A bus lane sharing optimization utilization method

Abstract

This invention provides a method for optimizing the shared use of dedicated bus lanes, relating to the field of transportation technology. The method includes: identifying congested road sections and obtaining vehicle speeds in each lane of the congested sections; comparing the congestion levels of dedicated bus lanes and regular lanes by calculating travel indices, and identifying routes where regular lanes are congested while dedicated bus lanes are unobstructed; establishing a multi-objective integer optimization model with the objectives of maximizing the number of shared vehicles available for reservation and minimizing overall road delays; solving for the maximum number of vehicles that can use the dedicated bus lane during a given time period; conducting simulations to verify the multi-objective integer optimization model and evaluating the impact of the shared reservation scheme on dedicated bus lanes and regular lanes; and, considering user acceptance, constructing a mechanism for regular vehicles to reserve bus lanes using carbon credits. This invention addresses the shortcomings of dynamic schemes and the one-size-fits-all approach to existing dedicated bus lane management models.

Description

Technical Field

[0001] This invention belongs to the field of transportation technology, and in particular relates to a method for optimizing the shared use of dedicated bus lanes. Background Technology

[0002] With the acceleration of urbanization, traffic congestion has become increasingly serious, making public transportation an important means of alleviating urban traffic pressure. While dedicated bus lanes have improved bus speeds to some extent, they often remain idle during peak hours, resulting in a waste of road resources. Especially during peak hours, some sections of road frequently experience empty bus lanes while long queues form in regular lanes, causing further congestion. Therefore, effectively utilizing bus lane resources to improve overall road efficiency has become a crucial issue in current urban traffic management.

[0003] The existing technology involves the following: First, dynamic adjustment control of the shared lane is implemented using real-time traffic flow information within the shared lane. When the shared lane meets specific control strategies, HOV vehicles are allowed to enter and share the shared lane with the bus lane. Relevant data (such as vehicle speed and traffic volume) is collected within a detection cycle of the shared lane, and the average delay of the shared lane is calculated based on the detected vehicle information. This data reflects the average delay of vehicles traveling in the shared lane. Then, the service level and congestion level of the shared lane are assessed according to the service level classification of urban roads (such as through delay time, traffic flow, and other indicators). Based on this assessment, the smoothness of the shared lane is determined. Finally, the usage of the shared lane is judged according to different service levels. To ensure smooth passage of vehicles in the shared lane, the system also coordinates and controls the traffic flow at adjacent intersection entrances and exits, adopting strategies such as extending the green light time of the bus lane and controlling interference from vehicles at adjacent intersection entrances and exits. However, the following drawbacks exist:

[0004] 1. Failure to fully consider changes in vehicle traffic flow at different times makes it difficult to effectively manage specific time periods.

[0005] 2. Establishing a dynamic control strategy requires a large amount of real-time data, and the acquisition and processing of this data are technically complex.

[0006] 3. The lack of visual information on the open status of bus lanes makes it difficult for users to understand the rationality of the current resources.

[0007] Existing technologies have also addressed the issue of vehicles using lanes to some extent, achieving intelligent recognition and scheduling. Specifically, this method determines whether and when intelligent connected vehicles need to use lanes by considering multiple factors, and optimizes traffic flow by controlling vehicle travel paths. However, it has the following drawbacks:

[0008] 1. It mainly focuses on intelligent connected vehicles and fails to cover all types of vehicle lane-sharing needs, and does not adequately support the reservation and sharing mechanism of traditional buses.

[0009] 2. For drivers and passengers of intelligent connected vehicles, frequent lane-changing may increase their anxiety and uncertainty, affecting their acceptance of intelligent connected vehicle technology.

[0010] 3. Intelligent connected vehicle technology is still in its early stages of development and requires long-term technological accumulation and optimization. Currently, its promotion and implementation in practical application scenarios face certain challenges.

