CN114638434A - Variable bus area station optimization method, device and computer storage medium - Google Patents
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Abstract
The embodiment of the invention discloses a method, equipment and a computer storage medium for optimizing a station of a variable bus area, wherein the method comprises the following steps: acquiring historical traffic data of passengers, and determining a regional station strategy, wherein the regional station strategy comprises a hot spot region for getting on and off a bus; acquiring bus operation data, and constructing a simulation evaluation model according to the bus operation data and the historical traffic volume data of passengers; and constructing a simulation optimization problem based on the simulation evaluation model, solving the simulation optimization problem based on a Kriging model global optimization algorithm of a multipoint filling sampling criterion, and determining the position of the target area site.
Description
Technical Field
The invention relates to the field of public transportation management, in particular to a method and equipment for optimizing a variable bus area stop and a computer storage medium.
Background
With the development of social economy and the improvement of the living standard of people, the holding amount of private automobiles is continuously improved. However, the traffic jam, the exhaust pollution and the energy consumption become social problems which cannot be ignored. Public transportation is an important means for solving the above problems, and has the advantages of large carrying capacity, low pollution degree and low energy consumption. The research on the public transportation problem and the rapid development of the public transportation are the urgent priority for the urban development in China. At present, the progress of urbanization in china is becoming faster, and low population density areas such as new urban areas and the like, which are generated along with urban expansion, are also increasing. Therefore, public transportation travel in low population density areas such as new urban areas becomes an urgent problem to be solved in public transportation development in China.
At present, public transportation can be divided into three categories: traditional fixed line public transportation, demand response type public transportation and flexible public transportation. Traditional fixed line public transportation (such as conventional public transportation, subway, etc.) has fixed circuit and fixed timetable, is applicable to the region that population density is high, and the passenger flows the trip greatly. The urban new area equal land inevitably causes the waste of the transport capacity of the traditional fixed line public transport due to the low population density. Demand-responsive buses do not have fixed lines and schedules and can respond to door-to-door service of passengers. But its operation cost is too high and it is difficult to couple with the urban backbone roads. The flexible bus is a new bus mode, has the characteristics of large traditional bus transportation volume and flexible service of demand response type bus door-to-door, and has strong applicability in low population density areas such as new urban areas. Among the operation modes of the variable-route buses, the variable-route buses are most widely applied. It is expected that, with the improvement of the requirements of residents in new urban areas and other places on the trip quality and the development of the internet technology, the variable-route public transport will gradually go to the daily life.
The working process of the variable-route bus (as shown in the figure I) can be briefly summarized as follows: a reference route is arranged in a preset service area, and a series of fixed stations are distributed on the reference route. The vehicle travels along the reference route, following the predetermined time of each fixed station and arriving at each fixed station in turn. If there is a need for getting on/off the vehicle from the reference route in the interval between the adjacent two fixed stops, the vehicle can select whether to serve the passenger according to whether the predetermined time of the fixed stop can be satisfied.
Disclosure of Invention
In view of this, the present invention provides a method, a device and a computer storage medium for optimizing a station in a variable bus area, which can improve the service level of variable-route bus operation.
In order to achieve the purpose, the technical scheme of the invention is realized as follows:
in a first aspect, the invention provides a method for optimizing a station in a variable bus area, which comprises the following steps:
obtaining historical traffic data of passengers and determining regional station strategies, wherein the regional station strategies comprise hot spot regions for getting on and off the bus;
acquiring public transport operation data, and constructing a simulation evaluation model according to the public transport operation data and the historical traffic volume data of passengers;
and constructing a simulation optimization problem based on a simulation evaluation model, solving the simulation optimization problem based on a Kriging model global optimization algorithm of a multipoint filling sampling criterion, and determining the position of a target area station.
Preferably, the acquiring historical traffic volume data of the passengers and determining the regional station strategy comprises:
obtaining historical traffic volume data of passengers, and dividing variable bus area stations into an aggregate formed by a plurality of sub-areas, wherein each sub-area comprises at least one area station;
obtaining the number of passengers getting on the bus in each sub-area based on the historical traffic volume data of the passengers and sequencing the passengers to obtain the passenger service rate in each sub-area;
when the passenger service rate is higher than a set first threshold value, determining that a sub-area corresponding to the passenger service rate is an boarding hot spot area.
Preferably, the acquiring historical traffic volume data of the passengers and determining the regional station strategy further include:
obtaining historical traffic volume data of passengers, and dividing variable bus area stations into an aggregate formed by a plurality of sub-areas, wherein each sub-area comprises at least one area station;
obtaining the number of alighting people of each subarea based on the historical traffic data of the passengers and sequencing the obtained number of the alighting people to obtain the passenger service rate of each subarea;
and when the passenger service rate is higher than a set second threshold value, determining that the sub-area corresponding to the passenger service rate is a get-off hot spot area.
