Method, device, terminal and medium for adjusting line carrying capacity based on vehicle-in-train encounter

Abstract

The present invention discloses a method, device, terminal and medium for adjusting the carrying capacity of a line based on a string of vehicles encountering. The method includes obtaining basic bus information of a target station, setting the initial number of carrying lines and bus operation time of the target station, calculating the average stop time of the station, and using a preset station string of vehicles encounter probability model to calculate the probability corresponding to the number of bus string vehicles arriving at the station in all time periods, and obtaining an average entry failure rate greater than the number of station berths of the target station; if the average entry failure rate is lower than the preset entry failure rate, the number of carrying lines is increased by 1, and the above steps are repeated until the average entry failure rate is higher than the preset entry failure rate, and the maximum number of carrying lines of the target station is obtained, and the station line carrying capacity of the target station is adjusted. Therefore, the embodiment of the present invention can reveal the impact of the "string of vehicles phenomenon" on the carrying capacity of station lines, and provide a theoretical basis for the subsequent planning and setting of bus stations and the configuration of the number of lines.

Description

Technical Field

[0001] The present invention relates to the field of network traffic technology, and in particular to a method, device, terminal and medium for adjusting the carrying capacity of a line based on a series of vehicles meeting. Background Art

[0002] Reasonable measurement of the carrying capacity of station lines is an important part of the configuration of transportation facilities. However, the current research on the carrying capacity of station lines is mostly based on queuing theory, which simply regards all the lines of the station as a whole and regards the arrival of the bus as a simple Poisson input. In fact, even the same bus station is located at different positions on different bus lines, and the section travel time and delay of buses on different lines are different. Although it is convenient to calculate and regard the arrival of the bus at the station as a simple Poisson input, it cannot take into account the bus operation characteristics of different lines. On the same bus line, the bus departs from the first station and passes through several stations and sections. Due to the cumulative diffusion effect of section delays, the bus that departs later may catch up with the bus in front and cause a "car-in-car" phenomenon, resulting in the situation where the buses that depart at the station departure time interval arrive at the station at the same time (i.e., meet). Since bus stops contain multiple lines, the "bus stringing" phenomenon will be more significant at the stops. However, queuing theory still has great limitations in describing the impact of bus "bus stringing" on the carrying capacity of station lines. The main limitation is that queuing theory focuses on the characteristics within the station and cannot measure the impact of the delay of the bus section outside the station on the carrying capacity of the station line. Therefore, starting from the "bus stringing" phenomenon and combining probability theory, a method for calculating the carrying capacity of station lines that is different from the solution of queuing theory can be proposed. Summary of the invention

[0003] The present invention provides a method, device, terminal and medium for adjusting the carrying capacity of a line based on the encounter of vehicles in a string. The method regards the encounter of vehicles at a station as a generalized phenomenon of vehicles in a string at a station from vehicles of multiple lines, integrates the number of carrying lines into the calculation of the average stop time of buses, constructs a probability model for the encounter of vehicles in a string at a station that takes into account the influencing factor of the number of carrying lines, and uses the designed entry failure rate as a judgment condition to solve the carrying capacity of the line at the station and obtain the upper limit of the number of carrying lines at the station.

[0004] In order to achieve the above-mentioned object, in a first aspect, an embodiment of the present invention provides a method for adjusting the carrying capacity of a line based on a train-in-train encounter, comprising:

[0005] S11, obtaining basic public transportation information of the target station; the basic public transportation information includes the number of parking spaces at the station, departure schedule, and public transportation operation hours;

[0006] S12, initializing parameters, setting the initial number of carrying lines of the target station to 1, dividing the bus operation time into q time periods, and calculating the average stop time of the station with the number of carrying lines;

[0007] S13, based on the average stop time, using a preset bus-bus encounter probability model to calculate the probability corresponding to the number of bus-bus encounters at the station in all time periods, and obtain an average entry failure rate for stations with a number of berths greater than the target station;

[0008] S14, if the average entry failure rate is lower than the preset entry failure rate, the number of the carrying lines is increased by 1, and the average stop time of the station with the increased number of carrying lines is calculated; return to step S13, and stop when the average entry failure rate is higher than the preset entry failure rate, and obtain the maximum number of carrying lines of the target station that is close to the preset entry failure rate critical value;

[0009] S15: Adjust the site line carrying capacity of the target site according to the maximum number of carrying lines.

