CN116911568A - Intelligent operation management method and system for public transportation - Google Patents
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Abstract
The application relates to the technical field of public transportation operation management, and discloses a public transportation intelligent operation management method, which comprises the following steps of firstly, collecting station flow to obtain a real-time flowing people flow value; step two, collecting the seating rate and the starting and ending time of the large-scale activities of the current city, and researching the scale change, the transport capacity and the passenger flow requirements of the operation vehicles to obtain large-scale activity transport capacity data; step three, carrying out capacity optimization on public transportation according to passenger flow statistics and analysis data by combining real-time flowing people flow value and large-scale activity capacity data; and step four, correlating real-time emergency events, calculating an emergency probability value and optimizing an emergency scheduling scheme. According to the intelligent public transportation comprehensive scheduling method, the real-time dynamic acquisition and analysis and the multi-aspect influence factor model analysis are carried out on the large-scale activities and the historical emergency time probability, so that the intelligent public transportation comprehensive scheduling service quality is improved.
Description
Technical Field
The application relates to the technical field of public transportation operation management, in particular to a public transportation intelligent operation management method and system.
Background
At present, many cities in China are built by strengthening an intelligent public transportation system, the intelligent cities are seriously dependent on the Internet of things, and nearly 60% of Internet of things equipment installed in the intelligent cities are used for intelligent commercial buildings and transportation. By 2020, smart cities will use 13.9 hundred million things to achieve sustainable development and climate change goals.
However, the current construction level of public traffic of the intelligent public transport system and the internet of things is far from that of a real intelligent public transport system, and the intelligent public transport system is considered to be provided with the GPS and the electronic stop board, so that the intelligent public transport system is only a little basic, and the most basic information service system in the intelligent public transport system is only stopped on static information service; in addition, real-time intelligent scheduling cannot be realized on one line, because real problems (such as the influence relationship of traffic control on bus operation, the determination of influence factors of a scheduling model and the like) in some urban intelligent bus systems are not solved.
Large-scale activities can be held in cities frequently, particularly under the conditions of large-scale activities starting and scattered fields, the problems of road section blockage, large waiting base number of passengers and the like caused by lack of dynamic information service in the operation process of single-line intelligent buses are caused, and the service level of buses is seriously influenced.
Disclosure of Invention
The application provides a public transportation intelligent operation management method, a public transportation intelligent operation management system and a public transportation intelligent operation management system, which have the advantages that real-time dynamic intelligent scheduling is realized, multiple-aspect influence factor model analysis is carried out on the probability of large-scale activities and historical emergency time, and further, the intelligent comprehensive scheduling service quality is improved, and the problems that in the background art, the intelligent scheduling cannot be realized on one line in addition are solved, namely the real problems (such as the influence relation of traffic control on bus operation, the determination of the influence factors of a scheduling model and the like) in some urban intelligent bus systems are not solved, the large-scale activities are usually held in cities, particularly, under the condition that the large-scale activities start and buses are scattered, the single-line intelligent buses lack of dynamic information service in the operation process, the road section is blocked, the waiting base number of passengers is large and the like are solved, and the service level is seriously influenced.
The application provides the following technical scheme: a public transportation intelligent operation management method comprises the following steps,
step one, collecting station flow to obtain a real-time flowing people flow value;
step two, collecting the seating rate and the starting and ending time of the large-scale activities of the current city, and researching the scale change, the transport capacity and the passenger flow requirements of the operation vehicles to obtain large-scale activity transport capacity data;
thirdly, carrying out capacity optimization on public transportation according to passenger flow statistics and analysis data by combining the real-time flowing people flow value and large-scale activity capacity data to obtain an optimization scheme;
and step four, associating real-time emergencies, calculating an emergency probability value by combining the historical emergencies, and optimizing an emergency scheduling scheme.
As an alternative to the public transportation intelligent operation management method of the present application, the method comprises: the specific method in the first step comprises the following steps:
1) Dynamically recognizing videos shot by cameras at the entrances and exits of public transportation means;
2) Carrying out time-period passenger flow volume statistical analysis according to the uploaded passenger flow data by taking a time axis as a reference; wherein the time intervals are as follows: including 6 am: 00-8:00, 11 pm: 30-13:00 pm 17:30-19:00, calculating the highest flow value of the early, the noon and the late peak, and obtaining the lowest flow value in the rest time;
3) And calculating the in-out direction and the number of passengers based on the video human body recognition and tracking technology so as to obtain passenger flow data and uploading the passenger flow data.