[0011] Existing technologies focus on optimizing the activation time of dedicated bus lanes during peak hours and the allocation of road capacity, aiming to improve the operational efficiency of urban transportation systems and alleviate traffic congestion during peak hours. However, they have the following drawbacks:

[0012] 1. Although the literature proposes methods for optimizing the activation time of dedicated bus lanes and the allocation of road capacity, actual implementation may require a significant investment of human, material, and financial resources. For example, it necessitates the installation and commissioning of relevant traffic monitoring equipment, and the establishment and maintenance of a traffic data management system. These costs may limit the promotion and application of this technical solution in certain regions.

[0013] 2. The optimization is primarily based on minimizing the total passenger delay. In reality, the average passenger capacity of buses and cars may lead to increased congestion in ordinary lanes and long-term periods of no traffic in bus lanes. Summary of the Invention

[0014] To address the aforementioned shortcomings in existing technologies, this invention provides a method for the shared and optimized utilization of dedicated bus lanes, solving the problem of low bus lane utilization; resolving the defects of dynamic schemes, such as vehicle-road cooperation and the high degree of technical support required; and overcoming the one-size-fits-all problem of existing dedicated bus lane management models. This invention proposes a systematic solution for whether and how dedicated bus lanes can be opened. Furthermore, this invention establishes a spatiotemporal distribution map of dedicated bus lanes in cities with open potential, thus obtaining a map of dedicated bus lanes in cities with open potential. 。

[0015] To achieve the above objectives, the technical solution adopted by this invention is: a method for optimizing the shared use of dedicated bus lanes, comprising the following steps:

[0016] S1. Filter out congested road sections during a certain time period and obtain the vehicle speeds in each lane of the congested road section;

[0017] S2. Based on congested road segment information and the obtained vehicle speed, the congestion level of bus lanes and ordinary lanes is compared by calculating the travel index, and the route where the ordinary lanes are congested but the bus lanes are unobstructed is selected.

[0018] S3. Based on the selected routes where ordinary lanes are congested but bus lanes are unobstructed, establish a multi-objective integer optimization model with the objectives of maximizing the number of open reservation shared vehicles and minimizing the overall road delay.

[0019] S4. Based on the multi-objective integer optimization model, the maximum number of vehicles that can be opened to the public for use in the bus lane during this period can be obtained.

[0020] S5. Based on the maximum number of vehicles that can be opened to external use of the bus lane during this period, a multi-objective integer optimization model is simulated and tested to evaluate the impact of the shared reservation scheme on the bus lane and ordinary lanes.

[0021] S6. Based on the impact assessment and taking into account user acceptance, and with the goal of maximizing emission reduction, construct a mechanism for ordinary vehicles to reserve bus lanes using carbon credits, thereby optimizing the bus lanes.

[0022] The beneficial effects of this invention are as follows: Based on urban road congestion information collected from the Gaode Open Platform API and vehicle driving conditions obtained through video recognition, this invention establishes a spatiotemporal distribution identification model for dedicated bus lanes with open sharing significance and an optimization model for the maximum number of vehicles that can be reserved for external use. Simultaneously, combined with a reasonable carbon credit exchange mechanism, it constructs an optimized scheme for dedicated bus lanes where users can reserve access through carbon credits, providing a new solution for the optimized utilization of dedicated bus lanes and the promotion of low-carbon travel. Based on historical road data, this invention obtains dedicated bus lanes with open sharing significance, calculates the maximum number of vehicles that can be opened, and proposes a static scheme for private cars to reserve access to dedicated bus lanes.

[0023] Further, step S1 includes the following steps:

[0024] S101. Make API calls to the Gaode Open Platform to collect the congestion index of road segments at different time periods, and determine that the road segment at a certain time period is a congested road segment when the congestion index of a road segment is greater than the preset threshold.

[0025] S102. Conduct video analysis on roads with dedicated bus lanes in congested sections to obtain vehicle speeds in different lanes.

[0026] The beneficial effects of the above-mentioned further solutions are: by screening out congested road sections and obtaining the vehicle speeds in each lane of the congested road sections, the present invention achieves the initial screening of congested road sections, laying the foundation for subsequent research.

