CN117474736A - Existing line traffic network transformation composite collaborative planning optimization method and system - Google Patents
Existing line traffic network transformation composite collaborative planning optimization method and system Download PDFInfo
- Publication number
- CN117474736A CN117474736A CN202311476938.6A CN202311476938A CN117474736A CN 117474736 A CN117474736 A CN 117474736A CN 202311476938 A CN202311476938 A CN 202311476938A CN 117474736 A CN117474736 A CN 117474736A
- Authority
- CN
- China
- Prior art keywords
- collaborative planning
- cost
- traffic
- traffic network
- collaborative
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000005457 optimization Methods 0.000 title claims abstract description 48
- 230000009466 transformation Effects 0.000 title claims abstract description 36
- 239000002131 composite material Substances 0.000 title claims abstract description 33
- 238000000034 method Methods 0.000 title claims abstract description 23
- 230000004048 modification Effects 0.000 claims description 41
- 238000012986 modification Methods 0.000 claims description 41
- 238000005265 energy consumption Methods 0.000 claims description 6
- 230000006870 function Effects 0.000 description 49
- 238000012545 processing Methods 0.000 description 3
- 230000008878 coupling Effects 0.000 description 2
- 238000010168 coupling process Methods 0.000 description 2
- 238000005859 coupling reaction Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 238000013480 data collection Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000010295 mobile communication Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000008569 process Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/10—Geometric CAD
- G06F30/18—Network design, e.g. design based on topological or interconnect aspects of utility systems, piping, heating ventilation air conditioning [HVAC] or cabling
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0637—Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/10—Office automation; Time management
- G06Q10/101—Collaborative creation, e.g. joint development of products or services
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Strategic Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Theoretical Computer Science (AREA)
- Tourism & Hospitality (AREA)
- Economics (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- Educational Administration (AREA)
- Operations Research (AREA)
- Geometry (AREA)
- Quality & Reliability (AREA)
- Development Economics (AREA)
- Computer Networks & Wireless Communication (AREA)
- Mathematical Analysis (AREA)
- Health & Medical Sciences (AREA)
- Data Mining & Analysis (AREA)
- Primary Health Care (AREA)
- Computational Mathematics (AREA)
- General Health & Medical Sciences (AREA)
- Game Theory and Decision Science (AREA)
- Mathematical Optimization (AREA)
- Pure & Applied Mathematics (AREA)
- Computer Hardware Design (AREA)
- Evolutionary Computation (AREA)
- General Engineering & Computer Science (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Traffic Control Systems (AREA)
Abstract
The invention discloses a method and a system for optimizing composite collaborative planning for existing line traffic network transformation, wherein the method comprises the following steps: acquiring pre-transformation information and post-transformation information of an existing line traffic network, and a plurality of collaborative planning decision variables; setting a collaborative planning optimization model, and calculating a collaborative planning comprehensive cost value under each collaborative planning decision variable according to the pre-transformation information and the post-transformation information. And finding out the minimum collaborative planning comprehensive cost value in all the collaborative planning comprehensive cost values, determining the collaborative planning decision variable corresponding to the minimum collaborative planning comprehensive cost value, and carrying out reconstruction composite collaborative planning optimization on the existing line traffic network according to the collaborative planning decision variable.
Description
Technical Field
The invention belongs to the technical field of existing line traffic network transformation composite collaborative planning optimization, and particularly relates to an existing line traffic network transformation composite collaborative planning optimization method and system.
Background
The improvement of the existing road network is a complex field related to urban traffic planning and optimization, and multiple factors need to be comprehensively considered. The following methods are generally employed:
data collection and analysis: detailed data of the urban traffic network is collected, including information of road network, public traffic line, traffic flow, population distribution, etc. And analyzing the bottleneck, the congestion condition and the demand mode of the existing line to determine the key area and the direction of transformation.
And (3) formulating a transformation strategy: and (3) formulating modification targets, such as reducing congestion, improving traffic efficiency, reducing pollution and the like. A schedule and budget for the retrofit is determined.
However, in the prior art, no technical scheme is available, so that the decision variable of the road network transformation can be automatically and efficiently determined, and the optimal decision variable can be found.
Disclosure of Invention
In order to solve the technical problems, the invention provides a composite collaborative planning optimization method for the transformation of an existing line traffic network, which comprises the following steps:
acquiring pre-transformation information and post-transformation information of an existing line traffic network, and a plurality of collaborative planning decision variables;
setting a collaborative planning optimization model, and calculating a collaborative planning comprehensive cost value under each collaborative planning decision variable according to the pre-transformation information and the post-transformation information.
