CN111951542A - Boarding point measuring and planning method for optimizing service efficiency of boarding area of taxi in airport - Google Patents
Boarding point measuring and planning method for optimizing service efficiency of boarding area of taxi in airport Download PDFInfo
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/20—Monitoring the location of vehicles belonging to a group, e.g. fleet of vehicles, countable or determined number of vehicles
- G08G1/207—Monitoring the location of vehicles belonging to a group, e.g. fleet of vehicles, countable or determined number of vehicles with respect to certain areas, e.g. forbidden or allowed areas with possible alerting when inside or outside boundaries
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/004—Artificial life, i.e. computing arrangements simulating life
- G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/20—Monitoring the location of vehicles belonging to a group, e.g. fleet of vehicles, countable or determined number of vehicles
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/20—Monitoring the location of vehicles belonging to a group, e.g. fleet of vehicles, countable or determined number of vehicles
- G08G1/205—Indicating the location of the monitored vehicles as destination, e.g. accidents, stolen, rental
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
Abstract
The invention relates to a passenger getting-on counting planning method for optimizing passenger getting-on area service efficiency of an airport taxi, which comprises the following steps: s1: defining an internal concept of the model; s2: establishing a passenger-carrying queuing model double-objective function of the taxi in the airport; s3: converting the double-target function into a single-target function; s4: establishing a passenger-carrying queuing model constraint condition of an airport taxi; s5: and performing model solution based on a particle swarm algorithm to obtain the optimal number of the boarding points. The invention solves the problem that both the taxi driver and the passenger stay in the passenger area of the airport, and leads the layout planning of the land side traffic system of the airport to be more scientific, reasonable and efficient.
Description
Technical Field
The invention belongs to the field of urban public transport system planning, and particularly relates to a passenger getting-on count planning method for optimizing passenger getting-on area service efficiency of taxis in an airport.
Background
The airport land side traffic system is an activity area which takes passengers as main bodies and is a link for connecting an airport and a city, and due to the characteristics of marginal property and intersection, the traffic problem between the airport and the city is always easily ignored, and the convenience degree for connecting the airport and the city center is more important in the level of urban infrastructure, so that the reasonable planning of the airport land side traffic is extremely important, and the airport land side traffic system is an important component of the airport land side traffic system, and the service efficiency of a passenger area on a taxi is not small.
According to the construction design and the actual use condition of most of the airports in China at present, the conditions that taxis are queued to carry passengers and passengers are queued to take passengers can appear in taxi pick-up areas on the land sides of the airports: if more passengers take the taxi, the passenger flow is large, and under the condition that the taxi supply amount in the passenger waiting area is enough, the passenger group waiting for taking the taxi can be quickly taken away by the taxi, so that the residence time of the taxi in the passenger waiting area is short in a certain period of time, the number of the vehicles is small, and the passenger carrying waiting time of a driver entering the storage pool is short; if the number of passengers taking the taxi is small, the passenger flow is small, and once the driver selects to enter the storage pool, the driver has to wait for the taxi to turn around, at the moment, the taxi entering the storage pool is detained because no passenger exists or the number of passengers is small, the time for waiting for carrying passengers by the driver entering the storage pool is longer, and the detained situation is more serious; if a taxi driver returns to an airport to wait for a waiting area when carrying passengers, and the traffic capacity and the service capacity in the area are poor, the phenomena of 'people waiting for cars' and 'people waiting for cars' can occur in the waiting area of the taxi in the airport, and if the taxi driver stops riding in a special period such as holidays, the blocking condition is more serious in the case of untimely planning, on one hand, the benefits of the taxi driver and the passenger are lost, on the other hand, the environmental pollution can be caused by the pollutant discharge amount of the motor vehicle, so that the reasonable planning of the composition setting of the waiting area of the taxi and the optimization of the service efficiency are particularly important.
In conclusion, the existing planning of the passenger boarding areas of the airports lacks a more scientific, reasonable and efficient method, and managers cannot flexibly and reasonably adjust the positions of the passenger boarding areas according to the actual operation conditions of the airports, so that the service efficiency of the passenger boarding areas of taxis of the airports is improved.
Disclosure of Invention
In order to solve the problems, the invention provides a passenger getting-on point measuring and planning method for optimizing the service efficiency of the passenger getting-on area of the taxi in the airport.