As an alternative to the public transportation intelligent operation management method of the present application, the method comprises: the specific method in the second step comprises the following steps:
1) The address, the time of entering and exiting the field and the time of scattering the field of the large-scale activity are intelligently acquired by combining with self-media, network and newspaper modes;
2) Obtaining audience boarding rate of each game item and audience public transport sharing quantity of each game item to predict, and predicting audience space distribution;
3) And establishing an integrated dispatching model of the bus line by using the seating rate and the audience space distribution predicted value.
As an alternative to the public transportation intelligent operation management method of the present application, the method comprises: the third step specifically includes:
1) Combining the real-time flowing people flow value Rl and the large-scale activity capacity data value Hd;
2) Establishing an analysis and evaluation model;
acquiring a public transportation schedule, a vehicle dispatching application model and a salesman dispatching model;
carrying out statistics on line driving plan data and actual driving record data, associating road conditions and real-time flowing people flow value Rl, and establishing an analysis and evaluation model;
3) The data analysis is carried out on an electronic road list, a dynamic whole-day vehicle number statistics table of the vehicles of the vehicle team, a summary table of operation indexes of the vehicle team, a dispatch daily report of the vehicle team, a passenger flow data statistics table of the vehicle team, a dynamic detail table of the vehicles, passenger flow analysis, a driving record and a driving schedule table, and the fitting data Nh are obtained by analysis;
4) Optimizing to obtain the public transportation dispatching capacity value YL.
As an alternative to the public transportation intelligent operation management method of the present application, the method comprises: the public transportation scheduling capacity YL is obtainable by the following formula:
wherein, the ≡represents the city bus line length and kilometers; v represents the average operating speed of the bus, kilometers per hour;
t represents the rest time of waiting for the first station and the last station, and the rest time is taken for 15-20 minutes; f represents the bus departure frequency in the peak period, and the value is 5-30;
b represents a margin coefficient of the vehicle which cannot be operated due to maintenance; nh identifies fitting data after data analysis; rl is expressed as a real-time flowing human flow value; hd is denoted as a large active capacity data value;
wherein f represents the bus departure frequency in the peak period, and the value of B is 1.2-1.5.
As an alternative to the public transportation intelligent operation management method of the present application, the method comprises: the fourth step of the method specifically comprises the steps of,
1) The history is called through artificial intelligence, the occurrence of the emergency and the congestion time of each month are accurate, and the probability value of the emergency is calculated;
2) And when the real-time emergency occurs, correlating the fitting data Nh, and calculating to obtain the nearest value of the road section of the dispatching car to obtain an emergency scheme.
As an alternative to the public transportation intelligent operation management method of the present application, the method comprises: the optimization scheme also comprises operation vehicle optimization, and the operation vehicle optimization method comprises the following steps:
1) The method comprises the steps of collecting the train running and the late time of public transportation in real time;
the train is characterized in that in a train dispatching plan of a rear train, when the train time of partial passengers and the like is increased due to the fact that the traffic flow is blocked and exceeds the linearity of a front train in the real-time operation process, the time of the next train is adjusted urgently;
the late time refers to the fact that the common operation vehicle reaches the same station time and exceeds the departure interval to hold a sword, and the deviation degree exceeds the set interval time threshold value, and at the moment, the situation of increasing vehicles or reducing vehicles is predicted according to the road traffic state.
As an alternative to the public transportation intelligent operation management method of the present application, the method comprises: the bus route integrated scheduling model is as follows:
where k represents a set of shifts, d k Representing the cost of shift K e K; e represents a collection of train number, see the non-passenger driving section, including stop waiting driving section; i, identifying a set of passenger driving sections, x, identifying decision variables, and identifying whether shift k is selected;
C x yx represents a decision variable, and identifies whether a vehicle will start the vehicle number J after the vehicle number i is completed;
and obtaining a driving plan based on artificial intelligence and the Internet of things, calculating a driver scheduling set coverage problem by utilizing a Lagrange heuristic algorithm in combination with a genetic algorithm, obtaining a shift plan, and generating a corresponding driving plan and a set coverage rate of the driving plan.