[0010] In a second aspect, an embodiment of the present invention provides a device for adjusting the carrying capacity of a line based on a train-in-train encounter, comprising:

[0011] An information acquisition module is used to acquire basic public transportation information of a target station; the basic public transportation information includes the number of parking spaces at the station, departure schedule, and public transportation operation hours;

[0012] Initialization module, initialization parameters, setting the initial number of carrying lines of the target station to 1, dividing the bus operation time into q time periods, and calculating the average stop time of the station with the number of carrying lines;

[0013] A probability calculation module, based on the average stop time, uses a preset bus-bus encounter probability model to calculate the probability corresponding to the number of bus-bus encounters at the station in all time periods, and obtains an average entry failure rate for stations with a number of berths greater than the target station;

[0014] The bearer route module increases the number of bearer routes by 1 if the average station entry failure rate is lower than the preset station entry failure rate, and calculates the average stop time of the station after the increase of the number of bearer routes; returns to the probability calculation module and stops when the average station entry failure rate is higher than the preset station entry failure rate, and obtains the maximum number of bearer routes of the target station that is close to the preset station entry failure rate critical value;

[0015] The line adjustment module adjusts the site line carrying capacity of the target site according to the maximum number of carrying lines.

[0016] In the third aspect, an embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, the method for adjusting the carrying capacity of the line based on the encounter of serial vehicles is implemented.

[0017] In addition, an embodiment of the present invention further provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the above-mentioned method for adjusting the carrying capacity of the line based on the encounter of serial vehicles.

[0018] Compared with the prior art, the embodiment of the present invention discloses a method, device, terminal and medium for adjusting the carrying capacity of the line based on the encounter of buses in series, which obtains the basic bus information of the target station; the basic bus information includes the number of station berths, departure schedule, and bus operation time; initializes parameters, sets the initial number of carrying lines of the target station to 1, divides the bus operation time into q time periods, and calculates the average stop time of the station with the number of carrying lines; based on the average stop time, uses a preset station bus encounter probability model to calculate the probability corresponding to the number of bus bus arrivals in all time periods, and obtains an average entry failure rate greater than the number of station berths of the target station; if the average entry failure rate is lower than the preset entry failure rate, the number of carrying lines is increased by 1, and the average stop time of the station with the increased number of carrying lines is calculated, and the above steps are repeated until the average entry failure rate is higher than the preset entry failure rate, and the maximum number of carrying lines of the target station that is close to the preset entry failure rate critical value is obtained; according to the maximum number of carrying lines, the station line carrying capacity of the target station is adjusted. Therefore, the embodiments of the present invention can reveal the impact of the "car stringing phenomenon" on the station line carrying capacity, provide a theoretical basis for the subsequent planning and setting of bus stations and the configuration of the number of lines, make adjustments and settings on the bus lines of existing stations, and improve the carrying capacity of specific station lines to meet current needs; avoid the limitations of queuing theory on the calculation of station line carrying capacity, and make a more accurate description and estimation of the station line carrying capacity of the station. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a flow chart of a method for adjusting the carrying capacity of a line based on a train-in-train encounter provided by an embodiment of the present invention;

[0020] Figure 2 It is a structural schematic diagram of a device for adjusting the carrying capacity of a line based on a train-in-train encounter provided by an embodiment of the present invention;

[0021] Figure 3 It is a schematic diagram of all events of a possible arrival sequence of a convoy of route i provided by an embodiment of the present invention;

[0022] Figure 4 The present invention is a schematic diagram of a passenger boarding a bus at a multi-berth location provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0023] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0024] It should be noted that the terms "comprises" and "specifically" and any variations of the present invention are intended to cover non-exclusive inclusions. For example, a process, method, system, product or apparatus comprising a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units that are not explicitly listed or inherent to these processes, methods, products or apparatuses.

