CN114066001B - Traffic service optimization method, system, terminal and medium based on travel intrinsic - Google Patents
Traffic service optimization method, system, terminal and medium based on travel intrinsic Download PDFInfo
- Publication number
- CN114066001B CN114066001B CN202111124961.XA CN202111124961A CN114066001B CN 114066001 B CN114066001 B CN 114066001B CN 202111124961 A CN202111124961 A CN 202111124961A CN 114066001 B CN114066001 B CN 114066001B
- Authority
- CN
- China
- Prior art keywords
- traffic
- data
- travel
- scheme
- intrinsic
- 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.)
- Active
Links
- 238000005457 optimization Methods 0.000 title claims abstract description 79
- 238000000034 method Methods 0.000 title claims abstract description 45
- 238000012216 screening Methods 0.000 claims abstract description 20
- 230000000694 effects Effects 0.000 claims description 41
- 238000004458 analytical method Methods 0.000 claims description 24
- 230000008859 change Effects 0.000 claims description 10
- 238000011156 evaluation Methods 0.000 claims description 10
- 238000004088 simulation Methods 0.000 claims description 5
- 230000006399 behavior Effects 0.000 claims description 3
- 230000007547 defect Effects 0.000 abstract 1
- 230000005284 excitation Effects 0.000 description 8
- 230000008569 process Effects 0.000 description 7
- 238000012545 processing Methods 0.000 description 7
- 238000007726 management method Methods 0.000 description 5
- 230000009286 beneficial effect Effects 0.000 description 3
- 238000010276 construction Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 238000000605 extraction Methods 0.000 description 3
- 238000013439 planning Methods 0.000 description 3
- 238000000638 solvent extraction Methods 0.000 description 3
- 238000004590 computer program Methods 0.000 description 2
- 238000007405 data analysis Methods 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 238000013468 resource allocation Methods 0.000 description 2
- 239000000919 ceramic Substances 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000002349 favourable effect Effects 0.000 description 1
- 238000007667 floating Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000010223 real-time analysis Methods 0.000 description 1
- 230000001105 regulatory effect Effects 0.000 description 1
- 230000003014 reinforcing effect Effects 0.000 description 1
- 230000000007 visual effect 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
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- 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—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
Abstract
The application relates to the technical field of urban public transportation, in particular to a travel intrinsic-based traffic service optimization method, a travel intrinsic-based traffic service optimization system, a travel intrinsic-based traffic service optimization terminal and a travel intrinsic-based traffic service medium, and aims to overcome the defect that in the prior art, management and control efficiency of traffic services in cities is low. Acquiring traffic intrinsic data; acquiring travel demand characteristics of residents in an initial area by analyzing the traffic intrinsic data; extracting a travel scheme between the initial area and the target area, and measuring and calculating simulated traffic data corresponding to the travel scheme; acquiring actual traffic data, comparing the actual traffic data with simulated traffic data, screening out the travel scheme exceeding the simulated traffic data, and marking the travel scheme as a problem scheme; the method comprises the steps of obtaining the remaining travel schemes between the starting area and the target area except the problem scheme and marking the travel schemes as priority schemes.
Description
Technical Field
The application relates to the technical field of urban public transportation, in particular to a travel intrinsic-based traffic service optimization method, system, terminal and medium.
Background
Urban traffic refers to public travel and passenger and cargo transportation between different road systems in cities, including urban and suburban areas. With the development of social productivity and the progress of technology level, people gather in cities, and the urban traffic system becomes huge and complex, so that the urban resident traffic travel demands can be better met, and the urban traffic development process becomes a difficult problem.
The configuration of traffic resources is one of important management links in an urban traffic system and is used for meeting the traffic travel demands of urban residents, and mainly comprises the planning construction of a highway network, the setting of public transportation lines and stations, the planning construction of rail traffic and the construction and deployment of other traffic facilities such as shared bicycles.
Generally, in order to meet the travel demands of urban residents, urban traffic service providers need to analyze the travel demands of the urban residents through collected traffic data, and according to the travel demands of the urban residents, the configuration of public traffic resources in the cities is adjusted, and the effects of relieving urban traffic jams and improving urban living environments are achieved through methods such as adding public traffic lines, building rail traffic and the like.
With respect to the related art in the above, the inventors consider that there are the following drawbacks: the selection of the travel routes of residents in the city is simply determined by the intention of the residents, so that the randomness is realized, and the management and control efficiency of traffic services in the city is low.
Disclosure of Invention
In order to guide the selection of traffic modes when residents travel, improve the efficiency of traffic control and further improve the traveling situation of urban residents, the application provides a traffic service optimization method, a traffic service optimization system, a traffic service optimization terminal and a traffic service optimization medium based on traveling characteristics.
