents the perceptual bias, ε g2 represents the prediction bias; E(ε g1 ) represents the perceptual expectation deviation, E(ε g2 ) represents the expected deviation of the forecast; μ z With μ o represent the share of habitual travelers and non-habitual travelers respectively; represents the traffic flow of inertial travelers in network w, represents the traffic flow of non-inertial travelers in network w;

[0044] Among them, f represents the overall travel demand of the road network, represents the traffic flow of the inertial travelers in the network w under the selected path r; The traffic flow of non-inertial travelers in network w under the selected path r; represents the actual travel time of the inertial travelers in network w under the selected path r, represents the actual travel time of a non-inertial traveler in network w under the selected path r, represents the actual travel time of a non-inertial traveler in network w under the selected path k; represents the expected travel time of an inertial traveler in network w under the selected path r; represents the perceived expected travel time of non-inertial travelers in network w after the inertial travelers choose path r, represents the expected travel time of a non-inertial traveler in network w under the selected k paths; is the coefficient to be calibrated;

[0045] S43. Conduct a mixed travel information guidance simulation experiment to obtain the overall travel time of the road network under the guidance of traffic information, and obtain the relationship between the overall travel time of the road network and the transfer rate of non-inertial travelers under the guidance of traffic information.

[0046] Furthermore, the specific steps of S5 are:

[0047] S51. Taking into account the characteristics of the expressway reconstruction and expansion construction area, the traffic congestion control area is divided into a congestion zone and a balance zone;

[0048] Based on the traffic characteristics of the reconstruction and expansion construction area, the traffic congestion control area is divided into a congestion zone and a balance zone. The congestion zone is the collection of road sections where the road traffic operation status is saturated, and the balance zone is the collection of traffic-carrying sections and interchanges upstream and adjacent to the boundary sections of the congestion zone.

[0049] S52. Using a multi-objective optimization model, establish a flow minimization model for the congested area, a traveler cost minimization model for the balanced area, and a road network travel time minimization model;

[0050] The congestion area flow minimization model is established as follows;

[0051] Control the inflow of congested areas and minimize it for path guidance. Set the decision variable of the guidance model as the diversion rate of the interconnected nodes in the congested area. Constrain the decision variable by the transfer rate and occupancy ratio of inertial travelers. The guidance model expression for the first-level goal of minimizing the flow in the congested area is established as follows:

[0052]

[0053] Where f1 represents the total traffic volume in the congestion area A during the control period, t0 and t1 represent the start and end time of a certain time period respectively; represents the node traffic volume entering congestion area A at node n; Q n in (t) represents the time function of the road flow; β n in (t) is the actual diversion ratio of node n on the upstream path at time t; μ1 represents the proportion of inertial drivers in the model, μ2 represents the proportion of non-inertial drivers in the model, η represents the transfer rate of inertial travelers in the model; p represents the penetration rate of intelligent connected vehicles; x n represents the compliance rate of intelligent connected vehicles and non-intelligent connected vehicles to the guidance information at node n in the model;

[0054] The model for minimizing the cost of travelers in the equilibrium area is established as follows;

[0055] With the goal of optimizing the traveler cost in the balance zone, the impact of guidance on the surrounding road network will be reduced to a minimum, that is, the comprehensive travel cost of vehicles on each road section in the balance zone B will be minimized;

[0056]

[0057] Among them, i and j represent the vertices of the road segment in the road network topology diagram, which represent the hub interchange diversion points and the ground interchange diversion points in the actual road network; represents the real-time road impedance of the section (i, j) in the balance area B at time t; and Respectively represent the time cost impedance and expense cost impedance of the road section (i, j) at time t; k1 and k2 are related model parameters; C i,j is the control parameter; S and D represent the starting point and the end point respectively; R(S,D) represents the set of paths between the starting points;

[0058] The model for minimizing the overall expected travel time of the road network is established as follows;

[0059]

