Traffic control method and system for multiple operation scenarios based on passenger flow forecast data

Abstract

The present invention discloses a traffic control method and system for multiple operation scenarios based on passenger flow prediction data, the method comprising: obtaining passenger flow information for each line section, wherein the passenger flow information comprises: a passenger flow prediction value for each line section, a passenger flow density for each line section, a critical value of the passenger flow density and a critical value of the passenger flow prediction; setting a traffic optimization model, and calculating a total traffic optimization index in real time based on the passenger flow information, and regulating a traffic organization plan based on the total traffic optimization index, wherein the traffic optimization model comprises: a transport capacity configuration function for configuring transport capacity and an energy consumption function for regulating energy consumption.

Description

Technical Field

[0001] The present invention belongs to the technical field of rail transportation organization and control, and more specifically, relates to a method and system for controlling train operation in multiple operating scenarios based on passenger flow forecast data. Background Art

[0002] Rail transit is an important urban infrastructure with a long planning and design cycle, large construction investment, a large customer base, high operating costs, a long operating cycle, and difficulty in rebuilding once built. Therefore, the determination of the engineering and technical plan requires a detailed demonstration and decision-making process.

[0003] Train operation organization design is an important part of the entire chain of rail transit planning and design. It is an important link connecting project planning and approval, feasibility study, overall design, and preliminary design. It is an important bridge for implementing project positioning, passenger flow demand, capacity supply, operation effect, and engineering technical solutions. It is a professional that provides information on multiple systems such as lines, vehicles, processes, limits, buildings, tracks, power supply, signals, and industrial economics. It has an important impact on determining project technical standards, major plans, service effects, and engineering investment.

[0004] In the past, driving organization design relied too much on passenger flow forecast data, and mostly studied redundant solutions to accommodate the predicted passenger flow transportation capacity from an inclusive perspective. The design points also focused on the peak hour operation status and the overall operation status throughout the day, and thus encompassed the functional requirements of various special operation scenarios. The system capacity involved in the operation scale was also mostly based on the maximum 2-minute interval. Generally speaking, there was little consideration of scenarios and the design content was relatively extensive.

[0005] With the gradual expansion of rail transit networks in various regions, the differentiation of line functions and operating characteristics has become increasingly obvious. The original extensive design ideas and methods are increasingly unable to adapt to the current development requirements. In some cities, newly opened lines are increasingly experiencing problems such as insufficient passenger flow, excess capacity, overly high standards, and oversized wiring. Summary of the Invention

[0006] To solve the above technical problems, the present invention proposes a traffic control method for multiple operating scenarios based on passenger flow forecast data, comprising:

[0007] Obtaining passenger flow information for each route section, wherein the passenger flow information includes: a passenger flow prediction value for each route section, a passenger flow density for each route section, a critical value of the passenger flow density, and a passenger flow prediction critical value;

[0008] A driving optimization model is set up, and a total driving optimization index is calculated in real time based on the passenger flow information, and driving is regulated based on the total driving optimization index, wherein the driving optimization model includes: a transport capacity configuration function for configuring transport capacity and an energy consumption function for regulating energy consumption.

[0009] Furthermore, the driving optimization model includes:

[0010]

[0011] Where R(t) is the total driving optimization index at time t, n is the total number of line sections, P i is the passenger flow forecast value of the i-th line section, F i (t) is the transport capacity configuration function of the i-th line section at time t, k is the adjustment factor of passenger flow density, D i is the passenger flow density of the i-th line section, D threshold is the critical value of passenger flow density, used to trigger peak shaving operation, E i (t) is the energy consumption function of the i-th line section at time t, and λ is the adjustment factor of the energy consumption function.

[0012] Furthermore, the transport capacity configuration function F of the i-th line section at time t is i (t) include:

[0013]

[0014] Among them, α i The first adjustment factor for the transport capacity of the i-th line section is configured, β i Configure the second adjustment factor for the transport capacity of the i-th line section, γ i The third adjustment factor for the transport capacity of the i-th line section is k′ i is the adjustment factor of the passenger flow forecast value of the i-th line section, δ i is the weight of the passenger flow density of the i-th line section, P threshold Predict critical value for passenger flow.

