CN117474736B - 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 PDF

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CN117474736B
CN117474736B CN202311476938.6A CN202311476938A CN117474736B CN 117474736 B CN117474736 B CN 117474736B CN 202311476938 A CN202311476938 A CN 202311476938A CN 117474736 B CN117474736 B CN 117474736B
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徐成永
贺鹏
李天石
仲莹萤
叶轩
刘畅
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Beijing Urban Construction Design and Development Group Co Ltd
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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

Existing line traffic network transformation composite collaborative planning optimization method and system
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)=α1Ccost(V)+α2Csafety(V)+α3Ccong(V)+α4Cmulti(V)
Wherein F (V) is a collaborative planning comprehensive cost value when collaborative planning decision variable V, alpha 1 is a weight of reconstruction cost, C cost (V) is a reconstruction cost function, alpha 2 is a weight of traffic safety cost, C safety (V) is a traffic safety cost function, alpha 3 is a weight of congestion cost, C cong (V) is a congestion cost function, alpha 4 is a weight of multi-mode traffic cost, and C multi (V) is a multi-mode traffic cost function.
Further, the modification cost function C cost (V) includes:
Wherein, C i_direct is the direct cost generated by the reconstruction of the traffic network through the ith cooperative planning decision variable, C i_indirect is the indirect cost generated by the reconstruction of the traffic network through the ith cooperative planning decision variable, C i_land is the land acquisition cost generated by the reconstruction of the traffic network through the ith cooperative planning decision variable, V i is the ith cooperative planning decision variable, and N is the total number of the cooperative planning decision variables.
Further, the traffic safety cost function C safety (V) includes:
wherein, A k_before is the average accident rate on the kth road section or area before the traffic network is reformed, A k_after is the average accident rate on the kth road section or area before the traffic network is reformed, C k is the traffic reforming cost on the kth road section or area, V k is the kth collaborative planning decision variable, and K is the total number of road sections or areas.
Further, the congestion cost function C cong (V) includes:
Where T j is the average traffic congestion time on the jth road segment or area, E j is the average energy consumption per hour on the jth road segment or area, P j is the traffic flow on the jth road segment or area, V j is the jth collaborative planning decision variable, and J is the total number of road segments or areas.
Further, the multi-modal traffic cost function C multi (V) includes:
Wherein, C m_before is the proportion of the mth traffic mode used before the traffic network is modified, C m_after is the proportion of the mth traffic mode used after the traffic network is modified, W m is the importance weight of the mth traffic mode, V m is the mth collaborative planning decision variable, and 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)=α1Ccost(V)+α2Csafety(V)+α3Ccong(V)+α4Cmulti(V)
Wherein F (V) is a collaborative planning comprehensive cost value when collaborative planning decision variable V, alpha 1 is a weight of reconstruction cost, C cost (V) is a reconstruction cost function, alpha 2 is a weight of traffic safety cost, C safety (V) is a traffic safety cost function, alpha 3 is a weight of congestion cost, C cong (V) is a congestion cost function, alpha 4 is a weight of multi-mode traffic cost, and C multi (V) is a multi-mode traffic cost function.
Further, the modification cost function C cost (V) includes:
Wherein, C i_direct is the direct cost generated by the reconstruction of the traffic network through the ith cooperative planning decision variable, C i_indirect is the indirect cost generated by the reconstruction of the traffic network through the ith cooperative planning decision variable, C i_land is the land acquisition cost generated by the reconstruction of the traffic network through the ith cooperative planning decision variable, V i is the ith cooperative planning decision variable, and N is the total number of the cooperative planning decision variables.
Further, the traffic safety cost function C safety (V) includes:
wherein, A k_before is the average accident rate on the kth road section or area before the traffic network is reformed, A k_after is the average accident rate on the kth road section or area before the traffic network is reformed, C k is the traffic reforming cost on the kth road section or area, V k is the kth collaborative planning decision variable, and K is the total number of road sections 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)=α1Ccost(V)+α2Csafety(V)+α3Ccong(V)+α4Cmulti(V)
Wherein F (V) is a collaborative planning integrated cost value when collaborative planning decision variables V (e.g., adding a bus lane, etc.), α 1 is a weight of reconstruction cost, C cost (V) is a reconstruction cost function, α 2 is a weight of traffic safety cost, C safety (V) is a traffic safety cost function, α 3 is a weight of congestion cost, C cong (V) is a congestion cost function, α 4 is a weight of multi-mode traffic cost, and C multi (V) is a multi-mode traffic cost function.