In order to achieve the purpose, the invention is realized by the following technical scheme:
the invention relates to a passenger getting-on counting planning method for optimizing passenger getting-on area service efficiency of an airport taxi, which comprises the following steps:
s1: defining an internal concept of the model;
s2: establishing a passenger-carrying queuing model double-objective function of the taxi in the airport;
s3: converting the double-target function into a single-target function;
s4: establishing a passenger-carrying queuing model constraint condition of an airport taxi;
s5: and performing model solution based on a particle swarm algorithm to obtain the optimal number of the boarding points.
The invention is further improved in that: the specific process of defining the internal concept of the model in the step S1 is as follows: defining the customer sources and the manner in which the customers arrive at the system, defining the probability distribution of service times, defining the service queue structure, and defining the service level of the service system.
The invention is further improved in that: the definition of the customer source and the arrival mode of the customer to the system specifically includes:
(1-1) in a service system mainly comprising passengers, the passengers receive passenger carrying services of taxi drivers, the passengers are 'customer sources', the drivers are 'service providers', the passengers are assumed as the customer sources at the moment, the passengers are infinite populations, namely, for a queuing model, the number of customers is large enough, the probability distribution of the arrival of the customers cannot be significantly influenced by the change of the total customer scale caused by the increase and decrease of the number of the customers, and the arrival conditions of the customers generally obey certain statistical distribution.
(1-2) in the taxi driver-based service system, the income that the driver gets to carry a passenger at an airport is provided by the passenger, so that it can be understood that the driver waits in line for the passenger to supply his income, and the driver is the "customer source" and the passenger is the "server" for the unlimited population.
The invention is further improved in that: defining a probability distribution of service times: the service time is one of the factors for measuring the service efficiency, the service capacity of a service person in the same time, namely the number of the customers to be received is larger, the service efficiency is higher, when the service time is relatively random, the service time is approximately distributed exponentially, and in a service system taking passengers as main bodies, the service efficiency refers to the number of passengers carried by a driver in unit time; in a taxi driver-based service system, the service efficiency refers to the amount of revenue a passenger provides to the driver per unit time.
The invention is further improved in that: defining the service queue structure means that the service queue structure can be judged according to the operation condition of the passenger area on the taxi of the actual airport: customers need to be arranged into a single queue to receive service according to the time sequence of arriving at service points, receive service according to the first-come first-serve criterion, and set a plurality of service points into 'multi-channel' in order to maintain the system order; the customer arriving at the service point from the home location completes the task and leaves the system as a "single stage", so the queues for both service systems follow a "multi-lane, single stage" structure.
The invention is further improved in that: the definition of the service level of the service system refers to the comprehensive consideration of the space scale limit of a passenger area on a taxi in an airport and the requirement on the service level, and the service level can be ensured to meet the requirement of the service time of a server with 95% confidence coefficient in the service system with n service points, namely, for different values i (i is more than or equal to 0 and less than or equal to n) of the number of the service points
The invention is further improved in that: the dual target function in step S2 is specifically:
(2-1) objective function one: min mu, which means that the shorter the time that the passenger wants to wait for the taxi to pick up the taxi, the better, and the service efficiency of the driver to the passenger mainly determines the time from the time of taking off the plane to the time of taking on the taxi;
(2-2) objective function two:
wherein
The formula (1) represents t that the taxi is stopped in the storage pool and waits for carrying passengersStagnation of bloodFor a period of time, the driver gives up
The average income of the passenger car is returned to the urban area without load, and the potential total income lost by all drivers waiting for carrying passengers in the storage pool is lossVehicle with wheels；
The formula (2) represents: the increased management cost of adding a boarding point in the airport is equal to the working time t of a newly added managerLength of operationMultiplying the average payroll per unit time of the manager at the boarding point
The invention is further improved in that: the step S3 of converting the dual objective function into the single objective function specifically includes: the double objective function is converted into by using an objective function division method:
the invention is further improved in that: the specific process of S4 is as follows:
(4-1) based on the constraints of the customer served submodel:
(4-2) constraint conditions based on taxi driver and airport manager loss value submodels:
wherein, the distribution of the number of the customers arriving is obeyed poisson distribution, the distribution of the service time of each time of the service persons is obeyed exponential distribution, PiFor each customer the probability of waiting to be serviced is
c is more than 1, c is the number of the opened passenger queuing passages, the number is dependent on the number of opened boarding points n, the values of the two are the same, the meanings are different, and the average number of the customers waiting for waiting in the queue is
The invention is further improved in that: the specific process of S5 is as follows: selecting particle swarm algorithm to solve and judge the model
Will not satisfy the confidence value of
The value of the condition is deleted from the feasible solution set, and the concrete steps of the solution are as follows:
(1) giving an initialization value;
(2) updating speed and position;
(3) updating the individual optimal fitness;
(4) and updating the global optimal fitness.