[0060] st0≤η≤0.6

[0061] Where f3 represents the overall expected travel time of the road network in the congestion area A and the balance area B during the control period;

[0062] E(T all ) represents the overall expected travel time of the road network under theoretical calculation; η represents the transfer rate of inertial travelers in the model;

[0063] S53, converting the multi-objective model into a single-objective model;

[0064] In this model, function f1 is not required to minimize the flow in the congested area, but only requires that the congestion in congested area A can be quickly dissipated. At the same time, function f3 does not require that the overall expected travel time is minimized, but only requires that the overall expected travel time after the implementation of guidance control is less than that before the implementation of guidance control, thereby reducing the loss of guidance control on the overall travel time of the road network.

[0065] Therefore, the comprehensive impedance function is used as the optimization target, and the guiding function is obtained by combining the minimum inflow formula in the congested area and the minimum cost formula for travelers in the balanced area:

[0066]

[0067] Where, F 总 represents the travel cost in the equilibrium area; K n It represents the effective reduction coefficient of the congestion index of the road section n, and uses the rounding function to reduce F 总 As 0-1 switch control, when K n ≥1 indicates that the road section is congested. n = 0, indicating that the road section is no longer congested; c is the congestion degree of the construction area section; ξ and Represent the decision maker's expected value, which respectively represents the critical value of node flow without congestion and the critical value of total expected travel time under no information guidance.

[0068] Beneficial effects: Compared with the prior art, the technical solution of the present invention has the following beneficial technical effects:

[0069] (1) The path guidance method proposed in the present invention is based on an intelligent network environment and the participation of network vehicles. It has excellent vehicle following behavior, vehicle-road perception ability and vehicle-to-vehicle communication ability, thereby effectively reducing the total travel time of the road network, improving the driving efficiency without interrupting traffic during the reconstruction and expansion construction, alleviating traffic congestion in the construction area, reducing driving safety hazards and lowering the accident rate, and reducing the impact of the reconstruction and expansion project on travelers.

[0070] (2) The present invention explores the following mode and comprehensive impedance modeling of connected vehicles and non-connected vehicles in the highway reconstruction and expansion construction area under a mixed networking environment, and establishes a path guidance model based on the optimization objectives such as the inflow volume in the congested area and the travel time of the road network. It provides a novel model and calculation method for the application of vehicle networking technology in traffic guidance in the reconstruction and expansion construction area, which can reduce the total travel time of the road network and alleviate traffic congestion in the construction area. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1Flowchart of a method for guiding a path in a renovation and expansion construction area under a hybrid network environment according to an embodiment of the present invention;

[0072] Figure 2 A simplified topological diagram of the road network in the highway reconstruction and expansion construction area according to an embodiment of the present invention;

[0073] Figure 3 Graph showing the relationship between the total travel time of a road network and the transfer rate of habitual travelers in an embodiment of the present invention;

[0074] Figure 4 Simulated road network diagram for expressways;

[0075] Figure 5 This is a comparison chart of average travel time before and after guidance in an embodiment of the present invention;

[0076] Figure 6 Comparison of the average travel time of the road network under different η. DETAILED DESCRIPTION

[0077] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0078] In one embodiment, Figure 1 As shown, the present invention provides a route guidance method during highway reconstruction and expansion construction in a hybrid network environment. The method is applied to the congestion problem of the Huai'an section of the Beijing-Shanghai Expressway reconstruction and expansion as an example, including the following steps:

[0079] S1. Calculate the time impedance under different penetration rates of connected vehicles in the highway reconstruction and expansion;

[0080] S2. Calculate the road cost impedance under different connected vehicle penetration rates;

[0081] S3. Combine time impedance and cost impedance to determine comprehensive impedance in hybrid network environment;

[0082] S4. Calculate the overall travel time of the road network under the guidance of traffic information based on the route selection of the inertial and non-inertial drivers after receiving guidance;

[0083] S5. Convert the two objectives of congested area flow and overall road network travel time into constraints, and solve the route guidance plan during the highway construction period with the goal of minimizing the travel cost calculated based on the comprehensive impedance.