In a first aspect, the present application provides a travel intrinsic-based traffic service optimization method, which adopts the following technical scheme:
a travel intrinsic based traffic service optimization method, the method comprising the steps of:
acquiring traffic intrinsic data, wherein the traffic intrinsic data at least comprises infrastructure data, resident trip data and traffic passing data related to traffic;
acquiring travel demand characteristics of residents in an initial area by analyzing the traffic intrinsic data, wherein the travel demand characteristics at least comprise a target area and demand intensity;
based on the infrastructure data, extracting a travel scheme between a starting area and a target area, and calculating simulated traffic data corresponding to the travel scheme according to the infrastructure data;
based on the resident trip data and the traffic data, acquiring actual traffic data corresponding to the trip scheme, comparing the actual traffic data with the simulated traffic data, screening out the trip scheme exceeding the simulated traffic data, and marking the trip scheme as a problem scheme;
and acquiring the rest travel schemes except the problem scheme between the starting region and the target region, and marking the travel schemes as priority schemes.
By adopting the technical scheme, the travel demand characteristics of residents are obtained after the intrinsic traffic data are analyzed, all travel schemes based on the demand characteristics are obtained, and further, selectable travel schemes of the residents are screened, travel schemes exceeding the actual traffic data by the simulated traffic data are screened, the simulated traffic data actually represent good traffic states of roads or traffic facilities involved in the travel schemes embodied in a data form, and therefore, the problem schemes obtained through screening are poor in running state; the priority scheme is acquired after the problem solving scheme is eliminated, so that the priority scheme is extracted from all travel schemes, and then the travel of residents is guided according to the priority scheme, so that the efficiency of traffic control is improved, and the urban traffic condition is improved.
Optionally, the acquiring the traffic intrinsic data includes:
periodically acquiring original traffic data in various fields related to traffic, and extracting traffic intrinsic data from the original traffic data;
and/or the number of the groups of groups,
and directly acquiring the traffic intrinsic data through an external network.
By adopting the technical scheme, the traffic intrinsic data can be directly acquired through an external network besides analyzing and acquiring the original traffic data, so that the source of the traffic intrinsic data is enlarged, the acquisition efficiency of the traffic intrinsic data is improved, the data volume of the traffic intrinsic data is increased, and the accuracy of the analysis result of the traffic intrinsic data is improved.
Optionally, the step of periodically acquiring the original traffic data of each field related to traffic, and after extracting the traffic intrinsic data from the original traffic data, further includes:
dividing an area covered by traffic service to generate a plurality of traffic cells;
and grouping the original traffic data by taking a traffic cell as a unit.
By adopting the technical scheme, the traffic data dispersed in the large traffic service coverage area can be divided, and the analysis is carried out by taking the smaller traffic cells as units, so that the correlation among the traffic cells can be analyzed, the analysis difficulty of the original traffic data is reduced, and the acquisition efficiency of the traffic intrinsic data is improved.
Optionally, the traffic intrinsic data further includes:
other influencing characteristics having an influence on the travel of the traffic, including at least weather characteristics, holidays.
By adopting the technical scheme, other influence characteristics related to traffic are added into the analysis of traffic conditions, so that the comprehensiveness of traffic data analysis is improved, and the accuracy of analysis results is improved.
Optionally, after the trip schemes between the starting area and the target area are obtained and the trip schemes are marked as priority schemes, the method further includes:
acquiring travel incentive measures, wherein the travel incentive measures are set as rewards which are available to residents when the residents execute a priority scheme;
and combining the priority scheme with travel incentive measures to generate a demand optimization scheme.
Through adopting above-mentioned technical scheme, through actual excitation strategy, initiative guide resident selects the priority scheme that obtains through above-mentioned step, helps reinforcing the implementation effect of trip demand optimization scheme, has improved the effect of traffic control, is favorable to improving resident's trip condition finally.
Optionally, after the combination of the priority scheme and the trip excitation measures and the generation of the requirement optimization scheme, the method further includes:
the traffic intrinsic data after the demand optimization scheme is generated is obtained regularly and marked as feedback traffic data;
and based on the feedback traffic data, performing effect evaluation on the demand optimization scheme to acquire an effect index corresponding to the demand optimization scheme.
By adopting the technical scheme, the traffic intrinsic data after implementing the demand optimization scheme is periodically acquired and analyzed, so that the implementation effect of the demand optimization scheme is evaluated by taking actual data as a basis, and the accuracy of an evaluation result is improved.
Optionally, based on the feedback traffic data, performing effect evaluation on the demand optimization scheme, and after obtaining an effect index corresponding to the demand optimization scheme, further includes:
screening out a specified requirement optimization scheme with the effect index lower than a preset effect threshold value;
and adjusting travel incentive measures corresponding to the appointed demand optimization scheme.