The invention has the beneficial effects that: the method is used for calculating the optimal number of the taxi-taking points in the taxi-taking areas of the airports in the planning field of urban public transport systems so as to improve the service efficiency of the taxi-taking areas. Firstly, defining the internal concept of a model, and dividing a service system of a boarding area into two subsystems of 'taking a passenger as a main body' and 'taking a taxi driver as a main body'; then establishing a double-objective function based on the loss and gain model of the subsystem; then, converting the double objective functions into single objective functions by using an objective function phase division method; then, establishing constraint conditions of a target function based on an airport taxi passenger-carrying queuing model; and finally, solving the objective function through a particle swarm algorithm to obtain the number of boarding points which enable the boarding area service efficiency to be optimal, thereby well solving the detention condition of both taxi drivers and passengers in the boarding area of the airport, and enabling the layout planning of the land side traffic system of the airport to be more scientific, reasonable and efficient.
The planning method provided by the invention calculates the number of the optimal passenger boarding points through overall planning, so that the service efficiency of a boarding area is scientifically, reasonably and effectively improved, and the operation capacity of an airport land side system is improved.
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FIG. 1 is a flow chart of the present invention.
Detailed Description
In the following description, for purposes of explanation, numerous implementation details are set forth in order to provide a thorough understanding of the embodiments of the invention. It should be understood, however, that these implementation details are not to be interpreted as limiting the invention. That is, in some embodiments of the invention, such implementation details are not necessary.
As shown in fig. 1, the present invention is a passenger boarding amount planning method for optimizing passenger boarding area service efficiency of taxis in an airport, comprising the following steps:
s1: defining the internal concept of the model, and the specific process is as follows:
(1) defining the customer source and the way the customer arrives at the system:
(1-1) in a service system mainly comprising passengers, the passengers receive passenger carrying services of taxi drivers, the passengers are 'customer sources', the drivers are 'service providers', the passengers are assumed as the customer sources at the moment, the passengers are infinite populations, namely, for a queuing model, the number of customers is large enough, the probability distribution of the arrival of the customers cannot be significantly influenced by the change of the total customer scale caused by the increase and decrease of the number of the customers, and the arrival conditions of the customers generally obey certain statistical distribution.
(1-2) in the service system mainly comprising taxi drivers, the income obtained by the drivers in the airport is provided by the passengers, so that the driver can be understood as waiting in line for the passengers to supply the income, and the driver is the 'customer source' and the passengers are the 'service provider' in the infinite population;
(2) defining a probability distribution of service times: the service time is one of the factors for measuring the service efficiency, the service capacity of a service person in the same time, namely the number of the customers to be received is larger, the service efficiency is higher, when the service time is relatively random, the service time is approximately distributed exponentially, and in a service system taking passengers as main bodies, the service efficiency refers to the number of passengers carried by a driver in unit time; in a service system taking a taxi driver as a main body, the service efficiency refers to a profit value provided for the driver in unit time by a passenger;
(3) defining a service queue structure: the operation condition of the taxi boarding area of the actual airport can be judged: customers need to be arranged into a single queue to receive service according to the time sequence of arriving at service points, receive service according to the first-come first-serve criterion, and set a plurality of service points into 'multi-channel' in order to maintain the system order; when a customer arrives at a service point from an initial position, the task is finished and the system leaves, and the system is in a single stage, so that the queues of the two service systems both follow the structure of multiple channels and single stage;
(4) defining a service level of a service system: comprehensively considering the space scale limitation of a passenger area on a taxi in an airport and the requirement on the service level, the service level can be defined in a service system with n service points, the service level can ensure that the service of a service provider is performed with 95% of confidence, namely, for different values i (i is more than or equal to 0 and less than or equal to n) of the number of the service points, the service levelTime is satisfied
S2: establishing a passenger-carrying queuing model dual-objective function of the taxi in the airport, which comprises the following specific processes:
(2-1) objective function one: min mu, which means that the shorter the time that the passenger wants to wait for the taxi to pick up the taxi, the better, and the service efficiency of the driver to the passenger mainly determines the time from the time of taking off the plane to the time of taking on the taxi;
(2-2) objective function two:
wherein
The formula (1) represents t that the taxi is stopped in the storage pool and waits for carrying passengersStagnation of bloodFor a period of time, the driver gives up
