A calculation method and system for the traffic capacity of a road section under the interference of a bus stop

Abstract

The present invention relates to the technical field of urban traffic management, and specifically relates to a method and system for calculating the traffic capacity of a road section under the interference of a bus stop. The method includes: determining the research scope and the research time period; obtaining the social vehicle trajectory data and the bus trajectory data within the research scope and the research time period; performing curve fitting based on the social vehicle trajectory data and the bus trajectory data to obtain a social vehicle trajectory fitting curve and a bus trajectory fitting curve; based on the social vehicle trajectory fitting curve, performing lane division according to the lane division method of peak positioning - spacing width determination, and determining the lane order; according to the lane order, determining multiple target lanes affected by the bus stop; based on the comparison and analysis of the overlapping area of the social vehicle and bus trajectory fitting curves and the lane driving data, comprehensively evaluating the traffic capacity reduction coefficient of each target lane, and then combining the basic traffic capacity of the lane to determine the actual traffic capacity of each target lane.

Description

Technical Field

[0001] The present invention relates to the technical field of urban traffic management, and particularly relates to a method and system for calculating the traffic capacity of a road section under the interference of a bus stop. Background Art

[0002] The road service level is a key evaluation index for the road service quality, and the road traffic capacity is the basis for calculating the service level index. Urban roads are often affected by many interference factors, resulting in a decrease in traffic capacity. Among them, bus stops have a greater interference on the traffic capacity of road sections. However, it is often very complicated to measure the impact caused by bus stops, which often involves the style of bus stops (bay or non-bay), whether there is an overflow in the bus parking area, the number of bus lines and the departure frequency, and whether it is affected by intersections, etc. These factors are intertwined, making it inconvenient to measure the daily reduction of traffic capacity. Therefore, it is necessary to propose a simplified and effective evaluation method to quickly and accurately estimate the impact of bus stops on road traffic capacity. Summary of the Invention

[0003] In order to scientifically and accurately evaluate the impact of bus stops on road traffic capacity, the purpose of the present invention is to provide a method and system for calculating the traffic capacity of a road section under the interference of a bus stop. The specific technical solutions adopted are as follows:

[0004] In the first aspect, the present application discloses a method for calculating the traffic capacity of a road section under the interference of a bus stop, and the method includes:

[0005] S1. Determine the research scope and the research time period;

[0006] S2. Obtain the social vehicle trajectory data and bus trajectory data within the research scope and research time period;

[0007] S3. Perform curve fitting based on the social vehicle trajectory data and the bus trajectory data to obtain a social vehicle trajectory fitting curve and a bus trajectory fitting curve;

[0008] S4. Based on the social vehicle trajectory fitting curve, perform lane division according to the lane division method of peak positioning - spacing width determination, and determine the lane order;

[0009] S5. Determine multiple target lanes affected by the bus stop according to the lane order;

[0010] S6. Based on the overlapping area of the social vehicle and bus trajectory fitting curves and the comparison and analysis of lane driving data, comprehensively evaluate the traffic capacity reduction coefficient of each target lane, and then combine the basic traffic capacity of the lane to determine the actual traffic capacity of each target lane.

[0011] Further, in step S3, the curve fitting based on the social vehicle trajectory data and the bus trajectory data to obtain the social vehicle trajectory fitting curve and the bus trajectory fitting curve includes:

[0012] S31. Based on the social vehicle trajectory data and the bus trajectory data, determine the first section where the social vehicle trajectory changes relatively concentratedly and the second section where the bus trajectory changes relatively concentratedly;

[0013] S32. In the first section and the second section, respectively use the mixture Gaussian distribution model for curve fitting to obtain the social vehicle trajectory fitting curve and the bus trajectory fitting curve.

[0014] Further, in step S4, the lane division and determination of the lane order based on the social vehicle trajectory fitting curve according to the lane division method of peak positioning - spacing fixed width include:

[0015] S41. Based on the social vehicle trajectory fitting curve, determine the peaks therein through peak detection;

[0016] S42. Taking each peak as a reference point, draw a perpendicular line along the peak direction to obtain the road center line;

[0017] S43. Taking the distance between adjacent road center lines as the lane width, conduct lane division and determine the lane order based on the relative position between the lane and the roadside.

[0018] Further, in step S6, the comprehensive evaluation of the traffic capacity reduction coefficient of each target lane based on the overlapping area between the social vehicle and bus trajectory fitting curves and the lane driving data comparison and analysis includes:

[0019] S61. Based on the overlapping area between the social vehicle trajectory fitting curve and the bus trajectory fitting curve, determine the bus mixed interference degree of each target lane;

[0020] S62. Based on the comparative analysis of the lane driving data of the lanes affected by bus stops and the non - affected lanes, obtain the travel time interference degree of each target lane;

[0021] S63. Comprehensively considering the bus mixed interference degree and the travel time interference degree, through weighted average and normalization processing calculations, obtain the traffic capacity reduction coefficient of each target lane.

