CN113657725A - Bus route and scheduling optimization method, system, device and medium - Google Patents
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Abstract
The invention discloses a method, a system, a device and a medium for optimizing bus routes and dispatching, wherein the method comprises the following steps: acquiring passenger IC transaction flow data and bus stop reporting data, and acquiring traveler OD data according to the passenger IC transaction flow data and the bus stop reporting data; carrying out data fusion on traveler OD data and preset resident trip survey data to obtain trip fusion data; constructing an evaluation index system for optimizing the public transport; and optimizing the bus operation scheduling and the bus lines by combining the travel fusion data, the evaluation index system and the bus line sub-network. The method is based on big data acquisition sample data, has wide sample coverage range, and can meet the requirements of multi-level, multi-scale and multi-type public transportation analysis; in addition, aiming at artificially judging the bus operation efficiency in the current bus line and operation scheduling optimization, the optimization method based on big data is more scientific and accurate. The invention can be widely applied to the field of bus dispatching.
Description
Technical Field
The invention relates to the field of bus scheduling, in particular to a bus route and scheduling optimization method, system, device and medium.
Background
The public transport vehicle operation and the passenger trip are basic units of the bus system trip, and the information of the public transport vehicle operation and the passenger trip comprises vehicle stop reporting data, shift sending data and passenger card swiping information. The GPS bus stop reporting data is generated by automatic stop reporting when the bus enters and exits the bus stop, and records the time-space information of the bus in the running process; the shift sending data records the current shift information of the line every day; the IC transaction data is transaction flow generated by swiping a card by a passenger getting on the bus, and card number, card type, transaction time, vehicle code, line information and the like of the IC are recorded; the resident trip survey is to collect information through entering a household, and records the number of trips of a resident every day, departure time, arrival time, Origin and Destination (OD) distribution, trip purposes, trip modes and other information of each trip. Therefore, the bus passenger travel time-space chain can be analyzed by fusing the IC transaction data with the vehicle stop reporting data and the shift sending data, and the travel purpose and the associated travel characteristics of the passenger can be analyzed by fusing the mined travel chain and the resident travel survey data; through deep recognition and mining analysis of the travel chain of the bus passengers, the travel rules of the passengers and the running characteristics of the vehicles are mastered, auxiliary decision information is provided for bus planning and management, and data-driven bus routes and operation scheduling optimization are realized.
The traditional public transportation OD analysis mainly takes manual investigation as a main part, a large amount of manpower and material resources are required to be input, the traditional public transportation network adjustment and operation scheduling optimization are also based on the traditional public transportation OD analysis, the problems of limited sample coverage, poor precision, low repeatability, large analysis time and space granularity and the like exist, and the limitation of the traditional public transportation OD analysis is difficult to meet the requirement of the current public transportation system analysis. With the continuous improvement of the mode and the intelligent level of bus information acquisition, the acquisition of mass travel information can provide rich data support for bus travel research and analysis, and a better working basis is provided for timely bus route adjustment and operation scheduling optimization.
Disclosure of Invention
In order to solve at least one of the technical problems in the prior art to a certain extent, the invention aims to provide a method, a system, a device and a medium for optimizing bus routes and dispatching.
The technical scheme adopted by the invention is as follows:
a bus route and scheduling optimization method comprises the following steps:
acquiring passenger IC transaction flow data and bus stop reporting data, and acquiring traveler OD data according to the passenger IC transaction flow data and the bus stop reporting data;
carrying out data fusion on traveler OD data and preset resident trip survey data to obtain trip fusion data;
constructing an evaluation index system for optimizing the public transport;
optimizing bus operation scheduling and lines by combining travel fusion data, an evaluation index system and a bus line subnet;
the travel fusion data comprise traveler characteristic data and public transportation characteristic data, and the public transportation line sub-network is a network formed by public transportation lines or subway lines which are most closely related to the lines in topology or passenger flow.
Further, the obtaining of traveler OD data according to passenger IC transaction flow data and bus stop reporting data includes:
acquiring first OD information containing getting-on and getting-off information and second OD information only containing getting-on information according to passenger IC transaction flow data and bus stop reporting data;
acquiring the attraction weight of the bus stop according to the first OD information, and correcting the attraction weight according to the second OD information;
and acquiring a shift OD matrix of the bus line according to the corrected attraction weight, and counting the OD quantity of each station in the bus line.