And finding out the minimum collaborative planning comprehensive cost value in all the collaborative planning comprehensive cost values, determining the collaborative planning decision variable corresponding to the minimum collaborative planning comprehensive cost value, and carrying out reconstruction composite collaborative planning optimization on the existing line traffic network according to the collaborative planning decision variable.
Further, the collaborative planning optimization model includes:
min F(V)＝α 1 C cost (V)+α 2 C safety (V)+α 3 C cong (V)+α 4 C multi (V)
wherein F (V) is a collaborative planning integrated cost value, alpha, when the collaborative planning decision variable V is 1 To reform the weight of the cost, C cost (V) is a modification cost function, alpha 2 Weight of traffic safety cost, C safety (V) is a traffic safety cost function, alpha 3 Weight of congestion cost, C cong (V) is a congestion cost function, alpha 4 Weight of multi-mode traffic cost, C multi (V) is a multimodal traffic cost function.
Further, the modification cost function C cost (V) comprises:
wherein C is i_direct Direct cost for traffic network reconstruction through ith collaborative planning decision-making variable, C i_indirect C, indirect cost generated by reconstruction of traffic network through ith collaborative planning decision-making variable i_land For land acquisition cost generated by reconstruction of traffic network through ith collaborative planning decision variable, V i For the ith collaborative planning decision variable, N is the total number of collaborative planning decision variables.
Further, the traffic safety cost function C safety (V) comprises:
wherein A is k_before For average accident rate on kth road section or area before traffic network modification, A k_after For average accident rate on kth road section or zone before traffic network modification, C k For cost of modification of traffic on kth road section or area, V k Decision variables are planned for the kth co-ordinates, K being the total number of road segments or areas.
Further, the congestion cost function C cong (V) comprises:
wherein T is j For average traffic congestion time on the jth road segment or area, E j To average energy consumption per hour on the jth road segment or area, P j For traffic flow on the jth road section or zone, V j Decision variables are planned for the J-th co-ordinates, J being the total number of road segments or areas.
Further, the multi-modal traffic cost function C multi (V) comprises:
wherein C is m_before C for using the proportion of the mth traffic mode before the traffic network is modified m_after To use the proportion of the mth traffic mode after the traffic network is modified, W m Is the importance weight of the mth traffic mode, V m And (3) for the mth collaborative planning decision variable, M is the total number of traffic modes.
The invention also provides a composite collaborative planning optimization system for the transformation of the existing line traffic network, which comprises the following steps:
the information acquisition module is used for acquiring pre-reconstruction information and post-reconstruction information of the existing line traffic network and a plurality of collaborative planning decision variables;
the setting model module is used for setting a collaborative planning optimization model and calculating a collaborative planning comprehensive cost value under each collaborative planning decision variable according to the pre-transformation information and the post-transformation information.
And the optimization module is used for finding out the minimum collaborative planning comprehensive cost value in all the collaborative planning comprehensive cost values, determining the collaborative planning decision variable corresponding to the minimum collaborative planning comprehensive cost value, and carrying out reconstruction composite collaborative planning optimization on the existing line traffic network according to the collaborative planning decision variable.
Further, the collaborative planning optimization model includes:
min F(V)＝α 1 C cost (V)+α 2 C safety (V)+α 3 C cong (V)+α 4 C multi (V)
wherein F (V) is a collaborative planning integrated cost value, alpha, when the collaborative planning decision variable V is 1 To reform the weight of the cost, C cost (V) is a modification cost function, alpha 2 Weight of traffic safety cost, C safety (V) is a traffic safety cost function, alpha 3 Weight of congestion cost, C cong (V) is a congestion cost function, alpha 4 Weight of multi-mode traffic cost, C multi (V) is a multimodal traffic cost function.
Further, the modification cost function C cost (V) comprises:
wherein C is i_direct Direct cost for traffic network reconstruction through ith collaborative planning decision-making variable, C i_indirect C, indirect cost generated by reconstruction of traffic network through ith collaborative planning decision-making variable i_land For land acquisition cost generated by reconstruction of traffic network through ith collaborative planning decision variable, V i For the ith collaborative planning decision variable, N is the total number of collaborative planning decision variables.