By adopting the technical scheme, the demand optimization scheme is adjusted according to the effect evaluation index, so that the demand optimization scheme is enabled to be close to the actual traffic condition, the instantaneity of the demand optimization scheme is improved, and the traffic condition in the city is continuously improved.
In a second aspect, the present application provides a travel intrinsic-based traffic service optimization system, which adopts the following technical scheme:
a travel eigen-based traffic service optimization system, the system comprising:
the system comprises a data acquisition module and a traffic management module, wherein the data acquisition module is used for acquiring traffic intrinsic data, and the traffic intrinsic data at least comprises infrastructure data related to traffic, resident trip data and traffic passing data;
the demand analysis module is used for acquiring travel demand characteristics of residents in the starting area by analyzing the traffic intrinsic data, wherein the travel demand characteristics at least comprise a target area and demand intensity;
the traffic simulation module is used for extracting a travel scheme between the initial area and the target area based on the infrastructure data, and calculating simulated traffic data corresponding to the travel scheme according to the infrastructure data;
the problem screening module is used for acquiring actual traffic data corresponding to the travel scheme based on the resident travel data and the traffic data, comparing the actual traffic data with the simulated traffic data, screening out the travel scheme exceeding the simulated traffic data, and marking the travel scheme as a problem scheme;
and the optimal scheme module is used for acquiring the remaining travel schemes except the problem scheme between the starting region and the target region and marking the travel schemes as priority schemes.
By adopting the technical scheme, the travel demand characteristics of residents are obtained after the intrinsic traffic data are analyzed, all travel schemes based on the demand characteristics are obtained, and further, selectable travel schemes of the residents are screened, travel schemes exceeding the actual traffic data by the simulated traffic data are screened, the simulated traffic data actually represent good traffic states of roads or traffic facilities involved in the travel schemes embodied in a data form, and therefore, the problem schemes obtained through screening are poor in running state; the priority scheme is acquired after the problem solving scheme is eliminated, so that the priority scheme is extracted from all travel schemes, and then the travel of residents is guided according to the priority scheme, so that the efficiency of traffic control is improved, and the urban traffic condition is improved.
In a third aspect, the present application provides an intelligent terminal, which adopts the following technical scheme:
an intelligent terminal comprising a processor and a memory, wherein at least one instruction, at least one program, code set or instruction set is stored in the memory, and the at least one instruction, the at least one program, the code set or instruction set is loaded and executed by the processor to implement a trip-intrinsic-based traffic service optimization method according to any one of the first aspects.
By adopting the technical scheme, the processor in the intelligent terminal can realize the travel demand support optimization method based on the intrinsic travel of the traffic according to the related computer program stored in the memory, thereby being beneficial to improving the rationality of traffic resource allocation and improving the travel condition of urban residents.
In a fourth aspect, the present application provides a computer readable storage medium, which adopts the following technical scheme:
a computer readable storage medium having stored therein at least one instruction, at least one program, code set, or instruction set loaded and executed by a processor to implement a trip-intrinsic based traffic service optimization method according to any one of the first aspects.
By adopting the technical scheme, the corresponding program can be stored, so that the reasonability of traffic resource allocation is improved, and the traveling condition of urban residents is improved.
In summary, the present application includes at least one of the following beneficial technical effects:
1. the method comprises the steps of analyzing traffic intrinsic data, obtaining travel demand characteristics of residents, obtaining all travel schemes based on the demand characteristics, screening travel schemes selected by the residents, screening travel schemes exceeding actual traffic data by the aid of the simulation traffic data, wherein the actual simulation traffic data represents a good traffic state of roads or traffic facilities related to the travel schemes in a data form, and therefore a problem scheme obtained through screening is poor in running state; the priority scheme is acquired after the problem is eliminated, so that the priority scheme is extracted from all travel schemes, and then the travel of residents is guided according to the priority scheme, so that the efficiency of traffic control is improved, and the urban traffic condition is improved;
2. the traffic data scattered in the large traffic service coverage area is divided, and the smaller traffic cells are used as units for analysis, so that the correlation among the traffic cells can be analyzed, the analysis difficulty of the original traffic data is reduced, and the acquisition efficiency of the traffic intrinsic data is improved;
3. through the actual incentive strategy, the resident is actively guided to select the priority scheme obtained through the steps, the implementation effect of the travel demand optimization scheme is enhanced, the effect of traffic control is improved, and finally the travel situation of the resident is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a block diagram of a travel eigen-based traffic service optimization system shown in an embodiment of the present application;
FIG. 2 is a method flow diagram of a travel eigen-based traffic service optimization method shown in an embodiment of the present application;
fig. 3 is a schematic structural diagram of an intelligent terminal in an embodiment of the present application.