[0084] This case study examines traffic congestion on the Huai'an section of the Beijing-Shanghai Highway expansion project. The construction area for this section is characterized by a half-road closure and an open median strip. Vehicles traveling in the original direction must merge through the median strip opening to join the oncoming lane, disrupting traffic flow. The typical two-lane "2+2" traffic pattern was changed to a two-lane "1+1" pattern, with two 3.75m lanes and a 2.5m emergency lane. A movable steel guardrail and isolation net were installed 1.25 meters inboard of the second lane, with orange reflective markings installed on the inside.

[0085] (1) Calculation of time impedance function in hybrid network environment

[0086] Based on the Davidson function, the relationship between time impedance and the proportion of intelligent connected vehicles and road network traffic flow is studied, and the expression of time impedance function is derived.

[0087] t n =t n0 [1+J·f n (p,μ)] (1)

[0088]

[0089] Where, t n0 is the unimproved road impedance; f n (p,μ) is a function of p and μ; p is the proportion of connected vehicles; μ is the saturation; Q n is the road flow; C n is the single lane capacity; J is the preset coefficient.

[0090] From the analysis of different vehicle distribution conditions in the road network, the Davidson function The highest power is 4, so it is initially set as a 2-variable 4-order function. By combining the corresponding p and μ, a nonlinear function model with 11 parameters can be constructed. The variable combination is shown in Table 1:

[0091] Table 1 Variable combinations

[0092]

[0093] Based on the possible combinations of variables in the table, multiple undetermined parameters are introduced to represent f n (p,μ), the function formula is:

[0094] f n (p,μ)=a1p+a2p 2 +a3μ+a4pμ+a5p 2 μ+a6μ 2 +a7pμ 2 +a8p 2 μ2 +a9μ 3 +a 10 pμ 3 +a 11 μ 4 (2)

[0095] a1 to a 11 is the parameter to be labeled. n (p, μ) is subjected to nonlinear regression, and the parameters J and a1 to a are obtained after regression analysis. 11 The value of . And the goodness of fit R 2 It reaches 0.992, indicating that the model has a good fitting effect.

[0096] Finally, the quantitative functional relationship between the time impedance function, the proportion of intelligent connected vehicles p, and the saturation μ is obtained as follows:

[0097] t n =t n0 [1+0.968(p+0.15p 2 -0.577μ-0.082pμ-0.15μ 2 +0.794pμ 2 -0.68μ 3 -0.759pμ 3 +0.663μ 4 )](3)

[0098] (2) Calculation of road cost impedance function

[0099] According to the cost impedance function expression, road driving costs mainly include depreciation, repair costs, fuel costs, etc. Road driving costs are run using a simplified fuel cost model. The fuel consumption per 100 kilometers of different vehicle models is shown in Table 2:

[0100] Table 2 Fuel consumption per 100 kilometers of different models

[0101]

[0102] In addition to the vehicle model, vehicle fuel consumption is also related to driving behaviors such as sudden acceleration, deceleration, and overtaking. Different driving behaviors cause fuel consumption to fluctuate. At the same time, in a mixed network environment, the traffic flow generated by platooning and beyond-visual-range perception can reduce the probability of sudden acceleration and deceleration, thereby significantly reducing fuel consumption. Therefore, the present invention will study the impact of the addition of intelligent connected vehicles on fuel consumption and emissions. By quantifying the impact of different intelligent connected vehicle penetration rates on fuel consumption, the vehicle fuel consumption per 100 kilometers under different intelligent connected vehicle penetration rates is obtained as shown in Table 3:

[0103] Table 3 Fuel consumption per 100 kilometers at different penetration rates of intelligent connected vehicles

[0104]

[0105] According to the "Vehicle Type Classification for Toll Road Vehicles", the highway toll standards (trucks are based on the total number of axles, and buses are based on the number of passengers) are shown in Table 4:

[0106] Table 4 Highway toll standards

[0107]

[0108] According to the specific data in Table 4, the travel cost per unit mileage and toll charges are calculated, and then the cost impedance value is obtained by formula (3).