Further, the traffic safety cost function C safety (V) comprises:
wherein A is k_before For average accident rate on kth road section or area before traffic network modification, A k_after For average accident rate on kth road section or zone before traffic network modification, C k For cost of modification of traffic on kth road section or area, V k Decision variables are planned for the kth co-ordinates, K being the total number of road segments or areas.
Compared with the prior art, the technical scheme of the invention has the following beneficial effects:
the method comprises the steps of obtaining pre-transformation information and post-transformation information of the existing line traffic network and a plurality of collaborative planning decision variables; setting a collaborative planning optimization model, and calculating a collaborative planning comprehensive cost value under each collaborative planning decision variable according to the pre-transformation information and the post-transformation information. And finding out the minimum collaborative planning comprehensive cost value in all the collaborative planning comprehensive cost values, determining the collaborative planning decision variable corresponding to the minimum collaborative planning comprehensive cost value, and carrying out reconstruction composite collaborative planning optimization on the existing line traffic network according to the collaborative planning decision variable. According to the technical scheme, the optimal decision variable can be found through a model formed by a plurality of calculation functions, so that the road network transformation is optimized.
Drawings
FIG. 1 is a flow chart of embodiment 1 of the present invention;
fig. 2 is a block diagram of a system of embodiment 2 of the present invention.
Detailed Description
In order to better understand the above technical solutions, the following detailed description will be given with reference to the accompanying drawings and specific embodiments.
The method provided by the invention can be implemented in a terminal environment, wherein the terminal can comprise one or more of the following components: processor, storage medium, and display screen. Wherein the storage medium has stored therein at least one instruction that is loaded and executed by the processor to implement the method described in the embodiments below.
The processor may include one or more processing cores. The processor connects various parts within the overall terminal using various interfaces and lines, performs various functions of the terminal and processes data by executing or executing instructions, programs, code sets, or instruction sets stored in the storage medium, and invoking data stored in the storage medium.
The storage medium may include a random access Memory (Random Access Memory, RAM) or a Read-Only Memory (ROM). The storage medium may be used to store instructions, programs, code sets, or instructions.
The display screen is used for displaying a user interface of each application program.
All subscripts in the formula of the invention are only used for distinguishing parameters and have no practical meaning.
In addition, it will be appreciated by those skilled in the art that the structure of the terminal described above is not limiting and that the terminal may include more or fewer components, or may combine certain components, or a different arrangement of components. For example, the terminal further includes components such as a radio frequency circuit, an input unit, a sensor, an audio circuit, a power supply, and the like, which are not described herein.
Example 1
As shown in fig. 1, an embodiment of the present invention provides a method for optimizing a composite collaborative planning for transformation of an existing line traffic network, including:
step 101, obtaining pre-reconstruction information and post-reconstruction information of an existing line traffic network and a plurality of collaborative planning decision variables;
and 102, setting a collaborative planning optimization model, and calculating a collaborative planning comprehensive cost value under each collaborative planning decision variable according to the pre-reconstruction information and the post-reconstruction information.
Specifically, the collaborative planning optimization model includes:
min F(V)＝α 1 C cost (V)+α 2 C safety (V)+α 3 C cong (V)+α 4 C multi (V)
wherein F (V) is a collaborative planning integrated cost value, alpha, when collaborative planning decision variable V (e.g. adding a bus lane, etc.) 1 To reform the weight of the cost, C cost (V) is a modification cost function, alpha 2 Weight of traffic safety cost, C safety (V) is a traffic safety cost function, alpha 3 Weight of congestion cost, C cong (V) is a congestion cost function, alpha 4 Weight of multi-mode traffic cost, C multi (V) is a multimodal traffic cost function.
Specifically, the modification cost function C cost (V) comprises:
wherein C is i_direct Direct cost for traffic network reconstruction through ith collaborative planning decision-making variable, C i_indirect C, indirect cost generated by reconstruction of traffic network through ith collaborative planning decision-making variable i_land For land acquisition cost generated by reconstruction of traffic network through ith collaborative planning decision variable, V i For the ith collaborative planning decision variable, N is the total number of collaborative planning decision variables.
Specifically, the traffic safety cost function C safety (V) comprises:
wherein A is k_before For average accident rate on kth road section or area before traffic network modification, A k_after For average accident rate on kth road section or zone before traffic network modification, C k For cost of modification of traffic on kth road section or area, V k Decision change for kth collaborative planningThe quantity, K, is the total number of road segments or areas.