The public transportation intelligent operation management system comprises a server, wherein the server comprises a GPS module, an Internet of things module, a geographic information module, a passenger flow real-time statistics module, a road traffic state acquisition module and a scheduling module;
GPS equipment installed on each public transportation operation vehicle collects vehicle positioning data, and map mapping of vehicle information and a target value are correspondingly displayed in a geographic information module after processing;
the passenger flow real-time statistics module is used for collecting, receiving and statistically monitoring the passenger flow data of the upper station and the lower station in real time in the first step of the method of the system, and is in communication connection with the scheduling module.
As an alternative scheme of the intelligent operation management system for public transportation, a road traffic state acquisition module acquires monitoring coil signals at the upstream and downstream of intersections of all road sections, and sends the monitoring coil signals to a scheduling module after processing the monitoring coil signals through occupancy rate, speed and flow information parameters of the intersections;
the scheduling module analyzes and calculates the information acquired by the GPS module, the Internet of things module, the geographic information module, the passenger flow real-time statistics module and the road traffic state acquisition module, and sends the processing result to each operation vehicle.
The application has the following beneficial effects:
1. the intelligent operation management method and system for public transportation are characterized in that under the condition of collecting real-time flowing human flow values, large-scale activity traffic capacity data are obtained after research and calculation by combining with the seating rate, traffic capacity and the like of large-scale activities, fitting is carried out, further, intelligent analysis is carried out according to passenger flow, optimization is carried out, and a scheduling scheme is optimized on the basis of correlating real-time and historical emergency probability values, so that the service quality of public transportation is greatly promoted, and the conditions of road section blockage and overlong multiplier waiting time caused by lack of dynamic information service are reduced
2. According to the intelligent operation management method and system for public transportation, the real-time flowing people flow rate Rl and the large-scale movable transportation capacity data value Hd are calculated, an analysis and evaluation model is built, the specific transportation data are summarized to obtain fitting data Nh, the public transportation scheduling capacity value YL is obtained through analysis and optimization, the service quality of real-time intelligent scheduling is improved, intelligent scheduling is promoted, and the public transportation service level is improved according to the scheduling capacity value YL.
3. According to the public transportation intelligent operation management method and system, the historical emergency probability value is scheduled through artificial intelligence, then when an emergency occurs in real time, fitting data Nh is associated, the nearest value of a car-dispatching road section is calculated and obtained, an emergency scheme is obtained, emergency scheduling is carried out when the time of waiting for passengers is increased due to the fact that the traffic of the public transportation operation is in a car-passing or a late state, and intelligent scheduling is promoted to improve the public transportation service level.
Drawings
FIG. 1 is a schematic flow chart of the method of the application.
FIG. 2 is a schematic flow chart of the system of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Example 1
At present, many cities in China are built by strengthening an intelligent public transportation system, the intelligent cities are seriously dependent on the Internet of things, and nearly 60% of Internet of things equipment installed in the intelligent cities are used for intelligent commercial buildings and transportation. By 2020, smart cities will use 13.9 hundred million things to achieve sustainable development and climate change goals.
However, the current construction level of public traffic of the intelligent public transport system and the internet of things is far from that of a real intelligent public transport system, and the intelligent public transport system is considered to be provided with the GPS and the electronic stop board, so that the intelligent public transport system is only a little basic, and the most basic information service system in the intelligent public transport system is only stopped on static information service; in addition, real-time intelligent scheduling cannot be realized on one line, because real problems (such as the influence relationship of traffic control on bus operation, the determination of influence factors of a scheduling model and the like) in some urban intelligent bus systems are not solved.
Large-scale activities can be held in cities frequently, particularly under the conditions of large-scale activities starting and scattered fields, the problems of road section blockage, large waiting base number of passengers and the like caused by lack of dynamic information service in the operation process of single-line intelligent buses are caused, and the service level of buses is seriously influenced.