[0109] (3) Comprehensive impedance function modeling under hybrid network environment, time cost equivalent conversion is shown in Table 5:

[0110] Table 5 Time cost equivalent conversion

[0111]

[0112] According to Table 5, the cost-time conversion coefficient is: γ = 2.2 × 0.66 + 1.1 × 0.34 ≈ 1.7.

[0113] After inquiring about the fuel prices in relevant areas of Jiangsu, the present invention adopts the calculated gasoline price of 7.45 yuan / L and the diesel price of 6.75 yuan / L, and determines the cost impedance based on the above values.

[0114] Based on historical road network data, multiple OD point pairs were selected and the time cost weight coefficients were calculated using SPSS software and the maximum likelihood calibration method: for expressways, k1 = 1.08, k2 = 0.062; for ordinary roads, k1 = 1.12, k2 = 0.088. Combined with the comprehensive road impedance function expression (4), the specific expression is:

[0115]

[0116] (4) Calculation of overall travel time of road network guided by traffic information

[0117] In the actual traffic guidance process, travelers are divided into two categories: inertial and non-inertial travelers. The present invention starts from the inertial and non-inertial choices of drivers and passengers after receiving traffic information guidance, and obtains the total expected time E(T) of the road network through experiments. all ) and the specific expression of the inertial traveler transfer rate η.

[0118] To this end, the highway network to be renovated and expanded is simplified into a road network topology diagram for experimentation, such as Figure 2As shown in the figure, sections abc and gbf are expressways, and the rest are ordinary national and provincial trunk roads. The network contains 7 nodes and 9 sections, which are the direction and information guidance selection of the main traffic flow in the expressway reconstruction and expansion. The main two OD pairs are selected, namely OD1(f, d) and OD2(c, d).

[0119] Record the attributes of each road segment, including free flow time and the section capacity c a , the expectation and variance of the travel time of each road section are calculated by the function shown in formula (5).

[0120]

[0121] Assume that the expected travel demand of OD1 is q1 = 1200 pcu / h and the standard deviation is σ q1 =12; OD2's expected travel demand q2 = 1200 pcu / h, standard deviation σ q2 =18, habitual travelers in the road network account for 60% of the total travel demand.

[0122] Assume that the set of non-inertial travelers is z = {1, 2, 3} and the set of inertial travelers is o = {1, 2, 3}. The basic assumptions are made for the three types of travelers without information guidance. Let the expected deviation of the prediction be E(θ) = 2 and the variance be var(θ) = 1.5. The impact of traffic guidance information on non-inertial travelers is b i =0.3, the impact on habitual travelers who tend to change their choices b j =0.3, maintain the selection effect b j Assuming that traffic guidance information is fully covered and the transfer rate of inertial travelers is 70%, that is, η = 0.7, it is substituted into formula (6) for calculation.

[0123]

[0124] The distribution of relevant travelers under the conditions of traffic guidance information can be obtained.

[0125] Table 6 Expected flow, travel time and traveler distribution of each path in equilibrium under information guidance

[0126]

[0127] Note: z4(o1), z5(o2), and z6(o3) represent travelers who break their habitual behavior under information guidance; o'1, o'2, and o'3 represent travelers who still insist on choosing habitual behavior under traffic information guidance.