[0022] Further, in step S62, the comparative analysis of the lane driving data of the lanes affected by bus stops and the non - affected lanes to obtain the travel time interference degree of each target lane includes:

[0023] S621. Obtain the driving time data of all vehicles on each lane within the research scope, and calculate the average travel time of each lane through averaging.

[0024] S622. Determine the average travel time of each target lane affected by the bus stop and the overall average travel time of the remaining non-interfered lanes, and calculate the travel time interference degree of each target lane through relative difference calculation.

[0025] Further, for each target lane, in step S63, by comprehensively considering the bus mixing interference degree and the travel time interference degree, through weighted average and normalization calculation, obtain the traffic capacity reduction coefficient of each target lane, including:

[0026] S631. Based on the calculation of the entropy weight method, obtain the first weight coefficient corresponding to the bus mixing interference degree and the second weight coefficient corresponding to the travel time interference degree.

[0027] S632. Based on the first weight coefficient and the second weight coefficient, perform weighted average calculation on the bus mixing interference degree and the travel time interference degree to obtain the comprehensive interference degree of the bus stop on the target lane.

[0028] S633. Based on the comprehensive interference degree, perform normalization processing to obtain the traffic capacity reduction coefficient of the target lane.

[0029] Further, in step S6, by combining the basic traffic capacity of the lane, determine the actual traffic capacity of each target lane, including:

[0030] S64. Obtain the basic traffic capacity of each target lane and adjust it through the corresponding traffic capacity reduction coefficient to obtain the actual traffic capacity of each target lane.

[0031] In the second aspect, the present application discloses a traffic capacity calculation system for a road section under bus stop interference. The system includes a scope determination module, a trajectory data acquisition module, a trajectory curve fitting module, a lane division module, a target lane identification module, and a road section traffic capacity evaluation module, where:

[0032] The scope determination module is used to determine the research scope and the research time period.

[0033] The trajectory data acquisition module is used to acquire the social vehicle trajectory data and bus trajectory data within the research scope and research time period.

[0034] The trajectory curve fitting module is used to perform curve fitting based on the social vehicle trajectory data and the bus trajectory data to obtain a social vehicle trajectory fitting curve and a bus trajectory fitting curve.

[0035] The lane division module is used to perform lane division based on the fitting curve of the social vehicle trajectory according to the lane division method of peak positioning - width determination by spacing, and determine the lane order.

[0036] The target lane recognition module is used to determine multiple target lanes affected by the bus stop according to the lane order.

[0037] The section traffic capacity evaluation module is used to comprehensively evaluate the reduction coefficient of the traffic capacity of each target lane based on the overlapping area of the fitting curves of the social vehicle and the bus trajectory and the comparison analysis of the lane driving data, and then determine the actual traffic capacity of each target lane in combination with the basic traffic capacity of the lane.

[0038] In a second aspect, the present application discloses a computer-readable storage medium storing a computer program, which when executed by a processor, implements the method for calculating the traffic capacity of a section under the interference of a bus stop.

[0039] In a second aspect, the present application discloses a traffic capacity calculation control device for a section under the interference of a bus stop, including a communication interface, a memory, a communication bus, and a processor, wherein the processor, the communication interface, and the memory complete communication with each other through the communication bus;

[0040] The memory is used to store a computer program;

[0041] The processor is used to implement the steps of the method for calculating the traffic capacity of a section under the interference of a bus stop when executing the program stored in the memory.

[0042] The present invention has the following beneficial effects:

[0043] 1) By adopting the lane division method of peak positioning - width determination by spacing, lane division can be performed based on the actual distribution of vehicle driving data, ensuring that the divided lanes are more in line with the actual traffic flow characteristics. Determining the target lanes affected by the bus stop according to the lane order can accurately locate the traffic impact area and provide a clear target for subsequent analysis and optimization;

[0044] 2) By comprehensively evaluating the overlapping area of the fitting curves of the social vehicle and the bus trajectory and the lane driving data, the reduction coefficient of the traffic capacity of each target lane can be calculated, and then the actual traffic capacity can be determined. This provides a scientific basis for traffic planning and decision-making and helps to optimize traffic organization and management. Description of the Drawings

[0045] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0046] Figure 1 It is a method flow chart of a method for calculating the road section passing capacity under the interference of a bus stop provided by an embodiment of the present invention;

[0047] Figure 2 It is a schematic diagram of lane division and trajectory curve fitting;

[0048] Figure 3 It is a schematic diagram of the overlapping area of the trajectory fitting curves of private cars and buses;

[0049] Figure 4 It is a system structure diagram of a system for calculating the road section passing capacity under the interference of a bus stop provided by an embodiment of the present invention. Detailed implementation manners

[0050] To further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following, in combination with the drawings and preferred embodiments, details the specific implementation manners, structures, features and effects of a method and system for calculating the road section passing capacity under the interference of a bus stop proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0051] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs.

[0052] The following specifically describes the specific solutions of a method and system for calculating the road section passing capacity under the interference of a bus stop provided by the present invention in combination with the drawings.

