Road section traffic capacity calculation method and system under bus station interference

Abstract

The invention relates to the technical field of urban traffic management, in particular to a road section traffic capacity calculation method and system under bus station interference, and the method comprises the steps: determining a research range and a research time period; social vehicle track data and bus track data in a research range and a research time period are acquired; 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; on the basis of the social vehicle track fitting curve, lane division is carried out according to a wave crest positioning-interval width fixing lane division method, and a lane sequence is determined; determining a plurality of target lanes influenced by the bus stations according to the lane sequence; and comprehensively evaluating the traffic capacity reduction coefficient of each target lane based on the overlapping area of the fitting curve of the social vehicle and the bus track and the comparative analysis of the lane driving data, and further determining the actual traffic capacity of each target lane in combination with the basic traffic capacity of the lane.

Description

Technical Field

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

[0002] Road service level is a key evaluation indicator of road service quality, and road capacity is the basis for calculating service level indicators. Urban roads are often affected by many interference factors, resulting in reduced capacity. Among them, bus stops have a greater impact on the capacity of road sections. However, it is often complicated to measure the impact of bus stops, which often involves the style of bus stops (bay or non-bay), whether there is overflow in the bus stop parking area, the number of bus lines and the frequency of departure, and whether it is affected by intersections, etc. These factors are intertwined and not convenient for daily capacity reduction. Therefore, it is necessary to propose a simplified and effective evaluation method to quickly and accurately estimate the impact of bus stops on road capacity. Summary of the invention

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

[0004] Further, in step S3, the 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.

[0005] Furthermore, 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.

[0006] Furthermore, 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.

[0007] Furthermore, in step S62, the comparative 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.

[0008] Furthermore, 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.

[0009] Furthermore, 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.

[0010] In a second aspect, the present application discloses a road section capacity calculation system under bus station interference, the system comprising 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 capacity evaluation 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 overlapping 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.

[0011] 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 road section capacity under bus station interference.

[0012] In a second aspect, the present application discloses a road section capacity calculation and control device under bus station interference, comprising a communication interface, a memory, a communication bus and a processor, wherein the processor, the communication interface and the memory communicate with each other via 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 when executing the program stored in the memory.

[0013] The present invention has the following beneficial effects: 1) The lane division method of peak location-spacing width can be used to divide lanes based on the actual distribution of vehicle driving data, ensuring that the divided lanes are more in line with the actual traffic flow characteristics. The target lanes affected by the bus station are determined according to the lane sequence, which can accurately locate the traffic impact area and provide a clear goal for subsequent analysis and optimization; 2) By comprehensively evaluating the overlapping area of ​​the social vehicle and bus trajectory fitting curves 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. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0015] Figure 1 A method flow chart of a method for calculating the road section traffic capacity under bus station interference provided by one embodiment of the present invention; Figure 2 Schematic diagram of lane division and trajectory curve fitting; Figure 3 It is a schematic diagram of the overlapping area of ​​the trajectory fitting curves of the social car and the bus; Figure 4 A system structure diagram of a system for calculating the road section traffic capacity under bus station interference provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0016] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the method and system for calculating the traffic capacity of a section under the interference of a bus station proposed by the present invention, its specific implementation method, structure, features and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0017] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0018] The specific scheme of the method and system for calculating the road section traffic capacity under bus station interference provided by the present invention is described in detail below with reference to the accompanying drawings.

[0019] See also Figure 1 , which shows a method flow chart of a method for calculating the road section traffic capacity under bus station interference provided by an embodiment of the present invention, the method comprising the following steps: Step S1, determine the research scope and research time period.

[0020] Specifically, this application takes the section between the bus stop and the previous intersection 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) as the research time period.

[0021] Step S2, obtaining social vehicle trajectory data and bus trajectory data within the research scope and research time period.

[0022] Specifically, this application collects social vehicle trajectory data and bus trajectory data within the research scope and research time period based on data sources such as vehicle-mounted GPS devices and traffic monitoring systems. These data include key information such as vehicle location information, speed, and driving direction, which can reflect the driving conditions of vehicles on the research section.

[0023] Step 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.