[0025] See also Figure 1 , Figure 1 1 is a flow chart of a method for adjusting the carrying capacity of a line based on a train-to-train encounter provided by an embodiment of the present invention. The method for adjusting the carrying capacity of a line based on a train-to-train encounter comprises steps S11 to S15:

[0026] S11, obtaining basic public transportation information of the target station; the basic public transportation information includes the number of parking spaces at the station, departure schedule, and public transportation operation hours;

[0027] S12, initializing parameters, setting the initial number of carrying lines of the target station to 1, dividing the bus operation time into q time periods, and calculating the average stop time of the station with the number of carrying lines;

[0028] S13, based on the average stop time, using a preset bus-bus encounter probability model to calculate the probability corresponding to the number of bus-bus encounters at the station in all time periods, and obtain an average entry failure rate for stations with a number of berths greater than the target station;

[0029] S14, if the average entry failure rate is lower than the preset entry failure rate, the number of the carrying lines is increased by 1, and the average stop time of the station with the increased number of carrying lines is calculated; return to step S13, and stop when the average entry failure rate is higher than the preset entry failure rate, and obtain the maximum number of carrying lines of the target station that is close to the preset entry failure rate critical value;

[0030] S15: Adjust the site line carrying capacity of the target site according to the maximum number of carrying lines.

[0031] Specifically, the preconditions of the preset site-sequential vehicle encounter probability model include:

[0032] (1) Real-time tracking and analysis of public transport vehicles. When a rear vehicle on the same route catches up with a front vehicle, the rear vehicle follows the front vehicle to form a convoy;

[0033] (2) The section travel time of the public transport vehicle follows a normal distribution, and the stop time delay is the diffusion effect of the section travel time delay. The stop time at the station also approximately follows a normal distribution;

[0034] (3) The impact of intersections (traffic lights) and emergencies on bus operations is not considered.

[0035] It should be noted that due to the randomness of bus stringing, the phenomenon of bus stringing in the middle section is relatively scattered, but because buses need to stop at stations to pick up passengers, bus stringing occurs frequently and data observation is relatively easy. In order to facilitate the analysis of the impact of bus stringing on the carrying capacity of station lines, the focus is on the bus stringing phenomenon at stations.

[0036] Specifically, the expected arrival probability density function of the preset site-by-site vehicle encounter probability model is:

[0037]

[0038] In the formula, is the first The expected arrival probability density function of a bus arriving at the target station s, where the target station s is the first bus on the route i. stations, all buses on line i need to pass through a total of Section; is the sum of the expected travel time of the jth section that the bus on the route i passes before arriving at the target station s and the expected stop time at the jth station; is the sum of the variance of the expected travel time of the jth section that the bus on the route i passes through before arriving at the target station s and the variance of the expected stop time at the jth station; is the average departure time of route i, and the bus operation time is [T s ,T e ], the total number of buses dispatched during the said bus operation period Public transport vehicles.

[0039] It should be noted that, assuming the average bus stop time at station s is It changes with the number of lines carried by the site n. Select any time period As the research object, if the arrival time of the first vehicle of the station-connected vehicle fleet is T fir ,but Except for the first vehicle, all other vehicles in the convoy were Follow to the station.

[0040] set up are the arrival times of the first and last trains in the evening peak period of line i respectively. In order to accurately calculate the probability of multiple trains meeting at a station in any selected period, we first assume that there are The vehicles meet at the station. For example, all possible arrival fleet sequences of route i See Figure 3 .