[0015] Furthermore, the energy consumption function E of the i-th line section at time t is i (t) include:

[0016]

[0017] Among them, η i is the energy consumption benchmark value of the i-th line section, ∈ is a positive constant to avoid the denominator being zero, ζ i is the time-decreasing first adjustment factor of the i-th line section, λ′ i The second adjustment factor is decremented for the time of the i-th line segment.

[0018] Furthermore, during peak hours, if the total driving optimization index R(t) at time t is lower than the preset threshold, it means that the current transport capacity is insufficient. In this case, the transport capacity can be improved by increasing the driving frequency.

[0019] During off-peak hours, if the total driving optimization index R(t) at time t is higher than the preset threshold, it means that the current transport capacity configuration is sufficient, and the energy consumption can be reduced by reducing the driving frequency, thus saving operating costs.

[0020] The present invention also proposes a traffic control system for multiple operating scenarios based on passenger flow forecast data, comprising:

[0021] An information acquisition module is used to acquire passenger flow information of each route section, wherein the passenger flow information includes: a passenger flow prediction value of each route section, a passenger flow density of each route section, a critical value of the passenger flow density, and a passenger flow prediction critical value;

[0022] The optimization module is used to set a driving optimization model, and calculate the total driving optimization index in real time based on the passenger flow information, and regulate driving according to the total driving optimization index, wherein the driving optimization model includes: a transportation capacity configuration function for configuring transportation capacity and an energy consumption function for regulating energy consumption.

[0023] Furthermore, the driving optimization model includes:

[0024]

[0025] Where R(t) is the total driving optimization index at time t, n is the total number of line sections, P i is the passenger flow forecast value of the i-th line section, F i (t) is the transport capacity configuration function of the i-th line section at time t, k is the adjustment factor of passenger flow density, D i is the passenger flow density of the i-th line section, D threshold is the critical value of passenger flow density, used to trigger peak shaving operation, E i (t) is the energy consumption function of the i-th line section at time t, and λ is the adjustment factor of the energy consumption function.

[0026] Furthermore, the transport capacity configuration function F of the i-th line section at time t is i (t) include:

[0027]

[0028] Among them, α i The first adjustment factor for the transport capacity of the i-th line section is configured, β i Configure the second adjustment factor for the transport capacity of the i-th line section, γ i The third adjustment factor for the transport capacity of the i-th line section is k′ i is the adjustment factor of the passenger flow forecast value of the i-th line section, δ iis the weight of the passenger flow density of the i-th line section, P threshold Predict critical value for passenger flow.

[0029] Furthermore, the energy consumption function E of the i-th line section at time t is i (t) include:

[0030]

[0031] Among them, η i is the energy consumption benchmark value of the i-th line section, ∈ is a positive constant to avoid the denominator being zero, ζ i is the time-decreasing first adjustment factor of the i-th line section, λ′ i The second adjustment factor is decremented for the time of the i-th line segment.

[0032] Furthermore, during peak hours, if the total driving optimization index R(t) at time t is lower than the preset threshold, it means that the current transport capacity is insufficient. In this case, the transport capacity can be improved by increasing the driving frequency.

[0033] During off-peak hours, if the total driving optimization index R(t) at time t is higher than the preset threshold, it means that the current transport capacity configuration is sufficient, and energy consumption can be reduced by reducing driving frequency, thus saving operating costs.

[0034] Compared with the prior art, the above technical solution conceived by the present invention has the following beneficial effects:

[0035] 1. Peak-shaving design

[0036] Through peak-shaving design, the number of driving pairs can be reduced by more than 10% and the number of vehicles in use can be saved by 10%.

[0037] 2. Unbalanced traffic density

[0038] By operating at an uneven density, the number of trains can be reduced by no less than 4 per hour, saving more than 10% of the number of vehicles in use and reducing the daily mileage by more than 3%.

[0039] 3. Reduce transportation capacity redundancy

[0040] By controlling the transport capacity surplus, the number of vehicles allocated in the initial stage can be reduced by 5%.

[0041] 4. Control system capabilities

[0042] By controlling the control system capabilities, we can achieve the goals of reducing the site size, reducing the interval ventilation shafts, reducing the number of traction stations and transformer capacity, optimizing wiring conditions such as return, and reducing project investment.

[0043] 5. Drive slowly without stopping in the direction of low passenger flow to save energy

[0044] By controlling the running speed of some trains and reducing train starts and stops, the goal of saving more than 3% in energy consumption can be achieved.