Preferably, the acquiring the bus operation data and constructing the simulation evaluation model according to the bus operation data and the historical traffic travel data of the passengers includes:
obtaining a fixed station position corresponding to each passenger according to the bus operation data, and obtaining a plurality of fixed station positions with the most trips;
obtaining the number of passengers getting on/off in each subarea according to the historical traffic volume data of the passengers;
obtaining a vehicle running path through a latest interpolation algorithm, and calculating a simulation model parameter evaluation index;
and constructing a simulation evaluation model based on the relaxation time, the number of passengers getting on/off the train in each sub-area and the policy model parameter evaluation index.
Preferably, the relaxation time includes:
TS＝2*(C-1)Td+2L/Vb
where C is the number of fixed stations, Td is the waiting time of each fixed station, and Vb is the vehicle traveling speed.
Preferably, the constructing a simulation optimization problem based on the simulation evaluation model, solving the simulation optimization problem based on a Kriging model global optimization algorithm of a multipoint filling sampling criterion, and determining the position of the target area site includes:
the method comprises the following steps: constructing a simulation optimization problem based on a simulation evaluation model, and inputting and calculating a corresponding response value of any regional site based on a Kriging model global optimization algorithm of a multipoint filling sampling criterion;
step two: judging whether the response value meets the set termination condition, if so, outputting the current response value, and if not, performing the third step;
step three: constructing a Kriging model;
step four: acquiring a new area site based on a multipoint filling sampling criterion, and calculating a corresponding response value;
step five: and updating the training set of the Kriging model, and returning to the step two.
In a second aspect, the present invention provides a computer apparatus comprising: a processor and a memory for storing a computer program capable of running on the processor;
the processor is configured to implement any of the above-described methods for optimizing a bus stop in a variable bus area when running the computer program.
In a third aspect, the present invention provides a computer storage medium having a computer program stored therein, the computer program being executed by a processor to implement any of the variable bus area stop optimization methods described above.
The embodiment of the invention provides a method, equipment and a computer storage medium for optimizing stations in a variable bus area, wherein the method comprises the following steps: acquiring historical traffic data of passengers, and determining a regional station strategy, wherein the regional station strategy comprises a hot spot region for getting on and off a bus; acquiring public transport operation data, and constructing a simulation evaluation model according to the public transport operation data and the historical traffic volume data of passengers; the method comprises the steps of constructing a simulation optimization problem based on a simulation evaluation model, solving the simulation optimization problem based on a Kriging model global optimization algorithm of a multipoint filling sampling criterion, and determining the position of a target area station.
Drawings
FIG. 1 is a schematic diagram of a variable-route bus of the prior art during operation;
fig. 2 is a schematic flow chart of a method for optimizing a station in a variable bus area according to an embodiment of the present invention;
fig. 3 is a schematic policy diagram of a regional site according to an embodiment of the present invention;
fig. 4 is a schematic diagram illustrating a hot spot area for confirming boarding according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of a variable bus area station optimization device according to an embodiment of the present invention;
fig. 6 is a schematic structural diagram of a computer device according to an embodiment of the present invention.
Detailed Description
The technical solution of the present invention is further described in detail with reference to the drawings and the specific embodiments of the specification. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the term and/or "includes any and all combinations of one or more of the associated listed items.
At present, although the practical experience of variable-line public transport in foreign countries exceeds twenty years, the variable-line public transport still does not form a mature system. In addition, the situation in China is very different from that in foreign countries. The main symptoms restricting the localization of the variable-line public transport can be summarized into three aspects:
1. the population of China is large, and even in areas with low population density, the travel demand often exceeds the bearing range of variable-route buses. In addition, closed cell layout is adopted in China, and accurate door-to-door service is difficult to realize.
2. The variable-route buses lack a reliable simulation evaluation model, and current research is to assume that the variable-route buses are in an ideal operation state (such as uniform distribution of fixed stations and random distribution of passengers). In fact, the travel demand of the passenger is often related to the road network structure, the land utilization condition and the like, and the travel distribution characteristics of the passenger should be mined by combining actual data.
3. The operation of variable-route buses lacks an effective optimization method, which limits further improvement of service performance thereof. This increases operating costs and is inefficient because decision makers can only determine their required operating parameter values based on past experience.
Aiming at the existing problems, the embodiment of the invention aims to provide a method for optimizing the stations in the variable bus area, which can be more comprehensively and objectively used, and the simulation optimization problem is solved by adopting a Kriging model global optimization algorithm based on a multipoint filling sampling criterion, so that the service level of the variable-line bus operation is further improved.