Specifically, the congestion cost function C cong (V) comprises:
wherein T is j For average traffic congestion time on the jth road segment or area, E j To average energy consumption per hour on the jth road segment or area, P j For traffic flow on the jth road section or zone, V j Decision variables are planned for the J-th co-ordinates, J being the total number of road segments or areas.
Specifically, the multimodal traffic cost function C multi (V) comprises:
wherein C is m_before C for the proportion of the mth traffic mode (such as walking, bicycle, public traffic, etc.) to be used before the traffic network is modified m_after To use the proportion of the mth traffic mode after the traffic network is modified, W m Is the importance weight of the mth traffic mode, V m And (3) for the mth collaborative planning decision variable, M is the total number of traffic modes.
And 103, finding out the minimum collaborative planning comprehensive cost value in all the collaborative planning comprehensive cost values, determining the collaborative planning decision variable corresponding to the minimum collaborative planning comprehensive cost value, and carrying out reconstruction composite collaborative planning optimization on the existing line traffic network according to the collaborative planning decision variable.
Example 2
As shown in fig. 2, the embodiment of the present invention further provides an existing wire-line network transformation composite collaborative planning optimization system, including:
the information acquisition module is used for acquiring pre-reconstruction information and post-reconstruction information of the existing line traffic network and a plurality of collaborative planning decision variables;
the setting model module is used for setting a collaborative planning optimization model and calculating a collaborative planning comprehensive cost value under each collaborative planning decision variable according to the pre-transformation information and the post-transformation information.
Specifically, the collaborative planning optimization model includes:
min F(V)＝α 1 C cost (V)+α 2 C safety (V)+α 3 C cong (V)+α 4 C multi (V)
wherein F (V) is a collaborative planning integrated cost value, alpha, when collaborative planning decision variable V (e.g. adding a bus lane, etc.) 1 To reform the weight of the cost, C cost (V) is a modification cost function, alpha 2 Weight of traffic safety cost, C safety (V) is a traffic safety cost function, alpha 3 Weight of congestion cost, C cong (V) is a congestion cost function, alpha 4 Weight of multi-mode traffic cost, C multi (V) is a multimodal traffic cost function.
Specifically, the modification cost function C cost (V) comprises:
wherein C is i_direct Direct cost for traffic network reconstruction through ith collaborative planning decision-making variable, C i_indirect C, indirect cost generated by reconstruction of traffic network through ith collaborative planning decision-making variable i_land For land acquisition cost generated by reconstruction of traffic network through ith collaborative planning decision variable, V i For the ith collaborative planning decision variable, N is the total number of collaborative planning decision variables.
Specifically, the traffic safety cost function C safety (V) comprises:
wherein A is k_before For average accident rate on kth road section or area before traffic network modification, A k_after For average accident rate on kth road section or zone before traffic network modification, C k For cost of modification of traffic on kth road section or area, V k Decision variables are planned for the kth co-ordinates, K being the total number of road segments or areas.
Specifically, the congestion cost function C cong (V) comprises:
wherein T is j For average traffic congestion time on the jth road segment or area, E j To average energy consumption per hour on the jth road segment or area, P j For traffic flow on the jth road section or zone, V j Decision variables are planned for the J-th co-ordinates, J being the total number of road segments or areas.
Specifically, the multimodal traffic cost function C multi (V) comprises:
wherein C is m_before C for the proportion of the mth traffic mode (such as walking, bicycle, public traffic, etc.) to be used before the traffic network is modified m_after To use the proportion of the mth traffic mode after the traffic network is modified, W m Is the importance weight of the mth traffic mode, V m And (3) for the mth collaborative planning decision variable, M is the total number of traffic modes.
And the optimization module is used for finding out the minimum collaborative planning comprehensive cost value in all the collaborative planning comprehensive cost values, determining the collaborative planning decision variable corresponding to the minimum collaborative planning comprehensive cost value, and carrying out reconstruction composite collaborative planning optimization on the existing line traffic network according to the collaborative planning decision variable.
Example 3
The embodiment of the invention also provides a storage medium which stores a plurality of instructions for realizing the existing line traffic network transformation composite collaborative planning optimization method.
Alternatively, in this embodiment, the storage medium may be located in any one of the computer terminals in the computer terminal group in the computer network, or in any one of the mobile terminals in the mobile terminal group.