[0045] 6. New functions for mainline parking lines improve economy and flexibility

[0046] Through precise parking line design, the goals of reducing the scale of vehicle base construction and reducing the number of kilometers traveled can be achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 is a flow chart of the method of embodiment 1 of the present invention;

[0048] Figure 2 This is a system structure diagram of Example 2 of the present invention. DETAILED DESCRIPTION

[0049] In order to better understand the above technical solution, the above technical solution will be described in detail below with reference to the accompanying drawings and specific implementation methods.

[0050] The method provided by the present invention can be implemented in the following terminal environment, wherein the terminal may include one or more of the following components: a processor, a storage medium, and a display screen. The storage medium stores at least one instruction, which is loaded and executed by the processor to implement the method described in the following embodiments.

[0051] A processor can include one or more processing cores. It connects various components within the terminal using various interfaces and circuits. It executes instructions, programs, code sets, or instruction sets stored in storage media, and accesses data stored in storage media to perform various terminal functions and process data.

[0052] The storage medium may include a random access memory (RAM) or a read-only memory (ROM). The storage medium may be used to store instructions, programs, codes, code sets, or instructions.

[0053] The display is used to show the interactive sections of each application.

[0054] All subscripts in the formulas of the present invention are only used to distinguish parameters and have no actual meaning.

[0055] In addition, those skilled in the art will appreciate that the structure of the terminal described above does not limit the terminal. The terminal may include more or fewer components, or a combination of certain components, or a different arrangement of components. For example, the terminal may also include a radio frequency circuit, an input unit, a sensor, an audio circuit, a power supply, and other components, which will not be described in detail here.

[0056] Example 1

[0057] like Figure 1 As shown, an embodiment of the present invention provides a traffic control method for multiple operating scenarios based on passenger flow prediction data, including:

[0058] Step 101: Obtain passenger flow information for each line section, wherein the passenger flow information includes: a passenger flow prediction value for each line section, a passenger flow density for each line section, a critical value for passenger flow density, and a critical value for passenger flow prediction. The passenger flow prediction value for each line section of the current station is estimated by using historical data of other stations of similar size to the current station, thereby obtaining the passenger flow prediction value for each line section of the current station.

[0059] Step 102: Set up a driving optimization model, and calculate the total driving optimization index in real time based on the passenger flow information, and regulate driving based on the total driving optimization index, wherein the driving optimization model includes: a transportation capacity configuration function for configuring transportation capacity and an energy consumption function for regulating energy consumption.

[0060] Specifically, the driving optimization model includes:

[0061]

[0062] Where R(t) is the total driving optimization index at time t, n is the total number of line sections, P i is the passenger flow forecast value of the i-th line section, F i (t) is the transport capacity configuration function of the i-th line section at time t, k is the adjustment factor of passenger flow density, D i is the passenger flow density of the i-th line section, D threshold is the critical value of passenger flow density, used to trigger peak shaving operation, E i (t) is the energy consumption function of the i-th line section at time t, and λ is the adjustment factor of the energy consumption function.

[0063] Specifically, the transport capacity configuration function F of the i-th line section at time t is i (t) include:

[0064]

[0065] Among them, α i The first adjustment factor for the transport capacity of the i-th line section is configured, β i Configure the second adjustment factor for the transport capacity of the i-th line section, γ i The third adjustment factor for the transport capacity of the i-th line section is k′ i is the adjustment factor of the passenger flow forecast value of the i-th line section, δ iis the weight of the passenger flow density of the i-th line section, P threshold Predict critical value for passenger flow.

[0066] Specifically, the energy consumption function E of the i-th line section at time t is i (t) include:

[0067]

[0068] Among them, η i is the energy consumption benchmark value of the i-th line section, ∈ is a positive constant to avoid the denominator being zero, ζ i is the time-decreasing first adjustment factor of the i-th line section, λ′ i The second adjustment factor is decremented for the time of the i-th line segment.

[0069] Specifically, during peak hours, if the total driving optimization index R(t) at time t is lower than the preset threshold, it means that the current transport capacity is insufficient. In this case, the transport capacity can be improved by increasing the driving frequency.