Referring to fig. 2, a method for optimizing a station in a variable bus area according to an embodiment of the present invention may be applied to optimizing the operation of buses with variable routes. The method can be executed by computer equipment, the computer equipment can be a terminal or a server and the like, and the terminal can be a desktop computer, a notebook computer, a smart phone, a personal digital assistant or a tablet computer and the like; the server may be a single server device or a cluster of servers, etc. The variable bus area stop optimization method comprises the following steps:
step S101: obtaining historical traffic data of passengers and determining regional station strategies, wherein the regional station strategies comprise hot spot regions for getting on and off the bus;
here, the traffic travel amount data is a traffic start/stop point survey, which is an OD survey, also referred to as an OD traffic volume survey, and the OD traffic volume is a traffic travel amount between start and stop points. "O" is derived from ORIGIN, english, and refers to the starting point of a trip, and "D" is derived from DESTINATION, english, and refers to the DESTINATION of a trip.
The OD traffic is typically acquired using personal trip surveys and automotive OD surveys, among others. This can be divided into passenger flow OD surveys and cargo flow OD surveys. The former survey content mainly includes the distribution of the starting points and the stopping points, the purpose of travel, the mode of travel, the time of travel, the distance of travel, the number of times of travel and the like. Therefore, the passenger distribution rule on the bus network can be determined, data are provided for the bus network optimization, the average passenger riding distance and the average passenger riding time of each line can also be determined, and the conversion relation between the resident running amount and the traffic flow is established. The data obtained through the individual trip survey is basic data for planning and evaluating the urban comprehensive transportation system.
Step 102: acquiring public transport operation data, and constructing a simulation evaluation model according to the public transport operation data and the historical traffic volume data of passengers;
step 103: and constructing a simulation optimization problem based on a simulation evaluation model, solving the simulation optimization problem based on a Kriging model global optimization algorithm of a multipoint filling sampling criterion, and determining the position of a target area station.
In one embodiment, the obtaining historical traffic volume data of passengers and determining regional station strategies includes:
obtaining historical traffic volume data of passengers, and dividing variable bus area stations into an aggregate formed by a plurality of sub-areas, wherein each sub-area comprises at least one area station;
obtaining the number of passengers getting on the bus in each sub-area based on the historical traffic volume data of the passengers and sequencing the passengers to obtain the passenger service rate in each sub-area;
when the passenger service rate is higher than a set first threshold value, determining that a sub-area corresponding to the passenger service rate is an boarding hot spot area.
In one embodiment, the obtaining the historical traffic volume data of the passenger and determining the regional site policy further includes:
obtaining historical traffic volume data of passengers, and dividing variable bus area stations into an aggregate formed by a plurality of sub-areas, wherein each sub-area comprises at least one area station;
obtaining the number of people getting off in each sub-area based on the historical traffic volume data of the passengers and sequencing to obtain the passenger service rate in each sub-area;
and when the passenger service rate is higher than a set second threshold value, determining that the sub-area corresponding to the passenger service rate is a get-off hot spot area.
Here, taking a variable bus as an example, the operation principle of the variable-route bus shows that the variable-route bus needs to go outside a reference route to accurately meet the getting-on and getting-off requirements of passengers. When the trip demand level of the passenger is increased, the rejection rate of the variable-route public transportation service is greatly increased, and the high rejection rate causes the trust degree of the passenger on the variable-route public transportation service to be reduced. In the embodiment, a data-driven regional stop strategy is provided, as shown in fig. three, by integrating the passenger getting-on/off demand in the hot spot region, the service performance of the variable-route buses under the high-going demand level is improved.
The regional station strategy regards the service region of the variable bus as an aggregate consisting of a plurality of sub-regions; historical traffic data of passengers are introduced, and the data are used for mining hot spot areas of passenger out-of-station travel. In this embodiment, taking boarding as an example, a passenger trip hot area is determined, and taking a boarding situation as an example, a boarding hot spot area is determined as shown in fig. four.
The specific steps can be as follows:
1) and counting the number of passengers getting on the bus in each area. According to the number of people getting on the bus, all the areas are sorted from big to small. The serial number of each sorted subarea is Si( i 1, 2.. said., Ns), the number of passengers getting on the train counted by each subarea is recorded as NSi(i＝1，2，....，Ns)。
2) Defining parameters: the passenger service rate Pr, participating in the following formula (1):
3) setting a threshold value P, a hot spot area evaluation criterion, see formula (2), and setting as follows:
min(Pr)≥P (2)
finally, each sub-area is preset with a fixed site, which is defined as the area site of the sub-area. If the sub-area is determined to be a boarding hot spot area, the fixed station is enabled to meet the boarding requirements of all passengers in the integrated sub-area; the getting-off situation is the same. It should be noted that if the sub-area is both an entry and exit hot spot area, then the regional station will integrate all the entry and exit needs in the area at the same time.
In an embodiment, the obtaining of the bus operation data and the building of the simulation evaluation model according to the bus operation data and the historical traffic volume data of the passengers include:
obtaining the position of each passenger fixed station according to the bus operation data, and obtaining a plurality of fixed station positions with the most trips;
obtaining the number of passengers getting on/off in each subarea according to the historical traffic volume data of the passengers;
obtaining a vehicle running path through a latest interpolation algorithm, and calculating a simulation model parameter evaluation index;
and constructing a simulation evaluation model based on the relaxation time, the number of passengers getting on/off the train in each sub-area and the policy model parameter evaluation index.