Alternatively, in the present embodiment, the storage medium is configured to store program code for performing the steps of: step 101, obtaining pre-reconstruction information and post-reconstruction information of an existing line traffic network and a plurality of collaborative planning decision variables;
and 102, setting a collaborative planning optimization model, and calculating a collaborative planning comprehensive cost value under each collaborative planning decision variable according to the pre-reconstruction information and the post-reconstruction information.
Specifically, the collaborative planning optimization model includes:
min F(V)＝α 1 C cost (V)+α 2 C safety (V)+α 3 C cong (V)+α 4 C multi (V)
wherein F (V) is a collaborative planning integrated cost value, alpha, when collaborative planning decision variable V (e.g. adding a bus lane, etc.) 1 To reform the weight of the cost, C cost (V) is a modification cost function, alpha 2 Weight of traffic safety cost, C safety (V) is a traffic safety cost function, alpha 3 Weight of congestion cost, C cong (V) is a congestion cost function, alpha 4 Weight of multi-mode traffic cost, C multi (V) is a multimodal traffic cost function.
Specifically, the modification cost function C cost (V) comprises:
wherein C is i_direct Direct cost for traffic network reconstruction through ith collaborative planning decision-making variable, C i_indirect C, indirect cost generated by reconstruction of traffic network through ith collaborative planning decision-making variable i_land For land acquisition cost generated by reconstruction of traffic network through ith collaborative planning decision variable, V i For the ith collaborative planning decision variable, N is the total number of collaborative planning decision variables.
Specifically, the traffic safety cost function C safety (V) comprises:
wherein A is k_before For average accident rate on kth road section or area before traffic network modification, A k_after For average accident rate on kth road section or zone before traffic network modification, C k For cost of modification of traffic on kth road section or area, V k Decision variables are planned for the kth co-ordinates, K being the total number of road segments or areas.
Specifically, the congestion cost function C cong (V) comprises:
wherein T is j For average traffic congestion time on the jth road segment or area, E j To average energy consumption per hour on the jth road segment or area, P j For traffic flow on the jth road section or zone, V j Decision variables are planned for the J-th co-ordinates, J being the total number of road segments or areas.
Specifically, the multimodal traffic cost function C multi (V) comprises:
wherein C is m_before C for the proportion of the mth traffic mode (such as walking, bicycle, public traffic, etc.) to be used before the traffic network is modified m_after To use the proportion of the mth traffic mode after the traffic network is modified, W m Is the importance weight of the mth traffic mode, V m And (3) for the mth collaborative planning decision variable, M is the total number of traffic modes.
And 103, finding out the minimum collaborative planning comprehensive cost value in all the collaborative planning comprehensive cost values, determining the collaborative planning decision variable corresponding to the minimum collaborative planning comprehensive cost value, and carrying out reconstruction composite collaborative planning optimization on the existing line traffic network according to the collaborative planning decision variable.
Example 4
The embodiment of the invention also provides electronic equipment, which comprises a processor and a storage medium connected with the processor, wherein the storage medium stores a plurality of instructions, and the instructions can be loaded and executed by the processor so that the processor can execute an existing wire transportation network transformation composite collaborative planning optimization method.
Specifically, the electronic device of the present embodiment may be a computer terminal, and the computer terminal may include: one or more processors, and a storage medium.
The storage medium can be used for storing software programs and modules, such as an existing line traffic network transformation composite collaborative planning optimization method in the embodiment of the invention, corresponding program instructions/modules are executed by the processor, and various functional applications and data processing are executed by the processor through running the software programs and the modules stored in the storage medium, namely the existing line traffic network transformation composite collaborative planning optimization method is realized. The storage medium may include a high-speed random access storage medium, and may also include a non-volatile storage medium, such as one or more magnetic storage systems, flash memory, or other non-volatile solid-state storage medium. In some examples, the storage medium may further include a storage medium remotely located with respect to the processor, and the remote storage medium may be connected to the terminal through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The processor may invoke the information stored in the storage medium and the application program via the transmission system to perform the steps of: step 101, obtaining pre-reconstruction information and post-reconstruction information of an existing line traffic network and a plurality of collaborative planning decision variables;
and 102, setting a collaborative planning optimization model, and calculating a collaborative planning comprehensive cost value under each collaborative planning decision variable according to the pre-reconstruction information and the post-reconstruction information.