[0053] Please refer to Figure 1 , which shows a method flow chart of a method for calculating the road section passing capacity under the interference of a bus stop provided by an embodiment of the present invention. The method includes the following steps:

[0054] Step S1, determine the research scope and the research time period.

[0055] Specifically, in this application, the section between a bus stop and the previous intersection is used as the research section, and the morning and evening peak hours (such as 7:00 to 9:00 in the morning and 17:00 to 19:00 in the evening) are used as the research time period.

[0056] Step S2: Obtain the social vehicle trajectory data and bus trajectory data within the research scope and research time period.

[0057] Specifically, in this application, based on data sources such as in-vehicle GPS devices and traffic monitoring systems, the social vehicle trajectory data and bus trajectory data within the research scope and research time period are collected. These data include key information such as the vehicle's position information, speed, and driving direction, and can reflect the driving conditions of vehicles on the research section.

[0058] Step S3: Perform curve fitting based on the social vehicle trajectory data and the bus trajectory data to obtain a social vehicle trajectory fitting curve and a bus trajectory fitting curve.

[0059] Specifically, in this application, a mixture Gaussian distribution model is used to fit the social vehicle trajectory data and the bus trajectory data. It should be noted that the application of the mixture Gaussian distribution model can more accurately capture and describe the distribution characteristics of vehicle trajectory data.

[0060] Step S4: Based on the social vehicle trajectory fitting curve, perform lane division according to the peak positioning-spacing fixed-width lane division method and determine the lane order.

[0061] Specifically, please refer to Figure 2 , in this application, significant peaks on the social vehicle trajectory fitting curve are identified through peak detection, and perpendicular lines are drawn from these peaks as reference points to obtain the road center line. Then, the lane width is determined by measuring the distance between adjacent road center lines, and the lane order is determined according to the relative position of the lane and the roadside, so as to achieve clear and reasonable lane division and provide support for traffic management and optimization.

[0062] Step S5: Determine multiple target lanes affected by the bus stop according to the lane order.

[0063] Specifically, according to the lane order, please refer to Figure 3 , considering that bus stops are usually located by the roadside, and passengers will frequently cross the adjacent lanes when getting on and off the bus, which may cause the traffic flow of the lane adjacent to the roadside and its adjacent lanes to be affected, resulting in congestion or a decrease in driving speed. Therefore, in this application, lane 1 (i.e., the lane adjacent to the roadside) shown in the figure and its adjacent lane 2 will be used as target lanes.

[0064] Step S6: Based on the comparison and analysis of the overlapping area between the trajectories of social vehicles and buses and the lane driving data, comprehensively evaluate the capacity reduction coefficient of each target lane, and then combine it with the basic lane capacity to determine the actual capacity of each target lane.

[0065] Specifically, this application will obtain the driving trajectories of social vehicles and buses during the same period, and perform curve fitting on them respectively to obtain the social vehicle trajectory fitting curve and the bus trajectory fitting curve. Then, using a Geographic Information System (GIS) or related software tools, calculate the overlapping area of these two fitting curves on each target lane. This overlapping area reflects the degree of mixed driving of buses and social vehicles on the same lane, that is, the degree of bus mixed interference. Then, by comparing and analyzing the driving data of the lanes affected by bus stops (target lanes) and non-interfered lanes, quantitatively evaluate the degree of travel time interference caused by buses to the target lanes. Finally, based on the degree of bus mixed interference and the degree of travel time interference, through a calculation method of weighted average and normalization, comprehensively consider the impact of both on the lane capacity, and obtain the capacity reduction coefficient of each target lane. This coefficient reflects the degree of reduction in the capacity of the target lane relative to the situation without interference due to bus interference. Further, combined with the basic lane capacity (that is, the maximum traffic flow that the lane can accommodate under non-interference conditions), use the capacity reduction coefficient for adjustment to obtain the actual capacity of each target lane.

[0066] As can be seen from the above, a method for calculating the section capacity under bus stop interference disclosed in this application adopts a lane division method of wave peak positioning - spacing fixed width, which can divide lanes based on the actual distribution of vehicle driving data, ensuring that the divided lanes are more in line with the characteristics of the actual traffic flow. Determining the target lanes affected by bus stops according to the lane sequence can accurately locate the traffic impact area, providing a clear target for subsequent analysis and optimization; by comprehensively evaluating the overlapping area between the trajectories of social vehicles and buses and the lane driving data, the capacity reduction coefficient of each target lane can be calculated, and then the actual capacity can be determined. This provides a scientific basis for traffic planning and decision-making, and helps to optimize traffic organization and management.

[0067] In one embodiment, in step S3, the curve fitting based on the social vehicle trajectory data and the bus trajectory data to obtain the social vehicle trajectory fitting curve and the bus trajectory fitting curve includes:

[0068] Step S31: Based on the social vehicle trajectory data and the bus trajectory data, determine the first section where the change of the social vehicle trajectory is relatively concentrated and the second section where the change of the bus trajectory is relatively concentrated.