In one embodiment, the relaxation time includes:
TS＝2*(C-1)Td+2L/Vb
where C is the number of fixed stations, Td is the waiting time of each fixed station, and Vb is the vehicle traveling speed.
Here, because no operation example of the variable-route public transport exists in China, an existing fixed public transport route can be selected as a reference route of the variable-route public transport. By introducing actual operation data of the bus line, a station with large passenger flow is selected as a fixed station of the variable-line bus, so that the service area length L of the variable-line bus and the position of the fixed station on the reference line can be determined. The width W/2 of the variable line public transportation service area may be 1.5 miles as specified by the american handicapped act for reference.
Specifically, the number of passengers getting on/off the bus in each sub-area is obtained according to the historical traffic volume data of the passengers; service passengers for variable-route buses can be classified into four categories:
getting on/off a fixed station;
getting on a vehicle at a fixed station and getting off the vehicle outside the station;
getting off at a fixed station and getting on outside the station;
getting on/off the vehicle outside the station.
In this embodiment, the passenger demand is generated based on the collected passenger historical traffic volume data. After the whole service area is divided into a plurality of sub-areas, the number of passengers getting on/off the bus in each sub-area is counted through the historical traffic data of the passengers, and the generation of the getting on/off demand is simulated by utilizing the probability PS of the passengers getting on/off the bus in each sub-area, the level of the passenger demand level and the preset proportion of four kinds of passengers. The get-on/get-off probability for each sub-area can be calculated by equation (3):
wherein Ni is the number of passengers getting on/off the ith sub-zone, and NS is the number of sub-zones. To explore the promotion of the proposed method for variable-line public transportation services at high demand levels, the demand level of this study was set at 50-100 passengers/hour.
Here, the nearest interpolation algorithm is a classic and efficient vehicle planning method, and the core idea is that each interpolation places coordinates at a position where the extra additional distance is shortest. Suppose there are n passengers with a reservation time of t1 < t2 < tn.
Specifically, a vehicle running path is obtained through a latest insertion algorithm, and a simulation model parameter evaluation index is calculated, wherein the insertion algorithm for the running of the variable-route public transport vehicle can be established as follows:
passenger 1: if the passenger is a first type passenger, it must be accepted by the system. If the passenger is a category 2, 3, 4 passenger, the passenger's off-station required coordinates are assigned to the fixed station block in which it is located. Obtaining a vehicle driving path by using a latest insertion algorithm, if the slack time constraint is met, accepting the passenger requirement, and updating the vehicle path; if the slack time constraint is not satisfied, the passenger demand is rejected, and the pre-insertion vehicle travel path is preserved.
The passenger 2: if the passenger is a first type passenger, it must be accepted by the system. If the passenger is a passenger of class 2, 3 or 4, the passenger's off-station required coordinates are assigned to the fixed station zone in which the passenger is located. Obtaining a vehicle driving path by using a latest insertion algorithm, if the slack time constraint is met, accepting the passenger requirement, and updating the vehicle path; if the slack time constraint is not satisfied, the passenger demand is rejected, and the pre-insertion vehicle travel path is preserved.
......
A passenger n: if the passenger is a first type passenger, it must be accepted by the system. If the passenger is a passenger of class 2, 3 or 4, the passenger's off-station required coordinates are assigned to the fixed station zone in which the passenger is located. Obtaining a vehicle driving path by using a latest insertion algorithm, if the slack time constraint is met, accepting the passenger requirement, and updating the vehicle path; if the slack time constraint is not satisfied, the passenger demand is rejected, and the pre-insertion vehicle travel path is preserved.
Furthermore, the invention adopts the passenger service quality function as the service performance evaluation index of the variable-route public transport, the concrete formula is shown as a formula (4),
F＝w1K+w2W+w3R (4)
wherein W1, W2 and W3 are proportionality coefficients, K is the average walking time of the passengers, W is the average waiting time of the passengers, and R is the average walking time of the passengers.
In one embodiment, the constructing a simulation optimization problem based on a simulation evaluation model, solving the simulation optimization problem based on a Kriging model global optimization algorithm of a multipoint filling sampling criterion, and determining the position of a target area site includes:
the method comprises the following steps: constructing a simulation optimization problem based on a simulation evaluation model, and inputting and calculating a corresponding response value of any regional site based on a Kriging model global optimization algorithm of a multipoint filling sampling criterion;
step two: judging whether the response value meets the set termination condition, if so, outputting the current response value, and if not, performing the third step;
step three: constructing a Kriging model;
step four: acquiring a new area site based on a multipoint filling sampling criterion, and calculating a corresponding response value;
step five: and updating the training set of the Kriging model, and returning to the step two.