The average income of the passenger car is returned to the urban area without load, and the potential total income lost by all drivers waiting for carrying passengers in the storage pool is lossVehicle with wheels；
The formula (2) represents: the increased management cost of adding a boarding point in the airport is equal to the working time t of a newly added managerLength of operationMultiplying the average payroll per unit time of the manager at the boarding point
S3: converting the double-target function into a single-target function, which specifically comprises the following steps: the double objective function is converted into by using an objective function division method:
s4: the method comprises the following steps of establishing a passenger-carrying queuing model constraint condition of an airport taxi, and the specific process comprises the following steps:
(4-1) based on the constraints of the customer served submodel:
(4-2) constraint conditions based on taxi driver and airport manager loss value submodels:
wherein, the distribution of the number of the customers arriving is obeyed poisson distribution, the distribution of the service time of each time of the service persons is obeyed exponential distribution, PiFor each customer the probability of waiting to be serviced is
c is more than 1, c is the number of the opened passenger queuing passages, the number is dependent on the number of opened boarding points n, the values of the two are the same, the meanings are different, and the average number of the customers waiting for waiting in the queue is
S5: carrying out model solution based on a particle swarm algorithm to obtain the optimal number of the boarding points, wherein the specific process is as follows: selecting particle swarm algorithm to solve and judge the model
Will not satisfy the confidence value of
The value of the condition is deleted from the feasible solution set, and the concrete steps of the solution are as follows:
(1) giving an initialization value;
(2) updating speed and position;
(3) updating the individual optimal fitness;
(4) and updating the global optimal fitness.
Taking the arrangement of the taxi boarding areas and the boarding points of the capital international airport as an example, the invention relates to a boarding point measuring and planning method for optimizing the service efficiency of the taxi boarding areas of the airport, which comprises the following steps:
step 1: method for establishing objective function model of passenger carrying list of taxi in airport
The overall single objective function is:
mu is the time length for waiting for the taxi to pick up; lossn+1The sum of the loss values generated for the ith pick-up point, including the potential gross loss of taxi driver due to staying in the pool waiting for passengersVehicle with wheelsAnd increased management cost loss of a new boarding point at an airportManager。
The constraint conditions are as follows:
based on the constraints of the customer served submodel:
constraint conditions based on taxi driver and airport manager loss value submodels:
step 2: solving the model based on an exhaustive traversal method, mainly comprising the following steps:
a) according to the maximum area of a passenger waiting area of the first international airport taxi, assuming that the maximum number n of the number of the class points in the passenger waiting area of the airport taxi is 10;
b) for constraint condition (1)
The trial calculation initial value of (2) is 0.5,
then in the value intervalSolving the value of the objective function based on the customer serviced submodel and the value of the objective function based on the taxi driver and airport supervisor loss value submodel in a 0.01 traversal offset range if for the current
The calculated confidence value which does not satisfy 0.95 is calculated by the value, the value of each current objective function is deleted from the possibility solution set, and the solution result of the total objective function value is shown in the following table:
| number of getting on bus n | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
| Total target value | 63.34 | 67.30 | 68.15 | 107.35 | 127.95 | 126.40 | 123.39 | 122.04 | 120.57 | 119.12 |
As can be seen from the data in the table, in the example analysis of the boarding area of the taxi in the capital international airport, 5 boarding points should be set for the highest total boarding efficiency of the boarding area under the condition of ensuring the safety of the vehicle and passengers.
And step 3: and performing solution optimization and solution result inspection based on the particle swarm algorithm. Because the operational efficiency of the exhaustive traversal method is unfavorable for the large-scale data set, on the premise of unknown data set scale, besides the exhaustive traversal method given in step 2, the invention provides a second model solving method for the large-scale data set, and the specific steps are as follows:
a) giving an initialization value to define an initialization particle swarm;
b) updating speed and position;
c) updating the individual optimal fitness;
d) outputting an optimal value;
the solving result based on the particle swarm optimization in this embodiment is shown in the following table:
the above description is only an embodiment of the present invention, and is not intended to limit the present invention. Various modifications and alterations to this invention will become apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.
Claims (10)
1. A passenger-service-area service efficiency optimization-oriented passenger service counting and planning method for taxis in an airport is characterized by comprising the following steps: the method comprises the following steps:
s1: defining an internal concept of the model;
s2: establishing a passenger-carrying queuing model double-objective function of the taxi in the airport;
s3: converting the double-target function into a single-target function;
s4: establishing a passenger-carrying queuing model constraint condition of an airport taxi;
s5: and performing model solution based on a particle swarm algorithm to obtain the optimal number of the boarding points.