[0041] Specifically, the calculation of the probability of bus vehicles encountering each other at a station of the preset bus encounter probability model includes:

[0042] If the time period No car arrives at the station, then the probability that no car arrives at the station on line i is:

[0043]

[0044] The total probability of no vehicle arriving at the target station s is expressed as:

[0045]

[0046] In the formula, the bus operation time [T s ,T e ] is divided into q segments, χ=0,1,2,3....q-1; L is the number of lines passing through the target site s;

[0047] If the time period The first bus arrives at the station, and the remaining buses are Follow the arrival station, then the line i has The probability of a bus meeting the target station s under the first possible arrival sequence of the fleet is for:

[0048]

[0049] The line i has at the target site s The buses met in a row. The probability of arriving at the convoy sequence is:

[0050]

[0051] In the formula, It represents the first possible arrival sequence event of the route i. The probability of a bus arriving at the station; represents the remaining buses on the route i that are not included in the first possible arrival fleet sequence event; r is the rth possible arrival fleet sequence event;

[0052] If the other lines of the target site are in the time period The probability that there is a car arriving at the station and no car arrives at the station on line i is:

[0053]

[0054] The total probability of n buses meeting at the target station is:

[0055]

[0056] It should be noted that if there are n vehicles meeting at a station, the vehicles come from the same or different routes, which is characterized by the situation in Indicates that line 1 has Cars meet each other, satisfying Taking route i as an example, there are three types of route vehicle arrival situations:

[0057] Case 1: If line i has The first car of the team comes from line i. If line i has The buses meet and arrive at the convoy sequence event in the first possible way. For example, considering that the rear vehicle does not overtake the front vehicle during the bus section, the first vehicle in the convoy sequence is the first vehicle. Therefore, under the first possible arrival convoy sequence event, route i has The probability of buses meeting at stop s It can be expressed as:

[0058]

[0059] Therefore, line i has The first bus of the bus convoy is included. The probability of this event can be expressed as:

[0060]

[0061] Case 2: Line i has The first car of the station-sequential car convoy does not come from line i. Since the first car of the station-sequential car convoy does not come from line i, the Vehicles in the period Arrive at the station.

[0062] Arrive at the convoy sequence in the first possible way For example, at this time line i has The probability that two buses meet at station s is expressed as:

[0063]

[0064] Similarly, line i has The first bus of the convoy does not come from the entire route i. The probability of this event can be expressed as:

[0065]

[0066] Scenario 3: No vehicle arrives at line i, but other lines have vehicles arriving. On the premise that other lines have vehicles arriving, line i has no vehicle arriving. Indicates that line i is in the selected time period If no train departs from the route i and arrives at the station, the probability that no train arrives at the station on route i is:

[0067]

[0068] Therefore, for any line i, there may be a car or no car arriving at the station during the selected time period. If there is a car arriving at the station, it is divided into whether it contains the first car or not. If Φ_tot is used to represent the satisfaction All situations of the condition The solution space, under the premise that the rear car does not overtake the front car, the probability of n cars meeting at the station s carrying L lines is It can be expressed as:

[0069]

[0070] Specifically, the average stop time at the station is:

[0071]

[0072] In the formula, P(v,n) represents the probability that there are v buses at the bus stop of n lines at the same time in time period t. represents the average bus boarding time, t oc Indicates the bus door opening and closing time.

[0073] It should be noted that the calculation of the average bus stop time in existing studies mostly adopts empirical values, ignoring the impact of the number of carrying lines as an important attribute of the station and the impact of the interference of the flow of people at the station, which will lead to deviations in the calculation of the average bus stop time. Studies have shown that most passengers arriving at the station are concentrated at the bus stop, and passengers waiting at the first berth can get on the bus directly. For buses parked at the back berths, if passengers at the bus stop want to get on the bus, they need to overcome the interference of the flow of people and move to the corresponding berth to get on the bus (see Figure 4), which results in different boarding times at different berths, with the further back the berth is, the longer it takes for each passenger to board the bus.