At present, research on the optimization field of the variable-route buses is less, and in the invention, a simulation optimization method is provided for optimizing parameters of a simulation evaluation model, namely position coordinates of regional stops.
After a simulation evaluation model is constructed to evaluate the regional station strategy, the position of the regional station is optimized by adopting a simulation optimization method, so that the service level of the variable-route bus is further improved. The simulation optimization means that an objective function or a constraint function value of an optimization model is calculated through the simulation model, and the actual situation can be better reflected by adopting a simulation optimization method, so that a decision maker is helped to plan and control the public transportation system before service operation.
The simulation evaluation model constructed by the invention is as follows:
(xj，yj)：j＝1，2，....，m (7)
wherein, SF is the mean value of the service quality function of the variable-line bus under different demand levels, and SR is the mean value of the passenger acceptance rate of the variable-line bus under different demand levels. xj and yj are coordinates of a horizontal axis and a vertical axis of the area site, and m is the number of all the getting-on/getting-off hot spot areas under a preset P value.
Here, the simulation optimization problem can be classified as one of the expensive black box optimization problems. Because its objective function or constraint function has no analytical expression and requires a value calculated by long-time simulation. The gradient-based precise optimization algorithm is not suitable for solving the simulation optimization problem, and gradient information is difficult to obtain because an objective function and a constraint function of the simulation optimization problem have no analytical expression. Metaheuristic algorithms that enable global optimization are also unsuitable because the search of these algorithms does not have directionality and often requires a large number of simulation calculations. Currently, the best way to deal with the simulation optimization problem is to use a global optimization algorithm based on a proxy model. The method adopts the Kriging model, and the Kriging model has good approximation capability on multimodal and nonlinear problems, so the Kriging model is often used for modeling of simulation optimization problems.
Here, the first step obtained by the global optimization algorithm of the Kriging model is initial sampling. Initial sampling requires the use of experimental design methods, the most common being latin hypercube sampling:
assuming that N sample points need to be generated for an M-dimension problem, N sample points can be generated by equally dividing each bit dimension of a design space into N subspaces and randomly generating one sample point in each subspace. The specific mathematical expression is as follows:
wherein j is the jth design variable, i is the ith level of the design variable, i is more than or equal to 1 and less than or equal to N, and j is more than or equal to 1 and less than or equal to M. U is random uniform distribution of [0, 1], and pi is random integer distribution of [0, N ].
According to previous studies, the initial number of sampling points of the Kriging global optimization method should be:
NS＝11*M-1 (9)
wherein, NS is the number of the initial sampling points, and M is the dimension of the problem.
Here, the termination condition is a judgment basis for stopping the algorithm, and since the simulation optimization problem has no explicit analytical expression, it is difficult to derive an accurate termination condition by a mathematical method. In engineering applications, a predetermined value with the maximum simulation calculation times is generally used as a termination condition, that is:
NFE＞NFEmax (10)
the NFE is the simulation calculation frequency, and the NFEmax is the preset maximum value of the simulation calculation frequency.
Here, for the Kriging model, it is assumed that there are m sample points X ═ X1, X2.., xm ] T, and the corresponding response values Y ═ Y1, Y2.., ym ] T. The Kriging model interpolates the target function based on a Gaussian process and a known point, and the basic predicted value expression is as follows:
y(x)＝fT(x)β+z(x) (11)
where β is a regression coefficient matrix, and f (x) is a polynomial matrix containing p regression values of x. z (x) is a spatially correlated randomly distributed error that can be considered an implementation of the gaussian process z (x) with a mean of 0 and a variance of σ 2. The covariance formula between design points is:
E(Z(xi，xj)＝σ2R(θ，xi，xj) (12)
here, the variance σ 2 is a scalar, and R (θ, xi, xj) is a spatial correlation function.
For m known sample points, the vector matrix F can be estimated by F (x), and the correlation matrix R can be defined by the spatial correlation function:
for an unknown sample point x, the correlation vector r between the unknown sample point x and m known sample points is:
r(x)＝{R(x，x1)，R(x，x2)，...，R(x，xm)}T (15)
the optimal linear unbiased estimate of y (x) is:
the variance s2 of y (x) can be calculated as:
here, the fill-sampling criterion is the core of the Kriging global optimization method, which determines which points will be used as new sampling points to update the Kriging model. In this study, a multi-point filling sampling criterion was proposed to achieve multi-point sampling by multi-objective optimization. The multi-objective problem of construction is as follows:
min：{f1，f2} (19)
wherein q is a control parameter, q is 10nAnd n generally has a value of 3 to 7. Y isminIs the minimum of the objective function, Y, in all known sample pointsmaxIs the maximum of the objective function in all known sample points. y (x) is the predicted value of the Kriging model, and s (x) is the predicted standard deviation of the Kriging model. Phi and
respectively, a cumulative distribution function and a probability density function of the standard normal distribution.