2. The method for optimizing the number of passengers getting on the taxi in the airport according to claim 1, wherein: the specific process of defining the internal concept of the model in the step S1 is as follows: defining the customer sources and the manner in which the customers arrive at the system, defining the probability distribution of service times, defining the service queue structure, and defining the service level of the service system.
3. The method for optimizing the number of passengers getting on the taxi in the airport according to claim 2, wherein: the definition of the customer source and the arrival mode of the customer to the system specifically includes:
(1-1) in the passenger-based service system, the passenger receives the passenger carrying service of a taxi driver, the passenger is the 'passenger source', the driver is the 'server', and the passenger is the infinite population as the passenger source.
(1-2) in the taxi driver-based service system, the income obtained by the driver carrying passengers at the airport is provided by the passengers, and the driver is the 'customer source' and the passengers are the 'service persons' in the infinite population.
4. The method for optimizing the number of passengers getting on the taxi in the airport according to claim 2, wherein: defining a probability distribution of service times: in a passenger-based service system, the service efficiency refers to the number of passengers carried by a driver in unit time; in a taxi driver-based service system, the service efficiency refers to the amount of revenue a passenger provides to the driver per unit time.
5. The method for optimizing the number of passengers getting on the taxi in the airport according to claim 2, wherein: defining the service queue structure means that the service queue structure can be judged according to the operation condition of the passenger area on the taxi of the actual airport: customers need to be arranged into a single queue to receive service according to the time sequence of arriving at service points, receive service according to the first-come first-serve criterion, and set a plurality of service points into 'multi-channel' in order to maintain the system order; when the customer arrives at the service point from the initial position, the task is completed and the system leaves, and the queue of the two service systems is in a single stage structure with multiple channels and single stage.
6. The method for optimizing the number of passengers getting on the taxi in the airport according to claim 2, wherein: the definition of the service level of the service system refers to the comprehensive consideration of the space scale limit of the passenger area on the taxi in the airport and the requirement on the service level, and the service level can be defined in the service system with n service points, the service level can ensure that the service time of a server meets the requirement with 95% of confidence coefficient, namely, for different values i (i is more than or equal to 0 and less than or equal to n) of the number of the service points, the service time of the server
7. The method for optimizing the number of passengers getting on the taxi in the airport according to claim 1, wherein: the dual target function in step S2 is specifically:
(2-1) objective function one: min mu, which means that the shorter the time that the passenger wants to wait for the taxi to pick up the taxi, the better, and the service efficiency of the driver to the passenger mainly determines the time from the time of taking off the plane to the time of taking on the taxi;
(2-2) objective function two:
wherein
The formula (1) represents t that the taxi is stopped in the storage pool and waits for carrying passengersStagnation of bloodFor a period of time, the driver gives up
The average income of the passenger car is returned to the urban area without load, and the potential total income lost by all drivers waiting for carrying passengers in the storage pool is lossVehicle with wheels；
The formula (2) represents: the increased management cost of adding a boarding point in the airport is equal to the working time t of a newly added managerLength of operationMultiplying the average payroll per unit time of the manager at the boarding point
8. The method for optimizing the number of passengers getting on the taxi in the airport according to claim 1, wherein: the step S3 of converting the dual objective function into the single objective function specifically includes: the double objective function is converted into by using an objective function division method:
9. the method for optimizing the number of passengers getting on the taxi in the airport according to claim 1, wherein: the specific process of S4 is as follows:
(4-1) based on the constraints of the customer served submodel:
(4-2) constraint conditions based on taxi driver and airport manager loss value submodels:
wherein, the distribution of the number of the customers arriving is obeyed poisson distribution, the distribution of the service time of each time of the service persons is obeyed exponential distribution, PiFor each customer the probability of waiting to be serviced is
c is the number of the opened passenger queuing channels, and the average number of the customers waiting for waiting in the queue is
10. The method for optimizing the number of passengers getting on the taxi in the airport according to claim 1, wherein: the specific process of S5 is as follows: selecting particle swarm algorithm to solve and judge the model
Will not satisfy the confidence value of
The value of the condition is deleted from the feasible solution set, and the concrete steps of the solution are as follows:
(1) giving an initialization value;
(2) updating speed and position;
(3) updating the individual optimal fitness;
(4) and updating the global optimal fitness.
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