[0128] To further explore the relationship between the transfer rate of inertial travelers and the total expected travel time of the road network, the value of the transfer rate η is changed from 0.1 to 1.0, and the above calculation steps are repeated. The corresponding table of the total expected travel time of the road network and the transfer rate of inertial travelers can be obtained, as shown in Table 7:

[0129] Table 7 Correspondence between total expected time of road network and transfer rate of inertial travelers

[0130]

[0131] The data in the table were fitted with parameters and compared between the no-information guidance state and the information guidance state. When the inertial traveler transfer rate is low, the total expected travel time of the road network is optimized to a certain extent. In particular, in this experiment, when the inertial traveler transfer rate is 0.15, the total expected travel time of the road network is minimized. However, as the inertial traveler transfer rate increases, representing an increase in the number of cardinal changes in traveler paths, the total expected travel time for the entire road network to reach equilibrium also increases, gradually exceeding the total expected travel time for the entire road network to reach equilibrium without information guidance. When the inertial traveler transfer rate reaches 0.7 or above, the overall expected travel time of the entire system reaches its peak and basically stops changing significantly.

[0132] Through this experiment, we can obtain Figure 3 As shown in the road network topology, the quantitative functional relationship between the total expected travel time and the transfer rate η of inertial travelers is as follows: Figure 4 As shown in Figure 2. Combining the curve changes of the image under different η values, the fitting results can be directly obtained by importing the data into SPSS, as shown in formula (7), R 2 The value is 0.91, which can fit the curve well.

[0133] E(T all )=32.009η 4 -893.07η 3 +8261.7η 2 -25531η+153723 (7)

[0134] (5) Solving the traffic guidance model for multi-objective optimization during highway construction

[0135] The parameters obtained from the various parameter expressions in the specific implementation plan are substituted into the guidance model (8) and the genetic algorithm is applied to optimize the solution to obtain the specific guidance plan.

[0136]

[0137] Where, F 总 represents the travel cost in the equilibrium area; K n It represents the effective reduction coefficient of the congestion index of the road section n, and uses the rounding function to reduce F总 As 0-1 switch control, when K n ≥1 indicates that the road section is congested. n = 0, indicating that the road section is no longer congested; c is the congestion degree of the construction area section; ξ and Represents the decision maker's expected value, representing the critical value of node flow without congestion and the critical value of total expected travel time under no information guidance. In order to eliminate the congestion of the highway expansion construction section in the congested area, the congestion degree of the section must be less than 0.75, that is, the critical value is 0.75 times the maximum traffic capacity, so according to Figure 3 From the function fitting graph, we can see that: ξ=0.3.

[0138] The road network is composed of the Huai'an section of the G2 Beijing-Shanghai Expressway and the surrounding national and provincial highways (such as Figure 4 As shown in the figure, the road network of the simulation experiment is taken as the experimental road network. The proportion of inertial travelers in the road network is 0.6, the proportion of non-inertial travelers is 0.4, the guidance compliance rate of non-inertial travelers of intelligent connected vehicles is set to 0.9, and the guidance compliance rate of ordinary non-inertial driving travelers is 0.8. In order to make E(T all ) is less than or equal to the total expected travel time of the road network without information guidance, according to Figure 3 From the function fitting graph, we can see that 0≤η≤0.3.

[0139] Obtained through simulation experiments Figure 5 The figure shows a comparison of average travel time in the regional road network before and after guidance. This figure demonstrates that, under mixed connectivity, for the same traffic demand conditions, average travel time decreases as the CAV (Connected Vehicle) penetration rate increases. When the CAV penetration rate reaches a certain level, the difference in average travel time before and after guidance increases and then gradually decreases until it approaches equality. This is due to the superior following characteristics of CAVs, which stabilize traffic flow and allow platooning to occur, significantly reducing the likelihood of severe congestion on the road network. Therefore, as the CAV penetration rate increases, the probability of congestion in construction zones decreases significantly.

[0140] Through solving, the results of the shortest path calculated by the model are reasonable and can provide drivers with reasonable path selection suggestions during actual driving.

[0141] Obtained through simulation experiments Figure 6 The comparison chart of average travel time of road network under different η is shown in the figure. Figure 6Figure 3 shows the impact of changes in the transfer rate η of different inertial travelers on the average travel time of the road network under different CAV occupancy rates during the guidance process. When η = 0.1 and 0.15, the average travel time of the road network gradually decreases with the increase of the CAV occupancy rate, approximately showing a linear decline. When η = 0.2, 0.25, and 0.3, the average travel time of the road network first increases with the increase of the CAV occupancy rate.