Detailed Description
The present embodiments are to be considered as merely illustrative and not restrictive, and modifications may occur to those skilled in the art upon reading the present specification and may, therefore, be made without inventive faculty, and it is intended that all such embodiments be protected by the patent laws for further clarity and the objects, technical solutions, and advantages of the embodiments of the present application, and in conjunction with the appended drawings, it is to be understood that the embodiments described are merely illustrative and not restrictive. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the present application will be described in further detail with reference to fig. 1 to 3.
The embodiment of the application provides a travel intrinsic-based traffic service optimization method, which can be applied to a travel intrinsic-based traffic service optimization system shown in fig. 1, wherein an execution subject can be an intelligent terminal and is assisted by an external network connected with the intelligent terminal. The intelligent terminal can be connected with an external network, directly or indirectly acquire data related to traffic through the external network, process and analyze the collected data, and output the analysis result.
The process flow shown in fig. 2 will be described in detail with reference to the specific embodiments, and the following may be included:
step 201, acquiring traffic intrinsic data.
The traffic intrinsic data at least comprises infrastructure data, resident trip data and traffic passing data related to traffic.
Specifically, the infrastructure data may include facility distribution of infrastructures such as road network, public transportation network, track network, and slow public bicycles, and the infrastructures may be integrated on an electronic map of a geographic information system of a system, so that visual comprehensive display is facilitated. The resident travel data may be data describing the travel and related behavior of the resident, for example: travel population density, travel mode statistical data, travel track and the like; the traffic data may be the actual running state of each infrastructure, such as the traffic flow of the road, the passenger flow of the track, the average running speed of the traffic flow of the road, the congestion index, etc., on the basis of the traffic infrastructure.
In one embodiment, since the traffic operating conditions are caused by a common influence of multiple factors, the traffic intrinsic data may also include other influencing features that have an influence on the traffic travel, accordingly.
Specifically, other influencing features may include several, such as:
has a continuous influence on the traffic running state, such as: weather features, climate features, etc.;
has a certain influence on the traffic running state, such as: holidays, etc.
In one embodiment, since the source of traffic data has a variety, the following process may be included in step 201: periodically acquiring original traffic data in various fields related to traffic, and extracting traffic intrinsic data from the original traffic data; and/or directly acquiring the traffic intrinsic data through an external network.
In implementation, the provider of the traffic service may obtain the original data related to traffic through its service object, which may be a manager or operator with traffic operation and regulatory rights, such as: traffic bureaus, big data bureaus, rail transit companies, public transportation companies, and other managers, operators, and the like. By connecting the intelligent terminal with the database of the operator or manager, the original traffic data can be accessed and acquired. The original traffic data may include, for example: bus IC card swiping data provided by a bus company, rail AFC data provided by a rail transit company, network bus GPS data provided by a taxi company and the like.
In this way, traffic-related, comprehensive raw traffic data may be obtained, which may then be formatted and calibrated for data of different sources. Specifically, unified data format standards may be preset, such as: and the international standard system performs format conversion on the original traffic data according to a preset data format standard, so that the original traffic data with various sources and different structures can be mutually referred. Further, for data from different sources, effective features contained in the data can be enumerated, after the effective features are extracted, the remaining data which are not used and have analysis value are removed, so that the data size of the original traffic data can be reduced, the acquisition efficiency of the traffic intrinsic data can be improved, and the method is specific: the effective data in the data from different sources can be preset by a worker and enumerated in a form, and after the intelligent terminal receives the original traffic data, the effective data is extracted by the form, so that the intrinsic traffic data is obtained.
In one embodiment, since the coverage of the traffic service is generally larger, the comprehensive analysis is difficult, so the following processing can be performed correspondingly: dividing an area covered by traffic service to generate a plurality of traffic cells; and grouping the original traffic data by taking a traffic cell as a unit.
In implementation, the coverage area of the traffic service can be rasterized and divided based on a geographic information system in the city, such as an electronic map, each grid is used as a traffic cell, and the coverage area of the traffic service can be divided into a plurality of traffic cells according to the division rule of administrative areas. In this embodiment, for convenience of explanation, taking dividing the traffic area in the city into the area a, the area B and the area C according to the division rule of the administrative area as an example, the following schemes will be explained, and the situation of different divisions after other divisions under the same division rule is similar to the situation of different divisions after other divisions under the same division rule, and the situation of different divisions after other divisions is similar to the situation of the same division rule, which will not be repeated.
Therefore, after the urban area is divided into the A area, the B area and the C area, the traffic intrinsic data related to each area are grouped, and the analysis efficiency of the traffic intrinsic data is improved.
In addition, the intelligent terminal can also directly acquire the traffic intrinsic data through an external network, the external network can be the internet external data, a data center of a traffic management operation department and the like, and the traffic intrinsic data is directly acquired, so that the acquisition efficiency of the traffic intrinsic data is improved, and the acquisition source of the traffic intrinsic data is expanded.