Specifically, the collaborative planning optimization model includes:
min F(V)＝α 1 C cost (V)+α 2 C safety (V)+α 3 C cong (V)+α 4 C multi (V)
wherein F (V) is a collaborative planning integrated cost value, alpha, when collaborative planning decision variable V (e.g. adding a bus lane, etc.) 1 To reform the weight of the cost, C cost (V) is a modification cost function, alpha 2 Weight of traffic safety cost, C safety (V) is a traffic safety cost function, alpha 3 Weight of congestion cost, C cong (V) is a congestion cost function, alpha 4 Weight of multi-mode traffic cost, C multi (V) is a multimodal traffic cost function.
Specifically, the modification cost function C cost (V) comprises:
wherein C is i_direct Direct cost for traffic network reconstruction through ith collaborative planning decision-making variable, C i_indirect C, indirect cost generated by reconstruction of traffic network through ith collaborative planning decision-making variable i_land Traffic network for decision variables through ith collaborative planningLand acquisition cost, V i For the ith collaborative planning decision variable, N is the total number of collaborative planning decision variables.
Specifically, the traffic safety cost function C safety (V) comprises:
wherein A is k_before For average accident rate on kth road section or area before traffic network modification, A k_after For average accident rate on kth road section or zone before traffic network modification, C k For cost of modification of traffic on kth road section or area, V k Decision variables are planned for the kth co-ordinates, K being the total number of road segments or areas.
Specifically, the congestion cost function C cong (V) comprises:
wherein T is j For average traffic congestion time on the jth road segment or area, E j To average energy consumption per hour on the jth road segment or area, P j For traffic flow on the jth road section or zone, V j Decision variables are planned for the J-th co-ordinates, J being the total number of road segments or areas.
Specifically, the multimodal traffic cost function C multi (V) comprises:
wherein C is m_before C for the proportion of the mth traffic mode (such as walking, bicycle, public traffic, etc.) to be used before the traffic network is modified m_after To use the proportion of the mth traffic mode after the traffic network is modified, W m Is the importance weight of the mth traffic mode, V m And (3) for the mth collaborative planning decision variable, M is the total number of traffic modes.
And 103, finding out the minimum collaborative planning comprehensive cost value in all the collaborative planning comprehensive cost values, determining the collaborative planning decision variable corresponding to the minimum collaborative planning comprehensive cost value, and carrying out reconstruction composite collaborative planning optimization on the existing line traffic network according to the collaborative planning decision variable.
The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
In the foregoing embodiments of the present invention, the descriptions of the embodiments are emphasized, and for a portion of this disclosure that is not described in detail in this embodiment, reference is made to the related descriptions of other embodiments.
In the embodiments provided in the present invention, it should be understood that the disclosed technology may be implemented in other manners. The system embodiments described above are merely exemplary, and for example, the division of the units is merely a logic function division, and there may be another division manner in actual implementation, for example, 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 through some interfaces, units or modules, or may be in electrical 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 on 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 invention 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 integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied essentially or partly in the form of a software product or all or part of the technical solution, which is stored in a storage medium, and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform 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 Read-Only Memory (ROM), a random-access Memory (RAM, random Access Memory), a removable hard disk, a magnetic disk, or an optical disk, or the like, which can store program codes.
It is apparent that the above examples are given by way of illustration only and are not limiting of the embodiments. Other variations or modifications of the above teachings will be apparent to those of ordinary skill in the art. It is not necessary here nor is it exhaustive of all embodiments. While still being apparent from variations or modifications that may be made by those skilled in the art are within the scope of the invention.
Claims (10)
1. The existing line traffic network transformation composite collaborative planning optimization method is characterized by comprising the following steps of:
acquiring pre-transformation information and post-transformation information of an existing line traffic network, and a plurality of collaborative planning decision variables;
setting a collaborative planning optimization model, and calculating a collaborative planning comprehensive cost value under each collaborative planning decision variable according to the pre-transformation information and the post-transformation information.
And finding out the minimum collaborative planning comprehensive cost value in all the collaborative planning comprehensive cost values, determining the collaborative planning decision variable corresponding to the minimum collaborative planning comprehensive cost value, and carrying out reconstruction composite collaborative planning optimization on the existing line traffic network according to the collaborative planning decision variable.
