Bus lane sharing optimization utilization method

Abstract

The invention provides a bus lane sharing optimization utilization method, and relates to the technical field of traffic, and the method comprises the steps: screening out a congested road section, and obtaining the vehicle speed of each lane of the congested road section; calculating a travel index to compare the congestion degree of the bus lane and the common lane, and screening to obtain a path in which the common lane is congested and the bus lane is unblocked; establishing a multi-target integer optimization model aiming at the maximum number of open and reserved shared vehicles and the minimum overall road total delay; solving to obtain the maximum number of vehicles which can open to the outside for tailgating at the time period of the bus lane; performing simulation test on the multi-target integer optimization model, and evaluating the influence of the shared reservation scheme on the bus lane and the common lane; on the basis of considering the user acceptability, an exchange mechanism that a common vehicle uses carbon points to carry out reservation supply on the bus lane is constructed, the defect problem existing in a dynamic scheme is solved, and the problem that an existing bus lane management mode is one-to-one is solved.

Description

Technical Field

[0001] The invention belongs to the field of traffic technology, and in particular relates to a method for optimizing the shared utilization of bus lanes. Background Art

[0002] How to effectively utilize bus lane resources and improve the overall traffic efficiency of roads has become an important issue in current urban traffic management.

[0003] The prior art involves the following: First, the real-time traffic information in the shared lane is used to perform dynamic adjustment control of the shared lane. When the shared lane meets a specific control strategy, HOV vehicles are allowed to enter the shared lane and share it with the bus lane. The relevant data (such as vehicle speed, traffic volume, etc.) within a detection cycle of the shared lane is collected, and the average delay of the shared lane is calculated through the detected vehicle information. This data reflects the average delay of vehicles traveling on the shared lane. Then, according to the service level classification of urban roads (such as through indicators such as delay time and traffic flow), the service level and congestion level of the shared lane are evaluated. Based on this evaluation, the smoothness of the shared lane is determined. Finally, the usage of the shared lane is judged according to different service levels. In order to ensure the smooth passage of vehicles in the shared lane, the system also coordinates and controls the traffic at the entrances and exits of the adjacent intersections, and adopts the strategy of extending the green light time of the bus lane and controlling the interference of vehicles at the entrances and exits of the adjacent intersections. However, there are the following disadvantages: 1. Failure to fully consider changes in vehicle flow in different time periods makes it difficult to effectively manage specific time periods.

[0004] 2. Establishing dynamic control strategies requires a large amount of real-time data, but the acquisition and processing of data involve technical complexity.

[0005] 3. No visual information on the opening status of bus lanes is provided, making it difficult for users to understand the rationality of current resources.

[0006] The existing technology has also solved the problem of vehicles borrowing lanes to a certain extent, and realized intelligent identification and scheduling. Specifically, this method determines whether and when the intelligent networked vehicle needs to borrow lanes by combining multiple factors, and optimizes traffic flow by controlling the vehicle's driving path. It has the following disadvantages: 1. It mainly focuses on intelligent connected vehicles, fails to cover the lane borrowing needs of all types of vehicles, and does not provide sufficient support for the reservation and sharing mechanism of traditional buses.

[0007] 2. For drivers and passengers of smart connected vehicles, frequent lane-changing behavior may increase their sense of uneasiness and uncertainty, and affect their acceptance of smart connected vehicle technology.

[0008] 3. Intelligent connected vehicle technology is still in its early stages of development and still requires long-term technological accumulation and optimization. Its current promotion and implementation in actual application scenarios face certain challenges.

[0009] The existing technology also focuses on optimizing the activation time of bus lanes during peak hours and the allocation of road capacity, aiming to improve the operating efficiency of urban transportation systems and alleviate traffic congestion during peak hours. It has the following disadvantages: 1. Although the literature proposes a method to optimize the activation time of bus lanes and the allocation of road capacity, it may require a lot of manpower, material and financial resources in actual implementation. For example, it is necessary to install and debug related traffic monitoring equipment, establish and maintain a traffic data management system, etc. These costs may limit the promotion and application of this technical solution in some areas.