[0070] During off-peak hours, if the total driving optimization index R(t) at time t is higher than the preset threshold, it means that the current transport capacity configuration is sufficient, and the energy consumption can be reduced by reducing the driving frequency, thus saving operating costs.

[0071] The following is a specific example:

[0072] 1. Peak-shaving design

[0073] The system's transport capacity should be reasonably designed according to the degree of sharpness of the passenger flow section. When the number of sections exceeding 90% of the maximum section during peak hours is no more than 3, peak-shaving design should be considered; the maximum section reduced by 10% or the third section value should be used as the calculation basis, and the transport capacity should be configured at 5 people / square meter with a 10% margin reserved. The standing density of the highest section of the entire line should not exceed 6 people / square meter, the number of sections exceeding 5 people / square meter should not be more than 3, and the train running time should not exceed 10 minutes.

[0074] Typically, the capacity design of rail transit lines uses individual maximum cross-sections as the controlling standard for full-line adaptation. This can easily lead to situations where individual prominent large cross-sections cause oversupply of capacity and waste of capacity across the entire line. This is particularly likely to occur in the current context of slowing urbanization and high passenger flow fluctuation forecast risks in China. For this reason, a peak-shaving design concept that takes comfort into consideration is proposed. Based on 0.9 times the maximum cross-section value, when there are fewer than three interval cross-sections, the cross-section sharpness is considered high, and the number of driving pairs is reduced by 10% to save costs. At the same time, the number of sections with a congestion level exceeding 5 people / square meter and the travel time are checked to avoid overly long congested sections. The goal is to reduce the number of driving pairs by more than 10% and save 10% of the number of vehicles in use.

[0075] 2. Unbalanced traffic density

[0076] When the ratio of the two-way controlled passenger flow section of the line exceeds 1.5, the traffic density in the direction of small passenger flow should be reduced on the basis of ensuring the service level, and an asymmetric operation mode should be adopted. The number of pairs reduced in one direction should be greater than 4 trains / h.

[0077] Rail transit primarily serves commuters, and the nature of commuting dictates that the vast majority of passengers travel in tidal waves, resulting in significant variations in passenger volume in both directions. While ensuring capacity in high-traffic areas and a basic service level in low-traffic areas, reducing the frequency of trains operating in these areas through operational organization can effectively save operating costs. For routes with high passenger volumes and a large-to-small cross-section ratio of 1.5 or more, we propose reducing trains operating in these areas, aiming to reduce the number of trains per hour by at least four, thereby saving over 10% of the number of vehicles in operation and reducing daily mileage by over 3%.

[0078] 3. Reduce transportation capacity redundancy

[0079] When the initial peak hourly cross-section exceeds 25,000 people / hour and the future growth rate is less than 50% compared to the initial period, the initial transport capacity margin should be controlled at 5% (calculated at 5 people / square meter); when the future peak hourly cross-section is more than three times that of the initial period, the future transport capacity margin should be controlled at 5% (calculated at 5 people / square meter). At the same time, the transport capacity margin should reach 10% after the backup vehicle is put into operation.

[0080] By referencing initial and long-term passenger flow fluctuations, we can anticipate risks in route passenger flow forecasts and accurately reserve capacity margins, thereby reducing the number of vehicles initially allocated and the future system scale. Based on general empirical evidence, when the future passenger flow cross-section shows a smaller growth rate than the initial one (0.5 times), it can be determined that the initial development along the route has reached a high level of maturity, and the risk of initial upward passenger flow fluctuations is generally controllable. A 5% reduction in capacity margin is recommended. When the ratio reaches three times or more, combined with the initial passenger flow intensity supporting the necessity of construction, it can be determined that the risk of future upward passenger flow is controllable, and a 5% reduction in capacity margin is recommended. At the same time, the number of spare vehicles should be configured to ensure that the margin remains at 10% after the spare vehicles are put online, in line with national standards. The goal is to reduce the number of vehicles initially allocated by at least 5%.

[0081] 4. Control system capabilities

[0082] This method primarily addresses the established practice of designing rail transit system capacity based on a single route at 30 pairs / h. It aims to accurately reserve capacity based on project characteristics and avoid overinvestment. System capacity should be determined by comprehensively considering transport capacity reserves and the cost-effectiveness of the civil engineering system. The rationality of reserving 30 pairs / h should be verified, especially for lines with future expansion plans. When the peak-hour cross-section of an outer line is less than 1 / 2 of the highest cross-section (or after peak shaving) and the length is over 5km, the system capacity should be designed in sections. The system capacity in low-section areas should be configured at 2 / 3 of the maximum system capacity of the entire line and should not be less than 15 pairs / h, leaving flexibility for development.

[0083] Specific approaches include reducing system size targets for high-traffic areas, such as those reserved for 24 or 20 pairs / hour. Secondly, system size should be determined based on demand for smaller passenger flows, such as peripheral and branch lines, taking into account passenger flow fluctuations. The goal is to reduce the size of depots, reduce interval ventilation shafts, reduce the number of traction stations and transformer capacity, and optimize wiring conditions such as turnarounds.

[0084] 5. Drive slowly without stopping in the direction of low passenger flow to save energy

[0085] When the ratio of two-way passenger flow sections is greater than 2:1, some trains can skip and slow down in the direction of small passenger flow to reduce the number of starts and stops and lower energy consumption. The number of trains skipping and stopping should be greater than 5 pairs per hour.

[0086] The tidal nature of rail transit passenger flow means there's excess capacity in low-traffic areas. While maintaining a basic service level for these areas, we can achieve energy savings by controlling train speeds and reducing train starts and stops by allowing some trains to run at normal speeds without stopping. The savings are greater than 3%.

[0087] 6. New functions for mainline parking lines improve economy and flexibility

[0088] On the premise of meeting the requirements for reversing access for nighttime vehicles, parking lines at the starting and ending stations or intermediate stations far from the vehicle base should have the function of parking trains at night and be counted towards the number of parking lines on the entire line; parking lines at both the starting and ending stations should be counted, double parking lines at intermediate stations should be counted as one line, and single parking lines may not be counted. Stations with parking conditions should provide conditions for crew members to rest at night. At the same time, the design of parking lines should focus on the function of parking hot standby vehicles, providing conditions for clearing passengers at stations with large passenger flow. The location should be close to high-section stations with large passenger flow and meet the "four-legged" access and exit conditions. If these conditions cannot be met, the parking lines should face the direction of large passenger flow.

[0089] With the current extended train inspection cycle and fully automated operation, parking at mainline stop lines can effectively reduce the size of the rolling stock base and prevent trains from running empty. By undercounting stop lines at intermediate stations by one train position, this prevents trains from being unable to stop when they serve as turnaround lines. By providing hot standby parking conditions on the main line and prioritizing stop lines near points of sudden passenger flow, station backlogs can be quickly cleared when passenger flow exceeds expectations, improving line operational flexibility and risk mitigation.

[0090] Example 2

[0091] like Figure 2 As shown, the embodiment of the present invention further proposes a traffic control system for multiple operating scenarios based on passenger flow prediction data, including:

[0092] An information acquisition module is used to acquire passenger flow information of each route section, wherein the passenger flow information includes: a passenger flow prediction value of each route section, a passenger flow density of each route section, a critical value of the passenger flow density, and a passenger flow prediction critical value;

[0093] The optimization module is used to set a driving optimization model, and calculate the total driving optimization index in real time based on the passenger flow information, and regulate driving according to the total driving optimization index, wherein the driving optimization model includes: a transportation capacity configuration function for configuring transportation capacity and an energy consumption function for regulating energy consumption.

[0094] Specifically, the driving optimization model includes:

[0095]

[0096] Where R(t) is the total driving optimization index at time t, n is the total number of line sections, P i is the passenger flow forecast value of the i-th line section, F i (t) is the transport capacity configuration function of the i-th line section at time t, k is the adjustment factor of passenger flow density, D i is the passenger flow density of the i-th line section, D threshold is the critical value of passenger flow density, used to trigger peak shaving operation, E i (t) is the energy consumption function of the i-th line section at time t, and λ is the adjustment factor of the energy consumption function.

[0097] Specifically, the transport capacity configuration function f of the i-th line section at time t is i (t) include:

[0098]

[0099] Among them, α i The first adjustment factor for the transport capacity of the i-th line section is configured, β iConfigure the second adjustment factor for the transport capacity of the i-th line section, γ i The third adjustment factor for the transport capacity of the i-th line section is k′ i is the adjustment factor of the passenger flow forecast value of the i-th line section, δ i is the weight of the passenger flow density of the i-th line section, P threshold Predict critical value for passenger flow.