[0069] Specifically, due to the interference of bus stops, social vehicles will change lanes to avoid the parked buses, decelerate to maintain a safe distance, or stop and wait for the bus to complete the stop and restart before the bus approaches the stop. These behaviors will occur within a certain road section range, resulting in changes in the traffic flow characteristics in this area. Based on this, this application will analyze the trajectory data of social vehicles and buses. Among them, for social vehicles, this application will, based on the changes in parameters such as the driving speed and direction of the vehicle, through statistical analysis, identify the road sections with large traffic flow and complex driving conditions as the first section. For buses, based on characteristics such as their stops, stop times, and interaction status with other vehicles, the road sections where the changes in the bus driving trajectories are relatively concentrated are used as the second section.

[0070] Step S32, within the first section and the second section respectively, use the mixture Gaussian distribution model for curve fitting to obtain the social vehicle trajectory fitting curve and the bus trajectory fitting curve.

[0071] Specifically, the definition formula of the mixture Gaussian distribution model includes:

[0072] ;

[0073] Among them, p ( x ) represents the mixture Gaussian distribution probability density function, j represents the vehicle type, wj represents the j proportion of the vehicle type, represents the j standard deviation of the distance of the vehicle type from the center line of the road, μj represents the j mean value of the distance of the vehicle type from the center line of the road, x represents the distance of the overall vehicle from the center line of the road.

[0074] In the above embodiment, by using the mixture Gaussian distribution model, which can capture the complex distribution characteristics of the data and improve the accuracy and robustness of the fitting, it is possible to obtain more realistic social vehicle and bus trajectory fitting curves, thereby providing reliable data support for subsequent data analysis.

[0075] In one of the embodiments, in step S4, based on the social vehicle trajectory fitting curve, according to the lane division method of peak positioning - spacing width determination, the lane division is carried out and the lane order is determined, including:

[0076] Step S41, based on the social vehicle trajectory fitting curve, through peak detection, determine the peaks therein.

[0077] Specifically, the present application analyzes the local maximum points on the fitted curve and uses these points as wave peaks. It should be noted that the positions of the wave peaks represent the relatively concentrated areas in the driving trajectories of social vehicles, usually corresponding to the center of the road.

[0078] Step S42: Using each wave peak as a reference point, draw a perpendicular line along the direction of the wave peak to obtain the road center line.

[0079] Specifically, the present application will determine a vertical direction at each wave peak and draw a perpendicular line along this direction. These perpendicular lines will serve as the preliminary estimate of the road center line.

[0080] Step S43: Using the distance between adjacent road center lines as the lane width, conduct lane division and determine the lane order based on the relative positions of the lanes and the roadside.

[0081] Specifically, the present application will calculate the interval distance between adjacent road lines, and this distance is the lane width. Then, the lane order will be determined according to the relative positions of the lanes and the roadside.

[0082] In one embodiment, please refer to Figure 2 , the present application will define the lane adjacent to the roadside as Lane 1, and then number them sequentially to the left (specifically depending on the driving direction of the road) to obtain Lane 2, Lane 3, etc. Among them, Lane 1 and its adjacent Lane 2 are defined as the lanes affected by buses.

[0083] In one embodiment, in step S6, the comprehensive evaluation of the traffic capacity reduction coefficient of each target lane based on the overlapping area between the social vehicle and the bus trajectory fitting curves and the lane driving data comparison analysis includes:

[0084] Step S61: Based on the overlapping area between the social vehicle trajectory fitting curve and the bus trajectory fitting curve, determine the bus mixing interference degree of each target lane.

[0085] Specifically, the present application will determine the overlapping area between the social vehicle trajectory fitting curve and the bus trajectory fitting curve according to the enclosed area formed by each trajectory fitting curve drawn by the mixture Gaussian distribution model and the horizontal axis of the unified reference coordinate system.

[0086] Further, please refer to Figure 3 , the overlapping area within the target lane range is the bus mixing interference degree of the target lane. The specific calculation formula is:

[0087] ;

[0088] Among them, represents lane iThe degree of mixed interference of buses represents the lane i The overlapping area between the fitted curve of the social vehicle trajectory and the fitted curve of the bus trajectory within the range

[0089] Step S62: Based on the comparative analysis of the lane driving data of the lanes affected by bus stops and the non - affected lanes, obtain the travel - time interference degree of each target lane

[0090] Specifically, this application will obtain the driving times of all vehicles on each lane within the research range, and through arithmetic - mean calculation, obtain the average travel time of each lane. Then, through screening and grouping, obtain the average travel time of the lanes affected by bus stops (i.e., target lanes) T i , and the overall average travel time of the non - affected lanes (all lanes except the target lanes) T o After that, based on the average travel time T i and the overall average travel time T o perform the calculation of relative difference, and then the travel - time interference degree of the target lane can be obtained, which reflects the additional time burden caused by bus stops on the traffic efficiency of the target lane

[0091] Step S63: Synthesize the degree of mixed interference of buses and the travel - time interference degree, and through weighted - average and normalization calculations, obtain the traffic - capacity reduction coefficient of each target lane