Further, the calculation formula of the attraction weight is as follows:
wherein d isijA station attraction weight for station j to i; si ′ jThe OD quantities of stations i to j are obtained through statistics of getting-on and getting-off deduction;
the total number of people getting on the bus at station i;
after the attraction weight is corrected according to the second OD information, the expression of the station OD amount obtained is as follows:
wherein the content of the first and second substances,
correction term for station OD, NiThe unknown passenger flow of the getting-on and getting-off stations of the station i is obtained; c is IC card occupancy;
for the bus line, the expressions of the number of getting-on persons and the number of getting-off persons of each stop obtained according to the OD matrix of the number of shifts are as follows:
wherein, the sites are arranged in sequence, i represents the ith site; n is the number of the route stations of the bus line; sijThe OD quantity of stations i to j in the line; u shapekThe number of passengers getting on the bus at the station k; dkRepresenting the number of alights at stop k.
Further, the data fusion of the traveler OD data and the preset resident travel survey data includes:
acquiring first OD information of a traveler m according to the OD data of the traveler;
matching the first OD information with second OD information recorded in resident trip survey data;
and after the matching is successful, acquiring corresponding traveler characteristics from the resident trip survey data and endowing the traveler characteristics to the traveler m.
Further, the bus operation scheduling is optimized by combining travel fusion data, an evaluation index system and a bus line subnet, and the method comprises the following steps:
the method comprises the steps that key characteristics of a bus line in operation scheduling are obtained by combining travel fusion data, an evaluation index body and a bus line sub-network system, wherein the key characteristics comprise the degree of congestion of a carriage, the running speed of a vehicle and the running time of the vehicle;
according to key characteristics, identifying problems existing in line operation scheduling from three dimensions of shift, station sections and line integrity of a line;
obtaining an optimization strategy according to the identified problems, and optimizing the bus operation scheduling;
the scheme for optimizing the bus operation scheduling comprises the following steps: service mode combination optimization, shift interval optimization, bus type optimization and vehicle in-transit control.
Further, the bus route is optimized by combining travel fusion data, an evaluation index system and a bus route sub-network, and the method comprises the following steps:
the method comprises the steps of obtaining key characteristics of a bus route by combining travel fusion data, an evaluation index body and a bus route sub-network system, wherein the key characteristics comprise route length, a repetition coefficient, a track traffic repetition rate and a transfer coefficient;
according to key characteristics, identifying problems of the bus line from two dimensions of physical characteristics and passenger flow characteristics of the line;
obtaining an optimization strategy according to the identified problems, and optimizing the bus route;
wherein, the scheme of optimizing the bus route includes: canceling, adjusting trend, shortening, splitting, extending and newly adding the line.
The other technical scheme adopted by the invention is as follows:
a bus route and schedule optimization system comprising:
the data mining module is used for acquiring passenger IC transaction stream data and bus stop reporting data and acquiring traveler OD data according to the passenger IC transaction stream data and the bus stop reporting data;
the data fusion module is used for carrying out data fusion on traveler OD data and preset resident trip survey data to obtain trip fusion data;
the index construction module is used for constructing an evaluation index system for optimizing the public transport;
the bus optimization module is used for optimizing bus operation scheduling and bus routes by combining travel fusion data, an evaluation index system and a bus route sub-network;
the travel fusion data comprise traveler characteristic data and public transportation characteristic data, and the public transportation line sub-network is a network formed by public transportation lines or subway lines which are most closely related to the lines in topology or passenger flow.
The other technical scheme adopted by the invention is as follows:
a bus route and schedule optimization apparatus comprising:
at least one processor;
at least one memory for storing at least one program;
when executed by the at least one processor, cause the at least one processor to implement the method described above.
The other technical scheme adopted by the invention is as follows:
a storage medium having stored therein a processor-executable program for performing the method as described above when executed by a processor.