On the basis, the two modes for acquiring the traffic intrinsic data can be used simultaneously or independently, so that the flexibility of acquiring the traffic intrinsic data is improved.
And 202, acquiring travel demand characteristics of residents in the initial area by analyzing the traffic intrinsic data.
The travel demand characteristics at least comprise a target area and demand intensity.
In this embodiment, for convenience of description, the above-mentioned area a is set forth as the start area, and the other areas are set forth as the start area similarly, and will not be described again.
In the implementation, a common contrast analysis method in a big data analysis method can be performed on the traffic intrinsic data, and the travel demand characteristics are obtained through analysis by combining time contrast analysis and space contrast analysis. The time comparison analysis can adopt the same-ratio time analysis, and traffic intrinsic data at the same time of each week is taken as a comparison analysis object for comparison, for example, the first week can be compared with the last week, and the second week can be compared with the last week; the time comparison analysis can also adopt ring ratio time analysis, and the total data of the Zhou Qitian is compared with the total data of the Zhou Qitian to obtain travel demand characteristics taking one week as a basic unit.
In this way, the travel demand characteristic with the area a as the starting area can be obtained, and the travel demand characteristic specifically can include the target area and the demand intensity relative to the starting area. When the area a is taken as the initial area, the target area may be the area B, the area C, and the like, and the area B is taken as an example. The required intensity may be the intensity of the flow of people from zone a to zone B over a certain time frame, the required intensity may be floating over a certain time frame, and may be as shown in table 1 below:
thus, taking the time of day as an example, and taking two hours as a statistical period, the required intensity from the start area to the target area in the time of day can be obtained, and the value of the required intensity is the average value obtained by analysis in the present embodiment.
And 203, extracting a travel scheme between the initial area and the target area based on the infrastructure data, and calculating simulated traffic data corresponding to the travel scheme according to the infrastructure data.
The travel scheme may include a travel route and a corresponding travel mode.
In the implementation, a plurality of travel schemes can be arranged between the area A and the area B, and the travel schemes can be extracted from the intrinsic traffic data, for example, based on the infrastructure data, a path planning algorithm is applied to obtain a plurality of travel schemes. It should be emphasized that several travel schemes may be constituted by a traffic means such as: the whole driving, whole riding and the like can also be combined by a plurality of traffic modes, such as: subway+bus, subway+riding, and the like.
Further, the travel scheme can be predicted based on the traffic intrinsic data, simulated traffic data corresponding to the travel scheme is estimated, and the simulated traffic data is specifically an ideal value of travel time corresponding to the travel scheme in a conventional traffic operation state and various data related to road operation conditions related to the travel scheme. Various data related to the road operation condition may include: the traffic flow of the road, the passenger flow of the track, the average running speed of the traffic flow of the road and the congestion index, and in the simulated traffic data, all the data can be set to 70% of the limit value, so that the data is taken as the ideal value of all the data related to the road operation condition in the conventional traffic operation state.
The conventional traffic operation state is used for reflecting the traffic operation state that the road is under the uncongested condition, and can be defined based on the average running speed of the vehicle, for example, the average running speed of the vehicle can be 70% of the speed limit value of the road vehicle in the conventional traffic operation state, and the travel time corresponding to the travel scheme can be estimated.
And 204, acquiring actual traffic data corresponding to the travel scheme based on the resident travel data and the traffic data, comparing the actual traffic data with the simulated traffic data, screening out the travel scheme exceeding the simulated traffic data, and marking the travel scheme as a problem scheme.
In implementation, the actual traffic data may be extracted from the traffic intrinsic data, and at least include travel time corresponding to the corresponding travel scheme and road operation conditions of the road section involved in the travel scheme. By comparing the actual traffic data with each item of data in the simulated traffic data one by one, a travel scheme with at least one item of data larger than the corresponding data in the simulated traffic data in a plurality of actual traffic data can be screened out.
Therefore, the screened scheme is a travel scheme with travel time exceeding predicted travel time or various data related to road operation conditions exceeding corresponding ideal values, and the travel scheme can be marked as a problem scheme, so that further processing is facilitated.
Step 205, obtaining the remaining travel schemes except the problem scheme between the starting area and the target area, and marking the travel schemes as priority schemes.
In the implementation, the travel schemes except the problem scheme in all the travel schemes can be marked as the priority scheme, and the priority scheme meets the condition that the travel time is in the estimated simulated traffic data and the road running condition of the corresponding time period is normal, so that residents can be guided to select the priority scheme, and the travel experience of the residents is improved.
In one embodiment, since the resident has a strong randomness in selecting the travel scheme, the following processing may be performed after step 205: acquiring travel incentive measures, wherein the travel incentive measures are set as rewards which are available to residents when the residents execute a priority scheme; and combining the priority scheme with travel incentive measures to generate a demand optimization scheme.