[0024] Specifically, the present application adopts a mixed Gaussian distribution model to fit social vehicle trajectory data and bus trajectory data. It should be noted that the application of the mixed Gaussian distribution model can more accurately capture and describe the distribution characteristics of vehicle trajectory data.

[0025] Step S4, based on the social vehicle trajectory fitting curve, lane division is performed according to the lane division method of peak positioning and spacing width determination, and the lane order is determined.

[0026] For details, please refer to Figure 2 This application uses peak detection to identify significant peaks on the social vehicle trajectory fitting curve, and uses these peaks as reference points to draw vertical lines to obtain the road centerline. After that, the lane width is determined by measuring the distance between adjacent road centerlines, and the lane order is determined according to the relative position of the lane and the roadside, thereby achieving clear and reasonable lane division and providing support for traffic management and optimization.

[0027] Step S5, determining multiple target lanes affected by the bus stop according to the lane sequence.

[0028] For details, please refer to Figure 3 Considering that bus stops are usually located on the roadside, and passengers frequently cross adjacent lanes when getting on and off the bus, this may affect the traffic flow of lanes adjacent to the roadside and adjacent lanes, causing congestion or reduced driving speed. Therefore, this application will use lane 1 (i.e., lane adjacent to the roadside) and lane 2 adjacent to it as target lanes.

[0029] Step S6, based on the overlapping area of ​​the social vehicle and bus trajectory fitting curves and the comparative analysis of lane driving data, comprehensively evaluate the capacity reduction coefficient of each target lane, and then determine the actual capacity of each target lane in combination with the basic lane capacity.

[0030] Specifically, this application will obtain the driving trajectories of social vehicles and buses in the same period, and perform curve fitting respectively to obtain the social vehicle trajectory fitting curve and the bus trajectory fitting curve. After that, the geographic information system (GIS) or related software tools are used to calculate the overlapping area of ​​the two fitting curves on each target lane. The overlapping area reflects the mixed driving degree of buses and social vehicles on the same lane, that is, the mixed interference degree of buses. After that, by comparing and analyzing the driving data of the lanes (target lanes) disturbed by bus stations and the non-interference lanes, the travel time interference degree caused by buses to the target lanes is quantitatively evaluated. Finally, based on the mixed interference degree of buses and the travel time interference degree, the weighted average and normalized calculation method is used to comprehensively consider the impact of the two on the lane capacity, and the capacity reduction coefficient of each target lane is obtained. This coefficient reflects the degree of reduction in the target lane capacity due to bus interference relative to the non-interference situation. Further, combined with the basic capacity of the lane (that is, the maximum traffic flow that the lane can accommodate under non-interference conditions), the capacity reduction coefficient is used for adjustment to obtain the actual capacity of each target lane.

[0031] From the above, it can be seen that the method for calculating the capacity of a road section under the interference of a bus station disclosed in this application adopts the lane division method of 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 actual traffic flow characteristics. Determining the target lanes affected by the bus station according to the lane sequence can accurately locate the traffic impact area and provide a clear target for subsequent analysis and optimization; by comprehensively evaluating the overlapping area of ​​the social vehicle and bus trajectory fitting curve 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.

[0032] In one embodiment, 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: Step 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.

[0033] Specifically, due to interference from bus stops, private vehicles will change lanes to avoid stopped buses, slow down to maintain a safe distance, or stop and wait for the bus to complete the stop and restart before approaching the bus. These behaviors will occur in a certain section of the road, causing changes in the traffic flow characteristics in the area. Based on this, this application will analyze the trajectory data of private vehicles and buses. For private vehicles, this application will identify the area with large traffic flow and complex driving conditions as the first section through statistical analysis based on the changes in parameters such as the vehicle's driving speed and direction. For buses, based on the characteristics of their stops, stop times, and interaction status with other vehicles, the area section where the bus's driving trajectory changes are more concentrated is used as the second section.

[0034] Step 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.

[0035] Specifically, the definition formula of the mixed Gaussian distribution model includes: ; in, p ( x ) represents the probability density function of mixed Gaussian distribution, j Indicates the vehicle type, wj Indicates j The proportion of vehicle types, Indicates j The standard deviation of the distance of vehicle type from the road centerline, μj Indicates j The mean distance of vehicle type from the road centerline, x Indicates the overall vehicle distance from the center line of the road.

[0036] In the above embodiment, the mixed Gaussian distribution model is used to capture the complex distribution characteristics of the data, improve the accuracy and robustness of the fitting, so that a social car and bus trajectory fitting curve that is closer to the actual can be obtained, thereby providing reliable data support for subsequent data analysis.

[0037] In one embodiment, 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: Step S41, based on the social vehicle trajectory fitting curve, determine the peak therein through peak detection.

[0038] Specifically, the present application analyzes the local maximum points on the fitting curve and uses these points as peaks. It should be noted that the position of the peak represents a relatively concentrated area in the driving trajectory of social vehicles, usually corresponding to the center of the road.

[0039] Step S42, taking each crest as a reference point, draw a vertical line along the crest direction to obtain the road centerline.

[0040] Specifically, the application will determine a vertical direction at each crest and make a vertical line along this direction. These vertical lines will serve as a preliminary estimate of the centerline of the road.

[0041] Step S43, lane division is performed using the distance between the center lines of adjacent roads as the lane width, and the lane order is determined based on the relative position of the lane and the roadside.

[0042] Specifically, the application calculates the distance between adjacent road lines, which is the lane width, and then determines the order of the lanes based on the relative position of the lanes to the roadside.

[0043] In one embodiment, please refer to Figure 2 , this application will define the lane adjacent to the roadside as lane 1, and then number them to the left (depending on the direction of travel of the road) to obtain lane 2, lane 3, etc. Among them, lane 1 and its adjacent lane 2 are defined as lanes affected by buses.

[0044] In one embodiment, 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: Step 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.

[0045] Specifically, the present application will determine the overlapping area between the social vehicle trajectory fitting curve and the bus trajectory fitting curve based on the trajectory fitting curves drawn by the mixed Gaussian distribution model and the enclosed area formed by the horizontal axis of the unified reference coordinate system.

[0046] For further information, please refer to Figure 3 , the overlapping area within the target lane is the bus mixed interference degree of the target lane. The specific calculation formula is: ; in, Indicates lane i The bus mix interference level, Indicates lane i The overlapping area between the social vehicle trajectory fitting curve and the bus trajectory fitting curve within the range.

[0047] Step S62, based on the comparative analysis of the lane driving data of the lane interfered by the bus station and the non-interfered lane, the travel time interference degree of each target lane is obtained.

[0048] Specifically, this application will obtain the travel time of all vehicles on each lane within the research scope, and can calculate the average travel time of each lane through arithmetic average. Then, through screening and grouping, the average travel time of the lane affected by the bus station (i.e. the target lane) is obtained. T i , and the overall average travel time for non-interfering lanes (all lanes except the target lane) T o After that, based on the average travel time T i The overall average travel time T o By calculating the relative difference, 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.

[0049] Step 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.

[0050] Specifically, this application will assign corresponding weights to the mixed interference degree of buses and the travel time interference degree according to the entropy weight method, wherein the entropy weight method is an objective weighting method, which can determine the weight according to the discrete degree of each indicator data, so that the indicator with a larger discrete degree has a greater impact on the comprehensive evaluation, thereby giving it a greater weight.

[0051] Furthermore, after the weights are determined, the bus mixed interference degree and the travel time interference degree (i.e., the index) are weighted averaged. That is, the application multiplies the value of each index by its corresponding weight, and then adds the weighted values ​​to complete.

[0052] Finally, the calculated weighted average is normalized to obtain the capacity reduction coefficient of each target lane. It should be noted that the normalized value is the capacity reduction coefficient, which reflects the degree of interference of each target lane by buses. The larger the value, the more serious the interference and the greater the reduction in capacity.

[0053] In the above embodiment, by analyzing the overlapping area between the social vehicle and bus trajectory fitting curves, the degree of mixed interference caused by the bus on the target lane can be accurately quantified. This quantification method is more accurate than the traditional qualitative evaluation and helps to have a deeper understanding of the impact of buses on traffic flow. In addition, by comparing and analyzing the lane driving data of lanes interfered by bus stops and non-interfering lanes, the specific impact of bus stops on lane travel time can be clarified. Finally, the capacity reduction coefficient obtained by weighted averaging and normalization based on the mixed interference degree of buses and the travel time interference degree can objectively reflect the actual impact of buses on lane capacity and provide a scientific basis for traffic planning and management.

[0054] In one embodiment, in step S62, the comparative analysis of lane travel data of the lane interfered by the bus station and the non-interfered lane to obtain the travel time interference degree of each target lane includes: Step S621, obtain the travel time data of all vehicles on each lane within the research range, and obtain the average travel time of each lane by calculating the average value.

[0055] Specifically, this application will collect the travel time data of all vehicles on each lane within the research scope through data collection equipment such as vehicle-mounted GPS devices or traffic monitoring systems. Then, the average travel time of each lane is obtained by summing up the travel time of all vehicles and dividing it by the number of vehicles.

[0056] Step S622, determining 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 obtaining the travel time interference degree of each target lane by calculating the relative difference.

[0057] Specifically, for the target lanes interfered by the bus station, the present application will calculate its average travel time by the method indicated in step S621. At the same time, for the remaining non-interfering lanes within the research scope, the present application will calculate their overall average travel time, which can be obtained by calculating the average travel time of all non-interfering lanes and performing weighted average processing to obtain the corresponding overall average travel time.

[0058] Furthermore, the present 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-interfering lanes. This can be achieved by subtracting the overall average travel time of the non-interfering lanes from the average travel time of the target lanes, and then dividing it by the overall average travel time of the non-interfering lanes. The percentage value obtained is the degree of travel time interference of each target lane.

[0059] In one embodiment, the travel time interference degree of each target lane can be calculated by the following formula: ; in, Indicates lane i (i.e. the target lane indicated above) travel time interference degree, Indicates lane i The average travel time, Represents the overall average travel time for non-interfering lanes.

[0060] In the above embodiment, by comparing the average travel time of the target lane disturbed by the bus stop with the average travel time of the non-interfering lane, we can quantitatively evaluate the degree of interference of the bus stop on the lane driving efficiency. This quantitative evaluation provides effective data support for traffic planning and optimization.

[0061] In one embodiment, 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: Step 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.

[0062] Specifically, the calculation steps of the entropy weight method include: The first step is to normalize the data of each indicator. The indicator data are: ; in, For the lane i A dataset of bus mix interference and travel time interference; For , The value after normalization.

[0063] After normalizing the data of each indicator, the normalized value is: ; in, is the standardized data in the data matrix, i Indicates lane, j Indicates bus / car; Represents the first i Lane No. j The index value, represents the minimum value of all interference coefficients in all lanes, Represents the maximum value of all interference coefficients in all lanes.

[0064] Step 2: Substitute the normalized value calculated in the first step into the following formula to calculate the j Under the indicator i The proportion of the indicator value in the overall indicator data : ; Step 3: According to the definition of information entropy in information theory, Substitute the following formula to calculate the j Entropy value of each indicator : ; Step 4: Substituting the following formula for calculation, we can obtain the first weight coefficient corresponding to the mixed interference degree of buses and the second weight coefficient corresponding to the interference degree of travel time: ; Right now is the first weight coefficient, is the second weight coefficient, where k is the number of indicators, that is, k=2.

[0065] Step 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.

[0066] Specifically, after obtaining the corresponding lane i The first weight coefficient , and the corresponding lane i The second weight coefficient Then, the bus station to lane interference degree is obtained by weighted average calculation of bus mixed interference degree and travel time interference degree using the following formula: i The comprehensive interference degree: ; in, Indicates the corresponding lane i The bus mix interference level, Indicates the corresponding lane i The degree of travel time interference.

[0067] Step S633: performing normalization processing based on the comprehensive interference degree to obtain a capacity reduction coefficient of the target lane.

[0068] Specifically, this application performs normalization processing through the following formula: ; in, Indicates the corresponding lane i capacity reduction factor.

[0069] It should be noted that the capacity reduction factor is a value less than or equal to 1, which represents the degree of reduction in lane capacity due to various unfavorable factors. i The closer the capacity reduction factor is to 1, the i The smaller the coefficient, the less the lane capacity is affected by adverse factors; on the contrary, if the coefficient is smaller, it means that the lane i The greater the lane capacity is, the more adverse factors will affect it.

[0070] In the above embodiment, the calculation based on the entropy weight method can accurately quantify the weights of the bus mixed interference degree and the travel time interference degree. And based on the quantified weights, the bus mixed interference degree and the travel time interference degree are weighted averaged to obtain the comprehensive interference degree of the bus station to the target lane. This comprehensive evaluation method can comprehensively consider the impact of various interference factors on the traffic capacity of the target lane, making the evaluation result more accurate and reliable.

[0071] In one embodiment, in step S6, determining the actual capacity of each target lane in combination with the basic capacity of the lane includes: Step S64, obtaining the basic lane capacity of each target lane, and adjusting it by a corresponding capacity reduction coefficient to obtain the actual capacity of each target lane.

[0072] Specifically, this application will obtain the basic lane capacity of each target lane and multiply it by the corresponding capacity reduction coefficient. The result is the actual capacity of the lane. This adjustment method can more accurately reflect the actual traffic conditions of road lanes, provide a more scientific basis for traffic planning, design and management, and help improve road traffic efficiency and reduce traffic congestion.

[0073] Please refer to Figure 4 The present application discloses a road section capacity calculation system under bus station interference, the system comprising 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 capacity evaluation module, wherein: The scope determination module is used to determine the research scope and research time period.

[0074] The trajectory data acquisition module is used to acquire social vehicle trajectory data and bus trajectory data within a research scope and research time period.

[0075] 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.

[0076] 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.

[0077] The target lane identification module is used to determine multiple target lanes affected by the bus stop according to the lane sequence.

[0078] The road section capacity assessment module is used to comprehensively assess the capacity reduction coefficient of each target lane based on the overlapping 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.

[0079] In one embodiment, the above modules are also used to implement the steps illustrated in any of the above method embodiments, which is not limited in this application.

[0080] From the above, it can be seen that the present application discloses a road section capacity calculation system under bus station interference, which adopts the lane division method of peak positioning-spacing width determination, and can divide lanes based on the actual distribution of vehicle driving data to ensure that the divided lanes are more in line with the actual traffic flow characteristics. Determining the target lanes affected by the bus station according to the lane sequence can accurately locate the traffic impact area and provide a clear target for subsequent analysis and optimization; by comprehensively evaluating the overlapping area of ​​the social vehicle and bus trajectory fitting curve 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.

[0081] The present application also discloses a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the method for calculating the road section capacity under bus station interference is implemented.

[0082] From the above, it can be seen that a computer-readable storage medium disclosed in the present application adopts a lane division method of peak positioning-spacing width determination, which can perform lane division based on the actual distribution of vehicle driving data to ensure 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 sequence can accurately locate the traffic impact area and provide a clear target for subsequent analysis and optimization; by comprehensively evaluating the overlapping area of ​​the social vehicle and bus trajectory fitting curve 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.

[0083] The present application also discloses a road section capacity calculation and control device under bus station interference, comprising a communication interface, a memory, a communication bus and a processor, wherein the processor, the communication interface and the memory communicate with each other via 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 when executing the program stored in the memory.

[0084] From the above, it can be seen that the present application discloses a road section capacity calculation and control device under the interference of bus stations, which adopts the lane division method of peak positioning-spacing width determination, and can divide lanes based on the actual distribution of vehicle driving data to ensure that the divided lanes are more in line with the actual traffic flow characteristics. Determining the target lanes affected by the bus station according to the lane sequence can accurately locate the traffic impact area and provide a clear target for subsequent analysis and optimization; by comprehensively evaluating the overlapping area of ​​the social vehicle and bus trajectory fitting curve 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.

[0085] It should be noted that the sequence of the above embodiments of the present invention is for description only and does not represent the advantages and disadvantages 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.

[0086] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for calculating the road section 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.

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 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.

5. The method according to claim 4, characterized in that 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.

6. The method according to claim 4, characterized in that 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.

7. The method according to claim 4, 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.

8. 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 overlapping 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.

9. 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 7 is implemented.

10. 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 7 when executing the program stored in the memory.

Images