Specifically, the modification cost function C cost (V) includes:
Wherein, C i_direct is the direct cost generated by the reconstruction of the traffic network through the ith cooperative planning decision variable, C i_indirect is the indirect cost generated by the reconstruction of the traffic network through the ith cooperative planning decision variable, C i_land is the land acquisition cost generated by the reconstruction of the traffic network through the ith cooperative planning decision variable, V i is the ith cooperative planning decision variable, and N is the total number of the cooperative planning decision variables.
Specifically, the traffic safety cost function C safety (V) includes:
wherein, A k_before is the average accident rate on the kth road section or area before the traffic network is reformed, A k_after is the average accident rate on the kth road section or area before the traffic network is reformed, C k is the traffic reforming cost on the kth road section or area, V k is the kth collaborative planning decision variable, and K is the total number of road sections or areas.
Specifically, the congestion cost function C cong (V) includes:
Where T j is the average traffic congestion time on the jth road segment or area, E j is the average energy consumption per hour on the jth road segment or area, P j is the traffic flow on the jth road segment or area, V j is the jth collaborative planning decision variable, and J is the total number of road segments or areas.
Specifically, the multi-modal traffic cost function C multi (V) includes:
Wherein, C m_before is the proportion of the mth traffic mode (such as walking, bicycle, public traffic, etc.) used before the traffic network is reformed, C m_after is the proportion of the mth traffic mode used after the traffic network is reformed, W m is the importance weight of the mth traffic mode, V m is the mth collaborative planning decision variable, and 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)=α1Ccost(V)+α2Csafety(V)+α3Ccong(V)+α4Cmulti(V)
Wherein F (V) is a collaborative planning integrated cost value when collaborative planning decision variables V (e.g., adding a bus lane, etc.), α 1 is a weight of reconstruction cost, C cost (V) is a reconstruction cost function, α 2 is a weight of traffic safety cost, C safety (V) is a traffic safety cost function, α 3 is a weight of congestion cost, C cong (V) is a congestion cost function, α 4 is a weight of multi-mode traffic cost, and C multi (V) is a multi-mode traffic cost function.
Specifically, the modification cost function C cost (V) includes:
Wherein, C i_direct is the direct cost generated by the reconstruction of the traffic network through the ith cooperative planning decision variable, C i_indirect is the indirect cost generated by the reconstruction of the traffic network through the ith cooperative planning decision variable, C i_land is the land acquisition cost generated by the reconstruction of the traffic network through the ith cooperative planning decision variable, V i is the ith cooperative planning decision variable, and N is the total number of the cooperative planning decision variables.
Specifically, the traffic safety cost function C safety (V) includes:
wherein, A k_before is the average accident rate on the kth road section or area before the traffic network is reformed, A k_after is the average accident rate on the kth road section or area before the traffic network is reformed, C k is the traffic reforming cost on the kth road section or area, V k is the kth collaborative planning decision variable, and K is the total number of road sections or areas.
Specifically, the congestion cost function C cong (V) includes:
Where T j is the average traffic congestion time on the jth road segment or area, E j is the average energy consumption per hour on the jth road segment or area, P j is the traffic flow on the jth road segment or area, V j is the jth collaborative planning decision variable, and J is the total number of road segments or areas.
Specifically, the multi-modal traffic cost function C multi (V) includes:
Wherein, C m_before is the proportion of the mth traffic mode (such as walking, bicycle, public traffic, etc.) used before the traffic network is reformed, C m_after is the proportion of the mth traffic mode used after the traffic network is reformed, W m is the importance weight of the mth traffic mode, V m is the mth collaborative planning decision variable, and 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)=α1Ccost(V)+α2Csafety(V)+α3Ccong(V)+α4Cmulti(V)
Wherein F (V) is a collaborative planning integrated cost value when collaborative planning decision variables V (e.g., adding a bus lane, etc.), α 1 is a weight of reconstruction cost, C cost (V) is a reconstruction cost function, α 2 is a weight of traffic safety cost, C safety (V) is a traffic safety cost function, α 3 is a weight of congestion cost, C cong (V) is a congestion cost function, α 4 is a weight of multi-mode traffic cost, and C multi (V) is a multi-mode traffic cost function.