In practice, the trip incentive means may include: pre-designed traffic fee coupons, points that can be accumulated and redeemed for rewards, and the like. The resident is actively guided to select the priority scheme, so that the requirements of the resident are passively met by the traffic service provider, two-way feedback is formed between the traffic service provider and the resident, adjustment is mutually influenced, and the improvement efficiency of traffic service is improved.
In one embodiment, the process of traffic service is dynamic, and the fixed mode is difficult to take effect for a long time, so the following processing can be performed correspondingly: the traffic intrinsic data after the demand optimization scheme is generated is obtained regularly and marked as feedback traffic data; and based on the feedback traffic data, performing effect evaluation on the demand optimization scheme to acquire an effect index corresponding to the demand optimization scheme.
In the implementation, as the traffic intrinsic data can embody the influence of travel excitation measures, the related traffic intrinsic data after the demand optimization scheme is issued can be marked as feedback traffic data, and the effect of the demand optimization scheme can be evaluated through the result of the feedback traffic data after the feedback traffic data is acquired. The evaluation of the effect is embodied in the comparison result between the simulated traffic data and the actual traffic data of the corresponding travel scheme. The actual traffic data corresponding to the demand optimization scheme can be obtained through feeding back the traffic data, the appointed data which is originally beyond the simulated traffic data in the actual traffic data is compared with the data before the demand optimization scheme is implemented, the effect index showing the change condition of the data is obtained, the effect index can be the change strength represented by the change percentage of the data, the change trend is represented by positive and negative, the positive is increased, and the negative is reduced.
Therefore, the effect of the requirement optimization scheme can be mastered in time, and real-time analysis and processing are facilitated.
In one embodiment, the following processing may be performed after the above steps: and screening out a specified demand optimization scheme with the effect index lower than a preset effect threshold value, and adjusting travel incentive measures corresponding to the specified demand optimization scheme.
In the implementation, if all data of the actual traffic data are lower than the simulated traffic data, the requirement optimization scheme is effective; if a certain item of data in the actual traffic data still exceeds the simulated traffic data and the difference between the data and the original actual traffic data is smaller than a preset threshold value, the corresponding priority scheme in the demand optimization scheme can be changed into other priority schemes; if a certain item of data in the actual traffic data still exceeds the simulated traffic data, but the comparison difference between the actual traffic data and the original actual traffic data is larger than a preset threshold value, the effectiveness of the priority scheme can be confirmed, and the strength of the excitation measures, such as increasing the coupon amount, increasing the point rewarding value, improving the point rewarding prize pool and the like, can be increased.
Thus, the demand optimization scheme can be updated in time, is beneficial to continuous feedback and adjustment, and enhances the effect of improving urban traffic service.
Based on the same technical conception, the embodiment of the invention also provides a travel intrinsic-based traffic service optimization system, which comprises:
the system comprises a data acquisition module and a traffic management module, wherein the data acquisition module is used for acquiring traffic intrinsic data, and the traffic intrinsic data at least comprises infrastructure data related to traffic, resident trip data and traffic passing data;
the demand analysis module is used for acquiring travel demand characteristics of residents in the starting area by analyzing the traffic intrinsic data, wherein the travel demand characteristics at least comprise a target area and demand intensity;
the traffic simulation module is used for extracting a travel scheme between the initial area and the target area based on the infrastructure data, and calculating simulated traffic data corresponding to the travel scheme according to the infrastructure data;
the problem screening module is used for acquiring actual traffic data corresponding to the travel scheme based on the resident travel data and the traffic data, comparing the actual traffic data with the simulated traffic data, screening out the travel scheme exceeding the simulated traffic data, and marking the travel scheme as a problem scheme;
and the optimal scheme module is used for acquiring the remaining travel schemes except the problem scheme between the starting region and the target region and marking the travel schemes as priority schemes.
In one embodiment, the data acquisition module further comprises:
the intrinsic extraction sub-module is used for periodically acquiring original traffic data in various fields related to traffic and extracting traffic intrinsic data from the original traffic data;
and/or the number of the groups of groups,
and the external network sub-module is used for directly acquiring the traffic intrinsic data through an external network.
In one embodiment, the intrinsic extraction sub-module further comprises:
the regional division sub-module is used for dividing the region covered by the traffic service to generate a plurality of traffic cells;
and the data grouping sub-module is used for grouping the original traffic data by taking a traffic cell as a unit.
In one embodiment, the intrinsic extraction sub-module further comprises:
other characteristics submodule, including other influence characteristics that have an influence on the travel of traffic, other influence characteristics include at least weather characteristics, holidays.