[0074] For example, let the average boarding time per passenger at the first berth be T 1 , the cumulative boarding time per passenger at the rear berth compared to the front berth is Δ, then the boarding time per passenger at different berth positions l is defined as for:

[0075]

[0076] Assume that the number of routes carried by the station is n. Since the arrival of buses follows a Poisson distribution and the bus routes are independent of each other, the arrival distribution of vehicles containing n routes can be regarded as a cumulative arrival rate of Poisson distribution, where represents the average departure frequency of the line, λ i is the actual departure frequency of the ith route, then the probability that a bus stop containing n routes has v buses at the same time in time period t can be expressed as:

[0077]

[0078] Formula (3) shows that the more the number of lines n carried by a station is, the higher the probability of multiple vehicles arriving at the station will be, and the degree of confusion of passengers boarding the vehicle will be aggravated, which will cause the vehicle to stay at the station for a longer time to complete the passenger service.

[0079] Taking the evening peak work area as an example, most passengers at the bus stop are going home by bus, and the number of people getting on the bus is greater than the number of people getting off the bus. The bus boarding time will cover the getting off time, and the station stop time is determined by the number of people getting on the bus. Assuming that passengers only choose a specific route to take the bus, let the average passenger arrival rate of the route be η, and the average number of passengers on the vehicle A is the cumulative number of passengers at the station within the average departure time of the route, which can be expressed as:

[0080]

[0081] Let the actual number of berths at a straight-line or shallow-harbor station be U. Considering the limited length of the station platform, when the number of vehicles v arriving at the station does not exceed the number of berths U, if the number of vehicles arriving is 1, the average boarding time of the bus is AT. 1 , the average boarding time of the bus when the number of buses is 2 is [AT 1 +A(T 1 +Δ)] / 2, and so on; when the number of buses v coming to the station exceeds the number of parking spaces U, it is assumed that the boarding time of each passenger on the buses queuing outside the station corresponds to the last parking space, so the average boarding time of the bus is:

[0082]

[0083] Formula (5) can be used to calculate the probability P(v,n) that v vehicles are at the same time at a station with n lines in the evening peak working area, and the expected number of vehicles arriving at the station is P(v,n)*v. Therefore, the average corrected stop time of a station with n lines can be expressed as the total stop time of vehicles arriving at the station divided by the expected number of vehicles arriving at the station, as shown in Formula (6).

[0084]

[0085] Formula (6) shows that when the number of bus routes at a station changes, the average bus stop time will also change accordingly.

[0086] Figure 2 1 is a schematic diagram of a structure of a device for adjusting the carrying capacity of a line based on a train-in-train encounter provided by an embodiment of the present invention, the device for adjusting the carrying capacity of a line based on a train-in-train encounter comprises:

[0087] The information acquisition module 21 acquires the basic public transportation information of the target station; the basic public transportation information includes the number of parking spaces at the station, the departure schedule, and the public transportation operation time;

[0088] Initialization module 22, initialization parameters, setting the initial number of carrying lines of the target station to 1, dividing the bus operation time into q time periods, and calculating the average stop time of the station with the number of carrying lines;

[0089] The probability calculation module 23 calculates the probability corresponding to the number of bus tandem buses arriving at the station in all time periods based on the average stop time and uses a preset station tandem bus encounter probability model to obtain an average station entry failure rate greater than the number of station berths of the target station;

[0090] The bearer route module 24 increases the number of bearer routes by 1 if the average station entry failure rate is lower than the preset station entry failure rate, and calculates the average stop time of the station after the increase of the number of bearer routes; returns to the probability calculation module 23, and stops when the average station entry failure rate is higher than the preset station entry failure rate, and obtains the maximum number of bearer routes of the target station that is close to the preset station entry failure rate critical value;

[0091] The line adjustment module 25 adjusts the site line carrying capacity of the target site according to the maximum number of carrying lines.

[0092] Specifically, the preconditions of the preset site-sequential vehicle encounter probability model include:

[0093] (1) Real-time tracking and analysis of public transport vehicles. When a rear vehicle on the same route catches up with a front vehicle, the rear vehicle follows the front vehicle to form a convoy;

[0094] (2) The section travel time of the public transport vehicle follows a normal distribution, and the stop time delay is the diffusion effect of the section travel time delay. The stop time at the station also approximately follows a normal distribution;

[0095] (3) The impact of intersections (traffic lights) and emergencies on bus operations is not considered.