Specifically, the modification cost function C cost (V) includes:
Wherein, C i_direct is the direct cost generated by the reconstruction of the traffic network through the ith cooperative planning decision variable, C i_indirect is the indirect cost generated by the reconstruction of the traffic network through the ith cooperative planning decision variable, C i_land is the land acquisition cost generated by the reconstruction of the traffic network through the ith cooperative planning decision variable, V i is the ith cooperative planning decision variable, and N is the total number of the cooperative planning decision variables.
Specifically, the traffic safety cost function C safety (V) includes:
wherein, A k_before is the average accident rate on the kth road section or area before the traffic network is reformed, A k_after is the average accident rate on the kth road section or area before the traffic network is reformed, C k is the traffic reforming cost on the kth road section or area, V k is the kth collaborative planning decision variable, and K is the total number of road sections or areas.
Specifically, the congestion cost function C cong (V) includes:
Where T j is the average traffic congestion time on the jth road segment or area, E j is the average energy consumption per hour on the jth road segment or area, P j is the traffic flow on the jth road segment or area, V j is the jth collaborative planning decision variable, and J is the total number of road segments or areas.
Specifically, the multi-modal traffic cost function C multi (V) includes:
Wherein, C m_before is the proportion of the mth traffic mode (such as walking, bicycle, public traffic, etc.) used before the traffic network is reformed, C m_after is the proportion of the mth traffic mode used after the traffic network is reformed, W m is the importance weight of the mth traffic mode, V m is the mth collaborative planning decision variable, and 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)=α1Ccost(V)+α2Csafety(V)+α3Ccong(V)+α4Cmulti(V)
Wherein F (V) is a collaborative planning integrated cost value when collaborative planning decision variables V (e.g., adding a bus lane, etc.), α 1 is a weight of reconstruction cost, C cost (V) is a reconstruction cost function, α 2 is a weight of traffic safety cost, C safety (V) is a traffic safety cost function, α 3 is a weight of congestion cost, C cong (V) is a congestion cost function, α 4 is a weight of multi-mode traffic cost, and C multi (V) is a multi-mode traffic cost function.
Specifically, the modification cost function C cost (V) includes:
Wherein, C i_direct is the direct cost generated by the reconstruction of the traffic network through the ith cooperative planning decision variable, C i_indirect is the indirect cost generated by the reconstruction of the traffic network through the ith cooperative planning decision variable, C i_land is the land acquisition cost generated by the reconstruction of the traffic network through the ith cooperative planning decision variable, V i is the ith cooperative planning decision variable, and N is the total number of the cooperative planning decision variables.
Specifically, the traffic safety cost function C safety (V) includes:
wherein, A k_before is the average accident rate on the kth road section or area before the traffic network is reformed, A k_after is the average accident rate on the kth road section or area before the traffic network is reformed, C k is the traffic reforming cost on the kth road section or area, V k is the kth collaborative planning decision variable, and K is the total number of road sections or areas.
Specifically, the congestion cost function C cong (V) includes:
Where T j is the average traffic congestion time on the jth road segment or area, E j is the average energy consumption per hour on the jth road segment or area, P j is the traffic flow on the jth road segment or area, V j is the jth collaborative planning decision variable, and J is the total number of road segments or areas.