The invention has the beneficial effects that: the method is based on big data acquisition sample data, has wide sample coverage range, and can meet the requirements of multi-level, multi-scale and multi-type public transportation analysis; in addition, aiming at artificially judging the bus operation efficiency in the current bus line and operation scheduling optimization, the optimization method based on big data is more scientific and accurate.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following description is made on the drawings of the embodiments of the present invention or the related technical solutions in the prior art, and it should be understood that the drawings in the following description are only for convenience and clarity of describing some embodiments in the technical solutions of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a general framework diagram of bus route optimization and operation scheduling optimization in an embodiment of the invention;
FIG. 2 is a schematic diagram of a bus OD inference principle in the embodiment of the invention;
FIG. 3 is a schematic diagram of a fusion process of bus data and resident travel survey data in the embodiment of the invention;
FIG. 4 is a schematic diagram of a digitized technical route of a public transportation system in the embodiment of the invention;
FIG. 5 is a schematic flow chart of a bus route subnet analysis technique in the embodiment of the invention;
FIG. 6 is a schematic flow chart of a bus operation scheduling optimization technique in the embodiment of the present invention;
FIG. 7 is a schematic flow chart of a bus route optimization technique in an embodiment of the invention;
fig. 8 is a flowchart illustrating steps of a bus route and schedule optimization method according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention. The step numbers in the following embodiments are provided only for convenience of illustration, the order between the steps is not limited at all, and the execution order of each step in the embodiments can be adapted according to the understanding of those skilled in the art.
In the description of the present invention, it should be understood that the orientation or positional relationship referred to in the description of the orientation, such as the upper, lower, front, rear, left, right, etc., is based on the orientation or positional relationship shown in the drawings, and is only for convenience of description and simplification of description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus, should not be construed as limiting the present invention.
In the description of the present invention, the meaning of a plurality of means is one or more, the meaning of a plurality of means is two or more, and larger, smaller, larger, etc. are understood as excluding the number, and larger, smaller, inner, etc. are understood as including the number. If the first and second are described for the purpose of distinguishing technical features, they are not to be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated or implicitly indicating the precedence of the technical features indicated.
In the description of the present invention, unless otherwise explicitly limited, terms such as arrangement, installation, connection and the like should be understood in a broad sense, and those skilled in the art can reasonably determine the specific meanings of the above terms in the present invention in combination with the specific contents of the technical solutions.
The embodiment provides a technical route and a key technology for integrated optimization of data-driven bus routes and operation scheduling, and a general research framework is shown in fig. 1. Firstly, fusing bus travel data, excavating a bus travel OD based on data resources such as a bus IC, station reporting, class sending, vehicles and the like, repairing a bus travel chain by combining models such as random forests, Boosting and the like, and performing data fusion with resident travel survey data to enhance a middle-end bus travel chain; secondly, digitalizing a public transport system, and constructing a multi-level evaluation index system of the public transport system on the basis of the fusion of public transport trip data; and finally, according to the digital analysis and visual expression of the public transportation system, the public transportation system operation characteristics, the public transportation operation problem revealing, the public transportation optimization suggestion giving, the analysis of the influence of the public transportation line subnet and the integral optimization scheme of the public transportation line and the operation scheduling are provided, and the method comprises four layers of line operation scheduling optimization, line planning and design optimization, regional line network planning and design optimization, regional customized public transportation design and the like. The above steps are explained in detail below with reference to fig. 1.
1. Bus trip data fusion
1.1 bus OD inference
The bus initial OD inference is to assume that the passengers have regularity in trip, and takes long-time trip behaviors and rules of the passengers as an assumed condition. For example, a traveler takes a bus on duty in the morning, gets on the bus at station A, and gets off the bus at station B; the system comprises a bus taking bus in the afternoon, a bus getting-on station B and a bus getting-off station A, wherein the traveling of the bus has regularity, and stable OD data can be obtained based on the regularity. And deducing the passenger flow of getting on or off the bus, the passenger flow of transferring and the bus OD according to the characteristics of a certain travel time threshold, the recognition of the transfer behavior, the spatial attribute of the bus stop and the like by combining passenger IC transaction flow data (including information such as card type, serial number, transaction time, vehicle attribute, bus route and the like), bus stop reporting data (including information such as vehicle attribute, stop name, stop reporting time, route driving direction and the like) and the route running direction.
In some optional embodiments, the step of mining the OD data of the traveler is as shown in fig. 2, the boarding station of the traveler is matched according to the passenger IC transaction flow data and the bus stop reporting data, and if the matching is successful, the next step is performed: and (5) deducing the get-off station. In the drop-off station, there are two cases: firstly, the traveler does not need to transfer, and the getting-off is the destination; and the travelers need to transfer, and need to take another regular bus after getting off the bus. When the information of the present person (i.e., the destination) is estimated, the OD data of the traveler is obtained. And acquiring a total bus stop OD based on the OD data of each traveler.