[0092] Specifically, this application will, according to the entropy - weight method, assign corresponding weights to the degree of mixed interference of buses and the travel - time interference degree. Among them, the entropy - weight method is an objective weight - assignment method, which can determine the weights according to the dispersion degree of each index data, so that the index with a larger dispersion degree has a greater impact on the comprehensive evaluation, and thus is given a greater weight

[0093] Furthermore, after determining the weights, perform weighted - average calculations on the degree of mixed interference of buses and the travel - time interference degree (i.e., the indexes). That is, this application will multiply the value of each index by its corresponding weight, and then add the weighted values to complete the calculation

[0094] Finally, perform normalization processing on the calculated weighted average value to obtain the traffic - capacity reduction coefficient of each target lane. It should be noted that the normalized value is the traffic - capacity reduction coefficient, which reflects the degree of bus interference on each target lane. Among them, the larger the value, the more serious the interference and the greater the reduction amplitude of the traffic capacity

[0095] In the above embodiments, by analyzing the overlapping area between the trajectory fitting curves of social vehicles and buses, the degree of mixed interference generated by buses on the target lane can be accurately quantified. This quantification method is more accurate than traditional qualitative evaluations and helps to understand the impact of buses on traffic flow more deeply. Additionally, by comparing and analyzing the lane travel data of the lanes affected by bus stops with those of non-affected lanes, the specific impact of bus stops on the lane travel time can be determined. Finally, by comprehensively considering the degree of bus mixed interference and the degree of travel time interference, and through weighted averaging and normalization processing, the capacity reduction factor can be obtained, which can objectively reflect the actual impact of buses on the lane capacity and provide a scientific basis for traffic planning and management.

[0096] In one of the embodiments, in step S62, the obtaining of the degree of travel time interference of each target lane through the comparative analysis of the lane travel data of the lanes affected by bus stops and non-affected lanes includes:

[0097] Step S621, obtaining the travel time data of all vehicles on each lane within the research scope, and through calculation of the average value, obtaining the average travel time of each lane.

[0098] Specifically, this application will collect the travel time data of all vehicles on each lane within the research scope through data collection devices such as in-vehicle GPS devices or traffic monitoring systems. After that, by summing up the travel times of all vehicles and dividing by the number of vehicles, the average travel time of each lane can be obtained.

[0099] Step S622, determining the average travel time of each target lane affected by bus stops and the overall average travel time of the remaining non-affected lanes, and through calculation of the relative difference, obtaining the degree of travel time interference of each target lane.

[0100] Specifically, for the target lanes affected by bus stops, this application will calculate their average travel time through the method illustrated in step S621. At the same time, for the remaining non-affected lanes within the research scope, this application will calculate their overall average travel time, which can be obtained by calculating the average travel times of all non-affected lanes and performing weighted averaging processing.

[0101] Furthermore, this application will also calculate the relative difference between the average travel time of the target lane and the overall average travel time of the non-affected lanes, which can be achieved by subtracting the overall average travel time of the non-affected lanes from the average travel time of the target lane and then dividing by the overall average travel time of the non-affected lanes. The resulting percentage value is the degree of travel time interference of each target lane.

[0102] In one of the embodiments, the travel time interference degree of each target lane can be calculated by the following formula:

[0103] ;

[0104] Wherein, represents the travel time interference degree of lane i (i.e., the aforementioned target lane), represents the average travel time of lane i , represents the overall average travel time of the non-interference lane.

[0105] In the above embodiment, by comparing the average travel times of the target lane affected by the bus stop and the non-interference lane, we can quantitatively evaluate the interference degree of the bus stop on the lane driving efficiency. This quantitative evaluation provides effective data support for traffic planning and optimization.

[0106] In one of the embodiments, for each target lane, in step S63, by comprehensively considering the bus mixing interference degree and the travel time interference degree, through weighted average and normalization processing calculations, the traffic capacity reduction coefficient of each target lane is obtained, including:

[0107] Step S631, based on the calculation of the entropy weight method, obtain the first weight coefficient corresponding to the bus mixing interference degree and the second weight coefficient corresponding to the travel time interference degree.

[0108] Specifically, the calculation steps of the entropy weight method include:

[0109] The first step is to normalize each index data. Among them, the index data are respectively:

[0110] ;

[0111] Wherein, is the dataset of the bus mixing interference degree and the travel time interference degree of lane i ;

[0112] is the value after standardizing , .

[0113] After normalizing each index data, the obtained normalized value is:

[0114] ;

[0115] Wherein, is the standardized data in the data matrix, i represents the lane,j Indicates bus / car; Indicates the i th lane and the j th index value in the data matrix, Indicates the minimum value of all interference coefficients among all lanes, Indicates the maximum value of all interference coefficients among all lanes.