Specifically, the multi-modal traffic cost function C multi (V) includes:
Wherein, C m_before is the proportion of the mth traffic mode (such as walking, bicycle, public traffic, etc.) used before the traffic network is reformed, C m_after is the proportion of the mth traffic mode used after the traffic network is reformed, W m is the importance weight of the mth traffic mode, V m is the mth collaborative planning decision variable, and 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, etc., 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 (2)

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, wherein the collaborative planning optimization model comprises:
minF(V)=α1Ccost(V)+α2Csafety(V)+α3Ccong(V)+α4Cmulti(V)
Wherein F (V) is a collaborative planning comprehensive cost value when a collaborative planning decision variable V is used, alpha 1 is a weight of reconstruction cost, C cost (V) is a reconstruction cost function, alpha 2 is a weight of traffic safety cost, C safety (V) is a traffic safety cost function, alpha 3 is a weight of congestion cost, C ong (V) is a congestion cost function, alpha 4 is a weight of multi-mode traffic cost, and C multi (V) is a multi-mode traffic cost function;
The modification cost function C cost (V) includes:
Wherein, C i_direct is the direct cost generated by the reconstruction of the traffic network through the ith cooperative planning decision variable, C i_indirect is the indirect cost generated by the reconstruction of the traffic network through the ith cooperative planning decision variable, C i_land is the land acquisition cost generated by the reconstruction of the traffic network through the ith cooperative planning decision variable, V i is the ith cooperative planning decision variable, and N is the total number of the cooperative planning decision variables;
The traffic safety cost function C safety (V) includes:
wherein, A k_before is the average accident rate on the kth road section or area before the traffic network is reformed, A k_after is the average accident rate on the kth road section or area before the traffic network is reformed, C k is the traffic reforming cost on the kth road section or area, V k is the kth collaborative planning decision variable, K is the total number of road sections or areas;
the congestion cost function C cong (V) includes:
Wherein T j is the average traffic congestion time on the jth road segment or area, F j is the average energy consumption per hour on the jth road segment or area, p j is the traffic flow on the jth road segment or area, V j is the jth collaborative planning decision variable, J is the total number of road segments or areas;
the multimodal traffic cost function C multi (V) includes:
Wherein, C m_before is the proportion of the mth traffic mode used before the traffic network is modified, C m_after is the proportion of the mth traffic mode used after the traffic network is modified, W m is the importance weight of the mth traffic mode, V m is the mth collaborative planning decision variable, and M is the total number of traffic modes;
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. 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, wherein the collaborative planning optimization model comprises:
minF(V)=α1Ccost(V)+α2Csafety(V)+α3Vvong(V)+α4Cmulti(V)
Wherein F (V) is a collaborative planning comprehensive cost value when a collaborative planning decision variable V is used, alpha 1 is a weight of reconstruction cost, C cost (V) is a reconstruction cost function, alpha 2 is a weight of traffic safety cost, C safety (V) is a traffic safety cost function, alpha 3 is a weight of congestion cost, C cong (V) is a congestion cost function, alpha 4 is a weight of multi-mode traffic cost, and C multi (V) is a multi-mode traffic cost function;
The modification cost function C cost (V) includes:
Wherein, C i_direct is the direct cost generated by the reconstruction of the traffic network through the ith cooperative planning decision variable, C i_indirect is the indirect cost generated by the reconstruction of the traffic network through the ith cooperative planning decision variable, C i_land is the land acquisition cost generated by the reconstruction of the traffic network through the ith cooperative planning decision variable, V i is the ith cooperative planning decision variable, and N is the total number of the cooperative planning decision variables;
The traffic safety cost function C safety (V) includes:
wherein, A k_before is the average accident rate on the kth road section or area before the traffic network is reformed, A k_after is the average accident rate on the kth road section or area before the traffic network is reformed, C k is the traffic reforming cost on the kth road section or area, V k is the kth collaborative planning decision variable, K is the total number of road sections or areas;
the congestion cost function C cong (V) includes:
Wherein T j is the average traffic congestion time on the jth road segment or area, F j is the average energy consumption per hour on the jth road segment or area, p j is the traffic flow on the jth road segment or area, V j is the jth collaborative planning decision variable, J is the total number of road segments or areas;
the multimodal traffic cost function C multi (V) includes:
Wherein, C m_before is the proportion of the mth traffic mode used before the traffic network is modified, C m_after is the proportion of the mth traffic mode used after the traffic network is modified, W m is the importance weight of the mth traffic mode, V m is the mth collaborative planning decision variable, and 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.
CN202311476938.6A 2023-11-07 2023-11-07 Existing line traffic network transformation composite collaborative planning optimization method and system Active CN117474736B (en)

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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

Patent Citations (4)

* Cited by examiner, † Cited by third party
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
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