The application provides the following technical scheme: referring to fig. 1-2, a public transportation intelligent operation management method includes the following steps,
step one, collecting station flow to obtain a real-time flowing people flow value;
step two, collecting the seating rate and the starting and ending time of the large-scale activities of the current city, and researching the scale change, the transport capacity and the passenger flow requirements of the operation vehicles to obtain large-scale activity transport capacity data;
thirdly, carrying out capacity optimization on public transportation according to passenger flow statistics and analysis data by combining the real-time flowing people flow value and large-scale activity capacity data to obtain an optimization scheme;
and step four, associating real-time emergencies, calculating an emergency probability value by combining the historical emergencies, and optimizing an emergency scheduling scheme.
In this embodiment, through steps one to four, under the condition of collecting the real-time flowing traffic value, the large-scale activity traffic data is obtained for fitting after combining with the research and calculation of the boarding rate, the traffic capacity and the like of the large-scale activity, and then the passenger flow intelligent analysis is optimized, and the scheduling scheme is optimized on the basis of correlating the real-time and historical emergency probability values, so that the service quality of public transportation is greatly promoted, and the conditions of road section blockage and overlong multiplier waiting time caused by the lack of dynamic information service are reduced.
Example 2
This embodiment is for explaining embodiment 1, and refer to fig. 1-2, in which: the specific method in the first step comprises the following steps:
1) Dynamically recognizing videos shot by cameras at the entrances and exits of public transportation means;
2) Carrying out time-period passenger flow volume statistical analysis according to the uploaded passenger flow data by taking a time axis as a reference; wherein the time intervals are as follows: including 6 am: 00-8:00, 11 pm: 30-13:00 pm 17:30-19:00, calculating the highest flow value of the early, the noon and the late peak, and obtaining the lowest flow value in the rest time;
3) And calculating the in-out direction and the number of passengers based on the video human body recognition and tracking technology so as to obtain passenger flow data and uploading the passenger flow data.
In this embodiment, passenger flow data is collected in real time at a public traffic entrance and analyzed statistically, so as to obtain the highest flow value and the lowest flow value of each period, and then relevant public traffic vehicles are scheduled timely according to the size of the passenger flow.
Example 3
This embodiment is for explaining embodiment 1, and refer to fig. 1-2, in which: the specific method in the second step comprises the following steps:
1) The address, the time of entering and exiting the field and the time of scattering the field of the large-scale activity are intelligently acquired by combining with self-media, network and newspaper modes;
2) Obtaining audience boarding rate of each game item and audience public transport sharing quantity of each game item to predict, and predicting audience space distribution;
3) And establishing an integrated dispatching model of the bus line by using the seating rate and the audience space distribution predicted value.
In the embodiment, the information of the large-scale activities is collected from multiple channels, the boarding rate and the bus sharing quantity of spectators are predicted, the integrated scheduling model of the bus route is built, and the problems that road sections are blocked, the waiting base number of passengers is large and the like due to the fact that traffic operation cannot be adjusted in time due to the large-scale activities are solved.
Example 4
This embodiment is for explaining embodiment 3, and refer to fig. 1-2, wherein: the third step specifically includes:
1) Combining the real-time flowing people flow value Rl and the large-scale activity capacity data value Hd;
2) Establishing an analysis and evaluation model;
acquiring a public transportation schedule, a vehicle dispatching application model and a salesman dispatching model;
carrying out statistics on line driving plan data and actual driving record data, associating road conditions and real-time flowing people flow value Rl, and establishing an analysis and evaluation model;
3) The data analysis is carried out on an electronic road list, a dynamic whole-day vehicle number statistics table of the vehicles of the vehicle team, a summary table of operation indexes of the vehicle team, a dispatch daily report of the vehicle team, a passenger flow data statistics table of the vehicle team, a dynamic detail table of the vehicles, passenger flow analysis, a driving record and a driving schedule table, and the fitting data Nh are obtained by analysis;
4) Optimizing to obtain the public transportation dispatching capacity value YL.
The public transportation scheduling capacity YL is obtainable by the following formula:
wherein, the ≡represents the city bus line length and kilometers; v represents the average operating speed of the bus, kilometers per hour;
t represents the rest time of waiting for the first station and the last station, and the rest time is taken for 15-20 minutes; f represents the bus departure frequency in the peak period, and the value is 5-30;
b represents a margin coefficient of the vehicle which cannot be operated due to maintenance; nh identifies fitting data after data analysis; rl is expressed as a real-time flowing human flow value; hd is denoted as a large active capacity data value;
wherein f represents the bus departure frequency in the peak period, and the value of B is 1.2-1.5.