[0116] Step 2: Substitute the normalized value calculated in the first step into the following formula to calculate the proportion of the j th index value under the i th index in the overall index data: :

[0117] ;

[0118] Step 3: According to the definition of information entropy in information theory, substitute into the following formula to calculate the entropy value of the j th index: :

[0119] ;

[0120] Step 4: Substitute into the following formula for calculation to obtain the first weight coefficient corresponding to the mixed interference degree of the bus and the second weight coefficient corresponding to the interference degree of the travel time:

[0121] ;

[0122] That is, is the first weight coefficient, is the second weight coefficient, where k is the number of indexes, that is, k = 2.

[0123] Step S632: Based on the first weight coefficient and the second weight coefficient, perform a weighted average calculation on the mixed interference degree of the bus and the interference degree of the travel time to obtain the comprehensive interference degree of the bus stop on the target lane.

[0124] Specifically, after obtaining the first weight coefficient i corresponding to the lane and the second weight coefficient i corresponding to the lane , perform a weighted average calculation on the mixed interference degree of the bus and the interference degree of the travel time through the following formula to obtain the comprehensive interference degree of the bus stop on the lane i :

[0125] ;

[0126] Among them, represents the degree of mixed interference of buses in the corresponding lane i and represents the degree of interference with the travel time in the corresponding lane. i

[0127] Step S633: Perform normalization processing based on the comprehensive interference degree to obtain the traffic capacity reduction coefficient of the target lane.

[0128] Specifically, the present application performs normalization processing through the following formula:

[0129] ;

[0130] Among them, represents the traffic capacity reduction coefficient of the corresponding lane i

[0131] It should be noted that the traffic capacity reduction coefficient is a value less than or equal to 1, which represents the degree of reduction of the lane traffic capacity due to various adverse factors. Specifically, if the traffic capacity reduction coefficient of lane i is closer to 1, it means that the traffic capacity of lane i is less affected by adverse factors; on the contrary, if the coefficient is smaller, it means that the traffic capacity of lane i is more affected by adverse factors.

[0132] In the above embodiment, based on the calculation of the entropy weight method, the weights of the degree of mixed interference of buses and the degree of interference with travel time can be accurately quantified. And based on the quantified weights, the degree of mixed interference of buses and the degree of interference with travel time are weighted and averaged to obtain the comprehensive interference degree of the bus stop on the target lane. This comprehensive evaluation method can comprehensively consider the influence of various interference factors on the traffic capacity of the target lane, making the evaluation result more accurate and reliable.

[0133] In one of the embodiments, in step S6, the determining the actual traffic capacity of each target lane by combining the basic traffic capacity of the lane includes:

[0134] Step S64: Obtain the basic traffic capacity of each target lane and adjust it through the corresponding traffic capacity reduction coefficient to obtain the actual traffic capacity of each target lane.

[0135] ​​Specifically, this application will obtain the basic lane passing capacity of each target lane and multiply it by the corresponding passing capacity reduction coefficient. The resulting value is the actual passing capacity of the lane. This adjustment method can more accurately reflect the actual passing conditions of road lanes, provide a more scientific basis for traffic planning, design, and management, help improve road passing efficiency, and reduce traffic congestion.

[0136] Please refer to Figure 4 , a calculation system for the passing capacity of a road section under the interference of a bus stop disclosed in this application. The system includes a range determination module, a trajectory data acquisition module, a trajectory curve fitting module, a lane division module, a target lane identification module, and a road section passing capacity evaluation module, where:

[0137] The range determination module is used to determine the research range and the research time period.

[0138] The trajectory data acquisition module is used to acquire the social vehicle trajectory data and the bus trajectory data within the research range and the research time period.

[0139] The trajectory curve fitting module is used to perform curve fitting based on the social vehicle trajectory data and the bus trajectory data to obtain a social vehicle trajectory fitting curve and a bus trajectory fitting curve.

[0140] The lane division module is used to perform lane division based on the social vehicle trajectory fitting curve according to the lane division method of peak positioning - spacing width determination, and determine the lane order.

[0141] The target lane identification module is used to determine multiple target lanes affected by the bus stop according to the lane order.

[0142] The road section passing capacity evaluation module is used to comprehensively evaluate the passing capacity reduction coefficient of each target lane based on the overlapping area of the social vehicle and bus trajectory fitting curves and the comparison analysis of lane driving data, and then combine the basic lane passing capacity to determine the actual passing capacity of each target lane.

[0143] In one embodiment, the above modules are also used to implement the steps illustrated in any of the above method embodiments, and this application does not make any limitations in this regard.

[0144] As can be seen from the above, a road section traffic capacity calculation system under the interference of a bus stop disclosed in this application adopts a lane division method of wave peak positioning - fixed width based on spacing, and can perform lane division based on the actual distribution of vehicle driving data, ensuring that the divided lanes are more in line with the characteristics of the actual traffic flow. By determining the target lanes affected by the bus stop according to the lane sequence, the traffic impact area can be accurately located, providing a clear target for subsequent analysis and optimization; by comprehensively evaluating the overlapping area of the trajectory fitting curves of social vehicles and buses and the lane driving data, the traffic capacity reduction coefficient of each target lane can be calculated, and then the actual traffic capacity can be determined. This provides a scientific basis for traffic planning and decision-making, and helps to optimize traffic organization and management.