[0100] Specifically, the energy consumption function E of the i-th line section at time t is i (t) include:

[0101]

[0102] Among them, η i is the energy consumption benchmark value of the i-th line section, ∈ is a positive constant to avoid the denominator being zero, ζ i is the time-decreasing first adjustment factor of the i-th line section, λ′ i The second adjustment factor is decremented for the time of the i-th line segment.

[0103] Specifically, during peak hours, if the total driving optimization index R(t) at time t is lower than the preset threshold, it means that the current transport capacity is insufficient. In this case, the transport capacity can be improved by increasing the driving frequency.

[0104] During off-peak hours, if the total driving optimization index R(t) at time t is higher than the preset threshold, it means that the current transport capacity configuration is sufficient, and energy consumption can be reduced by reducing driving frequency, thus saving operating costs.

[0105] Example 3

[0106] An embodiment of the present invention further proposes a storage medium storing a plurality of instructions, wherein the instructions are used to implement the aforementioned method for controlling traffic flow in multiple operating scenarios based on passenger flow prediction data.

[0107] Optionally, in this embodiment, the storage medium may be located in any computer terminal in a computer terminal group in a computer network, or in any mobile terminal in a mobile terminal group.

[0108] Optionally, in this embodiment, the storage medium is configured to store program codes for executing the following steps: Step 101, obtaining passenger flow information of each route section, wherein the passenger flow information includes: a passenger flow prediction value of each route section, a passenger flow density of each route section, a critical value of the passenger flow density, and a passenger flow prediction critical value;

[0109] Step 102: Set up a driving optimization model, and calculate the total driving optimization index in real time based on the passenger flow information, and regulate driving based on the total driving optimization index, wherein the driving optimization model includes: a transportation capacity configuration function for configuring transportation capacity and an energy consumption function for regulating energy consumption.

[0110] Specifically, the driving optimization model includes:

[0111]

[0112] Where R(t) is the total driving optimization index at time t, n is the total number of line sections, P i is the passenger flow forecast value of the i-th line section, F i (t) is the transport capacity configuration function of the i-th line section at time t, k is the adjustment factor of passenger flow density, D i is the passenger flow density of the i-th line section, D threshold is the critical value of passenger flow density, used to trigger peak shaving operation, E i (t) is the energy consumption function of the i-th line section at time t, and λ is the adjustment factor of the energy consumption function.

[0113] Specifically, the transport capacity configuration function F of the i-th line section at time t is i (t) include:

[0114]

[0115] Among them, α i The first adjustment factor for the transport capacity of the i-th line section is configured, β i Configure the second adjustment factor for the transport capacity of the i-th line section, γ i The third adjustment factor for the transport capacity of the i-th line section is k′ i is the adjustment factor of the passenger flow forecast value of the i-th line section, δ i is the weight of the passenger flow density of the i-th line section, P threshold Predict critical value for passenger flow.

[0116] Specifically, the energy consumption function E of the i-th line section at time t is i (t) include:

[0117]

[0118] Among them, η i is the energy consumption benchmark value of the i-th line section, ∈ is a positive constant to avoid the denominator being zero, ζ i is the time-decreasing first adjustment factor of the i-th line section, λ′ i The second adjustment factor is decremented for the time of the i-th line segment.

[0119] Specifically, during peak hours, if the total driving optimization index R(t) at time t is lower than the preset threshold, it means that the current transport capacity is insufficient. In this case, the transport capacity can be improved by increasing the driving frequency.

[0120] During off-peak hours, if the total driving optimization index R(t) at time t is higher than the preset threshold, it means that the current transport capacity configuration is sufficient, and the energy consumption can be reduced by reducing the driving frequency, thus saving operating costs.

[0121] Example 4

[0122] An embodiment of the present invention also proposes an electronic device, including a processor and a storage medium connected to the processor, wherein the storage medium stores multiple instructions, which can be loaded and executed by the processor to enable the processor to execute a driving control method for multiple operating scenarios based on passenger flow prediction data.

[0123] Specifically, the electronic device of this embodiment may be a computer terminal, which may include: one or more processors and a storage medium.