1.2 bus stop OD estimation
In some optional embodiments, the bus station OD is estimated by: the OD information of a single trip of a bus passenger can be obtained through getting-on/off deduction, but due to the fact that data are lost, trip chain breakage and the like, a getting-on/off information deduction algorithm has certain limitation, and partial deduction results only include getting-on information and lack of getting-off information. Therefore, the individual OD information is processed in a centralized manner, and the technical scheme of the processing is as follows: with the line shift as a statistical unit, for each line shift, OD information obtained by deducing the information of getting on or off the train is divided into two parts: firstly, complete OD information containing getting-on and getting-off information is subjected to station level statistics to obtain an initial station OD, station attraction weight is calculated according to each station OD, and the formula for calculating the station attraction weight is as follows:
wherein d isijA station attraction weight for station j to i; si ′ jThe OD quantities of stations i to j are obtained through statistics of getting-on and getting-off deduction;
the total number of cars on station i.
And secondly, only OD information containing the boarding information is counted as station output, the OD is distributed according to station attraction weight, and the previous initial OD is corrected. And finally, carrying out sample expansion on the station OD according to the IC card occupancy rate to obtain the final station OD quantity which can be expressed as:
wherein the content of the first and second substances,
correction term for station OD, NiThe unknown passenger flow of the getting-on and getting-off stations of the station i is obtained; and c is IC card occupancy.
1.3 bus stop trip information collection meter
In some optional embodiments, the bus stop travel information is collected by: the station trip information comprises two indexes of the number of people getting on or off the station and the passenger carrying capacity between the stations. And estimating the bus stop OD to obtain an OD matrix of each line shift, wherein the OD matrix represents the bus travel demands from the O stop to the D stop in the statistical range. Because each bus runs along the bus line in a single direction, and the station D is a downstream station of the station O, the OD matrix form of the line is an upper triangular matrix. For a bus route with n stops, the route OD matrix can be represented as shown in table 1.
Table 1 route OD matrix for n stations
The number of people getting on or off the station is calculated by accumulating the corresponding OD quantities of the station, the number of people getting on or off the station is the sum of the OD quantities of the station in one row taking the station as the O point, and the number of people getting off the station is the sum of the OD quantities of the station in one row taking the station as the D point, which can be specifically expressed as:
wherein, the sites are arranged in sequence, i represents the ith site; n is the number of the route stations of the bus line; sijThe OD quantity of stations i to j in the line; u shapekThe number of passengers getting on the bus at the station k; dkRepresenting the number of alights at stop k.
1.4 fusion of public transportation data and resident trip survey data
The public transportation data describes travel in a public transportation system, expresses the whole process of the whole public transportation travel and historical travel records of a single traveler, but can not obtain certain attributes of travel, such as travel purpose and social characteristics of the traveler; the bus travel information screened from the resident travel survey data (obtained by pre-counting and manually counting) has the travel purpose and social characteristics of travelers, such as sex, age, occupation and the like. Therefore, the bus trip characteristics are taken as connection, the attributes of the two types of data are complementary, and the resident trip data is fused to supplement the trip purposes (such as working and walking) of the bus passengers and the social characteristics (blue-collar and white-collar) of travelers.
In some optional embodiments, the data fusion process is as shown in fig. 3, the travel data of the bus traveler i is obtained, and the travel data is classified according to a travel mode, such as conventional travel or random travel; and after classification is finished, corresponding data are obtained from the resident trip survey data, and similarity calculation is carried out on the data and the trip data of the bus traveler i. And acquiring the characteristic information of the surrender i from the resident trip survey data according to the similarity, and generating the complete trip information of the surrender i. For example, the travel survey data of the resident j includes the travel time, the travel route, the OD data and the like, which are similar to the travel data of the public transport traveler i, and the travel purpose (such as office work) and the work characteristic (such as white collar) of the resident j can be given to the public transport traveler i.