[0145] This application also discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the method for calculating the traffic capacity of a road section under the interference of a bus stop.

[0146] As can be seen from the above, a computer-readable storage medium disclosed in this application adopts a lane division method of wave peak positioning - fixed width based on spacing, and can perform lane division based on the actual distribution of vehicle driving data, ensuring that the divided lanes are more in line with the characteristics of the actual traffic flow. By determining the target lanes affected by the bus stop according to the lane sequence, the traffic impact area can be accurately located, providing a clear target for subsequent analysis and optimization; by comprehensively evaluating the overlapping area of the trajectory fitting curves of social vehicles and buses and the lane driving data, the traffic capacity reduction coefficient of each target lane can be calculated, and then the actual traffic capacity can be determined. This provides a scientific basis for traffic planning and decision-making, and helps to optimize traffic organization and management.

[0147] This application also discloses a calculation control device for the traffic capacity of a road section under the interference of a bus stop, including a communication interface, a memory, a communication bus, and a processor. Among them, the processor, communication interface, and memory complete communication with each other through the communication bus;

[0148] The memory is used to store a computer program;

[0149] The processor is used to implement the steps of the method for calculating the traffic capacity of a road section under the interference of a bus stop when executing the program stored on the memory.

[0150] As can be seen from the above, a calculation and control device for road section traffic capacity under the interference of a bus stop disclosed in this application adopts a lane division method of wave peak positioning - spacing width determination, which can divide lanes based on the actual distribution of vehicle driving data to ensure that the divided lanes are more in line with the characteristics of the actual traffic flow. By determining the target lanes affected by the bus stop according to the lane sequence, the traffic impact area can be accurately located, providing a clear target for subsequent analysis and optimization; by comprehensively evaluating the overlapping area of the trajectory fitting curves of social vehicles and buses and the lane driving data, the traffic capacity reduction coefficient of each target lane can be calculated, and then the actual traffic capacity can be determined. This provides a scientific basis for traffic planning and decision-making, and helps to optimize traffic organization and management.

[0151] It should be noted that the above sequence of embodiments of the present invention is only for description and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0152] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for calculating the road section traffic capacity under bus station interference, characterized in that: The method comprises: S1. Determine the research scope and research time period; S2, obtain social vehicle trajectory data and bus trajectory data within the research scope and research time period; S3, performing curve fitting based on the social vehicle trajectory data and the bus trajectory data to obtain a social vehicle trajectory fitting curve and a bus trajectory fitting curve; S4. Based on the social vehicle trajectory fitting curve, lane division is performed according to the lane division method of peak positioning-spacing width determination, and the lane order is determined; S5. Determine multiple target lanes affected by the bus station according to the lane sequence; S6. Based on the overlapping area of ​​the social vehicle and bus trajectory fitting curves and the comparative analysis of lane driving data, the capacity reduction coefficient of each target lane is comprehensively evaluated, and then combined with the basic lane capacity, the actual capacity of each target lane is determined; In step S6, the capacity reduction coefficient of each target lane is comprehensively evaluated based on the overlapping area of ​​the social vehicle and bus trajectory fitting curves and the lane driving data comparison analysis, including: S61, determining the bus mixed interference degree of each target lane based on the overlapping area between the social vehicle trajectory fitting curve and the bus trajectory fitting curve; S62, obtaining the travel time interference degree of each target lane based on comparative analysis of lane driving data of the lane interfered by the bus station and the non-interfered lane; S63, comprehensively considering the mixed interference degree of buses and the interference degree of travel time, and calculating the capacity reduction coefficient of each target lane by weighted average and normalization processing; In step S62, the comparison and analysis of the lane driving data of the lane disturbed by the bus station and the non-interfering lane to obtain the travel time interference degree of each target lane includes: S621, obtaining the travel time data of all vehicles on each lane within the research scope, and obtaining the average travel time of each lane by calculating the average value; S622, determine the average travel time of each target lane affected by the bus station and the overall average travel time of the remaining non-interfering lanes, and obtain the travel time interference degree of each target lane by calculating the relative difference. The travel time interference degree of each target lane is calculated by the following formula: ； in, Indicates lane i The degree of travel time interference, Indicates lane i The average travel time, represents the overall average travel time for non-interfering lanes; For each target lane, in step S63, the comprehensive bus mixed interference degree and travel time interference degree are calculated by weighted average and normalized processing to obtain the capacity reduction coefficient of each target lane, including: S631, based on the calculation of the entropy weight method, obtain a first weight coefficient corresponding to the bus mixed interference degree and a second weight coefficient corresponding to the travel time interference degree; S632: Based on the first weight coefficient and the second weight coefficient, a weighted average calculation is performed on the bus mixed interference degree and the travel time interference degree to obtain a comprehensive interference degree of the bus station to the target lane; S633: Perform normalization processing based on the comprehensive interference degree to obtain a capacity reduction coefficient of the target lane.