[0010] 2. The optimization is mainly based on the principle of minimizing the total delay of passengers. The average passenger capacity relationship between buses and cars may lead to increased congestion time for vehicles in ordinary lanes and long-term absence of vehicles in bus lanes. Summary of the invention

[0011] In view of the above-mentioned deficiencies in the prior art, the present invention provides a method for optimizing the shared utilization of bus lanes, which solves the problem of low utilization rate of bus lanes; solves the defects of dynamic solutions, such as vehicle-road collaboration and high technical support requirements; solves the one-size-fits-all problem of the existing bus lane management model, and proposes a systematic solution for whether and how to open bus lanes. The present invention establishes a spatiotemporal distribution map of bus lanes with open significance in the city, and obtains bus lanes with open significance in the city. 。

[0012] In order to achieve the above objectives, the technical solution adopted by the present invention is: a method for optimizing the shared utilization of bus lanes, comprising the following steps: S1, screening out congested road sections, and obtaining the vehicle speeds on each lane of the congested road sections; S2. Based on the information of congested road sections and the obtained vehicle speed, the travel index is calculated to compare the congestion levels of the bus lane and the ordinary lane, and the path where the ordinary lane is congested and the bus lane is unobstructed is selected; S3. Based on the paths obtained by screening where ordinary lanes are congested but bus lanes are unobstructed, a multi-objective integer optimization model is established with the goal of maximizing the number of open reservation shared vehicles and minimizing the overall total delay of the road; S4. Based on the multi-objective integer optimization model, the maximum number of vehicles that can be opened to the public in the bus lane during this period is obtained; S5. Based on the maximum number of vehicles that can be used to borrow the bus lane during this period, a multi-objective integer optimization model is simulated to evaluate the impact of the shared reservation scheme on the bus lane and the ordinary lane; S6. Based on the impact assessment and taking into account user acceptance, with the maximum emission reduction as the goal, a redemption mechanism will be established for ordinary vehicles to use carbon credits to make reservations for use in bus lanes, thereby completing the optimization of bus lanes.

[0013] The beneficial effects of the present invention are as follows: based on the urban road congestion information collected from the AutoNavi open platform API and the vehicle driving conditions obtained by video recognition, the present invention establishes a spatiotemporal distribution recognition model of bus lanes with open sharing significance and an optimization model for the maximum number of vehicles that can be opened for reservation and borrowing, and at the same time, combined with a reasonable carbon credit exchange mechanism, constructs a bus lane optimization scheme in which users can make reservations for bus lanes through carbon credits, providing a new scheme for optimizing the use of bus lanes and advocating low-carbon travel. Based on historical road data, the present invention obtains bus lanes with open significance, calculates the maximum number of vehicles that can be opened, and proposes a static scheme for private cars to make reservations to enter bus lanes.

[0014] Furthermore, the step S1 includes the following steps: S101, calling the API of the AutoNavi open platform to collect the congestion index of the road section in each time period, and when the congestion index of the road section in a certain time period is greater than a preset threshold, determining that the road section in this time period is a congested road section; S102: Perform video analysis on a road with a dedicated bus lane on a congested road section to obtain vehicle speeds on different lanes.

[0015] The beneficial effect of the above further scheme is that the present invention realizes the initial screening of congested sections by screening out congested sections and obtaining the vehicle speeds on each lane of the congested sections, thus laying a foundation for subsequent research.

[0016] Furthermore, the step S102 includes the following steps: S1021, based on the road video with a bus lane on the congested road section, use a convolutional network to extract features of vehicles in the road video to obtain a vehicle classification result; S1022. Extracting pixel features based on the vehicle classification result; S1023. Based on the extracted pixel features, restore the original pixel in the fully connected layer of the convolutional network to obtain a predicted value; S1024. Count the vehicle flow in the period by using a Python script according to the predicted value. S1025, based on the vehicle traffic, obtaining the movement state of the vehicle through the ByteTrack tracking algorithm; S1026. Combining the motion state of the vehicle and the number of monitoring video frames, obtain the vehicle speed on each lane.

[0017] The beneficial effect of the above further scheme is that the present invention compares the congestion levels of the bus lane and the ordinary lane by calculating the travel index, and screens out the road sections where the ordinary lane is congested but the bus lane is unobstructed.

[0018] Furthermore, the expression for maximizing the number of open reservation shared vehicles in the multi-objective integer optimization model is as follows:

[0019] in, Indicates the maximum number of open reservation shared cars, Indicates i The bus lane j The number of vehicles available for sharing in a time period; The expression for minimizing the total delay of the road in the multi-objective integer optimization model is as follows:

[0020]

[0021]

[0022] in, Indicates that the overall delay of the road is minimal. Indicates the start time of a certain available reservation period. Indicates the end time of a certain appointment period. Indicates the cumulative number of people queuing in the bus lane. Indicates the cumulative number of people queuing in a single ordinary lane. Indicates the number of ordinary lanes on this road section. Indicates time, represents the differential, Indicates the cumulative number of buses queuing in the bus lane. represents the occupancy factor of the bus, Indicates the cumulative number of private cars queuing in the bus lane. represents the occupancy factor of a private car, Indicates the cumulative number of vehicles queuing for a single ordinary lane.

[0023] The beneficial effect of the above further scheme is that the present invention establishes a multi-objective optimization model with the goal of minimizing the total delay of people and maximizing the number of open reservation shared vehicles, thereby optimizing the application of the additional traffic capacity of the bus lane and alleviating the overall road congestion.

[0024] Furthermore, the constraints of the multi-objective integer optimization model include: Bus delay is less than threshold:

[0025]

[0026] in, represents the upper limit of the average delay for passengers who choose public transportation. Indicates the cumulative number of vehicles queuing in the bus lane. represents the bus arrival rate of the bus lane, Indicates the actual traffic capacity of the bus lane; The average delay per vehicle in the bus lane is less than that in the ordinary lane:

[0027]

[0028]

[0029]

[0030] in, Indicates the cumulative number of vehicles queuing in the bus lane. Indicates the accumulated queue of vehicles corresponding to a single ordinary lane. represents the proportion of buses in the bus lane traffic flow, represents the arrival rate of private cars in the ordinary lane of the road section, Indicates the actual traffic capacity of a single ordinary lane on the road section; Bus capacity allocation:

[0031] in, represents the bus arrival rate of the bus lane; Integer constraints: .

[0032] The beneficial effect of the above further scheme is: through the above constraints, the present invention realizes the optimized application of the extra traffic capacity of the bus lane, while ensuring the driving experience of private car owners who have made reservations to use the lane, and responds to the policy of "bus first". BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0034] The specific implementation modes of the present invention are described below so that those skilled in the art can understand the present invention. However, it should be clear that the present invention is not limited to the scope of the specific implementation modes. For those of ordinary skill in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the attached claims, these changes are obvious, and all inventions and creations utilizing the concept of the present invention are protected.

[0035] like Figure 1 As shown, the present invention provides a method for optimizing the shared utilization of bus lanes, and the implementation method is as follows: S1. Filter out the congested road sections and obtain the vehicle speeds on each lane of the congested road sections. The implementation method is as follows: S101, calling the API of the AutoNavi open platform to collect the congestion index of the road section in each time period, and when the congestion index of the road section in a certain time period is greater than a preset threshold, determining that the road section in this time period is a congested road section; S102: Perform video analysis on a road with a dedicated bus lane on a congested road section to obtain vehicle speeds on different lanes. The implementation method is as follows: S1021, based on the road video with a bus lane on the congested road section, use a convolutional network to extract features of vehicles in the road video to obtain a vehicle classification result; S1022. Extracting pixel features based on the vehicle classification result; S1023. Based on the extracted pixel features, restore the original pixel in the fully connected layer of the convolutional network to obtain a predicted value; S1024. Count the vehicle flow in the period by using a Python script according to the predicted value. S1025, based on the vehicle traffic, obtaining the vehicle's motion state through the ByteTrack tracking algorithm; S1026. Combining the motion state of the vehicle and the number of frames of the monitoring video, obtain the vehicle speed on each lane.

[0036] In this embodiment, in order to ensure the driving experience of the reserved users, the sections where the ordinary lanes are more congested and the bus lanes are relatively idle (i.e. the sections where the bus lanes are open) should be selected for research.

[0037] In this embodiment, in this part, the present invention first screens out the congested road section, and then obtains the vehicle speed on each lane of the congested road section. The specific method is as follows: First, the API call is made to the AutoNavi open platform to collect the driving speed and congestion index of vehicles in each time section (where congestion index = (actual driving time - smooth driving time) / smooth driving time * 100%). When the congestion index of a certain time section is greater than 2.2, the section is judged to be congested during this time period, and the initial screening of open sections is achieved. However, since the speed data of each lane and bus cannot be obtained from the AutoNavi open platform, the present invention performs video analysis on the roads with bus lanes in the above-mentioned sections, and obtains the driving speed of vehicles in different lanes through YOLOv5 and ByteTrack algorithms. The specific process is as follows: ① Video recognition: The bus number recognition algorithm based on the YOLOv8 algorithm focuses on quickly and accurately detecting and counting buses on different lanes in images or videos. This algorithm is a single-stage target detection algorithm that treats the target detection task as a regression problem. It completes the detection by regressing the location and category of the target to the bounding box of the image. In the absence of over-convergence, the smaller the value of the loss function, the better the result of the regression target. The YOLOv8 target detection algorithm is used to identify vehicles in the video, and a convolutional network is used to extract features. The VFL loss function is used in the convolution calculation:

[0038] in, Represents the predicted value of the recognition result, represents the target category or weight, represents the weight adjustment factor, γ Represents the balance factor.

[0039] The calculation of the VFL loss function accurately classifies vehicles in the image, such as cars and buses. After the classification is completed, the features of the pixels are extracted and the DFL loss function is used when giving the recognition prediction box:

[0040] in, express , The loss function is The model predicts the The scores of discrete distribution points, The model predicts the The scores of discrete distribution points, represents the upper bound of the corresponding discrete distribution point, Represents the actual target value, usually a continuous value.

[0041] Then, the original pixel value is restored in the fully connected layer of the convolution to get the predicted value. Based on the recognition result, we use Python script to count the car flow in that period.

[0042] ② Speed ​​calculation: The ByteTrack tracking algorithm is used to track the vehicle previously detected by the YOLOv8 algorithm. Multi-scale features such as pixels and frame rate are used to represent the target. The kinematic formula is used to solve the motion state of the vehicle. Finally, the speed of the vehicle is calculated by combining the motion state and the number of frames of the surveillance video.

[0043] The result of identifying a vehicle in a surveillance video is presented as a collection of pixels, so we only need to find the distance between the start and end pixels to represent the displacement. :

[0044] in, Indicates the final position pixel of the vehicle in the video, Indicates the starting pixel of the vehicle in the video.

[0045] time It can be calculated by dividing the number of frames of the target pixel by the frame rate of the video:

[0046] in, Indicates the number of video frames that the vehicle passes through during movement. Indicates the frame rate of the video.

[0047] speed Equal to the position divided by the time: , get the car speed in km / h.

[0048] Through the above method, the actual speed of the bus and the private cars in each lane can be accurately estimated in the surveillance video.

[0049] S2. Based on the information of congested road sections and the obtained vehicle speed, the travel index is calculated to compare the congestion levels of the bus lane and the ordinary lane, and the path where the ordinary lane is congested and the bus lane is unobstructed is selected; In this embodiment, based on the above-obtained data, the travel index TTI is calculated to compare the congestion levels of bus lanes and ordinary lanes, and the road sections where "ordinary lanes are congested but bus lanes are unobstructed" are screened to identify the spatiotemporal distribution of bus lanes that are meaningful to open. The specific formula is as follows:

[0050]

[0051] in, Indicates the road section at a certain time period k The travel index, Indicates the number of lanes in the corresponding time period. The point speed of the car, Indicates the first The point speed of the car, Indicates a single ordinary lane during this period Total number of vehicles in represents the threshold value, Indicates the first A car, j Indicates the first j A car, m Indicates the bus lane during this period The total number of vehicles in Indicates the number of a road section in a certain period of time. Indicates lanes in the same period The average speed of the inner vehicle points, Indicates bus lane The arithmetic mean of the vehicle point speeds.

[0052] That is, when the travel index TTI of a road section in a certain time period is greater than the threshold, the bus lane in the road section in this time period has meaning to be opened; when the travel index TTI of a road section in a certain time period is less than the threshold, the bus lane in the road section in this time period has no meaning to be opened.

[0053] S3. Based on the paths obtained by screening where ordinary lanes are congested but bus lanes are unobstructed, a multi-objective integer optimization model is established with the goal of maximizing the number of open reservation shared vehicles and minimizing the overall total delay of the road; In this embodiment, an optimization model is established to solve the maximum number of vehicles that can be opened to the public on the bus lane that has the meaning of opening.

[0054] In this embodiment, based on the selected bus lanes with the meaning of open lane reservation, and with the constraints that bus delays are within a certain range and the delays of private cars using the lanes are less than those of private cars in ordinary lanes, a multi-objective integer optimization model with the goals of maximizing the number of open reservation shared cars and minimizing the overall road delay is established, and ant colony, genetic and other algorithms are written using python, matlab, etc. to solve the maximum number of vehicles that can be used by private cars in the bus lane during this period.

[0055] In this embodiment, the model is a multi-objective optimization model. Since the increase in the number of shared vehicles will lead to an increase in the traffic volume in the bus lane, the total delay of people will also increase accordingly. This paper will take maximizing the number of shared vehicles and minimizing the total delay of people as the goals, and achieve relatively satisfactory results for both through weight assignment.

[0056] Max number of shared cars:

[0057] in, Indicates the maximum number of open reservation shared cars, Indicates i The bus lane j The number of vehicles available for sharing in a time period.

[0058] Min total delay per person, that is, the overall total delay of the road is the smallest:

[0059] in, Indicates that the overall delay of the road is minimal. Indicates the start time of a certain available reservation period. Indicates the end time of a certain reservation period. Indicates the cumulative number of people queuing in the bus lane. Indicates the cumulative number of people queuing in a single ordinary lane. Indicates the number of ordinary lanes on this road section. Indicates time, Represents differential.

[0060] The constraints are as follows: ①Bus delay < threshold: In order to ensure that the experience of passengers who choose to travel by bus is not significantly reduced, the service level of buses must still be guaranteed after the excess capacity in the bus lanes is allocated to cars. represents the upper limit of the average delay for passengers who choose public transportation, so the following conditions should be met:

[0061] in, represents the upper limit of the average delay for passengers who choose public transportation. Indicates the cumulative number of vehicles queuing in the bus lane. represents the bus arrival rate of the bus lane, Indicates the actual traffic capacity of the bus lane.

[0062] ② The average delay per vehicle in the reserved bus lane is less than the average delay per vehicle in the ordinary lane: When travelers choose to travel in reserved bus lanes, the travel efficiency and experience of the travelers should be guaranteed, that is, the average delay per person in reserved bus lanes is less than that per person in ordinary lanes. Therefore, the following conditions should be met:

[0063] in, Indicates the cumulative number of vehicles queuing in the bus lane. Indicates the accumulated vehicle queue corresponding to a single ordinary lane.

[0064] ③Bus capacity allocation: The capacity of the bus lane allocated to cars and buses meets the actual capacity limit constraints of the bus lane:

[0065] in, represents the bus arrival rate of the bus lane, Indicates the actual traffic capacity of the bus lane.

[0066] ④Integer constraint: .

[0067] In this embodiment, the cumulative number of people in the queue is the product of the number of vehicles in the queue on the corresponding lane and the number of people carried by the corresponding vehicle type:

[0068]

[0069] in, Indicates the cumulative number of buses queuing in the bus lane. represents the occupancy factor of the bus, Indicates the cumulative number of private cars queuing in the bus lane. represents the occupancy factor of a private car, Indicates the cumulative number of vehicles queuing in a single ordinary lane. Indicates the corresponding occupancy factor.

[0070] 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 traffic capacity, as follows: 1) Number of vehicles queueing in bus lanes a. Total number of vehicles queuing in the bus lane:

[0071] in, Indicates the cumulative number of vehicles queuing in the bus lane. represents the bus arrival rate of the bus lane, Indicates the actual traffic capacity of the bus lane.

[0072] b. Number of buses queueing in bus lanes:

[0073] in, Indicates the cumulative number of buses queuing in the bus lane. p Indicates the proportion of buses in the traffic flow of this lane.

[0074] c. Number of private cars queuing in the bus lane:

[0075] in, Indicates the cumulative number of private cars queuing in the bus lane.

[0076] 2) Number of vehicles queuing in the ordinary lane:

[0077] in, Indicates the accumulated queue of vehicles corresponding to a single ordinary lane. represents the arrival rate of private cars in the ordinary lane of the road section, Indicates the actual traffic capacity of a single ordinary lane on the road section.

[0078] In this embodiment, the actual traffic capacity is determined as follows: The actual capacity of a lane is related to its theoretical capacity and various influences, namely:

[0079]

[0080] in, The reduction factor that represents the impact of various factors on traffic capacity, represents the theoretical capacity, S represents the saturation flow rate, Indicates the ratio of effective green light time to cycle time.

[0081] The present invention introduces the basic theoretical traffic capacity obtained through field measurement in the prior art, and takes the basic saturation flow rate as 1800 vehicles / (h·lane).

[0082] Next, the present invention discusses the reduction coefficients corresponding to lane width, different vehicle models, and driver behavior.

[0083] Traffic composition: Traffic composition correction coefficient in this invention The formula is as follows:

[0084] in, Indicates the vehicle type is i The conversion factor, Indicates the type of vehicles in the traffic flow i % of.

[0085] In this embodiment, the conversion coefficient of passenger cars is 1.00, the average conversion coefficient of medium-sized cars is 1.25, the average conversion coefficient of large cars is 1.80, and the conversion coefficient of motorcycles is 0.58. Note: For urban signalized intersections, medium-sized cars are mainly single-section buses; large cars are mainly connecting buses.

[0086] Lane width: Lane width correction factor in this invention The formula is as follows:

[0087] Among them, the parameters The estimated value of is 6.22, Indicates the corresponding lane width.

[0088] Driver behavior: reduction factor of driver behavior on traffic capacity is an empirical value, and the present invention takes .

[0089] In this embodiment, the bus lane capacity is calculated as follows: Reduction factor for bus lanes :

[0090] At this time, take is 1.25, and the various parameters are substituted into the initial formula:

[0091] In this embodiment, the ordinary lane capacity is calculated as follows: Since only small passenger cars are used in the ordinary lanes, there is no need to reduce the traffic composition. The reduction coefficient of the ordinary lane is for:

[0092] At this point, substituting each parameter into the initial formula yields: .

[0093] Through the above model, the maximum number of vehicles that can be opened to the public on the bus lane with significance is solved.

[0094] S4. Based on the multi-objective integer optimization model, the maximum number of vehicles that can be opened to the public in the bus lane during this period is obtained; S5. Based on the maximum number of vehicles that can be used to borrow the bus lane during this period, a multi-objective integer optimization model is simulated to evaluate the impact of the shared reservation scheme on the bus lane and the ordinary lane; In this embodiment, after solving the above optimization model, the present invention obtains the maximum number of vehicles that can be opened to the public in the bus lane with opening significance. Then, the present invention draws and constructs the road section model where the bus lane is located in the VISSIM platform, configures the vehicle type, flow ratio, speed distribution, etc. in combination with real life, and allows the corresponding number of private cars that can be opened for reservation to enter the bus lane at a certain arrival rate according to the reservation sharing scheme studied above in a specific time period and under certain conditions, and completes the simulation experiment of the vehicle operation status of the entire road section after opening for reservation sharing. After the experiment, the traffic flow, average speed, queue length and other data of each lane are output for delay analysis to evaluate the impact of shared reservations on bus lanes and ordinary lanes.

[0095] S6. Based on the impact assessment and taking into account user acceptance, with the maximum emission reduction as the goal, a redemption mechanism will be established for ordinary vehicles to use carbon credits to make reservations for use in bus lanes, thereby completing the optimization of bus lanes.

[0096] In this embodiment, when constructing an exchange mechanism for private cars to use carbon credits to make reservations for use of bus lanes, as the number of carbon credits required for a reservation continues to increase, its attractiveness to users will decrease accordingly, while the emission reduction effect obtained from a single reservation will increase accordingly. Therefore, it is necessary to balance the attractiveness of the exchange mechanism to users and the emission reduction effect of a single reservation.

[0097] Taking the maximum emission reduction as the goal, the emission factor calculation formula (i.e. ) and the conversion formula between emission reduction and carbon credit (i.e. 1gCO_2e = 1 carbon credit), represents the total emissions, represents the emission factor, Represent activity data, evaluate the emission reduction achieved by the carbon credits used by private cars that have reserved the use of bus lanes in each time period after the implementation of the sharing plan, and achieve qualitative and quantitative evaluation of the actual emission reduction effect of reservation sharing, to ensure that the credit mechanism can promote low-carbon travel and achieve environmental protection goals. At the same time, conduct user surveys to understand users' acceptance of carbon credit redemption, to ensure that the mechanism can attract more users to participate, so as to achieve real emission reduction in practical and theoretical sense.

[0098] Based on the above content, an optimization plan for bus lanes has been constructed, in which users can make reservations for bus lanes using carbon points.

Claims

1. A method for optimizing the shared use of bus lanes, characterized in that: The following steps are involved: S1, screening out congested road sections, and obtaining the vehicle speeds on each lane of the congested road sections; S2. Based on the information of congested road sections and the obtained vehicle speed, the travel index is calculated to compare the congestion levels of the bus lane and the ordinary lane, and the path where the ordinary lane is congested and the bus lane is unobstructed is selected; S3. Based on the paths obtained by screening where ordinary lanes are congested but bus lanes are unobstructed, a multi-objective integer optimization model is established with the goal of maximizing the number of open reservation shared vehicles and minimizing the overall total delay of the road; S4. Based on the multi-objective integer optimization model, the maximum number of vehicles that can be opened to the public in the bus lane during this period is obtained; S5. Based on the maximum number of vehicles that can be used to borrow the bus lane during this period, a multi-objective integer optimization model is simulated to evaluate the impact of the shared reservation scheme on the bus lane and the ordinary lane; S6. Based on the impact assessment and taking into account user acceptance, with the maximum emission reduction as the goal, a redemption mechanism will be established for ordinary vehicles to use carbon credits to make reservations for use in bus lanes, thereby completing the optimization of bus lanes.

2. The method for optimizing shared utilization of bus lanes according to claim 1, characterized in that: The step S1 comprises the following steps: S101, calling the API of the AutoNavi open platform to collect the congestion index of the road section in each time period, and when the congestion index of the road section in a certain time period is greater than a preset threshold, determining that the road section in this time period is a congested road section; S102: Perform video analysis on a road with a dedicated bus lane on a congested road section to obtain vehicle speeds on different lanes.

3. The method for optimizing shared utilization of bus lanes according to claim 2, characterized in that: The step S102 includes the following steps: S1021, based on the road video with a bus lane on the congested road section, use a convolutional network to extract features of vehicles in the road video to obtain a vehicle classification result; S1022. Extracting pixel features based on the vehicle classification result; S1023. Based on the extracted pixel features, restore the original pixel in the fully connected layer of the convolutional network to obtain a predicted value; S1024. Count the vehicle flow in the period by using a Python script according to the predicted value. S1025, based on the vehicle traffic, obtaining the vehicle's motion state through the ByteTrack tracking algorithm; S1026. Combining the motion state of the vehicle and the number of monitoring video frames, obtain the vehicle speed on each lane.

4. The method for optimizing shared utilization of bus lanes according to claim 1, characterized in that: The expression for maximizing the number of open reservation shared vehicles in the multi-objective integer optimization model is as follows: in, Indicates the maximum number of open reservation shared cars, Indicates i The bus lane j The number of vehicles available for sharing in a time period; The expression for minimizing the total delay of the road in the multi-objective integer optimization model is as follows: in, Indicates that the overall delay of the road is minimal. Indicates the start time of a certain available reservation period. Indicates the end time of a certain appointment period. Indicates the cumulative number of people queuing in the bus lane. Indicates the cumulative number of people queuing in a single ordinary lane. Indicates the number of ordinary lanes on this road section. Indicates time, represents the differential, Indicates the cumulative number of buses queuing in the bus lane. represents the occupancy factor of the bus, Indicates the cumulative number of private cars queuing in the bus lane. represents the occupancy factor of a private car, Indicates the cumulative number of vehicles queuing for a single ordinary lane.

5. The method for optimizing shared utilization of bus lanes according to claim 4, characterized in that: The constraints of the multi-objective integer optimization model include: Bus delay is less than threshold: in, represents the upper limit of the average delay for passengers who choose public transportation. Indicates the cumulative number of vehicles queuing in the bus lane. represents the bus arrival rate of the bus lane, Indicates the actual traffic capacity of the bus lane; The average delay per vehicle in the bus lane is less than that in the ordinary lane: in, Indicates the cumulative number of vehicles queuing in the bus lane. Indicates the accumulated queue of vehicles corresponding to a single ordinary lane. represents the proportion of buses in the bus lane traffic flow, represents the arrival rate of private cars in the ordinary lane of the road section, Indicates the actual traffic capacity of a single ordinary lane on the road section; Bus capacity allocation: in, represents the bus arrival rate of the bus lane; Integer constraints: 。

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