After the multi-objective problem (19) is constructed, the multi-objective optimization algorithm is adopted to solve the problem, and the optimal Pareto frontier can be obtained. And finally, selecting a required new sampling point according to the size of the predicted value of the Kriging model corresponding to each point in the Pareto frontier, and obtaining the target area station.
An embodiment of the present invention further provides a device for optimizing a station in a variable bus area, where as shown in fig. 5, the device includes:
the first obtaining module 51 is configured to obtain historical traffic volume data of passengers, and determine a regional station strategy, where the regional station strategy includes a hot spot region for getting on and off a vehicle;
the first obtaining module 52 is configured to build a simulation evaluation model according to the bus operation data and the historical traffic travel data of the passengers;
and the determining module 53 is configured to construct a simulation optimization problem based on the simulation evaluation model, solve the simulation optimization problem based on a Kriging model global optimization algorithm of a multipoint filling sampling criterion, and determine a position of a target area site.
In an optional embodiment, the first obtaining module 51 is further configured to:
obtaining historical traffic volume data of passengers, and dividing variable bus area stations into an aggregate formed by a plurality of sub-areas, wherein each sub-area comprises at least one area station;
obtaining the number of passengers getting on the bus in each sub-area based on the historical traffic volume data of the passengers and sequencing the passengers to obtain the passenger service rate in each sub-area;
when the passenger service rate is higher than a set first threshold value, determining that a sub-area corresponding to the passenger service rate is an boarding hot spot area.
In an optional embodiment, the first obtaining module 51 is further configured to:
obtaining historical traffic volume data of passengers, and dividing variable bus area stations into an aggregate formed by a plurality of sub-areas, wherein each sub-area comprises at least one area station;
obtaining the number of people getting off in each sub-area based on the historical traffic volume data of the passengers and sequencing to obtain the passenger service rate in each sub-area;
and when the passenger service rate is higher than a set second threshold value, determining that the sub-area corresponding to the passenger service rate is a get-off hot spot area.
In an optional embodiment, the second obtaining module 52 is further configured to:
obtaining a fixed station position corresponding to each passenger according to the bus operation data, and obtaining a plurality of fixed station positions with the most trips;
obtaining the number of passengers getting on/off in each subarea according to the historical traffic volume data of the passengers; obtaining a vehicle running path through a latest interpolation algorithm, and calculating a simulation model parameter evaluation index;
and constructing a simulation evaluation model based on the relaxation time, the number of passengers getting on/off the train in each sub-area and the policy model parameter evaluation index.
In an optional embodiment, the determining module 53 is further configured to:
the method comprises the following steps: constructing a simulation optimization problem based on a simulation evaluation model, and inputting and calculating a corresponding response value of any regional site based on a Kriging model global optimization algorithm of a multipoint filling sampling criterion;
step two: judging whether the response value meets the set termination condition, if so, outputting the current response value, and if not, performing the third step;
step three: constructing a Kriging model;
step four: acquiring a new area site based on a multipoint filling sampling criterion, and calculating a corresponding response value;
step five: and updating the training set of the Kriging model, and returning to the step two.
It should be noted that: when the variable bus stop optimization device provided in the above embodiment implements the variable bus stop optimization method, only the division of the program modules is illustrated, and in practical applications, the processing may be distributed to different program modules as needed, so as to complete all or part of the processing described above. In addition, the variable bus regional station optimization device provided by the above embodiment and the corresponding variable bus regional station optimization method embodiment belong to the same concept, and the specific implementation process is described in detail in the method embodiment and is not described again here.
An embodiment of the present invention provides a computer device, as shown in fig. 6, where the computer device includes: a processor 110 and a memory 111 for storing computer programs capable of running on the processor 110; the processor 110 illustrated in fig. 6 is not used to refer to the number of the processors 110 as one, but is only used to refer to the position relationship of the processor 110 relative to other devices, and in practical applications, the number of the processors 110 may be one or more; similarly, the memory 111 illustrated in fig. 6 is also synonymous, that is, only used to refer to a position relationship of the memory 111 relative to other devices, and in practical applications, the number of the memory 111 may be one or more.
The processor 110 is configured to execute the following steps when running the computer program:
obtaining historical traffic data of passengers and determining regional station strategies, wherein the regional station strategies comprise hot spot regions for getting on and off the bus;
acquiring public transport operation data, and constructing a simulation evaluation model according to the public transport operation data and the historical traffic volume data of passengers;
and constructing a simulation optimization problem based on a simulation evaluation model, solving the simulation optimization problem based on a Kriging model global optimization algorithm of a multipoint filling sampling criterion, and determining the position of a target area station.
In an alternative embodiment, the processor 110 is further configured to execute the following steps when the computer program is executed:
obtaining historical traffic volume data of passengers, and dividing variable bus area stations into an aggregate formed by a plurality of sub-areas, wherein each sub-area comprises at least one area station;
obtaining the number of passengers getting on the bus in each sub-area based on the historical traffic volume data of the passengers and sequencing the passengers to obtain the passenger service rate in each sub-area;
when the passenger service rate is higher than a set first threshold value, determining that a sub-area corresponding to the passenger service rate is an boarding hot spot area.