In this embodiment, the method is used for calculating the real-time flowing traffic Rl and the large-scale movable transportation capacity data value Hd, establishing an analysis and evaluation model, summarizing specific traffic data to obtain fitting data Nh, analyzing and optimizing to obtain a public transportation dispatching capacity value YL, and dispatching vehicles according to the dispatching capacity value YL, so that the service effect of real-time intelligent dispatching is improved, intelligent dispatching is promoted, and the public transportation service level is improved.
Example 5
This embodiment is for explanation of embodiment 4, and concretely, please refer to fig. 1-2, wherein: the fourth step of the method specifically comprises the steps of,
1) The history is called through artificial intelligence, the occurrence of the emergency and the congestion time of each month are accurate, and the probability value of the emergency is calculated;
2) And when the real-time emergency occurs, correlating the fitting data Nh, and calculating to obtain the nearest value of the road section of the dispatching car to obtain an emergency scheme.
Wherein: the optimization scheme also comprises operation vehicle optimization, and the operation vehicle optimization method comprises the following steps:
1) The method comprises the steps of collecting the train running and the late time of public transportation in real time;
the train is characterized in that in a train dispatching plan of a rear train, when the train time of partial passengers and the like is increased due to the fact that the traffic flow is blocked and exceeds the linearity of a front train in the real-time operation process, the time of the next train is adjusted urgently;
the late time refers to the fact that the common operation vehicle reaches the same station time and exceeds the departure interval to hold a sword, and the deviation degree exceeds the set interval time threshold value, and at the moment, the situation of increasing vehicles or reducing vehicles is predicted according to the road traffic state.
The bus route integrated scheduling model is as follows:
where k represents a set of shifts, d k Representing the cost of shift K e K; e represents a collection of train number, see the non-passenger driving section, including stop waiting driving section; i, identifying a set of passenger driving sections, x, identifying decision variables, and identifying whether shift k is selected;
C x yx represents a decision variable, and identifies whether a vehicle will start the vehicle number J after the vehicle number i is completed;
and obtaining a driving plan based on artificial intelligence and the Internet of things, calculating a driver scheduling set coverage problem by utilizing a Lagrange heuristic algorithm in combination with a genetic algorithm, obtaining a shift plan, and generating a corresponding driving plan and a set coverage rate of the driving plan.
In this embodiment, the historical emergency probability value is scheduled through artificial intelligence, and then when an emergency occurs in real time, the fitting data Nh is associated, the latest value of the road section of the delivery road is calculated and obtained, an emergency scheme is obtained, and when the time of waiting for passengers is increased due to the occurrence of the train and the late time of public transportation operation, emergency scheduling is performed, so that the service effect of real-time intelligent scheduling is improved, and intelligent scheduling is promoted to improve the public transportation service level.
The public transportation intelligent operation management system comprises a server, wherein the server comprises a GPS module, an Internet of things module, a geographic information module, a passenger flow real-time statistics module, a road traffic state acquisition module and a scheduling module;
GPS equipment installed on each public transportation operation vehicle collects vehicle positioning data, and map mapping of vehicle information and a target value are correspondingly displayed in a geographic information module after processing;
the passenger flow real-time statistics module is used for collecting, receiving and statistically monitoring the passenger flow data of the upper station and the lower station in real time in the first step of the method of the system, and is in communication connection with the scheduling module.
The road traffic state acquisition module acquires monitoring coil signals at the upstream and downstream of intersections of all road sections, and sends the monitoring coil signals to the scheduling module after processing the monitoring coil signals through occupancy, speed and flow information parameters of the intersections;
the scheduling module analyzes and calculates the information acquired by the GPS module, the Internet of things module, the geographic information module, the passenger flow real-time statistics module and the road traffic state acquisition module, and sends the processing result to each operation vehicle to promote the improvement of the service quality of real-time intelligent scheduling.