[0142] (6) Comprehensive evaluation of the proposed guidance method

[0143] Assuming that the proportion of intelligent connected vehicles is 40%, and other conditions remain unchanged, the impact of the guidance method proposed in the patent on the traffic operation status of the experimental road network is evaluated by comparing the no guidance method and the all-or-no guidance method. Figure 5 According to Table 8, compared with the non-guidance method, the total vehicle travel time of the all-or-no guidance method and the proposed guidance method is reduced by about 6.3% and 18.5%, respectively.

[0144] Table 8 Comprehensive evaluation indicators

[0145]

[0146] The present invention has the beneficial effect of shortening vehicle travel times in hybrid connected vehicle environments. As the penetration rate of connected vehicles increases, travel times also show a trend of gradual reduction. The difference between travel times after guidance and before guidance increases and then decreases with increasing penetration. As connected vehicle penetration increases, information exchange between vehicles becomes smoother. The optimized guidance method in a hybrid connected vehicle environment can effectively alleviate traffic congestion in highway expansion and construction zones.

[0147] The above examples are intended to illustrate the technical concepts and features of the present invention. Their purpose is to enable those skilled in the art to understand the contents of the present invention and implement them accordingly. They are not intended to limit the scope of protection of the present invention. Any equivalent changes or modifications made in accordance with the spirit of the present invention are intended to be covered by the scope of protection of the present invention.

Claims

1. A method for guiding the path of a highway reconstruction and expansion construction area in a hybrid network environment, characterized in that: The method comprises the following steps: S1. Calculate the time impedance under different penetration rates of connected vehicles in the highway reconstruction and expansion; S2. Calculate the road cost impedance under different connected vehicle penetration rates; S3. Combine time impedance and cost impedance to determine comprehensive impedance in hybrid network environment; S4. Calculate the overall travel time of the road network under the guidance of traffic information based on the route selection of the inertial and non-inertial drivers after receiving guidance; S5. Convert the two objectives of congested area flow and overall road network travel time into constraints, and solve the route guidance plan during the highway construction period with the goal of minimizing the travel cost calculated based on the comprehensive impedance.

2. The method for guiding the path of a highway reconstruction and expansion construction area in a hybrid network environment according to claim 1 is characterized in that: Said S1 comprises the following steps: S11. Conduct hybrid connected vehicle simulation experiments based on traffic characteristics of the expanded and renovated expressways; S12. According to the penetration characteristics of connected vehicles, the time impedance model is improved based on the Davidson function; S13. Conduct simulation experiments with different connected vehicle occupancy rates and, combined with road saturation, obtain the time impedance under different connected vehicle penetration rates.

3. The method for guiding the path of a highway reconstruction and expansion construction area in a hybrid network environment according to claim 2 is characterized in that: The basic assumptions of S11 are as follows: in a connected environment, non-connected vehicles use the IDM following model, and connected vehicles use the CACC following model; due to the restrictions and speed limits on expressways during reconstruction and expansion, their road grades are degraded to national and provincial trunk road grades; the impact of intersection delays is ignored when calculating impedance; in a mixed connected environment, freight vehicles are all assumed to be non-connected vehicles, and each vehicle type is converted into a standard passenger car using the vehicle conversion coefficient.

4. The method for guiding the path of a highway reconstruction and expansion construction area in a hybrid network environment according to claim 2 is characterized in that: The improved time impedance model of S12 is as follows: Based on the Davidson function, the relationship between time impedance, the proportion of intelligent connected vehicles, and road network traffic flow is studied, and the time impedance is obtained as: t n =t n0 [1+J·f n (p,μ)] (1) Where, t n0 is the unimproved road impedance; f n (p,μ) is the fitting function of p and μ; p is the proportion of connected vehicles; μ is the saturation; Q n is the road flow; C n is the single lane capacity; J is the preset coefficient.

5. The method for guiding the path of a highway reconstruction and expansion construction area in a hybrid network environment according to claim 4 is characterized in that: The specific steps of S2 are: Simplifying the cost of driving on the highway into fuel costs and road tolls, the cost impedance expression is: E n =(K n +M n )L n +u (2) Where K n Indicates the cost per unit mileage; M n Indicates the road section charging rate, unit: yuan / km. If it is a non-toll road, its value is 0; L n It represents the length of road section n; u represents the toll collection value of ordinary road bridge or open highway, and is 0 if there is no toll.

6. The method for guiding the path of a highway reconstruction and expansion construction area in a hybrid network environment according to claim 5 is characterized in that: The specific steps of S3 are: S31. Convert the cost impedance into time impedance using a certain coefficient. The conversion is as follows: T n =γE n (3) Among them, T n represents the time impedance on section n; E n represents the cost impedance on road section n; γ represents the time / cost conversion coefficient; Taking the travel time of the road section as an influencing factor and taking the ratio of passenger and freight traffic into consideration, the calculation method of formula γ is obtained: γ=γ1×a1+γ2×a2 (4) Where a1 and a2 represent the traffic ratios of passenger cars and trucks, respectively; γ1 and γ2 represent the unit cost of passenger cars and trucks equivalent to time, respectively, in min / yuan; S32. Based on the actual road conditions during the traveler's route selection process, taking into account the fuel costs and highway tolls incurred during vehicle travel, the time impedance and the cost impedance are integrated to construct a comprehensive road impedance; F n =k1T n +k2E n (5) Among them, E n represents the impedance value of road section n; k1 and k2 represent the weight coefficients of time and cost for the comprehensive impedance of the road.

7. The method for guiding the path of a highway reconstruction and expansion construction area in a hybrid network environment according to claim 6 is characterized in that: The specific steps of S4 are: S41. Define the groups of travelers affected by traffic guidance information; Inertial travelers refer to those who, regardless of the information guidance process, believe that the travel time of the original route is less than or equal to other routes they can perceive and do not change their original route selection; non-inertial travelers refer to those who, during the information guidance process, choose other guided routes with expected travel time less than or equal to the original route and continuously make judgments and choices during the guidance process; S42, road network user equilibrium model guided by traffic information; Among them, E(T a ) represents the travel time of road segment a; represents the free flow time of section a; c a represents the capacity of road section a; T′ represents the reference formula for simplifying the model; x a and They represent the expectation and variance of the travel time of the road segment respectively; The road network equilibrium model is shown in formula (7): Where, represents the expected travel time of g people in network w under the selected path r, g is an indicator variable, and its value is only o or z; z represents inertial travelers, o represents non-inertial travelers, Z is the set of all inertial travelers, and O is the set of all non-inertial travelers; where, is the association matrix between segments and paths. When path r includes segment a, is equal to 1, otherwise it is 0; η represents the transfer rate of non-inertial travelers guided by traffic information; ε g1 represents the perceptual bias, ε g2 represents the prediction bias; E(ε g1 ) represents the perceptual expectation deviation, E(ε g2 ) represents the expected deviation of the forecast; μ z With μ o represent the share of habitual travelers and non-habitual travelers respectively; represents the traffic flow of inertial travelers in network w, represents the traffic flow of non-inertial travelers in network w; Among them, f represents the overall travel demand of the road network, represents the traffic flow of the inertial travelers in the network q under the selected path r; The traffic flow of non-inertial travelers in network w under the selected path r; represents the actual travel time of the inertial travelers in network w under the selected path r, represents the actual travel time of a non-inertial traveler in network w under the selected path r, represents the actual travel time of a non-inertial traveler in network w under the selected path k; represents the expected travel time of an inertial traveler in network w under the selected path r; represents the perceived expected travel time of non-inertial travelers in network w after the inertial travelers choose path r, represents the expected travel time of a non-inertial traveler in network w under the selected k paths; is the coefficient to be calibrated; S43. Conduct a mixed travel information guidance simulation experiment to obtain the overall travel time of the road network under the guidance of traffic information, and obtain the relationship between the overall travel time of the road network and the transfer rate of non-inertial travelers under the guidance of traffic information.

8. The method for guiding the path in a highway reconstruction and expansion construction area under a hybrid network environment according to claim 7 is characterized in that: The specific steps of S5 are: S51. Taking into account the characteristics of the expressway reconstruction and expansion construction area, the traffic congestion control area is divided into a congestion zone and a balance zone; Based on the traffic characteristics of the reconstruction and expansion construction area, the traffic congestion control area is divided into a congestion zone and a balance zone. The congestion zone is the collection of road sections where the road traffic operation status is saturated, and the balance zone is the collection of traffic-carrying sections and interchanges upstream and adjacent to the boundary sections of the congestion zone. S52. Using a multi-objective optimization model, establish a flow minimization model for the congested area, a traveler cost minimization model for the balanced area, and a road network travel time minimization model; The congestion area flow minimization model is established as follows; Control the inflow of congested areas and minimize it for path guidance. Set the decision variable of the guidance model as the diversion rate of the interconnected nodes in the congested area. Constrain the decision variable by the transfer rate and occupancy ratio of inertial travelers. The guidance model expression for the first-level goal of minimizing the flow in the congested area is established as follows: b n in (t)=(μ1η+μ2)x n Where f1 represents the total traffic volume in congestion area A during the control period, t0 and t1 represent the start and end time of a certain time period; represents the node traffic volume entering congestion area A at node n; Q n in (t) represents the time function of the road flow; β n in (t) is the actual diversion ratio of node n on the upstream path at time t; μ1 represents the proportion of inertial drivers in the model, μ2 represents the proportion of non-inertial drivers in the model, η represents the transfer rate of inertial travelers in the model; p represents the penetration rate of intelligent connected vehicles; x n represents the compliance rate of intelligent connected vehicles and non-intelligent connected vehicles to the guidance information at node n in the model; The model for minimizing the cost of travelers in the equilibrium area is established as follows; With the goal of optimizing the traveler cost in the balance zone, the impact of guidance on the surrounding road network will be reduced to a minimum, that is, the comprehensive travel cost of vehicles on each road section in the balance zone B will be minimized; Among them, i and j represent the vertices of the road segment in the road network topology diagram, which represent the hub interchange diversion points and the ground interchange diversion points in the actual road network; represents the real-time road impedance of the section (i, j) in the balance area B at time t; and Respectively represent the time cost impedance and expense cost impedance of the road section (i, j) at time t; k1 and k2 are related model parameters; C i,j is the control parameter; S and D represent the starting point and the end point respectively; R(S,D) represents the set of paths between the starting points; The model for minimizing the overall expected travel time of the road network is established as follows; st0≤η≤0.6 Where f3 represents the overall expected travel time of the road network in the congestion area A and the balance area B during the control period; E(T all ) represents the overall expected travel time of the road network under theoretical calculation; η represents the transfer rate of inertial travelers in the model; S53, converting the multi-objective model into a single-objective model; Taking the comprehensive impedance function as the optimization objective, the guiding function is obtained by combining the minimum inflow formula in the congested area and the minimum traveler cost formula in the balanced area: Where, F 总 represents the travel cost in the equilibrium area; K n It represents the effective reduction coefficient of the congestion index of the road section n, and uses the rounding function to reduce F 总 As 0-1 switch control, when K n ≥1 indicates that the road section is congested. n = 0, indicating that the road section is no longer congested; c is the congestion degree of the construction area section; ξ and Represent the decision maker's expected value, which respectively represents the critical value of node flow without congestion and the critical value of total expected travel time under no information guidance.

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