[0027] Furthermore, step S102 includes the following steps:

[0028] S1021. Based on the video of roads with dedicated bus lanes in congested sections, use a convolutional network to extract features from the vehicles in the video and obtain vehicle classification results.

[0029] S1022. Based on the vehicle classification results, extract the features of the pixels;

[0030] S1023. Based on the extracted pixel features, the original pixels are restored in the fully connected layer of the convolutional network to obtain the predicted value;

[0031] S1024. Based on the predicted values, count the traffic flow during this period using a Python script;

[0032] S1025. Based on traffic flow, the motion status of vehicles is obtained through the ByteTrack tracking algorithm;

[0033] S1026. By combining the vehicle's motion status and the number of monitoring video frames, the vehicle speed in each lane is obtained.

[0034] The beneficial effect of the above-mentioned further solution is that the present invention compares the congestion levels of bus lanes and ordinary lanes by calculating the travel index, and selects road sections where ordinary lanes are congested while bus lanes are unobstructed.

[0035] Furthermore, the expression for maximizing the number of open reservation shared vehicles in the multi-objective integer optimization model is as follows:

[0036]

[0037] in, This indicates maximizing the number of shared bikes available for reservation. Indicates the first i The first bus lane j The number of vehicles that can be shared during a given time period;

[0038] The expression for minimizing the overall total road delay in the multi-objective integer optimization model is as follows:

[0039]

[0040]

[0041]

[0042] in, This indicates that the overall road delay is minimized. Indicates the start time of a specific available booking time slot. This indicates the end time of a specific bookable time slot. This indicates the total number of people queuing in the dedicated bus lane. This indicates the total number of people queuing in a single regular lane. This indicates the number of regular lanes on this road section. Indicates time, To represent the differential, This indicates the cumulative number of buses queuing in the dedicated bus lane. This indicates the passenger capacity of the bus. This indicates the cumulative number of private cars queuing in the bus lane. This indicates the passenger capacity of a private car. This indicates the cumulative number of vehicles queuing for a single regular lane.

[0043] The beneficial effects of the above-mentioned further solutions are: the present invention establishes a multi-objective optimization model with the goal of minimizing total delays and maximizing the number of open reservation shared vehicles, thereby optimizing the application of the additional capacity of bus lanes and alleviating overall road congestion.

[0044] Furthermore, the constraints of the multi-objective integer optimization model include:

[0045] Bus delays are less than the threshold:

[0046]

[0047]

[0048] in, This indicates the maximum average delay for passengers who choose public transportation. This indicates the cumulative number of vehicles queuing in the bus lane. This indicates the bus arrival rate of the dedicated bus lane. This indicates the actual traffic capacity of the dedicated bus lane;

[0049] The average delay per vehicle for those booking a bus in a dedicated bus lane is less than the average delay per vehicle in a regular bus lane.

[0050]

[0051]

[0052]

[0053]

[0054] in, This indicates the cumulative number of buses queuing in the dedicated bus lane. This indicates the cumulative number of vehicles queuing in a single regular lane. This indicates the proportion of buses in the traffic flow of the bus lane. This indicates the arrival rate of private cars in the ordinary lanes of this road section. This indicates the actual traffic capacity of a single ordinary lane on this road section;

[0055] Bus capacity allocation:

[0056]

[0057] in, This indicates the bus arrival rate of the dedicated bus lane;

[0058] Integer constraints:

[0059] .

[0060] The beneficial effects of the above-mentioned further solutions are: through the above constraints, the present invention achieves optimized application of the additional capacity of bus lanes while ensuring the driving experience of private car owners who reserve lane access, and responds to the policy of "public transport first". Attached Figure Description

[0061] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0062] The specific embodiments of the present invention are described below to enable those skilled in the art to understand the present invention. However, it should be understood that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the present invention as defined and determined by the appended claims. All inventions utilizing the concept of the present invention are protected.

[0063] like Figure 1 As shown, this invention provides a method for optimizing the shared use of dedicated bus lanes, the implementation of which is as follows:

[0064] S1. Filter out congested road sections for a certain time period and obtain the vehicle speeds in each lane of the congested road sections. The implementation method is as follows:

[0065] S101. Make API calls to the Gaode Open Platform to collect the congestion index of road segments at different time periods, and determine that the road segment at a certain time period is a congested road segment when the congestion index of a road segment is greater than the preset threshold.

[0066] S102. Perform video analysis on roads with dedicated bus lanes in congested sections to obtain vehicle speeds in different lanes. The implementation method is as follows:

[0067] S1021. Based on the video of roads with dedicated bus lanes in congested sections, use a convolutional network to extract features from the vehicles in the video and obtain vehicle classification results.

[0068] S1022. Based on the vehicle classification results, extract the features of the pixels;

[0069] S1023. Based on the extracted pixel features, the original pixels are restored in the fully connected layer of the convolutional network to obtain the predicted value;

[0070] S1024. Based on the predicted values, count the traffic flow during this period using a Python script;

[0071] S1025. Based on traffic flow, the motion status of vehicles is obtained through the ByteTrack tracking algorithm;

[0072] S1026. By combining the vehicle's motion status and the number of monitoring video frames, the vehicle speed in each lane is obtained.

[0073] In this embodiment, to ensure the driving experience of users who make reservations, the study should be conducted on road sections where ordinary lanes are relatively congested and bus lanes are relatively empty (i.e., road sections where bus lanes are open).

[0074] In this embodiment, the present invention first identifies congested road sections, and then obtains the vehicle speeds in each lane of the congested road sections. The specific method is as follows:

[0075] First, the API of the Gaode Open Platform is called to collect vehicle speeds and congestion indices for different time periods (where congestion index = (actual travel time - free-flowing travel time) / free-flowing travel time * 100%). When the congestion index for a certain time period is greater than 2.2, the road segment is judged to be congested, thus achieving initial screening of open road segments. However, since the speed data of each lane and buses cannot be obtained from the Gaode Open Platform, this invention performs video analysis on roads with dedicated bus lanes in the above-mentioned road segments. The YOLOv5 and ByteTrack algorithms are used to obtain the vehicle speeds in different lanes. The specific process is as follows:

[0076] ① Video Recognition: The bus counting algorithm based on YOLOv8 focuses on quickly and accurately detecting and counting buses in different lanes in images or videos. This algorithm is a single-stage object detection algorithm that treats object detection as a regression problem. It completes detection by regressing the location and category of the object to the bounding box of the image. Without overconvergence, the smaller the value of the loss function, the better the regression result. When recognizing vehicles in videos, the YOLOv8 object detection algorithm is used, employing a convolutional network to extract features. The VFL loss function is used in the convolution calculation.

[0077]

[0078] in, This represents the predicted value for the recognition result. Indicates the target category or weight. Indicates the weight adjustment factor. γ This represents the balance factor.

[0079] The VFL loss function is used to accurately classify vehicles, such as cars and buses, in images. After classification, pixel features are extracted, and the DFL loss function is used when providing the predicted bounding boxes.

[0080]

[0081] in, express , loss function, The model predicts the first The score of each discrete distribution point The model predicts the first The score of each discrete distribution point This represents the upper bound of the corresponding discrete distribution points. This represents the actual target value, which is usually a continuous value.

[0082] Then, the original pixel values ​​are restored in the fully connected layer of the convolution to obtain the predicted values. Based on the recognition results, we then use a Python script to count the traffic flow during that time period.

[0083] ②Speed ​​Calculation: The vehicle detected by the YOLOv8 algorithm is tracked by the ByteTrack tracking algorithm. The target is represented by multi-scale features such as pixels and frame rate. The motion state of the vehicle is obtained by solving the kinematic formula. Finally, the speed of the vehicle is calculated by combining the motion state and the number of frames in the monitoring video.

[0084] The vehicle identification result in surveillance video is presented as a set of pixels. Therefore, the displacement can be represented by finding the distance between the beginning and end pixels. :

[0085]

[0086] in, This indicates the final position of the vehicle in pixels within the video. This indicates the pixel position of the vehicle at the beginning of the video.

[0087] time It can be calculated by dividing the number of frames per target pixel by the video's frame rate:

[0088]

[0089] in, This indicates the number of video frames the vehicle passes through during its movement. This indicates the video's frame rate.

[0090] speed Equals bit removal by time: This gives the car's speed in km / h.

[0091] Using the above methods, accurate estimates of the actual speeds of buses and private cars in each lane can be obtained simultaneously from surveillance video.

[0092] S2. Based on congested road segment information and the obtained vehicle speed, the congestion level of bus lanes and ordinary lanes is compared by calculating the travel index, and the route where the ordinary lanes are congested but the bus lanes are unobstructed is selected.

[0093] In this embodiment, based on the data obtained above, the congestion levels of bus lanes and regular lanes are compared by calculating the Travel Index (TTI), and road segments where "regular lanes are congested while bus lanes are unobstructed" are selected, thus identifying the spatiotemporal distribution of bus lanes with meaningful accessibility. The specific formula is as follows:

[0094]

[0095]

[0096] in, Indicates the road segment at a certain time period k Travel index Indicates the number of ordinary lanes within the corresponding time period The point speed of the vehicle, Indicates the first bus lane The point speed of the vehicle, This indicates a single regular lane during that time period. Total number of vehicles in the area Indicates the threshold. Indicates the first lane of the ordinary lane a car, j Indicates the first bus lane j a car, m This indicates the bus lane during this time period. Total number of vehicles inside, This indicates the number of a specific road segment within a certain time period. Indicates lanes during the same time period The average speed of the vehicles at the point inside the vehicle, Bus lane The arithmetic mean of the point speeds of vehicles inside the vehicle.

[0097] That is, when the Travel Index (TTI) of a road segment during a certain time period is greater than the threshold, the bus lane in that road segment during that time period is meaningful to open; when the Travel Index (TTI) of a road segment during a certain time period is less than the threshold, the bus lane in that road segment during that time period is not meaningful to open.

[0098] S3. Based on the selected routes where ordinary lanes are congested but bus lanes are unobstructed, establish a multi-objective integer optimization model with the objectives of maximizing the number of open reservation shared vehicles and minimizing the overall road delay.

[0099] In this embodiment, an optimization model is established to solve for the maximum number of vehicles that can be opened to external traffic in a bus lane with openness.

[0100] In this embodiment, based on the selected bus lanes with open lane reservation significance during the selected time periods, and with constraints such as bus delays within a certain range and private car delays in the bus lanes being less than those in ordinary lanes, a multi-objective integer optimization model is established with the objectives of maximizing the number of shared vehicles available for open reservation and minimizing the overall road delay. Algorithms such as ant colony optimization and genetic optimization, written in Python and MATLAB, are used to solve for the maximum number of vehicles that can be used by private cars in the bus lanes during the specified time periods.

[0101] In this embodiment, the model is a multi-objective optimization model. Since an increase in the number of shared vehicles leads to an increase in traffic flow in bus lanes, the total delay for passengers will also increase accordingly. This paper aims to maximize the number of shareable vehicles and minimize the total delay for passengers, and uses weights to achieve satisfactory results for both.

[0102] Maximum number of vehicles that can be shared:

[0103]

[0104] in, This indicates maximizing the number of shared bikes available for reservation. Indicates the first i The first bus lane j The number of vehicles that can be shared during a given time period.

[0105] Minimize total passenger delay, i.e., minimize the overall total road delay:

[0106]

[0107] in, This indicates that the overall road delay is minimized. Indicates the start time of a specific available booking time slot. This indicates the end time of a specific bookable time slot. This indicates the total number of people queuing in the dedicated bus lane. This indicates the total number of people queuing in a single regular lane. This indicates the number of regular lanes on this road section. Indicates time, It represents the differential.

[0108] The constraints are as follows:

[0109] ① Bus ​​delay < threshold:

[0110] To ensure that the passenger experience for those choosing public transportation is not significantly diminished, even after allocating surplus capacity in bus lanes to private cars, the service level of buses must still be maintained. This is where... This represents the maximum average delay for passengers choosing public transportation, and therefore must meet the following conditions:

[0111]

[0112] in, This indicates the maximum average delay for passengers who choose public transportation. This indicates the cumulative number of vehicles queuing in that bus lane.

[0113] ② Average delay per vehicle in bus lane reservations < Average delay per vehicle in regular lanes:

[0114] When travelers choose to book a dedicated bus lane, their travel efficiency and experience should be guaranteed, meaning the average delay per vehicle in a reserved bus lane should be less than the average delay per vehicle in a regular bus lane. Therefore, the following conditions should be met:

[0115]

[0116] in, This indicates the cumulative number of buses queuing in the dedicated bus lane. This indicates the cumulative number of vehicles queuing for a single regular lane.

[0117] ③ Bus capacity allocation:

[0118] The capacity allocated to cars and buses in the dedicated bus lane meets the actual capacity constraints of the bus lane:

[0119]

[0120] in, This indicates the bus arrival rate of the dedicated bus lane. This indicates the actual traffic capacity of the dedicated bus lane.

[0121] ④ Integer constraints:

[0122] .

[0123] In this embodiment, the total number of people queuing is the product of the number of vehicles queuing in the corresponding lane and the number of passengers that the corresponding vehicle type can carry:

[0124]

[0125]

[0126] in, This indicates the cumulative number of buses queuing in the dedicated bus lane. This indicates the passenger capacity of the bus. This indicates the cumulative number of private cars queuing in the bus lane. This indicates the passenger capacity of a private car. This indicates the cumulative number of vehicles queuing for a single regular lane.

[0127] In this embodiment, the cumulative number of queued vehicles is the difference between the actual number of vehicles arriving on the road and the road's capacity, as follows:

[0128] 1) Number of vehicles queuing in the bus lane

[0129] a. Total number of vehicles queuing in the bus lane:

[0130]

[0131] in, This indicates the cumulative number of vehicles queuing in the bus lane. This indicates the bus arrival rate of the dedicated bus lane. This indicates the actual traffic capacity of the dedicated bus lane.

[0132] b. Number of buses queuing in the bus lane:

[0133]

[0134] in, This indicates the cumulative number of buses queuing in the dedicated bus lane. p This indicates the proportion of buses in the traffic flow of that lane.

[0135] c. Number of private cars queuing in the bus lane:

[0136]

[0137] in, This indicates the cumulative number of private cars queuing in the bus lane.

[0138] 2) Number of vehicles queuing in regular lanes:

[0139]

[0140] in, This indicates the cumulative number of vehicles queuing in a single regular lane. This indicates the arrival rate of private cars in the ordinary lanes of this road section. This indicates the actual traffic capacity of a single ordinary lane on that road section.

[0141] In this embodiment, the actual traffic capacity is determined as follows:

[0142] The actual capacity of a lane is related to its theoretical capacity and various influencing factors, namely:

[0143]

[0144]

[0145] in, This represents the reduction factor for the impact of various factors on traffic capacity. Indicates theoretical passability. S Indicates saturation flow rate. It represents the ratio of the effective green light time to the cycle duration.

[0146] This invention introduces the basic theoretical traffic capacity obtained through field measurements in the prior art, taking a basic saturation flow rate of 1800 vehicles / (h·lane).

[0147] The present invention will now discuss the reduction factors corresponding to lane width, different vehicle models, and driver behavior.

[0148] Traffic composition: Traffic composition correction coefficient in this invention The formula is as follows:

[0149]

[0150] in, Indicates vehicle type i The conversion factor, Indicates the vehicle type in the traffic flow. i The percentage.

[0151] In this embodiment, the conversion factor for passenger cars is 1.00, the average conversion factor for medium-sized vehicles is 1.25, the average conversion factor for large vehicles is 1.80, and the conversion factor for motorcycles is 0.58. Note: For urban signalized intersections, medium-sized vehicles mainly refer to single-car buses; large vehicles mainly refer to connecting buses.

[0152] Lane width: Lane width correction coefficient in this invention The formula is as follows:

[0153]

[0154] Among them, parameters The estimated value is 6.22. This indicates the width of the corresponding lane.

[0155] Driver behavior: The factor by which driver behavior reduces traffic capacity As an empirical value, this invention takes .

[0156] In this embodiment, the bus lane capacity is calculated as follows:

[0157] bus lane reduction factor :

[0158]

[0159] At this time, take The value is 1.25. Substituting the parameters into the initial formula, we get:

[0160]

[0161] In this embodiment, the capacity of the ordinary lanes is calculated as follows:

[0162] Since only passenger cars travel in the ordinary lanes, no reduction in traffic composition is required. The reduction factor for the ordinary lanes is... for:

[0163]

[0164] At this point, substituting each parameter into the initial formula yields:

[0165] .

[0166] Using the above model, the maximum number of vehicles that can be opened to external traffic in a bus lane with openness can be obtained.

[0167] S4. Based on the multi-objective integer optimization model, the maximum number of vehicles that can be opened to the public for use in the bus lane during this period can be obtained.

[0168] S5. Based on the maximum number of vehicles that can be opened to external use of the bus lane during this period, a multi-objective integer optimization model is simulated and tested to evaluate the impact of the shared reservation scheme on the bus lane and ordinary lanes.

[0169] In this embodiment, after solving the aforementioned optimization model, the present invention obtains the maximum number of vehicles that can be opened for use in a bus lane with openness significance. Next, the present invention draws and constructs a road segment model of the bus lane in the VISSIM platform, and configures vehicle types, traffic flow ratios, speed distributions, etc., in conjunction with real-world scenarios. Based on the reservation and sharing scheme studied above, under specific time periods and conditions, the present invention allows the corresponding number of private cars that can be reserved to enter the bus lane at a certain arrival rate, completing a simulation experiment of the overall road segment vehicle operation status after opening reservation and sharing. After the experiment, data such as traffic flow, average speed, and queue length for each lane are output for delay analysis to evaluate the impact of reservation sharing on the bus lane and ordinary lanes.

[0170] S6. Based on the impact assessment and taking into account user acceptance, and with the goal of maximizing emission reduction, construct a mechanism for ordinary vehicles to reserve bus lanes using carbon credits, thereby optimizing the bus lanes.

[0171] In this embodiment, when constructing the exchange mechanism for private cars to reserve bus lanes using carbon credits, the attractiveness to users decreases as the number of carbon credits required for a reservation increases, while the emission reduction effect of a single reservation increases. Therefore, it is necessary to balance the attractiveness of the exchange mechanism to users and the emission reduction effect of a single reservation.

[0172] With the goal of maximum emission reduction, the emission factor is calculated using the formula (i.e.) And the formula for converting emission reductions to carbon credits (i.e., 1gCO_2e = 1 carbon credit). This represents total emissions. Indicates emission factor, This data represents the activity data and assesses the emission reduction corresponding to the carbon credits used by private cars that reserve bus lanes in different time periods and road sections after the implementation of the sharing scheme. It achieves a qualitative and quantitative assessment of the actual emission reduction effect of reservation sharing, ensuring that the credit mechanism can promote low-carbon travel and achieve environmental protection goals. At the same time, user surveys are conducted to understand users' acceptance of carbon credit redemption, ensuring that the mechanism can attract more users to participate, so as to achieve real emission reduction in both practical and theoretical terms.

[0173] Based on the above content, a bus lane optimization scheme has been developed that allows users to reserve bus lanes using carbon credits.

Claims

1. A method for optimizing the shared use of dedicated bus lanes, characterized in that, Includes the following steps: S1. Filter out congested road sections during a certain time period and obtain the vehicle speeds in each lane of the congested road section; S2. Based on congested road information and the obtained vehicle speed, the congestion levels of bus lanes and ordinary lanes are compared by calculating the travel index, and routes with congested ordinary lanes but unobstructed bus lanes are selected. S3. Based on the selected routes where ordinary lanes are congested but bus lanes are unobstructed, establish a multi-objective integer optimization model with the objectives of maximizing the number of shared vehicles available for reservation and minimizing the overall road delay; S4. Based on the multi-objective integer optimization model, solve for the maximum number of vehicles that can be opened to the public for use in the bus lane during this time period. The expression for maximizing the number of open reservation shared vehicles in the multi-objective integer optimization model is as follows: in, This indicates maximizing the number of shared bikes available for reservation. Indicates the first i The first bus lane j The number of vehicles that can be shared during a given time period; The expression for minimizing the overall total road delay in the multi-objective integer optimization model is as follows: in, This indicates that the overall road delay is minimized. Indicates the start time of a specific available booking time slot. This indicates the end time of a specific bookable time slot. This indicates the total number of people queuing in the dedicated bus lane. This indicates the total number of people queuing in a single regular lane. This indicates the number of regular lanes on this road section. Indicates time, To represent the differential, This indicates the cumulative number of buses queuing in the dedicated bus lane. This indicates the passenger capacity of the bus. This indicates the cumulative number of private cars queuing in the bus lane. This indicates the passenger capacity of a private car. This indicates the cumulative number of vehicles queuing for a single regular lane. The constraints of the multi-objective integer optimization model include: Bus delays are less than the threshold: in, This indicates the maximum average delay for passengers who choose public transportation. This indicates the bus arrival rate of the dedicated bus lane. This indicates the actual traffic capacity of the dedicated bus lane; The average delay per vehicle for those booking a bus in a dedicated bus lane is less than the average delay per vehicle in a regular bus lane. in, This indicates the proportion of buses in the traffic flow of the bus lane. This indicates the arrival rate of private cars in the ordinary lanes of this road section. This indicates the actual traffic capacity of a single ordinary lane on this road section; Bus capacity allocation: ； Integer constraints: ； S5. Based on the maximum number of vehicles that can be opened to external use of the bus lane during this period, a multi-objective integer optimization model is simulated and tested to evaluate the impact of the shared reservation scheme on the bus lane and ordinary lanes. S6. Based on the impact assessment and taking into account user acceptance, and with the goal of maximizing emission reduction, construct a mechanism for ordinary vehicles to reserve bus lanes using carbon credits, thereby optimizing the bus lanes.

2. The method for shared and optimized utilization of dedicated bus lanes according to claim 1, characterized in that, Step S1 includes the following steps: S101. Make API calls to the Gaode Open Platform to collect the congestion index of road segments at different time periods, and determine that the road segment at a certain time period is a congested road segment when the congestion index of a road segment is greater than the preset threshold. S102. Conduct video analysis on roads with dedicated bus lanes in congested sections to obtain vehicle speeds in different lanes.

3. The method for optimizing the shared use of bus lanes according to claim 2, characterized in that, Step S102 includes the following steps: S1021. Based on the road video with bus lanes on congested road sections, use a convolutional network to extract features of vehicles in the road video and obtain vehicle classification results. S1022. Based on the vehicle classification results, extract the features of the pixels; S1023. Based on the extracted pixel features, the original pixels are restored in the fully connected layer of the convolutional network to obtain the predicted value; S1024. Based on the predicted values, count the traffic flow during this period using a Python script; S1025. Based on traffic flow, the motion status of vehicles is obtained through the ByteTrack tracking algorithm; S1026. By combining the vehicle's motion status and the number of monitoring video frames, the vehicle speed in each lane is obtained.

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