[0096] Specifically, the expected arrival probability density function of the preset site-by-site vehicle encounter probability model is:

[0097]

[0098] In the formula, is the first The expected arrival probability density function of a bus arriving at the target station s, where the target station s is the first bus on the route i. stations, all buses on line i need to pass through a total of Section; is the sum of the expected travel time of the jth section that the bus on the route i passes before arriving at the target station s and the expected stop time at the jth station; is the sum of the variance of the expected travel time of the jth section that the bus on the route i passes through before arriving at the target station s and the variance of the expected stop time at the jth station; is the average departure time of route i, and the bus operation time is [T s ,T e ], the total number of buses dispatched during the said bus operation period Public transport vehicles.

[0099] A device for adjusting the carrying capacity of a line based on the meeting of trains in series provided in an embodiment of the present invention can implement all processes of the method for adjusting the carrying capacity of a line based on the meeting of trains in series in the above-mentioned embodiment. The functions of each module in the device and the technical effects achieved are respectively the same as the functions of the method for adjusting the carrying capacity of a line based on the meeting of trains in series and the technical effects achieved in the above-mentioned embodiment, and will not be repeated here.

[0100] An embodiment of the present invention provides a terminal device, the terminal device comprising: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps in the above-mentioned method for adjusting the carrying capacity of the line based on the encounter of trains are implemented. Alternatively, when the processor executes the computer program, the functions of each module in the above-mentioned device for adjusting the carrying capacity of the line based on the encounter of trains are implemented.

[0101] The terminal device may be a computing device such as a desktop computer, a notebook, a PDA, and a cloud server. The terminal device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that the schematic diagram is merely an example of a terminal device and does not constitute a limitation on the terminal device. The terminal device may include more or fewer components than shown in the diagram, or may combine certain components, or different components. For example, the terminal device may also include an input / output device, a network access device, a bus, etc.

[0102] The processor may be a central processing unit, or other general-purpose processors, digital signal processors, application-specific integrated circuits, field programmable gate arrays or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the terminal device, and uses various interfaces and lines to connect various parts of the entire terminal device.

[0103] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the terminal device by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card, at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0104] It should be noted that the device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0105] An embodiment of the present invention also provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the method for adjusting the carrying capacity of the line based on the encounter of trains in series as described in the above embodiment.

[0106] In summary, the embodiments of the present invention disclose a method, device, terminal and medium for adjusting the carrying capacity of a line based on a string vehicle encounter, which obtains the basic bus information of a target station; the basic bus information includes the number of station berths, departure schedule, and bus operation time; initializes parameters, sets the initial number of carrying lines of the target station to 1, divides the bus operation time into q time periods, and calculates the average stop time at the station with the number of carrying lines; based on the average stop time, uses a preset station string vehicle encounter probability model to calculate the probability corresponding to the number of bus string vehicles arriving at the station in all time periods, and obtains an average entry failure rate greater than the number of station berths of the target station; if the average entry failure rate is lower than the preset entry failure rate, the number of carrying lines is increased by 1, and the average stop time at the station with the increased number of carrying lines is calculated, and the above steps are repeated until the average entry failure rate is higher than the preset entry failure rate, and the maximum number of carrying lines of the target station that is close to the preset entry failure rate critical value is obtained; according to the maximum number of carrying lines, the station line carrying capacity of the target station is adjusted. Therefore, the embodiments of the present invention can reveal the impact of the "car stringing phenomenon" on the station line carrying capacity, provide a theoretical basis for the subsequent planning and setting of bus stations and the configuration of the number of lines, make adjustments and settings on the bus lines of existing stations, and improve the carrying capacity of specific station lines to meet current needs; avoid the limitations of queuing theory on the calculation of station line carrying capacity, and make a more accurate description and estimation of the station line carrying capacity of the station.