In one embodiment, after the priority scheme module, the method further comprises:
the system comprises an excitation module, a control module and a control module, wherein the excitation module is used for acquiring travel excitation measures, and the travel excitation measures are set to be rewards available to residents when the residents execute a priority scheme;
and the optimization scheme module is used for combining the priority scheme with travel incentive measures to generate a demand optimization scheme.
In one embodiment, after optimizing the solution module, the method further includes:
the feedback collection module is used for periodically acquiring the traffic intrinsic data after the demand optimization scheme is generated and marking the traffic intrinsic data as feedback traffic data;
and the effect evaluation module is used for evaluating the effect of the demand optimization scheme based on the feedback traffic data and acquiring an effect index corresponding to the demand optimization scheme.
In one embodiment, after the effect evaluation module, the method further comprises:
and the scheme adjusting module is used for screening out a specified requirement optimizing scheme with the effect index lower than a preset effect threshold value and adjusting travel incentive measures corresponding to the specified requirement optimizing scheme.
The embodiment of the application also discloses an intelligent terminal, which comprises a memory and a processor, wherein the memory stores a computer program which can be loaded by the processor and execute the traffic service optimization method based on travel intrinsic.
Based on the same technical concept and the same invention concept, the embodiment of the application also discloses a computer readable storage medium which comprises the steps in the travel intrinsic-based traffic service optimization method flow when being loaded and executed by a processor.
The computer-readable storage medium includes, for example: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
It will be clearly understood by those skilled in the art that, for convenience and simplicity of description, only the above-mentioned division of the functional modules is used for illustration, in practical application, the above-mentioned functional allocation may be performed by different functional modules according to needs, that is, the internal structure of the apparatus is divided into different functional modules to perform all or part of the above-mentioned functions, and the specific working processes of the above-mentioned system, apparatus and unit may refer to the corresponding processes in the foregoing method embodiments, which are not repeated herein.
In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and the partitioning of hard blocks or units is merely a logical functional partitioning, and there may be additional partitioning in actual implementation, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. .
The units described as separate units may or may not be physically separate, and units displayed 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, and part or all of the units may be selected according to actual needs to achieve the purpose of the embodiment.
In addition, in the embodiments of the present application, each functional unit 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 that the technical solution of the present application is essentially or partly contributing to the prior art or all or part of the technical solution may be embodied in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) or a processor (processor) to perform all or part of the steps of the methods of the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a U disk, a mobile hard disk, a read-only memory, a random access memory, a ceramic disk or an optical disk.
The foregoing embodiments are only used to describe the technical solution of the present application in detail, but the descriptions of the foregoing embodiments are only used to help understand the method and the core idea of the present application, and should not be construed as limiting the present application. Variations or alternatives that are readily contemplated by those skilled in the art within the scope of the present disclosure are intended to be encompassed within the scope of the present disclosure.
Claims (6)
1. A travel intrinsic based traffic service optimization method, the method comprising:
acquiring traffic intrinsic data, wherein the traffic intrinsic data at least comprises infrastructure data, resident trip data and traffic passing data related to traffic;
acquiring travel demand characteristics of residents in an initial area by analyzing the traffic intrinsic data, wherein the travel demand characteristics at least comprise a target area and demand intensity;
based on the infrastructure data, extracting a travel scheme between a starting area and a target area, and calculating simulated traffic data corresponding to the travel scheme according to the infrastructure data;
based on the resident trip data and the traffic data, acquiring actual traffic data corresponding to the trip scheme, comparing the actual traffic data with the simulated traffic data, screening out the trip scheme exceeding the simulated traffic data, and marking the trip scheme as a problem scheme;
acquiring the rest travel schemes except the problem schemes between the starting region and the target region, and marking the travel schemes as priority schemes;
the infrastructure data includes: facility distribution of road network, public transport network, track network and slow public bicycles; the resident trip data is data for describing the trips and related behaviors of residents, and comprises the following steps: travel population density, travel mode statistics and travel track; the traffic data is the actual running state of each infrastructure, and comprises: traffic flow of the road, passenger flow of the track, average running speed of the traffic flow of the road, congestion index;
the simulated traffic data are travel time corresponding to a travel scheme in a conventional traffic operation state and ideal values of various data related to road operation conditions related to the travel scheme;
the actual traffic data is extracted from the traffic intrinsic data and at least comprises travel time corresponding to a corresponding travel scheme and road operation conditions of road sections related to the travel scheme;
the method further comprises the steps of:
acquiring travel incentive measures, wherein the travel incentive measures are set as rewards which can be obtained by residents when the residents execute a priority scheme; combining the priority scheme with travel incentive measures to generate a demand optimization scheme;
the traffic intrinsic data after the demand optimization scheme is generated is obtained regularly and marked as feedback traffic data; based on the feedback traffic data, performing effect evaluation on the demand optimization scheme to obtain an effect index corresponding to the demand optimization scheme;
screening out a specified requirement optimization scheme with the effect index lower than a preset effect threshold value; adjusting travel incentive measures corresponding to the appointed demand optimization scheme;
the traffic intrinsic data further includes: other influencing characteristics which influence the travel of the traffic, wherein the other influencing characteristics at least comprise weather characteristics and holidays;
the effect index comprises the change intensity and the change trend of the data change.