In an alternative embodiment, the processor 110 is further configured to execute the following steps when the computer program is executed:
obtaining historical traffic data of passengers, and dividing variable bus area stops into an aggregate formed by a plurality of sub-areas, wherein each sub-area comprises at least one area stop;
obtaining the number of people getting off in each sub-area based on the historical traffic volume data of the passengers and sequencing to obtain the passenger service rate in each sub-area;
and when the passenger service rate is higher than a set second threshold value, determining that the sub-area corresponding to the passenger service rate is a get-off hot spot area.
In an alternative embodiment, the processor 110 is further configured to execute the following steps when the computer program is executed:
obtaining the position of each passenger fixed station according to the bus operation data, and obtaining a plurality of fixed station positions with the most trips;
obtaining the number of passengers getting on/off in each subarea according to the historical traffic volume data of the passengers;
obtaining a vehicle running path through a latest interpolation algorithm, and calculating a simulation model parameter evaluation index;
and constructing a simulation evaluation model based on the relaxation time, the number of passengers getting on/off the train in each sub-area and the policy model parameter evaluation index.
In an alternative embodiment, the processor 110 is further configured to execute the following steps when the computer program is executed:
the method comprises the following steps: constructing a simulation optimization problem based on a simulation evaluation model, and inputting and calculating a corresponding response value of any regional site based on a Kriging model global optimization algorithm of a multipoint filling sampling criterion;
step two: judging whether the response value meets the set termination condition, if so, outputting the current response value, and if not, performing the third step;
step three: constructing a Kriging model;
step four: acquiring a new area site based on a multipoint filling sampling criterion, and calculating a corresponding response value;
step five: and updating the training set of the Kriging model, and returning to the step two.
The computer device further includes: at least one network interface 112. The various components in the device are coupled together by a bus system 113. It will be appreciated that the bus system 113 is used to enable communications among the components. The bus system 113 includes a power bus, a control bus, and a status signal bus in addition to the data bus. For clarity of illustration, however, the various buses are labeled as bus system 113 in FIG. 6.
The memory 111 may be a volatile memory or a nonvolatile memory, or may include both volatile and nonvolatile memories. Among them, the nonvolatile Memory may be a Read Only Memory (ROM), a Programmable Read Only Memory (PROM), an Erasable Programmable Read-Only Memory (EPROM), an Electrically Erasable Programmable Read-Only Memory (EEPROM), a magnetic random access Memory (FRAM), a Flash Memory (Flash Memory), a magnetic surface Memory, an optical disk, or a Compact Disc Read-Only Memory (CD-ROM); the magnetic surface storage may be disk storage or tape storage. Volatile Memory can be Random Access Memory (RAM), which acts as external cache Memory. By way of illustration and not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Synchronous Static Random Access Memory (SSRAM), Dynamic Random Access Memory (DRAM), Synchronous Dynamic Random Access Memory (SDRAM), Double Data Rate Synchronous Dynamic Random Access Memory (DDRSDRAM), Enhanced Synchronous Dynamic Random Access Memory (ESDRAM), Enhanced Synchronous Dynamic Random Access Memory (Enhanced DRAM), Synchronous Dynamic Random Access Memory (SLDRAM), Direct Memory (DRmb Access), and Random Access Memory (DRAM). The memory 111 described in connection with the embodiments of the invention is intended to comprise, without being limited to, these and any other suitable types of memory.
The memory 111 in embodiments of the present invention is used to store various types of data to support the operation of the device. Examples of such data include: any computer program for operating on the device, such as operating systems and application programs; contact data; telephone book data; a message; a picture; video, etc. The operating system includes various system programs, such as a framework layer, a core library layer, a driver layer, and the like, and is used for implementing various basic services and processing hardware-based tasks. The application programs may include various application programs such as a Media Player (Media Player), a Browser (Browser), etc. for implementing various application services. Here, a program that implements the method of the embodiment of the present invention may be included in the application program.
The present embodiment also includes a computer storage medium, in which a computer program is stored, where the computer storage medium may be a Memory such as a magnetic random access Memory (FRAM), a Read Only Memory (ROM), a Programmable Read Only Memory (PROM), an Erasable Programmable Read-Only Memory (EPROM), an Electrically Erasable Programmable Read-Only Memory (EEPROM), a Flash Memory (Flash Memory), a magnetic surface Memory, an optical Disc, or a Compact Disc Read-Only Memory (CD-ROM); or may be a variety of devices including one or any combination of the above memories, such as a mobile phone, computer, tablet device, personal digital assistant, etc. The vehicle identification method is implemented when a computer program stored in the computer storage medium is executed by a processor. Please refer to the description of the embodiment shown in fig. 2 for a specific step flow realized when the computer program is executed by the processor, which is not described herein again.
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
As used herein, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, including not only those elements listed, but also other elements not expressly listed.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (8)
1. A variable bus area stop optimization method is characterized by comprising the following steps:
obtaining historical traffic data of passengers and determining regional station strategies, wherein the regional station strategies comprise hot spot regions for getting on and off the bus;
acquiring public transport operation data, and constructing a simulation evaluation model according to the public transport operation data and the historical traffic volume data of passengers;
and constructing a simulation optimization problem based on a simulation evaluation model, solving the simulation optimization problem based on a Kriging model global optimization algorithm of a multipoint filling sampling criterion, and determining the position of a target area station.
2. The method for optimizing the bus stop zone according to claim 1, wherein the step of obtaining historical traffic volume data of passengers and determining the strategy of the bus stop zone comprises the following steps:
obtaining historical traffic volume data of passengers, and dividing variable bus area stations into an aggregate formed by a plurality of sub-areas, wherein each sub-area comprises at least one area station;
obtaining the number of passengers getting on the bus in each sub-area based on the historical traffic volume data of the passengers and sequencing the passengers to obtain the passenger service rate in each sub-area;
and when the passenger service rate is higher than a set first threshold value, determining that the sub-area corresponding to the passenger service rate is an boarding hot spot area.
3. The method for optimizing the bus stop zone according to claim 1, wherein the step of obtaining the historical traffic volume data of the passengers and determining the strategy of the bus stop zone further comprises the steps of:
obtaining historical traffic volume data of passengers, and dividing variable bus area stations into an aggregate formed by a plurality of sub-areas, wherein each sub-area comprises at least one area station;
obtaining the number of people getting off in each sub-area based on the historical traffic volume data of the passengers and sequencing to obtain the passenger service rate in each sub-area;
and when the passenger service rate is higher than a set second threshold value, determining that the sub-area corresponding to the passenger service rate is a get-off hot spot area.
4. The method for optimizing the bus stop of the variable bus area according to claim 1, wherein the obtaining of the bus operation data and the construction of the simulation evaluation model according to the bus operation data and the historical traffic volume data of the passengers comprise:
obtaining a fixed station position corresponding to each passenger according to the bus operation data, and obtaining a plurality of fixed station positions with the most trips;
obtaining the number of passengers getting on/off in each subarea according to the historical traffic volume data of the passengers;
obtaining a vehicle running path through a latest interpolation algorithm, and calculating a simulation model parameter evaluation index;
and constructing a simulation evaluation model based on the relaxation time, the number of passengers getting on/off the train in each sub-area and the policy model parameter evaluation index.
5. The method of optimizing a variable transit zone stop according to claim 4, wherein the slack time comprises:
TS＝2*(C-1)Td+2L/Vb
where C is the number of fixed stations, Td is the waiting time of each fixed station, and Vb is the vehicle traveling speed.
6. The method as claimed in claim 1, wherein the step of constructing a simulation optimization problem based on a simulation evaluation model, the step of solving the simulation optimization problem based on a Kriging model global optimization algorithm of a multipoint filling sampling criterion, and the step of determining the position of a target area station comprises the steps of:
the method comprises the following steps: constructing a simulation optimization problem based on a simulation evaluation model, and inputting and calculating a corresponding response value of any regional site based on a Kriging model global optimization algorithm of a multipoint filling sampling criterion;
step two: judging whether the response value meets the set termination condition, if so, outputting the current response value, and if not, performing the third step;
step three: constructing a Kriging model;
step four: acquiring a new area site based on a multipoint filling sampling criterion, and calculating a corresponding response value;
step five: and updating the training set of the Kriging model, and returning to the step two.
7. A computer device, comprising: a processor and a memory for storing a computer program capable of running on the processor;
wherein the processor is configured to implement the method for optimizing a variable bus area stop according to any one of claims 1 to 6 when running the computer program.
8. A computer storage medium, in which a computer program is stored, characterized in that the computer program is executed by a processor to implement the variable bus area stop optimization method according to any one of claims 1 to 6.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116564080A (en) * | 2023-04-25 | 2023-08-08 | 内蒙古大学 | Bus route information acquisition and simulation modeling platform |
| CN116704778A (en) * | 2023-08-04 | 2023-09-05 | 创意(成都)数字科技有限公司 | Intelligent traffic data processing method, device, equipment and storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116564080A (en) * | 2023-04-25 | 2023-08-08 | 内蒙古大学 | Bus route information acquisition and simulation modeling platform |
| CN116704778A (en) * | 2023-08-04 | 2023-09-05 | 创意(成都)数字科技有限公司 | Intelligent traffic data processing method, device, equipment and storage medium |
| CN116704778B (en) * | 2023-08-04 | 2023-10-24 | 创意(成都)数字科技有限公司 | Intelligent traffic data processing method, device, equipment and storage medium |
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