[0107] The above is a preferred embodiment of the present invention. It should be pointed out that a person skilled in the art can make several improvements and modifications without departing from the principle of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for adjusting the carrying capacity of a line based on a train-in-train encounter, characterized in that: include: S11, obtaining basic public transportation information of the target station; the basic public transportation information includes the number of parking spaces at the station, departure schedule, and public transportation operation hours; S12, initializing parameters, setting the initial number of carrying lines of the target station to 1, dividing the bus operation time into q time periods, and calculating the average stop time of the station with the number of carrying lines; S13, based on the average stop time, using a preset bus-bus encounter probability model to calculate the probability corresponding to the number of bus-bus encounters at all time periods, and obtain an average entry failure rate when the number of bus-bus encounters at the target station is greater than the number of bus berths at the target station; S14, if the average station entry failure rate is lower than the preset station entry failure rate, the number of the carrying lines is increased by 1, and the average stop time of the station of the increased number of carrying lines is calculated; Return to step S13, and stop when the average entry failure rate is higher than a preset entry failure rate, to obtain the maximum number of carrying lines of the target site that is close to the preset entry failure rate critical value; S15, adjusting the site line carrying capacity of the target site according to the maximum number of carrying lines; The expected arrival probability density function of the preset site-to-site vehicle encounter probability model is: In the formula, is the first The expected arrival probability density function of a bus arriving at the target station s, where the target station s is the first bus on the route i. stations, all buses on the route i need to pass through a total of Section; is the sum of the expected travel time of the jth section that the bus on the route i passes before arriving at the target station s and the expected stop time at the jth station; is the sum of the variance of the expected travel time of the jth section that the bus on the route i passes through before arriving at the target station s and the variance of the expected stop time at the jth station; is the average departure time of route i, and the bus operation time is [T s ,T e ], the total number of buses dispatched during the said bus operation period public transport vehicles; The calculation of the probability of bus-bus encounter at a station of the preset bus-bus encounter probability model specifically includes: If the time period No car arrives at the station, then the probability that no car arrives at the station on line i is: The total probability of no vehicle arriving at the target station s is expressed as: In the formula, the bus operation time [T s ,T e ] is divided into q segments, χ=0,1,2,3....q-1; L is the number of lines passing through the target site s; If the time period The first bus arrives at the station, and the remaining buses are Follow the arrival station, then the line i has The probability of a bus meeting the target station s under the first possible arrival sequence of the fleet is for: The line i has at the target site s The buses met in a row. The probability of arriving at the convoy sequence is: In the formula, is the average bus stop time at the target station s; It represents the first possible arrival sequence event of the route i. The probability of a bus arriving at the station; represents the remaining buses on the route i that are not included in the first possible arrival fleet sequence event; Indicates that the line i has The probability of a bus meeting the target station s under the rth possible arrival sequence event of the convoy; If the other lines of the target site are in the time period The probability that there is a car arriving at the station and no car arrives at the station on line i is: The total probability of n buses meeting at the target station is: In the formula, Φ_tot means satisfying All situations of the condition The solution space is: n is the total number of vehicles that meet at the target station s. Indicates that line 1 has A car meets another car. Indicates that line 2 has A car meets another car. Indicates that line L has Cars meet in a row.

2. The method for adjusting the carrying capacity of a line based on a train-in-train encounter as claimed in claim 1, characterized in that: The prerequisites of the preset site-to-site vehicle encounter probability model specifically include: (1) Real-time tracking and analysis of public transport vehicles. When a rear vehicle on the same route catches up with a front vehicle, the rear vehicle follows the front vehicle to form a convoy; (2) The section travel time of the public transport vehicle follows a normal distribution, and the stop time delay is the diffusion effect of the section travel time delay. The stop time at the station follows a normal distribution; (3) The impact of intersections and emergencies on bus operations is not considered.

3. The method for adjusting the carrying capacity of a line based on a train-in-train encounter as claimed in claim 1, characterized in that: The average stop time at the station is: In the formula, P(v,n) represents the probability that there are v buses at the bus stop of n lines at the same time in time period t. represents the average bus boarding time, t oc Indicates the bus door opening and closing time.