In this embodiment, in the public transportation operation management system server, not only the information acquired by the GPS module, the internet of things module, the geographic information module, the passenger flow real-time statistics module, and the road traffic state acquisition module is combined to perform analysis and calculation, but also the information is intelligently transmitted to each operation vehicle after the result is processed, so that the public transportation operation management system server is convenient for timely scheduling, and the improvement is promoted
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions described in accordance with embodiments of the present application are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable devices. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center by wired (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains one or more sets of available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium may be a solid state disk.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a read-only memory (ROM), a random access memory (random access memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Finally: the foregoing description of the preferred embodiments of the application is not intended to limit the application, but to enable any modification, equivalent or improvement to be made without departing from the spirit and principles of the application.
The foregoing is merely a preferred embodiment of the present application, and it should be noted that it will be apparent to those skilled in the art that several modifications and variations can be made without departing from the technical principle of the present application, and these modifications and variations should also be regarded as the scope of the application.
Claims (10)
1. A public transportation intelligent operation management method is characterized in that: comprises the steps of,
step one, collecting station flow to obtain a real-time flowing people flow value;
step two, collecting the seating rate and the starting and ending time of the large-scale activities of the current city, and researching the scale change, the transport capacity and the passenger flow requirements of the operation vehicles to obtain large-scale activity transport capacity data;
thirdly, carrying out capacity optimization on public transportation according to passenger flow statistics and analysis data by combining the real-time flowing people flow value and large-scale activity capacity data to obtain an optimization scheme;
and step four, associating real-time emergencies, calculating an emergency probability value by combining the historical emergencies, and optimizing an emergency scheduling scheme.
2. The public transportation intelligent operation management method according to claim 1, wherein: the specific method in the first step comprises the following steps:
1) Dynamically recognizing videos shot by cameras at the entrances and exits of public transportation means;
2) Carrying out time-period passenger flow volume statistical analysis according to the uploaded passenger flow data by taking a time axis as a reference; wherein the time intervals are as follows: including 6 am: 00-8:00, 11 pm: 30-13:00 pm 17:30-19:00, calculating the highest flow value of the early, the noon and the late peak, and obtaining the lowest flow value in the rest time;
3) And calculating the in-out direction and the number of passengers based on the video human body recognition and tracking technology so as to obtain passenger flow data and uploading the passenger flow data.
3. The public transportation intelligent operation management method according to claim 2, wherein: the specific method in the second step comprises the following steps:
1) The address, the time of entering and exiting the field and the time of scattering the field of the large-scale activity are intelligently acquired by combining with self-media, network and newspaper modes;
2) Obtaining audience boarding rate of each game item and audience public transport sharing quantity of each game item to predict, and predicting audience space distribution;
3) And establishing an integrated dispatching model of the bus line by using the seating rate and the audience space distribution predicted value.
4. A public transportation intelligent operation management method according to claim 3, wherein: the third step specifically includes:
1) Combining the real-time flowing people flow value Rl and the large-scale activity capacity data value Hd;
2) Establishing an analysis and evaluation model;
acquiring a public transportation schedule, a vehicle dispatching application model and a salesman dispatching model;
carrying out statistics on line driving plan data and actual driving record data, associating road conditions and real-time flowing people flow value Rl, and establishing an analysis and evaluation model;
3) The data analysis is carried out on an electronic road list, a dynamic whole-day vehicle number statistics table of the vehicles of the vehicle team, a summary table of operation indexes of the vehicle team, a dispatch daily report of the vehicle team, a passenger flow data statistics table of the vehicle team, a dynamic detail table of the vehicles, passenger flow analysis, a driving record and a driving schedule table, and the fitting data Nh are obtained by analysis;
4) Optimizing to obtain the public transportation dispatching capacity value YL.
5. The public transportation intelligent operation management method according to claim 4, wherein: the public transportation scheduling capacity YL is obtainable by the following formula:
wherein, the ≡represents the city bus line length and kilometers; v represents the average operating speed of the bus, kilometers per hour;
t represents the rest time of waiting for the first station and the last station, and the rest time is taken for 15-20 minutes; f represents the bus departure frequency in the peak period, and the value is 5-30;
b represents a margin coefficient of the vehicle which cannot be operated due to maintenance; nh identifies fitting data after data analysis; rl is expressed as a real-time flowing human flow value; hd is denoted as a large active capacity data value;
wherein f represents the bus departure frequency in the peak period, and the value of B is 1.2-1.5.