2. The method according to claim 1, characterized in that In step S3, curve fitting is performed based on the social vehicle trajectory data and the bus trajectory data to obtain a social vehicle trajectory fitting curve and a bus trajectory fitting curve, including: S31, based on the social vehicle trajectory data and the bus trajectory data, determining a first section where social vehicle trajectory changes are relatively concentrated, and a second section where bus trajectory changes are relatively concentrated; S32. In the first section and the second section, a mixed Gaussian distribution model is used to perform curve fitting to obtain a social vehicle trajectory fitting curve and a bus trajectory fitting curve.

3. The method according to claim 1, characterized in that: In step S4, the lane division is performed based on the social vehicle trajectory fitting curve according to the lane division method of peak positioning-spacing width determination, and the lane order is determined, including: S41, determining the peak of the social vehicle trajectory fitting curve through peak detection; S42, taking each crest as a reference point, draw a vertical line along the crest direction to obtain the road centerline; S43. Lane division is performed based on the distance between the center lines of adjacent roads as the lane width, and the lane sequence is determined based on the relative position of the lane and the roadside.

4. The method according to claim 1, characterized in that: In step S6, the actual capacity of each target lane is determined in combination with the basic capacity of the lane, including: S64. Obtain the basic lane capacity of each target lane, and adjust it using a corresponding capacity reduction coefficient to obtain the actual capacity of each target lane.

5. A road section capacity calculation system under bus station interference, characterized in that: The system includes a range determination module, a trajectory data acquisition module, a trajectory curve fitting module, a lane division module, a target lane identification module, and a road section traffic capacity assessment module, wherein: The scope determination module is used to determine the research scope and research time period; The trajectory data acquisition module is used to acquire social vehicle trajectory data and bus trajectory data within the research scope and research time period; The trajectory curve fitting module is used to perform curve fitting based on the social vehicle trajectory data and the bus trajectory data to obtain a social vehicle trajectory fitting curve and a bus trajectory fitting curve; The lane division module is used to divide lanes based on the social vehicle trajectory fitting curve and determine the lane order according to the lane division method of peak positioning-spacing width determination; The target lane identification module is used to determine multiple target lanes affected by the bus stop according to the lane sequence; The road section capacity assessment module is used to comprehensively assess the capacity reduction coefficient of each target lane based on the overlap area of ​​the social vehicle and bus trajectory fitting curves and the lane driving data comparison analysis, and then determine the actual capacity of each target lane in combination with the basic lane capacity; The road section traffic capacity assessment module is based on the overlapping area of ​​social vehicle and bus trajectory fitting curves and lane driving data comparison analysis, and the specific implementation of comprehensively assessing the traffic capacity reduction coefficient of each target lane is as follows: Determining the bus mixed interference degree of each target lane based on the overlapping area between the social vehicle trajectory fitting curve and the bus trajectory fitting curve; Based on the comparative analysis of lane travel data between lanes disturbed by bus stops and non-interfered lanes, the travel time interference degree of each target lane is obtained; The mixed interference degree of buses and the interference degree of travel time are comprehensively considered, and the capacity reduction coefficient of each target lane is obtained through weighted average and normalized calculation; The road section traffic capacity evaluation module obtains the travel time interference degree of each target lane based on the comparative analysis of the lane driving data of the lane interfered by the bus station and the non-interfered lane as follows: Obtain the travel time data of all vehicles on each lane within the research scope, and calculate the average travel time of each lane by averaging; Determine the average travel time of each target lane affected by the bus station and the overall average travel time of the remaining non-interfering lanes. By calculating the relative difference, the travel time interference degree of each target lane is obtained. The travel time interference degree of each target lane is calculated by the following formula: ； in, Indicates lane i The degree of travel time interference, Indicates lane i The average travel time, represents the overall average travel time for non-interfering lanes; For each target lane, the road section traffic capacity assessment module comprehensively considers the mixed interference degree of buses and the interference degree of travel time, and obtains the traffic capacity reduction coefficient of each target lane by weighted average and normalization calculation, which is specifically implemented as follows: Based on the calculation of the entropy weight method, a first weight coefficient corresponding to the mixed interference degree of buses and a second weight coefficient corresponding to the interference degree of travel time are obtained; Based on the first weight coefficient and the second weight coefficient, a weighted average calculation is performed on the bus mixed interference degree and the travel time interference degree to obtain a comprehensive interference degree of the bus station to the target lane; A normalization process is performed based on the comprehensive interference degree to obtain a capacity reduction coefficient of the target lane.

6. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for calculating the road section capacity under bus station interference as described in any one of claims 1 to 4 is implemented.

7. A road section capacity calculation and control device under bus station interference, characterized in that: It includes a communication interface, a memory, a communication bus and a processor, wherein the processor, the communication interface and the memory communicate with each other through the communication bus; The memory is used to store computer programs; The processor is used to implement the steps of the method for calculating the road section capacity under bus station interference as described in any one of claims 1 to 4 when executing the program stored in the memory.

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