[0124] Among them, the storage medium can be used to store software programs and modules, such as a method for controlling traffic flow in multiple operating scenarios based on passenger flow prediction data in an embodiment of the present invention, and corresponding program instructions / modules. The processor executes various functional applications and data processing by running the software programs and modules stored in the storage medium, that is, realizing the above-mentioned method for controlling traffic flow in multiple operating scenarios based on passenger flow prediction data. The storage medium may include high-speed random access storage media, and may also include non-volatile storage media, such as one or more magnetic storage systems, flash memory, or other non-volatile solid-state storage media. In some instances, the storage medium may further include storage media remotely located relative to the processor, and these remote storage media may be connected to the terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, corporate intranet, local area network, mobile communication network, and combinations thereof.

[0125] The processor may call the information and application stored in the storage medium through the transmission system to execute the following steps: Step 101, obtaining passenger flow information of each route section, wherein the passenger flow information includes: a passenger flow prediction value of each route section, a passenger flow density of each route section, a critical value of the passenger flow density, and a passenger flow prediction critical value;

[0126] Step 102: Set up a driving optimization model, and calculate the total driving optimization index in real time based on the passenger flow information, and regulate driving based on the total driving optimization index, wherein the driving optimization model includes: a transportation capacity configuration function for configuring transportation capacity and an energy consumption function for regulating energy consumption.

[0127] Specifically, the driving optimization model includes:

[0128]

[0129] Where R(t) is the total driving optimization index at time t, n is the total number of line sections, P i is the passenger flow forecast value of the i-th line section, F i (t) is the transport capacity configuration function of the i-th line section at time t, k is the adjustment factor of passenger flow density, D i is the passenger flow density of the i-th line section, D threshold is the critical value of passenger flow density, used to trigger peak shaving operation, E i (t) is the energy consumption function of the i-th line section at time t, and λ is the adjustment factor of the energy consumption function.

[0130] Specifically, the transport capacity configuration function E of the i-th line section at time t is i (t) include:

[0131]

[0132] Among them, α i The first adjustment factor for the transport capacity of the i-th line section is configured, β i Configure the second adjustment factor for the transport capacity of the i-th line section, γ i The third adjustment factor for the transport capacity of the i-th line section is k′ i is the adjustment factor of the passenger flow forecast value of the i-th line section, δ i is the weight of the passenger flow density of the i-th line section, P threshold Predict critical value for passenger flow.

[0133] Specifically, the energy consumption function E of the i-th line section at time t is i (t) include:

[0134]

[0135] Among them, η i is the energy consumption benchmark value of the i-th line section, ∈ is a positive constant to avoid the denominator being zero, ζ i is the time-decreasing first adjustment factor of the i-th line section, λ′ i The second adjustment factor is decremented for the time of the i-th line segment.

[0136] Specifically, during peak hours, if the total driving optimization index R(t) at time t is lower than the preset threshold, it means that the current transport capacity is insufficient. In this case, the transport capacity can be improved by increasing the driving frequency.

[0137] During off-peak hours, if the total driving optimization index R(t) at time t is higher than the preset threshold, it means that the current transport capacity configuration is sufficient, and the energy consumption can be reduced by reducing the driving frequency, thus saving operating costs.

[0138] The serial numbers of the above embodiments of the present invention are for description only and do not represent the advantages or disadvantages of the embodiments.

[0139] In the above embodiments of the present invention, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0140] In the several embodiments provided by the present invention, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the system embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, and can be electrical or other forms.

[0141] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0142] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0143] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only storage medium (ROM, Read-Only Memory), random access storage medium (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk, and other media that can store program code.

[0144] Obviously, the above embodiments are merely examples for clarity of explanation and are not intended to limit the implementation methods. Those skilled in the art will readily appreciate that other variations or modifications based on the above descriptions are possible. It is not necessary and impossible to enumerate all implementation methods here. Obvious variations or modifications arising therefrom remain within the scope of protection of the present invention.