2. Bus system digitization technology
The bus system digitization technology is based on bus lines and an operation scheduling problem set, an index system for comprehensively expressing, evaluating and analyzing bus operation characteristics and service capacity is constructed, expression of the bus lines and operation scheduling operation characteristics is achieved by means of fusion and analysis of geographic information and multi-source heterogeneous big data of a bus system, and then monitoring and evaluation, intelligent bus planning and self-adaptive bus scheduling of the whole process of the bus system are achieved. The analysis is carried out according to the thought of object → target → index → condition → problem diagnosis → countermeasure and the problem diagnosis and countermeasure optimization of how to drive the public traffic system through data by combing analysis from three dimensions of government, enterprise and passengers, wherein the problems of overlong length, high overlapping rate with other lines or rail traffic, serious detour, overlarge inter-station distance, unsatisfied accessibility requirement, poor passenger flow efficiency and the like mainly exist in line setting, and the problems of long waiting time of passengers, crowded carriage or low boarding rate, low running speed, instable running time, unbalanced passenger flow and the like mainly exist in line operation scheduling. As shown in fig. 4 and table 2.
TABLE 2 bus route setup and operation scheduling problem diagnosis and optimization strategy combing
Based on the indexes in table 2, the operation status of each station can be judged and analyzed by combining the obtained data such as the OD amount of the bus station.
3. Bus line subnet analysis technology
The bus line sub-network is a network formed by bus lines or subway lines which are most closely related to the lines in topology or passenger flow, the line sub-network construction is equivalent to the determination of the influence range of the lines, and the influence of line operation scheduling adjustment is suitable for being analyzed on the line sub-network. The bus line subnet comprises three aspects: a subway sub-network, a subway line where target lines intersect; topological overlapping subnets: the method is characterized in that a network formed by other bus lines with higher overlapping rate with the line is used for drawing the line standard of 50% overlapping rate of bus trunk lines, 70% overlapping rate of bus branch lines and 30% overlapping rate of microcirculation lines into a topological overlapping sub-network according to the line function; the passenger flow associates the subnet: the line with the stronger traffic associated with the target line.
The analysis technical route is shown in fig. 5, for the passenger flow of the target line, the passenger flow tracing is firstly carried out: and tracing the corresponding travel quantity by the passenger flow, tracing the corresponding OD quantity by the travel, and distributing by the actual path corresponding to the OD quantity. And (3) constructing a full passenger flow associated subnet in combination with other traffic lines, and optimizing and refining according to the constructed subnet: and (4) deleting accidental trips of the target line, deleting weak related paths of the target line, and balancing the scale of the subnet and trip loss. And after optimization and refinement, obtaining the passenger flow associated subnet.
4. Bus line and operation scheduling optimization technology
4.1 bus operation scheduling optimization technique
Firstly, judging key characteristics of a bus line in operation scheduling through a line operation scheduling evaluation index system, wherein the key characteristics comprise indexes such as carriage crowdedness, vehicle running speed and running time; secondly, identifying and diagnosing problems existing in line operation scheduling from three dimensions of shift-giving times, station sections, line integrity and the like of the line, and providing a related optimization strategy suggestion; and finally, analyzing the passenger service level change and passenger flow transfer of the bus route subnet according to the optimization scheme, judging the feasibility of the optimization scheme, and finally outputting the bus route operation scheduling optimization scheme, wherein the bus route operation scheduling optimization scheme comprises service mode combination optimization, duty interval optimization, bus type optimization, vehicle on-road control and the like. The technical flow is shown in fig. 6.
4.2 bus route optimization technology
Firstly, judging key characteristics of a bus route through a route optimization evaluation index system, wherein the key characteristics comprise indexes such as route length, repetition coefficient, repetition rate with rail transit, transfer coefficient and the like; secondly, identifying and diagnosing problems existing in the line setting from two dimensions of physical characteristics, passenger flow characteristics and the like of the line, and providing a related optimization strategy suggestion; and finally, analyzing the passenger service level change and passenger flow transfer of the bus route subnet according to the optimization scheme, judging the feasibility of the optimization scheme, and finally outputting the bus route optimization scheme, wherein the bus route optimization scheme comprises the steps of canceling, adjusting trend, shortening, splitting, extending, newly adding and the like of the route. The technical flow is shown in fig. 7.
In some optional embodiments, as shown in fig. 8, this embodiment provides a bus route and schedule optimization method, including the following steps:
s1, acquiring passenger IC transaction flow data and bus stop reporting data, and acquiring traveler OD data according to the passenger IC transaction flow data and the bus stop reporting data;
s2, carrying out data fusion on traveler OD data and preset resident travel survey data to obtain travel fusion data;
s3, constructing an evaluation index system for optimizing the bus;
and S4, optimizing the bus operation scheduling and the bus lines by combining the travel fusion data, the evaluation index system and the bus line sub-network. The bus line sub-network is a network formed by bus lines or subway lines which are most closely related to the lines in topology or passenger flow.
This embodiment still provides a bus route and dispatch optimal system, includes:
the data mining module is used for acquiring passenger IC transaction stream data and bus stop reporting data and acquiring traveler OD data according to the passenger IC transaction stream data and the bus stop reporting data;
the data fusion module is used for carrying out data fusion on traveler OD data and preset resident trip survey data to obtain trip fusion data;
the index construction module is used for constructing an evaluation index system for optimizing the public transport;
the bus optimization module is used for optimizing bus operation scheduling and bus routes by combining travel fusion data, an evaluation index system and a bus route sub-network;
the travel fusion data comprise traveler characteristic data and public transportation characteristic data, and the public transportation line sub-network is a network formed by public transportation lines or subway lines which are most closely related to the lines in topology or passenger flow.
The bus route and scheduling optimization system of the embodiment can execute the bus route and scheduling optimization method provided by the embodiment of the method of the invention, can execute any combination implementation steps of the embodiment of the method, and has corresponding functions and beneficial effects of the method.
This embodiment still provides a bus route and dispatch optimizing apparatus, includes:
at least one processor;
at least one memory for storing at least one program;
when executed by the at least one processor, cause the at least one processor to implement the method of fig. 8.
The bus route and scheduling optimization device of the embodiment can execute the bus route and scheduling optimization method provided by the embodiment of the method of the invention, can execute any combination implementation steps of the embodiment of the method, and has corresponding functions and beneficial effects of the method.
The embodiment of the application also discloses a computer program product or a computer program, which comprises computer instructions, and the computer instructions are stored in a computer readable storage medium. The computer instructions may be read by a processor of a computer device from a computer-readable storage medium, and the computer instructions executed by the processor cause the computer device to perform the method illustrated in fig. 8.
The embodiment also provides a storage medium, which stores instructions or programs capable of executing the bus route and scheduling optimization method provided by the embodiment of the method of the invention, and when the instructions or the programs are run, the steps can be implemented by any combination of the embodiment of the method, and the method has corresponding functions and beneficial effects.
In alternative embodiments, the functions/acts noted in the block diagrams may occur out of the order noted in the operational illustrations. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality/acts involved. Furthermore, the embodiments presented and described in the flow charts of the present invention are provided by way of example in order to provide a more thorough understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is changed and in which sub-operations described as part of larger operations are performed independently.
Furthermore, while the invention is described in the context of functional modules, it should be understood that one or more of the described functions or features may be integrated in a single physical device or software module or may be implemented in a separate physical device or software module unless stated to the contrary. It will also be appreciated that a detailed discussion of the actual implementation of each module is not necessary for an understanding of the present invention. Rather, the actual implementation of the various functional modules in the apparatus disclosed herein will be understood within the ordinary skill of an engineer, given the nature, function, and internal relationship of the modules. Accordingly, those skilled in the art can, using ordinary skill, practice the invention as set forth in the claims without undue experimentation. It is also to be understood that the specific concepts disclosed are merely illustrative of and not intended to limit the scope of the invention, which is defined by the appended claims and their full scope of equivalents.
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 such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
In the foregoing description of the specification, reference to the description of "one embodiment/example," "another embodiment/example," or "certain embodiments/examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.
While the preferred embodiments of the present invention have been illustrated and described, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (9)
1. A bus route and scheduling optimization method is characterized by comprising the following steps:
acquiring passenger IC transaction flow data and bus stop reporting data, and acquiring traveler OD data according to the passenger IC transaction flow data and the bus stop reporting data;
carrying out data fusion on traveler OD data and preset resident trip survey data to obtain trip fusion data;
constructing an evaluation index system for optimizing the public transport;
optimizing bus operation scheduling and bus lines by combining travel fusion data, an evaluation index system and a bus line subnet;
the bus line sub-network is a network formed by bus lines or subway lines which are most closely related to the lines in topology or passenger flow.
2. The bus route and dispatching optimization method according to claim 1, wherein the obtaining of traveler OD data according to passenger IC transaction flow data and bus stop reporting data comprises:
acquiring first OD information containing getting-on and getting-off information and second OD information only containing getting-on information according to passenger IC transaction flow data and bus stop reporting data;
acquiring the attraction weight of the bus stop according to the first OD information, and correcting the attraction weight according to the second OD information; and acquiring a shift OD matrix of the bus line according to the corrected attraction weight, and counting the OD quantity of each station in the bus line.
3. The bus route and dispatching optimization method according to claim 2, wherein the calculation formula of the attraction weight is as follows:
wherein d isijA station attraction weight for station j to i; s'ijThe OD quantities of stations i to j are obtained through statistics of getting-on and getting-off deduction;
the total number of people getting on the bus at station i;
after the attraction weight is corrected according to the second OD information, the expression of the station OD amount obtained is as follows:
wherein the content of the first and second substances,
correction term for station OD, NiThe unknown passenger flow of the getting-on and getting-off stations of the station i is obtained; c is IC card occupancy;
for the bus line, the expressions of the number of getting-on persons and the number of getting-off persons of each stop obtained according to the OD matrix of the number of shifts are as follows:
wherein, the sites are arranged in sequence, i represents the ith site; n is the number of the route stations of the bus line; sijThe OD quantity of stations i to j in the line; u shapekThe number of passengers getting on the bus at the station k; dkRepresenting the number of alights at stop k.
4. The bus route and dispatching optimization method according to claim 1, wherein the data fusion of traveler OD data and preset resident trip survey data comprises:
acquiring first OD information of a traveler m according to the OD data of the traveler;
matching the first OD information with second OD information recorded in resident trip survey data;
and after the matching is successful, acquiring corresponding traveler characteristics from the resident trip survey data and endowing the traveler characteristics to the traveler m.
5. The bus route and schedule optimization method according to claim 1, wherein optimizing bus operation schedules in combination with travel fusion data, an evaluation index system and a bus route sub-network comprises:
the method comprises the steps that key characteristics of a bus line in operation scheduling are obtained by combining travel fusion data, an evaluation index body and a bus line sub-network system, wherein the key characteristics comprise the degree of congestion of a carriage, the running speed of a vehicle and the running time of the vehicle;
according to key characteristics, identifying problems existing in line operation scheduling from three dimensions of shift, station sections and line integrity of a line;
obtaining an optimization strategy according to the identified problems, and optimizing the bus operation scheduling;
the scheme for optimizing the bus operation scheduling comprises the following steps: service mode combination optimization, shift interval optimization, bus type optimization and vehicle in-transit control.
6. The bus route and dispatching optimization method according to claim 1, wherein optimizing the bus route by combining travel fusion data, an evaluation index system and a bus route sub-network comprises:
the method comprises the steps of obtaining key characteristics of a bus route by combining travel fusion data, an evaluation index body and a bus route sub-network system, wherein the key characteristics comprise route length, a repetition coefficient, a track traffic repetition rate and a transfer coefficient;
according to key characteristics, identifying problems of the bus line from two dimensions of physical characteristics and passenger flow characteristics of the line;
obtaining an optimization strategy according to the identified problems, and optimizing the bus route;
wherein, the scheme of optimizing the bus route includes: canceling, adjusting trend, shortening, splitting, extending and newly adding the line.
7. A bus route and schedule optimization system, comprising:
the data mining module is used for acquiring passenger IC transaction stream data and bus stop reporting data and acquiring traveler OD data according to the passenger IC transaction stream data and the bus stop reporting data;
the data fusion module is used for carrying out data fusion on traveler OD data and preset resident trip survey data to obtain trip fusion data;
the index construction module is used for constructing an evaluation index system for optimizing the public transport;
the bus optimization module is used for optimizing bus operation scheduling and bus routes by combining travel fusion data, an evaluation index system and a bus route sub-network;
the travel fusion data comprise traveler characteristic data and public transportation characteristic data, and the public transportation line sub-network is a network formed by public transportation lines or subway lines which are most closely related to the lines in topology or passenger flow.
8. A bus route and schedule optimization device, comprising:
at least one processor;
at least one memory for storing at least one program;
when executed by the at least one processor, cause the at least one processor to implement the method of any one of claims 1-6.
9. A storage medium having stored therein a program executable by a processor, wherein the program executable by the processor is adapted to perform the method of any one of claims 1-6 when executed by the processor.
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