2. The method for optimizing existing line traffic network transformation composite collaborative planning according to claim 1, wherein the collaborative planning optimization model comprises:
min F(V)＝α 1 C cost (V)+α 2 C safety (V)+α 3 C cong (V)+α 4 C multi (V)
wherein F (V) is a collaborative planning integrated cost value, alpha, when the collaborative planning decision variable V is 1 To reform the weight of the cost, C cost (V) is a modification cost function, alpha 2 Weight of traffic safety cost, C safety (V) is a traffic safety cost function, alpha 3 Weight of congestion cost, C cong (V) is a congestion cost function, alpha 4 Weight of multi-mode traffic cost, C multi (V) is a multimodal traffic cost function.
3. The method for optimizing existing line traffic network transformation composite collaborative planning according to claim 2, wherein the transformation cost function C cost (V) comprises:
wherein C is i_direct Direct cost for traffic network reconstruction through ith collaborative planning decision-making variable, C i_indirect C, indirect cost generated by reconstruction of traffic network through ith collaborative planning decision-making variable i_land For land acquisition cost generated by reconstruction of traffic network through ith collaborative planning decision variable, V i For the ith collaborative planning decision variable, N is the total number of collaborative planning decision variables.
4. The method for optimizing existing wire-line network transformation composite collaborative planning according to claim 2, wherein the traffic safety cost function C safety (V) comprises:
wherein A is k_before For average accident rate on kth road section or area before traffic network modification, A k_after For average accident rate on kth road section or zone before traffic network modification, C k For cost of modification of traffic on kth road section or area, V k Decision variables are planned for the kth co-ordinates, K being the total number of road segments or areas.
5. The method for optimizing existing line traffic network transformation composite collaborative planning according to claim 2, wherein the congestion cost function C cong (V) comprises:
wherein T is j For average traffic congestion time on the jth road segment or area, E j To average energy consumption per hour on the jth road segment or area, P j For traffic flow on the jth road section or zone, V j Decision variables are planned for the J-th co-ordinates, J being the total number of road segments or areas.
6. The method for optimizing existing wire-line network transformation composite collaborative planning according to claim 2, wherein the multi-modal traffic cost function C multi (V) comprises:
wherein C is m_before C for using the proportion of the mth traffic mode before the traffic network is modified m_after To use the proportion of the mth traffic mode after the traffic network is modified, W m Is the importance weight of the mth traffic mode, V m And (3) for the mth collaborative planning decision variable, M is the total number of traffic modes.
7. An existing line traffic network transformation composite collaborative planning optimization system is characterized by comprising:
the information acquisition module is used for acquiring pre-reconstruction information and post-reconstruction information of the existing line traffic network and a plurality of collaborative planning decision variables;
the setting model module is used for setting a collaborative planning optimization model and calculating a collaborative planning comprehensive cost value under each collaborative planning decision variable according to the pre-transformation information and the post-transformation information.
And the optimization module is used for finding out the minimum collaborative planning comprehensive cost value in all the collaborative planning comprehensive cost values, determining the collaborative planning decision variable corresponding to the minimum collaborative planning comprehensive cost value, and carrying out reconstruction composite collaborative planning optimization on the existing line traffic network according to the collaborative planning decision variable.
8. The existing wire-line network retrofit composite collaborative planning optimization system of claim 7, wherein said collaborative planning optimization model comprises:
min F(V)＝α 1 C cost (V)+α 2 C safety (V)+α 3 C cong (V)+α 4 C multi (V)
wherein F (V) is a collaborative planning integrated cost value, alpha, when the collaborative planning decision variable V is 1 To reform the weight of the cost, C cost (V) is a modification cost function, alpha 2 Weight of traffic safety cost, C safety (V) is a traffic safety cost function, alpha 3 Weight of congestion cost, C cong (V) is a congestion cost function, alpha 4 Weight of multi-mode traffic cost, C multi (V) is a multimodal traffic cost function.
9. An existing wire-line network retrofit composite collaborative planning optimization system according to claim 8 wherein said retrofit cost function C cost (V) comprises:
wherein C is i_direct Direct cost for traffic network reconstruction through ith collaborative planning decision-making variable, C i_indirect C, indirect cost generated by reconstruction of traffic network through ith collaborative planning decision-making variable i_land For land acquisition cost generated by reconstruction of traffic network through ith collaborative planning decision variable, V i For the ith collaborative planning decision variable, N is the total number of collaborative planning decision variables.