4. A device for adjusting the carrying capacity of a line based on a train-in-train encounter, characterized in that: include: Information acquisition module, to obtain basic bus information of the target station; The basic public transportation information includes the number of parking spaces at the station, departure schedule, and public transportation operation hours; Initialization module, initialization parameters, setting the initial number of carrying lines of the target station to 1, dividing the bus operation time into q time periods, and calculating the average stop time of the station with the number of carrying lines; A probability calculation module, based on the average stop time, uses a preset bus-bus encounter probability model to calculate the probability corresponding to the number of bus-bus encounters at all time periods, and obtains an average entry failure rate when the number of bus-bus encounters at the target station is greater than the number of bus berths at the target station; The load-carrying route module increases the number of load-carrying routes by 1 if the average entry failure rate is lower than the preset entry failure rate, and calculates the average stop time of the increased number of load-carrying routes; Returning to the probability calculation module, the process stops when the average entry failure rate is higher than a preset entry failure rate, and obtains the maximum number of carrying lines of the target site that is close to the preset entry failure rate critical value; A line adjustment module, adjusting the site line carrying capacity of the target site according to the maximum number of carrying lines; The expected arrival probability density function of the preset site-to-site vehicle encounter probability model is: In the formula, is the first The expected arrival probability density function of a bus arriving at the target station s, where the target station s is the first bus on the route i. stations, all buses on the route i need to pass through a total of Section; is the sum of the expected travel time of the jth section that the bus on the route i passes before arriving at the target station s and the expected stop time at the jth station; is the sum of the variance of the expected travel time of the jth section that the bus on the route i passes through before arriving at the target station s and the variance of the expected stop time at the jth station; is the average departure time of route i, and the bus operation time is [T s ,T e ], the total number of buses dispatched during the said bus operation period public transport vehicles; The calculation of the probability of bus-bus encounter at a station of the preset bus-bus encounter probability model specifically includes: If the time period No car arrives at the station, then the probability that no car arrives at the station on line i is: The total probability of no vehicle arriving at the target station s is expressed as: In the formula, the bus operation time [T s ,T e ] is divided into q segments, χ=0,1,2,3....q-1; L is the number of lines passing through the target site s; If the time period The first bus arrives at the station, and the remaining buses are Follow the arrival station, then the line i has The probability of a bus meeting the target station s under the first possible arrival sequence of the fleet is for: The line i has at the target site s The buses met in a row. The probability of arriving at the convoy sequence is: In the formula, is the average bus stop time at the target station s; It represents the first possible arrival sequence event of the route i. The probability of a bus arriving at the station; represents the remaining buses on the route i that are not included in the first possible arrival fleet sequence event; Indicates that the line i has The probability of a bus meeting the target station s under the rth possible arrival sequence event of the convoy; If other lines of the target site are in the time period The probability that there is a car arriving at the station and no car arrives at the station on line i is: The total probability of n buses meeting at the target station is: In the formula, Φ_tot means satisfying All situations of the condition The solution space is: n is the total number of vehicles that meet at the target station s. Indicates that line 1 has A car meets another car. Indicates that line 2 has A car meets another car. Indicates that line L has Cars meet in a row.

5. The device for adjusting the carrying capacity of a line based on a train-in-train encounter as claimed in claim 4, characterized in that: The prerequisites of the preset site-to-site vehicle encounter probability model specifically include: (1) Real-time tracking and analysis of public transport vehicles. When a rear vehicle on the same route catches up with a front vehicle, the rear vehicle follows the front vehicle to form a convoy; (2) The section travel time of the public transport vehicle follows a normal distribution, and the stop time delay is the diffusion effect of the section travel time delay. The stop time at the station follows a normal distribution; (3) The impact of intersections and emergencies on bus operations is not considered.

6. A terminal device, characterized in that: It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, it implements the method for adjusting the carrying capacity of the line based on the encounter of trains in series as described in any one of claims 1-3.

7. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the method for adjusting the carrying capacity of the line based on the encounter of trains in series as described in any one of claims 1-3.

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