Claims

1. A traffic control method for multiple operation scenarios based on passenger flow forecast data, characterized in that: include: Obtaining passenger flow information for each route section, wherein the passenger flow information includes: a passenger flow prediction value for each route section, a passenger flow density for each route section, a critical value of the passenger flow density, and a passenger flow prediction critical value; Setting a driving optimization model, and calculating a total driving optimization index in real time based on the passenger flow information, and regulating driving based on the total driving optimization index, wherein the driving optimization model includes: a transportation capacity configuration function for configuring transportation capacity and an energy consumption function for regulating energy consumption; The driving optimization model includes: Where R(t) is the total driving optimization index at time t, n is the total number of line sections, P i is the passenger flow forecast value of the i-th line section, F i (t) is the transport capacity configuration function of the i-th line section at time t, k is the adjustment factor of passenger flow density, D i is the passenger flow density of the i-th line section, D threshold is the critical value of passenger flow density, used to trigger peak shaving operation, E i (t) is the energy consumption function of the i-th line section at time t, λ is the adjustment factor of the energy consumption function; The transport capacity configuration function F of the i-th line section at time t i (t) include: Among them, α i The first adjustment factor for the transport capacity of the i-th line section is configured, β i Configure the second adjustment factor for the transport capacity of the i-th line section, γ i The third adjustment factor for the transport capacity of the i-th line section is k′ i is the adjustment factor of the passenger flow forecast value of the i-th line section, δ i is the weight of the passenger flow density of the i-th line section, P threshold Predict critical values ​​for passenger flow; The energy consumption function E of the i-th line section at time t i (t) include: Among them, η i is the energy consumption benchmark value of the i-th line section, ∈ is a positive constant to avoid the denominator being zero, ζ i is the time-decreasing first adjustment factor of the i-th line section, λ′ i The second adjustment factor is decremented for the time of the i-th line segment.

2. The method for controlling traffic flow in multiple operation scenarios based on passenger flow forecast data according to claim 1, characterized in that: During peak hours, if the total driving optimization index R(t) at time t is lower than the preset threshold, it means that the current transport capacity is insufficient. In this case, the transport capacity can be improved by increasing the driving frequency. During off-peak hours, if the total driving optimization index R(t) at time t is higher than the preset threshold, it means that the current transport capacity configuration is sufficient, and the energy consumption can be reduced by reducing the driving frequency, thus saving operating costs.

3. A traffic control system for multiple operating scenarios based on passenger flow forecast data, characterized in that: include: An information acquisition module is used to acquire passenger flow information of each route section, wherein the passenger flow information includes: a passenger flow prediction value of each route section, a passenger flow density of each route section, a critical value of the passenger flow density, and a passenger flow prediction critical value; an optimization module, configured to set a driving optimization model, calculate a total driving optimization index in real time based on the passenger flow information, and regulate driving based on the total driving optimization index, wherein the driving optimization model includes: a transport capacity configuration function for configuring transport capacity and an energy consumption function for regulating energy consumption; The driving optimization model includes: Where R(t) is the total driving optimization index at time t, n is the total number of line sections, P i is the passenger flow forecast value of the i-th line section, F i (t) is the transport capacity configuration function of the i-th line section at time t, k is the adjustment factor of passenger flow density, D i is the passenger flow density of the i-th line section, D threshold is the critical value of passenger flow density, used to trigger peak shaving operation, E i (t) is the energy consumption function of the i-th line section at time t, λ is the adjustment factor of the energy consumption function; The transport capacity configuration function F of the i-th line section at time t i (t) include: Among them, α i The first adjustment factor for the transport capacity of the i-th line section is configured, β i Configure the second adjustment factor for the transport capacity of the i-th line section, γ i The third adjustment factor for the transport capacity of the i-th line section is k′ i is the adjustment factor of the passenger flow forecast value of the i-th line section, δ i is the weight of the passenger flow density of the i-th line section, P threshold Predict critical values ​​for passenger flow; The energy consumption function E of the i-th line section at time t i (t) include: Among them, η i is the energy consumption benchmark value of the i-th line section, ∈ is a positive constant to avoid the denominator being zero, ζ i is the time-decreasing first adjustment factor of the i-th line section, λ′ i The second adjustment factor is decremented for the time of the i-th line segment.

4. The multi-operation scenario traffic control system based on passenger flow forecast data according to claim 3, characterized in that: During peak hours, if the total driving optimization index R(t) at time t is lower than the preset threshold, it means that the current transport capacity is insufficient. In this case, the transport capacity can be improved by increasing the driving frequency. During off-peak hours, if the total driving optimization index R(t) at time t is higher than the preset threshold, it means that the current transport capacity configuration is sufficient, and the energy consumption can be reduced by reducing the driving frequency, thus saving operating costs.

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