10. An existing wire-line network retrofit composite collaborative planning optimization system according to claim 8 wherein said traffic safety cost function C safety (V) comprises:
wherein A is k_before For average accident rate on kth road section or area before traffic network modification, A k_after For average accident rate on kth road section or zone before traffic network modification, C k For cost of modification of traffic on kth road section or area, V k Decision variables are planned for the kth co-ordinates, K being the total number of road segments or areas.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311476938.6A CN117474736B (en) | 2023-11-07 | 2023-11-07 | Existing line traffic network transformation composite collaborative planning optimization method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311476938.6A CN117474736B (en) | 2023-11-07 | 2023-11-07 | Existing line traffic network transformation composite collaborative planning optimization method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117474736A true CN117474736A (en) | 2024-01-30 |
| CN117474736B CN117474736B (en) | 2024-06-07 |
Family
ID=89623473
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311476938.6A Active CN117474736B (en) | 2023-11-07 | 2023-11-07 | Existing line traffic network transformation composite collaborative planning optimization method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117474736B (en) |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108681788A (en) * | 2018-04-27 | 2018-10-19 | 东南大学 | A kind of city discrete network design problem method based on active safety |
| CN112614366A (en) * | 2020-12-11 | 2021-04-06 | 国汽(北京)智能网联汽车研究院有限公司 | Automobile collaborative decision method and device, electronic equipment and computer storage medium |
| CN116524715A (en) * | 2023-04-17 | 2023-08-01 | 东南大学 | Rolling double-layer planning method combining trunk line green wave coordination and emergency path decision |
| CN116822769A (en) * | 2023-06-29 | 2023-09-29 | 郑州轻工业大学 | Intelligent traffic route optimizing system based on artificial intelligence |
-
2023
- 2023-11-07 CN CN202311476938.6A patent/CN117474736B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108681788A (en) * | 2018-04-27 | 2018-10-19 | 东南大学 | A kind of city discrete network design problem method based on active safety |
| CN112614366A (en) * | 2020-12-11 | 2021-04-06 | 国汽(北京)智能网联汽车研究院有限公司 | Automobile collaborative decision method and device, electronic equipment and computer storage medium |
| CN116524715A (en) * | 2023-04-17 | 2023-08-01 | 东南大学 | Rolling double-layer planning method combining trunk line green wave coordination and emergency path decision |
| CN116822769A (en) * | 2023-06-29 | 2023-09-29 | 郑州轻工业大学 | Intelligent traffic route optimizing system based on artificial intelligence |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117474736B (en) | 2024-06-07 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110084377B (en) | Method and device for constructing decision tree | |
| CN104299151A (en) | Online advertising flow distribution system and method | |
| CN106997577A (en) | A kind of order strategy dynamic adjusting method and device | |
| CN108960797B (en) | Block generation and verification method, device, equipment and storage medium | |
| CN116485475A (en) | Internet of things advertisement system, method and device based on edge calculation | |
| CN103995749B (en) | The calculation task allocating method and system of cell cloud system | |
| CN111934315A (en) | Source network load storage cooperative optimization operation method considering demand side and terminal equipment | |
| CN113837631B (en) | Employee evaluation method and device, electronic equipment and readable storage medium | |
| CN110992123B (en) | Order distribution method and device | |
| CN117474736B (en) | Existing line traffic network transformation composite collaborative planning optimization method and system | |
| CN110489142A (en) | Appraisal procedure and device, storage medium, the terminal of equipment software upgrading | |
| CN117539619A (en) | Computing power scheduling method, system, equipment and storage medium based on cloud edge fusion | |
| CN107679635A (en) | Client's follow-up method, system and readable storage medium storing program for executing based on priority | |
| CN104933110B (en) | A kind of data prefetching method based on MapReduce | |
| CN111429237A (en) | Order price determining method and device, server and storage medium | |
| CN114492849B (en) | Model updating method and device based on federal learning | |
| CN110135918A (en) | A kind of running time exchanging method and device | |
| CN113409571B (en) | Judging method and device for setting bus lane, storage medium and terminal | |
| CN110598930B (en) | Bus route optimization adjustment method and device | |
| CN117474735B (en) | Rail transit network and city planning fusion evaluation method and system | |
| CN113554300A (en) | Shared parking space real-time allocation method based on deep reinforcement learning | |
| CN117474734B (en) | Existing line transformation and urban line network line and station multi-element fusion method and system | |
| CN114091263B (en) | Dual-tie-line power grid power transmission and reception capacity simulation analysis method, system and storage medium | |
| CN115964499B (en) | Knowledge graph-based social management event mining method and device | |
| CN113657488B (en) | User grading method, device, equipment and storage medium based on driving behaviors |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant |