A full-life-cycle expressway main line statistical buffer point design method

Abstract

This invention discloses a method for designing statistical slow points on highway mainlines throughout their entire lifecycle. The method includes acquiring actual data on statistical slow points, establishing a decision-making model for lane selection behavior at statistical slow points, acquiring selection behavior data at statistical slow points, and establishing a simulation model. The method compares the output of the simulation model with the actual data on statistical slow points to verify the model's authenticity and effectiveness. Based on the simulation model, a vehicle delay model and a vehicle fuel consumption model are obtained, and then the vehicle delay cost, energy cost, and environmental cost are obtained respectively. The method designs statistical slow points throughout their entire lifecycle and obtains the total cost of constructing statistical slow points and their plazas. Finally, a layout optimization model for statistical slow points is obtained, and the actual data on the actual route is used to obtain the optimization results. These results are then compared with existing statistical slow points to determine which layout optimization model is superior.

Description

Technical Field

[0001] This invention relates to the field of highway management technology, and in particular to a method for designing statistical slow points on the main line of a highway throughout its entire life cycle. Background Technology

[0002] Highways are vital infrastructure for promoting national economic and social development, playing an irreplaceable role in improving transportation efficiency and reducing traffic accidents. However, toll stations have become a major bottleneck restricting highway capacity and service levels.

[0003] However, with the removal of provincial border toll stations, the arrival frequency of upstream vehicles has increased, and congestion has gradually shifted to the mainline toll stations. Therefore, the mainline toll stations have become the main bottleneck restricting the efficiency of highway transportation, and the congestion problem urgently needs to be addressed. In particular, the wide, horizontal layout of toll stations results in large land occupation, uneven service across toll booths, and uneven vehicle trajectories, leading to severe congestion in toll plazas and toll lanes during periods of high traffic volume. ETC vehicles only need to slow down when passing through toll stations without stopping to pay, and they can pass through at relatively high speeds. The rapid popularization of ETC has led to difficulties for vehicles in the exit merging area to merge into the main road during periods of high traffic volume, causing congestion. Therefore, the future congestion point is likely to shift from the toll station entrance to the exit merging area.

[0004] Scholars both at home and abroad have conducted research on toll stations, which can be mainly divided into four aspects: toll station layout, lane layout, alignment indicators, and layout optimization.

[0005] Regarding toll station layout, the "Research on Optimization and Configuration of Toll Collection Methods on the Beijing-Hong Kong-Macau Expressway in Henan Province" summarizes the classification methods of expressway toll stations; the "Research on the Location of Mainline Toll Stations and Mainline Alignment Indicators" mainly studies the specific locations where mainline toll stations should be set up and explains the workflow during the setup; the "Demonstration and Analysis of Toll Station Setup Scheme for Provincial Highway 519 Wutong Daquan to Shaquanzi Expressway" analyzes multiple toll station layout schemes using a certain expressway project as background; and the "Discrete-event modelling of the capacity of the toll collection exit point and the formation of congestion" uses AnyLogic software to establish discrete event simulation models of toll plazas at toll road exits under low and high traffic flow conditions to demonstrate the methodology of transportation micro-modeling, and studies and solves the problem of evaluating the capacity of toll plazas and traffic congestion during the operation of toll roads.

[0006] Regarding lane layout, the study "Research on ETC Lane Configuration Strategy for Highway Toll Stations" investigated ETC lanes based on a queuing theory model, obtaining the optimal number of ETC lanes considering traffic flow. The study "Exploration of Calculation Methods for the Number of Toll Lanes" referenced the traffic capacity of each lane and calculated and adjusted the number of lanes that should be set up at toll stations based on other specific needs. The study "Evaluation of toll plaza performance after addition of express toll lanes at mainline toll plaza" researched and designed toll station lane layout, adding fast lanes during peak hours to improve the user experience for ETC vehicles. The study "Optimization of Opening Scheme of ETC / MTC Toll Lane Based on Cost and Benefit Analysis" aimed to minimize the cost and maximize the benefits of toll stations under normal operating conditions. Within the scope of seeking a balance between cost and benefit, a multi-objective nonlinear optimization model was established to optimize the toll lane opening scheme.

[0007] Regarding linear indicators, the study "Research on Key Parameters for Mainline Toll Station Design Based on Congestion Relief" proposes an improved and optimized single-entry, double-exit dual-toll station model based on the advantages and disadvantages of existing highway toll stations, and determines technical indicators such as linear parameters and signage design. The study "Design and Capacity and Cost Estimation of High-Safety Highway Toll Plazas with 'Four Lanes-Seven Channels'" redistributes the linear indicators of toll lanes based on lane classification and asymmetric fan-shaped progressive diversion, designs highway toll plazas according to a "four lanes-seven channels" layout, and analyzes their safety. The study "Traffic micro-simulation model for design and operational analysis of barrier toll stations" uses a simulation model to analyze scenarios with different traffic volumes, toll station capacity, driver types, and toll station configurations, finding that the number of toll lanes and toll methods significantly affect the average delay and maximum queue length of toll stations, and finally proposes recommendations regarding the number of toll booths. The study "Modeling traffic operations at electronic tollcollection and traffic management systems" comprehensively considers factors such as lane configuration, the proportion of vehicles using different payment methods, and the number of ETC lanes, and establishes a toll station capacity calculation model.

[0008] In terms of layout optimization, the paper "Discussion on the Advantages and Disadvantages of Highway Toll Station Reconstruction and Expansion" established an optimized ETC lane layout method for the rapid development of ETC in highway networks; "Optimized Layout Method of Non-stop Toll Collection Lanes in Highway Networks" comprehensively considered various situations and listed four different forms of toll station reconstruction and expansion; "Design of Expressway Toll Station Based on Neural Network and Traffic Flow" constructed a fuzzy-BP neural network model with capacity, cost, and safety factor as input layers and performance as output layer based on the traffic flow velocity-density flow model, and studied the design problem of highway toll stations based on neural networks and traffic flow; "A novel methodology for enhancing the capacity of toll plaza under mixed traffic condition" analyzed various performance aspects of toll stations, clarified the impact of parameters such as vehicle type and traffic volume on toll stations, redesigned the toll plaza based on this, and calculated the optimized queuing and delay, finding that the results were significantly reduced and the toll station's throughput capacity was significantly improved. The study on the layout of toll stations at the beginning and end of expressways established a toll station layout design model that comprehensively considers construction costs, operating costs and delay costs, and provided an optimization scheme for the layout of toll station lanes. However, the model failed to take the gradient rate into account and simply treated it as a constant value; at the same time, it did not take energy and environmental costs into account, which has certain limitations.

[0009] Based on the above, this invention improves and optimizes the design method of a full life-cycle toll plaza, which is more in line with the laws of reality. Summary of the Invention

[0010] The purpose of this invention is to provide a method for designing statistical slow points on the main line of a highway throughout its entire life cycle. It establishes an optimization model for the layout of statistical slow point plazas throughout the entire life cycle with the goal of minimizing total cost, thereby reducing the load on the statistical slow points, improving their throughput efficiency, enhancing the travel experience for the public, and saving construction costs.

[0011] To achieve the above objectives, this invention provides a method for designing statistical slowdown points on the mainline of a highway throughout its entire lifecycle, comprising the following steps:

[0012] Obtain actual data on traffic bottlenecks in the route statistics;

[0013] A decision-making model for lane selection behavior at the statistical slow points of the route is established based on the actual data of the statistical slow points of the route.

[0014] Based on the route statistical slowdown island lane selection behavior decision model, obtain route statistical slowdown selection behavior data;

[0015] A simulation model is established based on the actual data of the route's statistical slow points, the decision-making model for lane selection behavior at the route's statistical slow points, and the selection behavior data of the route's statistical slow points.

[0016] The simulation model's output is compared with the actual data of the line's statistical slow points to verify the model's authenticity and effectiveness.

[0017] Based on the verification of the effectiveness of the simulation model, a vehicle delay model and a vehicle fuel consumption model are obtained according to the simulation model.

[0018] Based on the vehicle delay model and the vehicle fuel consumption model, the vehicle delay cost and energy and environmental costs are obtained respectively.

[0019] Based on the aforementioned delay costs and energy and environmental costs, a full life-cycle route statistical slowdown point is designed, and the total cost of constructing the route statistical slowdown point and the route statistical slowdown point plaza is obtained.

[0020] Based on the total cost of constructing route statistical slow points and route statistical slow point plazas, obtain a route statistical slow point layout optimization model;

[0021] By substituting actual data of the statistical slow points of the lines into the optimization model for the layout of the line statistical slow points, the optimization results were obtained and compared with the existing statistical slow points of the lines. It was determined that the optimization model for the layout of the statistical slow points of the lines was better.

[0022] Furthermore, the actual data on the statistical slow points of the route includes: the structural data of the statistical slow points of the route, traffic flow data, vehicle trajectory data, lane information data, and lane service data;

[0023] The statistical slow point structure data of the route includes the number of lanes at the statistical slow point, the number of highway lanes, the length and width of the transition section, the width of the statistical slow point, and the area of ​​the statistical slow point.

[0024] The traffic flow data includes traffic volume, vehicle type, vehicle toll type, vehicle performance data, and driver behavior data.

[0025] The vehicle trajectory data includes the distance between the target lane and the central divider, the number of lanes required to reach the target lane, and the trajectory the vehicle travels during its operation.

[0026] The lane information data includes the target lane location and the target lane toll type;

[0027] The lane service data includes the number of queues in each lane, the queuing time in each lane, the service time in each lane, the lane capacity, the average pre-station delay time in each lane, and the average post-station delay time in each lane.

[0028] The vehicle type, the vehicle toll type, the number of queues in the target lane, the distance between the target lane and the central divider, and the number of vehicles heading towards the target lane are selected as feature vectors to establish a route statistical slow-point island lane selection behavior decision model.

[0029] The decision-making model for lane selection behavior at the statistical slow-down islands of the route is observed, and the lane selection behavior at the statistical slow-down islands of the route is summarized. The decision-making model for lane selection behavior at the statistical slow-down islands of the route includes a decision tree model for the entrance of the statistical slow-down islands of the route and a decision tree model for the exit of the statistical slow-down islands of the route.

[0030] Furthermore, the simulation model is a VISSIM simulation model. After the VISSIM simulation model outputs results, the box plot is compared with the actual data to obtain the confidence interval of the average delay time.

[0031] The actual data of the line statistical slow points were divided into three groups: low flow, high flow, and oversaturated flow for simulation. The independent delay time was tested for each group. If the data of the three groups were not significant, the authenticity and effectiveness of the simulation model were verified.

[0032] Based on the verification of the effectiveness of the simulation model, vehicle delay data and fuel consumption data are output according to the simulation model.

[0033] Based on vehicle delay data and fuel consumption data, the independent variables are determined according to the actual data of the slow points on the route.

[0034] The vehicle delay time and fuel consumption at the entrance and exit of the route are calculated by inputting independent variables using the symbolic regression method.

[0035] After data fitting and evaluation based on the goodness of fit, mean absolute error, and complexity of the output function, vehicle delay models and vehicle fuel consumption models for the entry and exit points of the route statistical slow points are obtained.

[0036] Furthermore, based on the actual data of the statistical slowdown points on the route, independent variables are determined. Using the symbolic regression method, the vehicle delay time and fuel consumption at the entrance and exit of the statistical slowdown points on the route are calculated. Specifically,

[0037] The dependent variables are determined based on the actual traffic volume, number of lanes, gradient rate, vehicle type, and vehicle toll type in the route statistics. The dependent variables include traffic volume, ETC traffic volume, MTC traffic volume, number of lanes, number of ETC lanes, number of MTC lanes, gradient rate, proportion of ETC vehicles, and proportion of passenger vehicles.

[0038] The vehicle delay model fitting result at the statistical slowdown point entrance of the route is obtained by calculating the following formula using the symbolic regression method:

[0039]

[0040] Where t1 is the vehicle delay time at the entrance of the route's statistical slow point, L is the gradient rate, and Q is the vehicle delay time at the entrance of the slow point. b Let Q represent the traffic volume of b MTC lanes. a Let α represent the traffic volume of 'a' ETC lanes, where 'a' is the number of ETC lanes, 'b' is the number of MTC lanes, and α is the proportion of ETC vehicles.

[0041] The vehicle delay model fitting result for the statistical slowdown exits of the route is obtained by calculating the following formula using the symbolic regression method:

[0042]

[0043] Where t2 is the vehicle delay time at the exit of the route's statistical slow point, L is the gradient rate, and Q is the vehicle delay time at the exit of the slow point. b Let Q represent the traffic volume of b MTC lanes. a This represents the traffic volume of 'a' ETC lanes, where 'a' is the number of ETC lanes and 'α' is the proportion of ETC vehicles.

[0044] The vehicle fuel consumption model fitting result at the route statistical slowdown point entrance is obtained by calculating the following formula using the symbolic regression method:

[0045] B1=0.26×[6.79+L+2.19N+0.00172Q-0.00718(Q a / a)-6.75α] (3)

[0046] Where B1 is fuel consumption, L is the gradient rate, N is the number of lanes, and Q is the number of lanes. a Let Q represent the traffic volume of a ETC lanes, where a is the number of ETC lanes and α is the proportion of ETC vehicles.

[0047] The vehicle fuel consumption model fitting result at the exit of the route statistical slow point is obtained by calculating the following formula using the symbolic regression method:

[0048] B2 = 0.26 × (0.00911Q) b +0.00286Q +0.00111Q a L-2.53) (4)

[0049] Where B2 is fuel consumption, Q b Let Q represent the traffic volume of b MTC lanes. a Let L represent the traffic volume of lane a in ETC lanes, and L be the rate of change.

[0050] Furthermore, based on the total cost of constructing route statistical slow points and route statistical slow point plazas, an optimization model for the layout of route statistical slow points is obtained, specifically as follows:

[0051] The total cost of constructing route statistical deceleration points and route statistical deceleration point plazas according to the entire life cycle includes five parts: construction cost, operating cost, delay cost, energy and environmental cost, and demolition cost;

[0052] Based on construction costs, operating costs, delay costs, energy and environmental costs, and demolition costs, corresponding cost models in daily units are obtained;

[0053] Based on a cost model that includes construction costs, operating costs, delay costs, energy and environmental costs, and demolition costs, the system inputs input variables and obtains output variables based on the statistical slowdown data of the construction lines, determines variable parameters, and establishes an optimization model for the layout of statistical slowdown points based on the actual situation of the construction lines.

[0054] Furthermore, based on construction costs, operating costs, delay costs, energy and environmental costs, and demolition costs, corresponding daily cost models are obtained, specifically as follows:

[0055] Based on the factors affecting construction costs, the costs are divided into five parts: construction and installation engineering costs, land use and demolition compensation costs, other construction costs, contingency costs, and construction period loan interest. Among these factors, the factors affecting construction costs include toll lanes, the number of toll lanes, and the area of ​​traffic slowdown points in the route statistics.

[0056] The construction cost is determined based on five components of the cost that affect the construction cost. Then, combined with the capital recovery coefficient, a construction cost model on a daily basis is obtained.

[0057] The construction cost model in daily units is determined by the following formula:

[0058]

[0059] Among them, C 建 A1 represents construction and installation costs, A2 represents land use and demolition compensation costs, A3 represents other construction costs, A4 represents contingency funds, A5 represents construction period loan interest, i represents the rate of return on investment, and n represents the investment discount period.

[0060] A1 represents construction and installation engineering costs, which include direct costs, equipment purchase costs, measures costs, enterprise management fees, regulatory fees, profits, and taxes.

[0061] Furthermore, based on construction costs, operating costs, delay costs, energy and environmental costs, and demolition costs, corresponding daily cost models are obtained, specifically as follows:

[0062] Based on the factors affecting operating costs, the costs are divided into three parts: labor costs, facility operation costs, and maintenance and repair costs. Based on these three parts, an operating cost model is determined on a daily basis.

[0063] The daily operating cost model obtained by the following formula is as follows:

[0064] C 运 =(B1+B2+B3) / 30 (6)

[0065] Among them, C 运 The figures represent operating costs, with B1 representing labor costs, B2 representing facility operation costs, and B3 representing maintenance and repair costs.

[0066] Furthermore, based on construction costs, operating costs, delay costs, energy and environmental costs, and demolition costs, corresponding daily cost models are obtained, specifically as follows:

[0067] Factors affecting delay costs include vehicle delay time, average time value per person, and average passenger capacity per vehicle;

[0068] Factors affecting delay costs: The average passenger capacity of buses and freight trucks is set as P. 客 P 货 The average traffic volume for passenger cars and freight cars is set as follows: The average time value per person is set as V. t Time utilization rate is set as γ; delay time is set as t, and t includes vehicle delay time t1 at the entrance of the line statistical slow point and vehicle delay time t2 at the exit of the line statistical slow point.

[0069] The delay cost model in daily units is obtained by the following formula:

[0070]

[0071] Among them, C 延 P represents the cost of delay. 客 This indicates the average passenger capacity of a bus. P represents the average passenger vehicle traffic volume. 货 This indicates the average passenger capacity of a truck. Average truck traffic volume, V t Let γ represent the average time value per person, γ be the time utilization rate, and t be the delay time.

[0072] Furthermore, based on construction costs, operating costs, delay costs, energy and environmental costs, and demolition costs, corresponding daily cost models are obtained, specifically as follows:

[0073] Based on the factors affecting energy and environmental costs, the costs are divided into two parts: non-operational and operational phases. An operational cost model is then determined based on these two parts of costs, using the non-operational and operational phases as the basis.

[0074] Non-operation phase costs include fuel consumption costs and carbon emission costs, while operation phase costs include carbon emission costs during the construction and demolition phases and carbon emission costs during the building material production and transportation phases.

[0075] The energy and environmental cost model in daily units, obtained by the following formula, is as follows:

[0076]

[0077] Among them, C 能 The values ​​represent energy and environmental costs, with D1 representing non-operational costs and D2 representing operational costs.

[0078] Furthermore, based on construction costs, operating costs, delay costs, energy and environmental costs, and demolition costs, corresponding daily cost models are obtained, specifically as follows:

[0079] Based on the factors affecting demolition costs, the costs are divided into three parts: labor costs, material costs, and machinery usage costs. A demolition cost model based on these three parts is then determined on a daily basis.

[0080] The demolition cost model in daily units, obtained by the following formula, is as follows:

[0081] C 拆 = (E1+E2+E3)×1 / 365 (9)

[0082] Among them, C 拆 E1 represents the demolition cost, E2 represents the material cost, and E3 represents the machinery usage cost.

[0083] Furthermore, based on the total cost of constructing route statistical slow points and route statistical slow point plazas, an optimization model for the layout of route statistical slow points is obtained, specifically as follows:

[0084] To summarize all the above formulas, the variables are divided into two categories: one is parameters with real-world significance, which can be directly assigned or calculated; the other is environment variables, which need to be set according to different situations. These variables are the input variables.

[0085] The final decision variable obtained from the model output is the number of MTC lanes, n. M Number of ETC lanes n E And the gradient rate L;

[0086] Based on the actual situation of the statistical slow points of the construction route, the cost model design constraints are used to determine the layout optimization model of the statistical slow points of the route. Among them, the main relevant parameters in the model have been determined, but some parameters change with different years and regions. These parameters need to be determined according to the actual situation of the statistical slow points of the construction route.

[0087] Let the objective function be f, with the goal of minimizing the total cost. The total cost is C1, which is the sum of construction costs, operating costs, delay costs, and energy and environmental costs.

[0088] The design constraints for this model include:

[0089] Both the number of ETC lanes and the number of MTC lanes are integers that are greater than zero;

[0090] To ensure that vehicles can pass through normally, the route statistics for slow points must ensure that there is one ETC lane and one MTC lane.

[0091] To ensure reasonable construction costs, the total number of lanes N is designed based on the maximum daily hourly traffic volume, according to regulations. max And use it as the upper limit;

[0092] According to the specifications, the gradient rate of the transition points and plazas in the route statistics should be 1 / 6 to 1 / 7.

[0093] In order to ensure that the efficiency of traffic flow at slow points on the route meets the demand and to maximize the proportion of ETC lanes, the ratio of ETC lanes to MTC lanes is defined as [γ1, γ2] based on actual traffic volume and capacity.

[0094] To ensure service levels during peak traffic periods, a standard should be established for the average service level of these peak periods throughout the day. Research indicates that when traffic volume... With traffic capacity C 总 When the ratio is less than or equal to 0.75, the service level of the statistical slow points of the line is relatively good and can maintain basic smoothness;

[0095] Based on the aforementioned constraints, a statistical slowdown point layout optimization model for the route is calculated.

[0096] The optimization model for the layout of slow points in the line statistics is substituted with actual data of slow points in the field. The optimization results are obtained and compared with the existing slow points in the line statistics. If the total cost of the optimization model for the layout of slow points in the line statistics is less than the total cost of the existing slow points in the line statistics, then the optimization model for the layout of slow points in the line statistics is determined to be better.

[0097] Furthermore, based on the aforementioned constraints, a route statistical slowdown point layout optimization model is calculated, specifically as follows:

[0098] The route statistical slow point layout optimization model obtained by the following formula is:

[0099]

[0100] Where f is the objective function with the goal of minimizing the total cost, min C1 represents the minimum total cost, C1 represents the total cost, and C 建 C represents the construction cost. 运 C represents operating costs. 延 C represents the cost of delay. 能 C represents energy and environmental costs. 拆 The following represents the demolition cost: A1 represents construction and installation costs; A2 represents land use and demolition compensation costs; A3 represents other construction costs; A4 represents contingency funds; A5 represents construction period loan interest; i represents the rate of return on investment; n represents the discount period of the investment; B1 represents labor costs; B2 represents facility operation costs; B3 represents maintenance and repair costs; D1 represents non-operational phase costs; D2 represents operational phase costs; E1 represents labor costs; E2 represents material costs; E3 represents machinery usage costs; n M Indicates the number of MTC lanes, n E N represents the number of ETC lanes. max This represents the total number of lanes designed based on the maximum hourly traffic volume, where L is the gradient rate, γ1 and γ2 represent the ratio range of ETC lanes and MTC lanes, Q is the traffic volume, and C... 总 Indicates traffic capacity.

[0101] The technical effects and advantages of this invention are as follows:

[0102] 1. Taking into account construction costs, operating costs, delay costs, energy and environmental costs, as well as demolition costs, a full life-cycle toll plaza layout optimization model was established with the goal of minimizing total costs. This reduces the load on slow points in the route statistics, improves its throughput efficiency, and enhances the travel experience for the public.

[0103] 2. By using the gradient rate and the number of toll lanes as decision variables, the problem of excessively high gradient rates in the common slow points of the route statistics is solved. This allows vehicles to maintain a reasonable following distance from the vehicles in front and behind when entering and leaving the slow points of the route statistics. At the same time, it improves the congestion situation in the downstream gradient section of the slow point plaza after the promotion of ETC technology.

[0104] 3. The design of route statistical slow points and route statistical slow point plazas takes into account energy and environmental costs, saving energy and avoiding excessive carbon emissions and pollution.

[0105] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures pointed out in the description, claims and drawings. Attached Figure Description

[0106] Figure 1 This is a flowchart of a method for designing statistical slow points on a highway mainline throughout its entire lifecycle, as described in an embodiment of the present invention.

[0107] Figure 2 This is a decision tree model for selecting the entrance lane of the toll island in this embodiment of the invention;

[0108] Figure 3 This is a decision tree model for selecting the exit lane of the toll island in this embodiment of the invention;

[0109] Figure 4 This is the fitting result of the vehicle delay model at the toll station entrance in this embodiment of the invention;

[0110] Figure 5 This is the fitting result of the vehicle delay model at the toll station exit in this embodiment of the invention;

[0111] Figure 6 This is the fitting result of the vehicle fuel consumption model at the toll station entrance in this embodiment of the invention;

[0112] Figure 7 This is the fitting result of the vehicle fuel consumption model at the toll station exit in this embodiment of the invention;

[0113] Figure 8 This is a schematic diagram of the existing Xihongmen toll station layout in an embodiment of the present invention;

[0114] Figure 9 This is a schematic diagram of the optimized layout of the Xihongmen toll station in an embodiment of the present invention. Detailed Implementation

[0115] 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 embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0116] To address the shortcomings of existing technologies, this invention provides a method for designing statistical slowdown points on highway mainlines throughout their entire lifecycle. This embodiment uses a toll station as an example to illustrate the statistical slowdown points, but it is not limited to toll stations; any point where data is collected by reducing vehicle speed is included. The process steps are as follows: Figure 1 As shown, it includes:

[0117] Obtain actual data from toll stations during peak hours; specifically:

[0118] A data survey was conducted using the Xihongmen toll station as a case study to obtain actual data on toll stations during peak hours; such as Figure 8 The diagram shows the current layout of the Xihongmen toll station.

[0119] The actual data for toll stations during peak hours includes: toll station structure data, traffic flow data, vehicle trajectory data, lane information data, and lane service data.

[0120] The data includes: toll station structure data (number of toll station lanes, number of highway lanes, length and width of transition sections, width of toll station, and area of ​​toll station); traffic flow data (traffic volume, vehicle type (passenger / freight vehicle), vehicle toll type (ETC vehicle, MTC vehicle), vehicle performance data, and driver behavior data); lane trajectory data (distance between the target lane and the center divider, number of lanes required to reach the target lane, and the trajectory traveled by the vehicle during operation); lane information data (target lane location and target lane toll type (ETC vehicle, MTC vehicle); and lane service data (number of queues in the target lane, queue time in the target lane, toll collection time in the target lane (ETC lane, MTC lane), lane capacity (ETC lane, MTC lane), average pre-station delay time for each lane, and average post-station delay time for each lane).

[0121] A decision-making model for lane selection behavior on toll islands was established based on peak-hour toll station data; specifically:

[0122] Vehicle type, vehicle toll type, number of queues in the target lane, distance between the target lane and the central divider, and number of vehicles heading towards the target lane are selected from the actual data of toll stations during peak hours as feature vectors to establish a toll island lane selection behavior decision model.

[0123] The decision-making models for lane selection behavior on toll islands include decision tree models for lane selection at toll station entrances and exits, such as... Figure 2 , Figure 3 As shown: Vehicle types include passenger cars and freight cars, and toll collection types include ETC and MTC. Figure 2The model represents a decision tree model for lane selection at the toll island entrance, categorized into two types based on vehicle toll type: ETC and MTC. For ETC, the required number of lanes to reach the target lane is further divided into <1.5 and ≥1.5. When the required number of lanes is <1.5, it is further divided into ≥0.5 and <0.5 cases. Lanes with a required number of lanes <0.5 are designated as Lane 1, and the selection rate for Lane 1 is 13.60%.

[0124] When the number of lanes required to reach the destination is ≥0.5, it is further divided into two cases based on the number of queues in the target lane: <0.5 and ≥0.5. The proportion of people choosing lane 3 with a target lane queue of <0.5 is 16.26%, while the proportion of people choosing lane 2 with a target lane queue of ≥0.5 is 20.49%. When the number of lanes required is ≥1.5, it is divided into two cases based on vehicle type: passenger cars and freight cars. The proportion of passenger cars choosing lane 4 with a required number of lanes of ≥1.5 is 10.48%, while the proportion of freight cars choosing lane 5 with a required number of lanes of ≥1.5 is 5.77%.

[0125] MTC categorizes vehicles into two types based on the number of lanes required to reach the target lane: <4.5 and ≥4.5. When the required number of lanes is <4.5, it further categorizes vehicles into passenger cars and freight cars. Similarly, when the required number of lanes is ≥4.5, it also categorizes vehicles into passenger cars and freight cars. 14.42% of passenger cars chose lane 6 (requiring <4.5 lanes), while 7.11% of freight cars chose lane 8 (requiring ≥4.5 lanes). Lane 7, chosen by both passenger cars and freight cars (requiring ≥4.5 lanes), accounted for 11.87% of all passenger cars.

[0126] Figure 3 The decision tree model for lane selection at the toll island exit is shown. Based on the number of lanes required to reach the target lane, there are two cases: <9.5 and ≥9.5. The number of lanes required for <9.5 is further divided into ≥1.5 and <1.5. The number of lanes required for <1.5 is further divided into ≥0.5 and <0.5. The proportion of lane 1 (requiring <0.5 lanes) is selected. The number of lanes required for ≥0.5 is divided into ≥0.5 and <0.5 based on the queue length of the target lane. The proportion of lane 2 (requiring ≥0.5 lanes) is selected is 19.78%, and the proportion of lane 3 (requiring <0.5 lanes) is selected is 14.99%.

[0127] The required number of lanes greater than or equal to 1.5 is divided into two cases based on the toll type: ETC and MTC. ETC is further divided into two cases based on the number of lanes required to reach the target lane: ≥3.5 and <3.5. The required number of lanes less than 3.5 is further divided into two cases: ≥2.5 and <2.5. The proportion of people choosing lane 4 (requiring ≥2.5 lanes) is 9.97%. The required number of lanes less than 2.5 is further divided into two cases based on the queue length of the target lane: ≥0.5 and <0.5. The proportion of people choosing lane 5 (with a queue length ≥0.5) is 7.06%, and the proportion of people choosing lane 6 (with a queue length <0.5) is 4.66%. The required number of lanes greater than or equal to 3.5 is further divided into two cases based on the queue length of the target lane: ≥0.5 and <0.5. The proportion of people choosing lane 7 (with a queue length ≥0.5) is 2.83%, and the proportion of people choosing lane 8 (with a queue length <0.5) is 2.44%.

[0128] MTC is further divided into two categories based on vehicle type: passenger cars and freight cars. Passenger cars selected lane 9 (requiring ≥1.5 lanes and designated as MTC) at a rate of 5.18%, while freight cars selected lane 10 (requiring ≥1.5 lanes and designated as MTC) at a rate of 4.50%.

[0129] The required number of lanes ≥9.5 is further divided into two categories based on vehicle type: passenger cars and freight cars. For passenger cars, the proportion of those choosing lane 11 with a required number of lanes ≥9.5 is 3.34%. For freight cars, the proportion of those choosing lane 12 with a required number of lanes >2.5 and those choosing lane 13 with a required number of lanes ≤2.5 is 2.67%.

[0130] Toll station selection behavior data is obtained based on a toll island lane selection behavior decision model; specifically:

[0131] By observing the decision tree models at the entrance and exit of the toll island, we can summarize the lane toll station selection behavior.

[0132] Based on the observed model, the lane selection behavior of toll islands was summarized, and the lane selection rules were found to be as follows:

[0133] Regardless of whether it is an ETC vehicle or an MTC vehicle, a passenger vehicle or a freight vehicle, priority is given to the toll lane that is closer to the central median, and only then is the selection based on the number of queues in the target lane and the number of lanes required to reach the target lane.

[0134] For ETC vehicles, since they don't need to stop to pay and can pass through toll booths more quickly, the number of lane changes is more important. Even if the queues for the nearest toll lane are longer, they will tend to choose the closer lane. Moreover, compared to trucks, passenger cars are more likely to choose lanes with longer queues but fewer lane changes.

[0135] For MTC (Manual Toll Collection) vehicles, the manual toll lanes are generally located on the outer edge. Since they already need to change lanes more frequently, the number of vehicles in the queue is the primary factor for MTC vehicles. Furthermore, because MTC requires stopping to pay, the toll collection time is longer, making them more inclined to choose lanes with shorter queues. Moreover, compared to passenger vehicles, trucks are more likely to choose lanes that require more lane changes but have shorter queues.

[0136] A VISSIM simulation model was established based on actual data from toll stations during peak hours, a decision-making model for lane selection behavior on toll islands, and toll station selection behavior data; specifically...

[0137] The lane selection probability of a vehicle is obtained by summarizing the vehicle's lane selection behavior decision model based on the vehicle's toll station selection behavior.

[0138] The VISSIM simulation model was calibrated by taking into account factors such as lane selection probability, number of highway lanes (specifically, number of expressway lanes), number of toll station lanes (including ETC lanes and MTC lanes), length and width of transition sections, width of toll stations, proportion of vehicle types, toll station service time (including ETC vehicles and MTC vehicles), driver behavior parameters (including expected speed and reaction time), and vehicle performance parameters (including maximum speed and acceleration). A total of 192 simulation scenarios were set for toll station entrances and 1040 for exits.

[0139] The driver behavior parameters and vehicle performance parameters are built into the VISSIM simulation model and need to be reconfigured according to the different toll station scenarios.

[0140] The output results of the simulation model were compared with actual data from toll stations during peak hours to verify the authenticity and effectiveness of the VISSIM simulation model; specifically...

[0141] After the VISSIM simulation model outputs the results, the box plot and the actual data are compared. It can be seen that the average delay time is within the 95% confidence interval.

[0142] Independent delay times before and after vehicles pass through toll stations were tested separately. The actual data were divided into three groups: low flow, high flow, and oversaturated flow. Simulations were performed for each of the three cases, and the simulation outputs were compared with the actual data. The average, variance, and p-value were compared, as shown in Table 1. It was found that the data differences under the three cases were not significant, thus verifying the authenticity and effectiveness of the VISSIM simulation model.

[0143] Table 1 Verification of Simulation Data and Measured Data

[0144]

[0145]

[0146] Based on the verification of the effectiveness of the simulation model, a vehicle delay model and a vehicle fuel consumption model are obtained according to the simulation model; specifically,

[0147] Based on the verification of the effectiveness of the simulation model, vehicle delay data and fuel consumption data are output according to the simulation model.

[0148] Based on vehicle delay data and fuel consumption data, a symbolic regression model based on genetic programming is used to obtain vehicle delay model and vehicle fuel consumption model;

[0149] Specifically, based on vehicle delay data and fuel consumption data, independent variables are determined according to the actual data of the toll station during peak hours; the vehicle delay time and fuel consumption at the toll station entrance and exit are calculated by inputting the independent variables using the symbolic regression method; after data fitting and evaluation based on the goodness of fit, mean absolute error, and complexity index of the output function, vehicle delay models and vehicle fuel consumption models at the toll station entrance and exit are obtained.

[0150] Specifically, the dependent variables are determined based on the actual traffic volume, number of lanes, gradient rate, vehicle type, and vehicle toll type data of the toll station during peak hours. The dependent variables include traffic volume, ETC traffic volume, MTC traffic volume, number of lanes, number of ETC lanes, number of MTC lanes, gradient rate, proportion of ETC vehicles, and proportion of passenger vehicles.

[0151] The fitting result of the vehicle delay model at the toll station entrance is obtained by calculating the following formula using the symbolic regression method:

[0152]

[0153] Where t1 is the vehicle delay time at the toll station entrance, L is the rate of change, and Q is the rate of change. b Let Q represent the traffic volume of b MTC lanes. a Let α represent the traffic volume of 'a' ETC lanes, where 'a' is the number of ETC lanes, 'b' is the number of MTC lanes, and α is the proportion of ETC vehicles.

[0154] The fitting results of the vehicle delay model at the toll station entrance are as follows: Figure 4 As shown, there is a small error between the model's predicted delay value and the actual delay value.

[0155] The fitting result of the vehicle delay model at the toll station exit is obtained by calculating the following formula using the symbolic regression method:

[0156]

[0157] Where t2 is the vehicle delay time at the toll station exit, L is the rate of change, and Q is the rate of change. b Let Q represent the traffic volume of b MTC lanes. a This represents the traffic volume of 'a' ETC lanes, where 'a' is the number of ETC lanes.

[0158] The fitting results of the vehicle delay model at the toll station exit are as follows: Figure 5 As shown, there is a small error between the model's predicted delay value and the actual delay value.

[0159] The vehicle fuel consumption model fitting result at the toll station entrance is calculated using the following formula based on the symbolic regression method:

[0160] B1=0.26×[6.79+L+2.19N+0.00172Q-0.00718(Q a / a)-6.75α] (3)

[0161] Where B1 is fuel consumption, L is the gradient rate, N is the number of lanes, and Q is the number of lanes. a Let Q represent the traffic volume of a ETC lanes, where a is the number of ETC lanes and α is the proportion of ETC vehicles.

[0162] The fitting results of the vehicle fuel consumption model at the toll station entrance are as follows: Figure 6 As shown, the error between the model's predicted fuel consumption and the actual fuel consumption is small.

[0163] The vehicle fuel consumption model fitting result at the toll station exit is calculated using the following formula based on the symbolic regression method:

[0164] B2 = 0.26 × (0.00911Q) b +0.00286Q +0.00111Q a L-2.53) (4)

[0165] Where B2 is fuel consumption, Q b Let Q represent the traffic volume of b MTC lanes. a This represents the traffic volume of 'a' ETC lanes, where 'a' is the number of ETC lanes and 'L' is the rate of change.

[0166] The fitting results of the vehicle fuel consumption model at the toll station exit are as follows: Figure 7 As shown, the error between the model's predicted fuel consumption and the actual fuel consumption is small.

[0167] Based on the vehicle delay model and the vehicle fuel consumption model, the vehicle delay cost, energy cost, and environmental cost are obtained respectively; specifically,

[0168] Factors affecting delay costs include vehicle delay time, average time value per passenger, and average passenger capacity per vehicle. Let P be the average passenger capacity of passenger cars and freight cars. 客 P 货 The average traffic volume for passenger cars and freight cars is The average time value per person is V t The time utilization rate is γ, and the delay time is set as t, where t includes the vehicle delay time t1 at the toll station entrance and the vehicle delay time t2 at the toll station exit.

[0169] The delay cost model in daily units is obtained by the following formula:

[0170]

[0171] Among them, C 延 P represents the cost of delay. 客 This indicates the average passenger capacity of a bus. P represents the average passenger vehicle traffic volume. 货 This indicates the average passenger capacity of a truck. Average truck traffic volume, V t Let γ represent the average time value per person, γ be the time utilization rate, and t be the delay time.

[0172] The factors affecting energy and environmental costs are toll station area, fuel consumption, and carbon emissions; fuel consumption is calculated using the fitting results of the fuel consumption model for vehicles entering the toll station (Formula 3) and the fitting results of the fuel consumption model for vehicles exiting the toll station (Formula 4).

[0173] Based on the factors influencing energy and environmental costs, expenses are divided into two parts: non-operational and operational phases. An operational cost model is then determined based on these two parts, calculated on a daily basis. Non-operational phase costs include fuel consumption costs and carbon emission costs; operational phase costs include carbon emission costs during construction and demolition, and carbon emission costs during building material production and transportation. As shown in Table 4, non-operational phase costs are denoted as D1, operational phase costs as D2, and carbon emission costs during construction and demolition as D... 11 The carbon emission cost during the building materials production and transportation stages is set as D. 12 Fuel consumption cost is set as D. 21 Carbon emission cost is set as D 22 The calculation formulas for each part are shown in Table 2:

[0174] Table 2 Energy and Environmental Cost Calculation

[0175]

[0176] The energy and environmental cost model in daily units is obtained through the following formula:

[0177] C 能=D1×1 / 365+D2×24 (8)

[0178] Among them, C 能 The values ​​represent energy and environmental costs, with D1 representing non-operational costs and D2 representing operational costs.

[0179] Based on the aforementioned delay costs and energy and environmental costs, a toll station with a full life cycle is designed, and the total cost of constructing the toll station and toll plaza is obtained; specifically,

[0180] The input passenger flow is obtained based on the actual data of the toll station during peak hours. The designed passenger flow data of the toll station is obtained based on the passenger flow. The total cost of building the toll station and toll plaza is divided into five parts based on the whole life cycle: construction cost, operating cost, delay cost, energy and environmental cost, and demolition cost.

[0181] Based on the costs of the five parts, corresponding cost models in daily units are obtained.

[0182] Specifically,

[0183] The factors influencing construction costs are the number and type of toll lanes and the area of ​​the toll station. Based on these factors, construction costs are divided into five parts: construction and installation costs, land use and demolition compensation costs, other construction costs, contingency funds, and construction period loan interest. Construction and installation costs include direct costs, equipment purchase costs, facility costs, enterprise management fees, regulatory fees, profit, and taxes. The construction cost is set as C. 建 The construction and installation costs are set as A1, and the direct costs are set as A. 11 The equipment purchase cost is set as A. 12 The measure fee is set as A. 13 Enterprise management fee is set as A. 14 Fees are set as A 15 Let the profit be A. 16 Let the tax be A. 17 The calculation formulas for each part are shown in Table 3:

[0184] Table 3 Construction Cost Calculation

[0185]

[0186]

[0187] Therefore, the construction cost C 建 =A1+A2+A3+A4+A5, where A1 is the construction and installation engineering cost, A2 is the land use and demolition compensation cost, A3 is other construction costs, A4 is the contingency fund, and A5 is the construction period loan interest.

[0188] Combining this with the capital recovery factor, the final construction cost model on a daily basis is as follows:

[0189]

[0190] Among them, C 建 The values ​​represent construction costs, A1 represents construction and installation costs, A2 represents land use and demolition compensation costs, A3 represents other construction costs, A4 represents contingency costs, A5 represents construction period loan interest, i represents the rate of return on investment, and n represents the investment discount period.

[0191] Operating costs are influenced by operational, institutional, and technological factors, and are divided into three parts: labor costs, facility operation costs, and maintenance costs. Operating costs are denoted as C. 运 Labor costs are set as B1, facility operation costs as B2, and maintenance and repair costs as B3; the calculation formulas for each part are shown in Table 4:

[0192] Table 4 Calculation of Operating Costs

[0193]

[0194] Therefore, operating cost C 运 =B1+B2+B3, where B1 represents labor costs, B2 represents facility operation costs, and B3 represents maintenance and repair costs.

[0195] Finally, the daily operating cost model is obtained through the following formula:

[0196] C 运 =(B1+B2+B3) / 30 (6)

[0197] Among them, C 运 The figures represent operating costs, with B1 representing labor costs, B2 representing facility operation costs, and B3 representing maintenance and repair costs.

[0198] The factors influencing demolition costs are the toll station area, lane type, and number, which are divided into three parts: labor costs, material costs, and machinery usage costs. The demolition cost is denoted as C. 拆 Labor costs are set as E1, material costs as E2, and machinery usage costs as E3; the calculation methods for each part are shown in Table 5.

[0199] Table 5 Demolition Cost Calculation

[0200] Serial Number project symbol Fee description and calculation formula 1 labor costs <![CDATA[E1]]> <![CDATA[E1 = Labor cost per unit area * Toll station area]]> 2 Material costs <![CDATA[E2]]> <![CDATA[E2 = Material cost required for a single lane * Total number of lanes]]> 3 Machinery usage fee <![CDATA[E3]]> <![CDATA[E3 = Demolition machinery usage fee per unit area * Toll station area]]> total Demolition costs <![CDATA[C 拆 ]]> <![CDATA[C 拆 =E1+E2+E3]]>

[0201] Therefore, the demolition cost C 拆 =E1+E2+E3;

[0202] The demolition cost model in daily units, obtained by the following formula, is as follows:

[0203] C拆 = (E1+E2+E3)×1 / 365 (9)

[0204] Among them, C 拆 E1 represents the demolition cost, E2 represents the material cost, and E3 represents the machinery usage cost.

[0205] A toll station layout optimization model is obtained based on the total cost of constructing toll stations and toll plazas; specifically...

[0206] To summarize all the above formulas, the variables are divided into two categories: one is parameters with real-world significance, which can be directly assigned or calculated; the other is environment variables, which need to be set according to different situations. These variables are the input variables.

[0207] The final decision variable obtained from the model output is the number of MTC lanes, n. M Number of ETC lanes n E And the gradient rate L;

[0208] All variables are organized into tables, as shown in Tables 6 and 7:

[0209] Table 6 Environmental Variables

[0210]

[0211]

[0212] Table 7 Actual Parameters

[0213]

[0214]

[0215]

[0216] Based on the actual situation of toll station construction, the cost model design constraints are used to determine the toll station layout optimization model. Among them, the main relevant parameters in the model have been determined, but some parameters change with different years and regions. These parameters need to be determined according to the actual situation of the toll stations to be constructed.

[0217] Let the objective function be f, aiming to minimize the total cost, where the total cost is C1, which is the sum of construction costs, operating costs, delay costs, and energy and environmental costs. To better reflect reality, the model is designed with the following constraints:

[0218] 1) Decision variable: Number of ETC lanes n E Number of MTC lanes n M All are integers greater than zero.

[0219] 2) To ensure that vehicles can pass through normally, toll stations must ensure that there is one ETC lane and one MTC lane.

[0220] 3) To ensure reasonable construction costs, the total number of lanes N is designed based on the maximum daily hourly traffic volume, according to regulations. max And use it as the upper limit.

[0221] 4) According to the standard, the recommended value for the gradient rate of the toll plaza should be 1 / 6 to 1 / 7.

[0222] 5) In order to meet the demand for toll station traffic efficiency and maximize the proportion of ETC lanes, the ratio range of ETC lanes and MTC lanes is defined as [γ1, γ2] based on actual traffic volume and traffic capacity.

[0223] 6) To ensure the service level of toll stations, the average daily service level of toll stations should be standardized. Research shows that when traffic volume... With traffic capacity C 总 When the ratio is less than or equal to 0.75, the service level of the toll station is relatively good and it can maintain basic smooth traffic.

[0224] In summary, the final optimized model for the layout of toll plazas is as follows:

[0225]

[0226] Where f is the objective function with the goal of minimizing total cost, minC1 represents the minimum total cost, C1 represents the total cost, and C 建 C represents the construction cost. 运 C represents operating costs. 延 C represents the cost of delay. 能 C represents energy and environmental costs. 拆 The following represents the demolition cost: A1 represents construction and installation costs; A2 represents land use and demolition compensation costs; A3 represents other construction costs; A4 represents contingency funds; A5 represents construction period loan interest; i represents the rate of return on investment; n represents the discount period of the investment; B1 represents labor costs; B2 represents facility operation costs; B3 represents maintenance and repair costs; D1 represents non-operational phase costs; D2 represents operational phase costs; E1 represents labor costs; E2 represents material costs; E3 represents machinery usage costs; n M Indicates the number of MTC lanes, n E n represents the number of ETC lanes. M n E Take values ​​from positive integers, N max This represents the total number of lanes designed based on the maximum hourly traffic volume, where L is the gradient rate, γ1 and γ2 represent the ratio range of ETC lanes and MTC lanes, Q is the traffic volume, and C... 总 Indicates traffic capacity.

[0227] By substituting actual data from toll station layout optimization models into the model, optimization results were obtained and compared with existing toll station layouts. The results determined that the optimized layout model was superior. Specifically...

[0228] Based on the toll station layout optimization model and substituting actual data from real-world toll stations, specifically...

[0229] Substituting the actual data of Xihongmen toll station, in terms of construction costs, the rate of return on investment i is 8%, the discount period is 13 years, and the construction period loan interest A5 is negligible;

[0230] In terms of delay costs, the average time value per person, V t It costs 333.4 yuan per day;

[0231] In terms of energy and environmental costs, the unit diesel consumption E during the construction and demolition phases 11 E 21 The value is 44.44 kg / m², corresponding to a carbon emission factor λ1 of 2.63 kg CO₂ / m². 2 Concrete consumption per unit area (M) during the building materials production and transportation stages 11 The carbon emission factor η1 is 0.17 kg CO2 / m2, corresponding to a construction area S1 of the toll station area S; the carbon emission factor η1 is 0.17 kg CO2 / m2; the stainless steel consumption per unit area is M. 12 The concentration is 9.5 kg / m², and the corresponding construction area S2 is related to the number of toll lanes. The calculation method is (n E +n M The carbon emission factor η2 is 1.1 kg CO2 / m2, and the carbon emission factor μ1 per unit weight of transport distance is 0.07 kg CO2 / (t·km).

[0232] Average hourly traffic volume in the entrance model The capacity is 2112 pcu / h, the proportion of passenger vehicles is 85%, the proportion of ETC vehicles is 70%, the maximum total number of lanes is 15, and the ratio of ETC lanes to MTC lanes is between 75% and 200%; the optimization results of the entrance model are shown in Table 8:

[0233] Table 8. Optimization results of the ingress model

[0234]

[0235]

[0236] Average hourly traffic volume in the export model The efficiency is 3365 pcu / h, the proportion of passenger vehicles is 85%, the proportion of ETC vehicles is 80%, the maximum total number of lanes is 20, and the other constraints are the same as those at the entrance. The optimization results of the exit model are shown in Table 9.

[0237] Table 9. Optimization Results of the Export Model

[0238]

[0239] The optimization results in Tables 8 and 9 include (number of ETC lanes, number of MTC lanes), gradient rate, toll plaza area, construction cost, operating cost, delay cost, energy and environmental cost, and demolition cost. The optimization results are compared between existing toll plazas and the model toll plazas, and the total cost is calculated and compared using the toll plaza layout optimization model. If the total cost of the toll plaza layout optimization model is less than the total cost of the existing toll plazas, then the toll plaza layout optimization model is considered better. Therefore, observing Tables 8 and 9 shows that the optimized toll plaza entrance layout is better; the optimized toll plaza exit layout is better; in conclusion, the optimized toll plaza layout is better.

[0240] The existing layout diagram of Xihongmen toll station is as follows: Figure 8 As shown, the Xihongmen toll station has parallel toll booths and toll islands. Both toll booths and toll islands are equipped with toll canopies. Both toll booths and toll islands are located in the middle of the toll lanes. The toll booths are located in the direction of vehicle entrance. The toll booths are 45 meters wide. Vehicles need to travel 225 meters from the regular lane to the toll booth, and then 200 meters from the toll booth to the regular lane. The toll islands are 75 meters wide and 100 meters long. Vehicles need to travel 230 meters from the regular lane to the toll island, and 270 meters from the toll island to the regular lane.

[0241] The optimized layout diagram of Xihongmen toll station is shown below. Figure 9 As shown, the Xihongmen toll station has parallel toll booths and toll islands. Both toll booths and toll islands are equipped with toll canopies. Both toll booths and toll islands are located in the middle of the toll lanes. The toll booths are located in the direction of vehicle entrance. The toll booths are 39 meters wide. Vehicles need to travel 194 meters from the toll booth to the toll booth, and then another 194 meters from the toll booth to the toll lane. The toll islands are 56 meters wide and 200 meters long. Vehicles need to travel 289 meters from the toll booth to the toll island, and then another 289 meters from the toll island to the toll lane.

[0242] Comparing the schematic diagrams of the toll station layout before and after optimization, it was found that the existing toll stations have problems such as an excessive number of lanes and an excessive gradient between upstream and downstream lanes. This shows that in order to alleviate toll station congestion, we cannot only focus on the number and layout of toll lanes, but also have to consider the gradient factor, which proves that the present invention is real and effective.

[0243] The present invention has the following technical effects:

[0244] (1) The lane selection of toll stations was studied using measured data, and a decision tree model for lane selection was established as the input rule for the simulation model. Simulation and symbolic regression methods were used to obtain the vehicle delay and fuel consumption under different toll station settings.

[0245] (2) The impact of the gradient rate on the layout of toll stations and toll plazas was considered, and the problem that when the gradient rate is too high, the following distance between vehicles cannot reach a reasonable standard during the driving process before and after the vehicle selects the toll lane and after leaving the toll lane, and when the traffic volume is large, the vehicle is congested at the end of the acceleration gradient section, thereby reducing the toll station's traffic capacity.

[0246] (3) When designing the toll plaza, energy and environmental costs were taken into consideration. This solved the problems of existing toll stations either having too large a land area, wasting construction and operation costs, or having low traffic efficiency that could not meet the existing traffic demand, thus causing a large amount of delay costs and energy and environmental costs.

[0247] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for designing statistical slowdown points on a highway mainline throughout its entire life cycle, characterized in that, Obtain actual data on traffic bottlenecks in the route statistics; A decision-making model for lane selection behavior at the statistical slow points of the route is established based on the actual data of the statistical slow points of the route. Based on the route statistical slowdown island lane selection behavior decision model, obtain route statistical slowdown selection behavior data; A simulation model is established based on the actual data of the route's statistical slow points, the decision-making model for lane selection behavior at the route's statistical slow points, and the selection behavior data of the route's statistical slow points. The simulation model's output is compared with the actual data of the line's statistical slow points to verify the model's authenticity and effectiveness. Based on the verification of the effectiveness of the simulation model, a vehicle delay model and a vehicle fuel consumption model are obtained according to the simulation model. Based on the vehicle delay model and the vehicle fuel consumption model, the vehicle delay cost and energy and environmental costs are obtained respectively. Based on the aforementioned delay costs and energy and environmental costs, a full life-cycle route statistical slowdown point is designed, and the total cost of constructing the route statistical slowdown point and the route statistical slowdown point plaza is obtained. Based on the total cost of constructing route statistical slow points and route statistical slow point plazas, obtain a route statistical slow point layout optimization model; By substituting actual data of the statistical slow points of the lines into the optimization model for the layout of the line statistical slow points, the optimization results were obtained and compared with the existing statistical slow points of the lines. It was determined that the optimization model for the layout of the statistical slow points of the lines was better.

2. The method for statistical drawdown design of highway mainline routes throughout their entire life cycle, as described in claim 1, is characterized in that... The actual data on the statistical slow points of the route includes: the structural data of the statistical slow points of the route, traffic flow data, vehicle trajectory data, lane information data, and lane service data; The statistical slow point structure data of the route includes the number of lanes at the statistical slow point, the number of highway lanes, the length and width of the transition section, the width of the statistical slow point, and the area of ​​the statistical slow point. The traffic flow data includes traffic volume, vehicle type, vehicle toll type, vehicle performance data, and driver behavior data. The vehicle trajectory data includes the distance between the target lane and the central divider, the number of lanes required to reach the target lane, and the trajectory the vehicle travels during its operation. The lane information data includes the target lane location and the target lane toll type; The lane service data includes the number of queues in each lane, the queuing time in each lane, the service time in each lane, the lane capacity, the average pre-station delay time in each lane, and the average post-station delay time in each lane. The vehicle type, the vehicle toll type, the number of queues in the target lane, the distance between the target lane and the center divider, and the number of lanes required to reach the target lane are selected as feature vectors to establish a route statistical slow-point island lane selection behavior decision model. The decision-making model for lane selection behavior at the statistical slow-down islands of the route is observed, and the lane selection behavior at the statistical slow-down islands of the route is summarized. The decision-making model for lane selection behavior at the statistical slow-down islands of the route includes a decision tree model for the entrance of the statistical slow-down islands of the route and a decision tree model for the exit of the statistical slow-down islands of the route.

3. The method for statistical drawdown design of highway mainline routes throughout their entire life cycle, as described in claim 1 or 2, is characterized in that... The simulation model is the VISSIM simulation model. After the VISSIM simulation model outputs the results, the box plot is compared with the actual data to obtain the confidence interval of the average delay time. The actual data of the line statistical slow points were divided into three groups: low flow, high flow, and oversaturated flow for simulation. Independent delay time tests were performed on each group. If the data corresponding to the three groups were not significant, the authenticity and effectiveness of the simulation model were verified. Based on the verification of the effectiveness of the simulation model, vehicle delay data and fuel consumption data are output according to the simulation model. Based on vehicle delay data and fuel consumption data, the independent variables are determined according to the actual data of the slow points on the route. The vehicle delay time and fuel consumption at the entrance and exit of the route are calculated by inputting independent variables using the symbolic regression method. After data fitting and evaluation based on the goodness of fit, mean absolute error, and complexity of the output function, vehicle delay models and vehicle fuel consumption models for the entry and exit points of the route statistical slow points are obtained.

4. The method for statistical drawdown design of highway mainline routes throughout their entire life cycle, as described in claim 3, wherein, The independent variables are determined based on the actual data of the statistical slow points of the route. The vehicle delay time and fuel consumption at the entrance and exit of the statistical slow points of the route are obtained by calculating the independent variables using the symbolic regression method. Specifically, The dependent variables are determined based on the actual traffic volume, number of lanes, gradient rate, vehicle type, and vehicle toll type in the route statistics. The dependent variables include traffic volume, ETC traffic volume, MTC traffic volume, number of lanes, number of ETC lanes, number of MTC lanes, gradient rate, proportion of ETC vehicles, and proportion of passenger vehicles. The vehicle delay model fitting result at the statistical slowdown point entrance of the route is obtained by calculating the following formula using the symbolic regression method: （1） in, t 1 represents the vehicle delay time at the slow-moving entrance of the route. L For the rate of change, Q b express b Traffic volume per MTC lane Q a express a Traffic volume per ETC lane a For the number of ETC lanes, b For the number of MTC lanes, α The proportion of vehicles using ETC (Electronic Toll Collection). The vehicle delay model fitting result for the statistical slowdown exits of the route is obtained by calculating the following formula using the symbolic regression method: （2） in, t 2 represents the vehicle delay time at slow-moving exits on the route. L For the rate of change, Q b express b Traffic volume per MTC lane Q a express a Traffic volume per ETC lane a For the number of ETC lanes, α The proportion of vehicles using ETC (Electronic Toll Collection). The vehicle fuel consumption model fitting result at the route statistical slowdown point entrance is obtained by calculating the following formula using the symbolic regression method: （3） in, B 1 represents fuel consumption. L For the rate of change, N For the number of lanes, Q a express a Traffic volume per ETC lane Q For traffic volume, a For the number of ETC lanes, α The proportion of vehicles using ETC (Electronic Toll Collection). The vehicle fuel consumption model fitting result at the exit of the route statistical slow point is obtained by calculating the following formula using the symbolic regression method: （4） in, B 2 represents fuel consumption. Q b express b Traffic volume per MTC lane Q For traffic volume, Q a express a Traffic volume per ETC lane L The rate of change is denoted by .

5. The method for statistical drawdown design of highway mainline routes throughout their entire life cycle, as described in claim 4, wherein, Based on the total cost of constructing route slowdown points and the corresponding plazas, an optimization model for the layout of route slowdown points is obtained. The total cost of constructing route statistical deceleration points and route statistical deceleration point plazas according to the entire life cycle includes five parts: construction cost, operating cost, delay cost, energy and environmental cost, and demolition cost; Based on construction costs, operating costs, delay costs, energy and environmental costs, and demolition costs, corresponding cost models in daily units are obtained; Based on a cost model that includes construction costs, operating costs, delay costs, energy and environmental costs, and demolition costs, the system inputs input variables and obtains output variables based on the statistical slowdown data of the construction lines, determines variable parameters, and establishes an optimization model for the layout of statistical slowdown points based on the actual situation of the construction lines.

6. The method for statistical slowdown design of highway mainline routes throughout their entire life cycle, as described in claim 5, wherein, Based on construction costs, operating costs, delay costs, energy and environmental costs, and demolition costs, corresponding daily cost models are obtained, specifically as follows: Based on the factors affecting construction costs, the costs are divided into five parts: construction and installation engineering costs, land use and demolition compensation costs, other construction costs, contingency costs, and construction period loan interest. Among these factors, the factors affecting construction costs include toll lanes, the number of toll lanes, and the area of ​​traffic slowdown points in the route statistics. The construction cost is determined based on five components of the cost that affect the construction cost. Then, combined with the capital recovery coefficient, a construction cost model on a daily basis is obtained. The construction cost model in daily units is determined by the following formula: （5） in, Indicates construction cost, This indicates the cost of construction and installation work. This refers to land use and demolition compensation fees. This indicates other construction costs. This indicates a contingency fund. This indicates the interest rate on loans during the construction period. i For the rate of return on investment, n The discount period for the investment; in, This refers to the cost of construction and installation engineering, which includes direct costs, equipment purchase costs, measures costs, enterprise management fees, regulatory fees, profits, and taxes.

7. The method for statistical drawdown design of highway mainline routes throughout their entire life cycle, as described in claim 5, wherein, Based on construction costs, operating costs, delay costs, energy and environmental costs, and demolition costs, corresponding daily cost models are obtained, specifically as follows: Based on the factors affecting operating costs, the costs are divided into three parts: labor costs, facility operation costs, and maintenance and repair costs. Based on these three parts, an operating cost model is determined on a daily basis. The daily operating cost model obtained by the following formula is as follows: （6） in, Indicates operating costs, This indicates labor costs. Indicates facility operating costs, This indicates maintenance and repair costs.

8. The method for statistical slowdown design of highway mainline routes throughout their entire life cycle, as described in claim 5, wherein, Based on construction costs, operating costs, delay costs, energy and environmental costs, and demolition costs, corresponding daily cost models are obtained, specifically as follows: Factors affecting delay costs include vehicle delay time, average time value per person, and average passenger capacity per vehicle; Factors affecting delay costs include the average passenger capacity of buses and freight trucks. , The average traffic volume for passenger cars and freight cars is set as follows: , The average time value per person is set as Time utilization rate is set to The delay time is set as t, and t includes the vehicle delay time t1 at the entrance of the route statistical slow point and the vehicle delay time t2 at the exit of the route statistical slow point. The delay cost model in daily units is obtained by the following formula: （7） Among them, C 延 Indicates the cost of delay. This indicates the average passenger capacity of a bus. This indicates the average passenger vehicle traffic volume. This indicates the average passenger capacity of a truck. Average truck traffic volume Indicates the average value of time per person. Let t represent time utilization rate and t represent delay time.

9. The method for statistical drawdown design of highway mainline routes throughout their entire life cycle, as described in claim 5, wherein, Based on construction costs, operating costs, delay costs, energy and environmental costs, and demolition costs, corresponding daily cost models are obtained, specifically as follows: Based on the factors affecting energy and environmental costs, the costs are divided into two parts: non-operational and operational phases. An operational cost model is then determined based on these two parts of costs, using the non-operational and operational phases as the basis. Non-operation phase costs include fuel consumption costs and carbon emission costs, while operation phase costs include carbon emission costs during the construction and demolition phases and carbon emission costs during the building material production and transportation phases. The energy and environmental cost model in daily units, obtained by the following formula, is as follows: （8） in, Indicates energy and environmental costs, This indicates non-operational phase expenses. This indicates operating phase costs.

10. The method for statistical drawdown design of highway mainline routes throughout their entire life cycle, as described in claim 5, wherein, Based on construction costs, operating costs, delay costs, energy and environmental costs, and demolition costs, corresponding daily cost models are obtained, specifically as follows: Based on the factors affecting demolition costs, the costs are divided into three parts: labor costs, material costs, and machinery usage costs. A demolition cost model based on these three parts is then determined on a daily basis. The demolition cost model in daily units, obtained by the following formula, is as follows: （9） in, Indicates the demolition cost. This indicates labor costs. This indicates the cost of materials. This indicates the cost of using the machinery.

11. A method for designing statistical slowdown points on a highway mainline throughout its entire life cycle, as described in any one of claims 5-10, wherein, The route statistical slowdown point layout optimization model obtained by the following formula is: (10) in, Let min be the objective function that aims to minimize the total cost. This represents the minimum total cost. Represents the total cost. Indicates construction cost, Indicates operating costs, Indicates the cost of delay. Indicates energy and environmental costs, Indicates demolition cost, This indicates the cost of construction and installation work. This refers to land use and demolition compensation fees. This indicates other construction costs. This indicates a contingency fund. Let represent the loan interest during the construction period, i be the rate of return on investment, and n be the discount period for the investment. This indicates labor costs. Indicates facility operating costs, This indicates maintenance and repair costs. This indicates non-operational phase expenses. This indicates operating phase costs. This indicates labor costs. This indicates the cost of materials. This indicates the cost of using the machinery. Indicates the number of MTC lanes, Indicates the number of ETC lanes. , Take values ​​from positive integers. This indicates the total number of lanes designed based on the maximum hourly traffic volume. L For the rate of change, γ 1 and γ 2 indicates the ratio range of ETC lanes and MTC lanes. Q For traffic volume, Indicates traffic capacity.

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