6. The public transportation intelligent operation management method according to claim 5, wherein: the fourth step of the method specifically comprises the steps of,
1) The history is called through artificial intelligence, the occurrence of the emergency and the congestion time of each month are accurate, and the probability value of the emergency is calculated;
2) And when the real-time emergency occurs, correlating the fitting data Nh, and calculating to obtain the nearest value of the road section of the dispatching car to obtain an emergency scheme.
7. The public transportation intelligent operation management method according to claim 6, wherein: the optimization scheme also comprises operation vehicle optimization, and the operation vehicle optimization method comprises the following steps:
1) The method comprises the steps of collecting the train running and the late time of public transportation in real time;
the train is characterized in that in a train dispatching plan of a rear train, when the train time of partial passengers and the like is increased due to the fact that the traffic flow is blocked and exceeds the linearity of a front train in the real-time operation process, the time of the next train is adjusted urgently;
the late time refers to the fact that the common operation vehicle reaches the same station time and exceeds the departure interval to hold a sword, and the deviation degree exceeds the set interval time threshold value, and at the moment, the situation of increasing vehicles or reducing vehicles is predicted according to the road traffic state.
8. A public transportation intelligent operation management method according to claim 3, wherein: the bus route integrated scheduling model is as follows:
where k represents a set of shifts, d k Representing the cost of shift K e K; e represents a collection of train number, see the non-passenger driving section, including stop waiting driving section; i, identifying a set of passenger driving sections, x, identifying decision variables, and identifying whether shift k is selected;
C x yx represents a decision variable, and identifies whether a vehicle will start the vehicle number J after the vehicle number i is completed;
and obtaining a driving plan based on artificial intelligence and the Internet of things, calculating a driver scheduling set coverage problem by utilizing a Lagrange heuristic algorithm in combination with a genetic algorithm, obtaining a shift plan, and generating a corresponding driving plan and a set coverage rate of the driving plan.
9. A public transportation wisdom operation management system, characterized by: the system comprises a server, wherein the server comprises a GPS module, an Internet of things module, a geographic information module, a passenger flow real-time statistics module, a road traffic state acquisition module and a scheduling module;
GPS equipment installed on each public transportation operation vehicle collects vehicle positioning data, and map mapping of vehicle information and a target value are correspondingly displayed in a geographic information module after processing;
the passenger flow real-time statistics module is used for collecting, receiving and statistically monitoring the passenger flow data of the upper station and the lower station in real time in the first step of the method of the system, and is in communication connection with the scheduling module.
10. The public transportation intelligent operation management system according to claim 9, wherein: the road traffic state acquisition module acquires monitoring coil signals at the upstream and downstream of intersections of all road sections, and sends the monitoring coil signals to the scheduling module after processing the monitoring coil signals through occupancy, speed and flow information parameters of the intersections;
the scheduling module analyzes and calculates the information acquired by the GPS module, the Internet of things module, the geographic information module, the passenger flow real-time statistics module and the road traffic state acquisition module, and sends the processing result to each operation vehicle.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117495204A (en) * | 2023-12-29 | 2024-02-02 | 济南市城市交通研究中心有限公司 | Urban bus running efficiency evaluation method and system based on data analysis |
| CN117593167A (en) * | 2024-01-18 | 2024-02-23 | 山东国建土地房地产评估测绘有限公司 | Intelligent city planning management method and system based on big data |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117495204A (en) * | 2023-12-29 | 2024-02-02 | 济南市城市交通研究中心有限公司 | Urban bus running efficiency evaluation method and system based on data analysis |
| CN117495204B (en) * | 2023-12-29 | 2024-04-16 | 济南市城市交通研究中心有限公司 | Urban bus running efficiency evaluation method and system based on data analysis |
| CN117593167A (en) * | 2024-01-18 | 2024-02-23 | 山东国建土地房地产评估测绘有限公司 | Intelligent city planning management method and system based on big data |
| CN117593167B (en) * | 2024-01-18 | 2024-04-12 | 山东国建土地房地产评估测绘有限公司 | Intelligent city planning management method and system based on big data |
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Application publication date: 20231020 |