2. The travel eigen-based traffic service optimization method according to claim 1, wherein: the acquiring traffic intrinsic data comprises:
periodically acquiring original traffic data in various fields related to traffic, and extracting traffic intrinsic data from the original traffic data;
and/or the number of the groups of groups,
and directly acquiring the traffic intrinsic data through an external network.
3. The travel eigen-based traffic service optimization method according to claim 2, characterized in that: the method comprises the steps of periodically acquiring original traffic data of various fields related to traffic, extracting traffic intrinsic data from the original traffic data, and further comprising:
dividing an area covered by traffic service to generate a plurality of traffic cells;
and grouping the original traffic data by taking a traffic cell as a unit.
4. A travel eigen-based traffic service optimization system, the system comprising:
the system comprises a data acquisition module, a traffic management module and a traffic management module, wherein the data acquisition module is used for acquiring traffic intrinsic data, and the traffic intrinsic data at least comprises infrastructure data, resident trip data and traffic passing data related to traffic;
the demand analysis module is used for acquiring travel demand characteristics of residents in the starting area by analyzing the traffic intrinsic data, wherein the travel demand characteristics at least comprise a target area and demand intensity;
the traffic simulation module is used for extracting a travel scheme between the initial area and the target area based on the infrastructure data, and calculating simulated traffic data corresponding to the travel scheme according to the infrastructure data;
the problem screening module is used for acquiring actual traffic data corresponding to the travel scheme based on the resident travel data and the traffic data, comparing the actual traffic data with the simulated traffic data, screening out the travel scheme exceeding the simulated traffic data, and marking the travel scheme as a problem scheme;
the optimization scheme module is used for acquiring the rest travel schemes except the problem schemes between the starting region and the target region and marking the travel schemes as priority schemes;
the infrastructure data includes: facility distribution of road network, public transport network, track network and slow public bicycles; the resident trip data is data for describing the trips and related behaviors of residents, and comprises the following steps: travel population density, travel mode statistics and travel track; the traffic data is the actual running state of each infrastructure, and comprises: traffic flow of the road, passenger flow of the track, average running speed of the traffic flow of the road, congestion index;
the simulated traffic data are travel time corresponding to a travel scheme in a conventional traffic operation state and ideal values of various data related to road operation conditions related to the travel scheme;
the actual traffic data is extracted from the traffic intrinsic data and at least comprises travel time corresponding to a corresponding travel scheme and road operation conditions of road sections related to the travel scheme;
the optimization scheme module is also used for: acquiring travel incentive measures, wherein the travel incentive measures are set as rewards which can be obtained by residents when the residents execute a priority scheme; combining the priority scheme with travel incentive measures to generate a demand optimization scheme; the traffic intrinsic data after the demand optimization scheme is generated is obtained regularly and marked as feedback traffic data; based on the feedback traffic data, performing effect evaluation on the demand optimization scheme to obtain an effect index corresponding to the demand optimization scheme; screening out a specified requirement optimization scheme with the effect index lower than a preset effect threshold value; adjusting travel incentive measures corresponding to the appointed demand optimization scheme;
the traffic intrinsic data further includes: other influencing characteristics which influence the travel of the traffic, wherein the other influencing characteristics at least comprise weather characteristics and holidays;
the effect index comprises the change intensity and the change trend of the data change.
5. An intelligent terminal, characterized in that the intelligent terminal comprises a processor and a memory, wherein at least one instruction, at least one section of program, a code set or an instruction set is stored in the memory, and the at least one instruction, the at least one section of program, the code set or the instruction set is loaded and executed by the processor to realize a travel intrinsic based traffic service optimization method according to any one of claims 1 to 3.
6. A computer readable storage medium having stored therein at least one instruction, at least one program, code set, or instruction set, the at least one instruction, the at least one program, the code set, or instruction set being loaded and executed by a processor to implement a trip eigen-based traffic service optimization method of any one of claims 1 to 3.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111124961.XA CN114066001B (en) | 2021-09-25 | 2021-09-25 | Traffic service optimization method, system, terminal and medium based on travel intrinsic |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111124961.XA CN114066001B (en) | 2021-09-25 | 2021-09-25 | Traffic service optimization method, system, terminal and medium based on travel intrinsic |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114066001A CN114066001A (en) | 2022-02-18 |
| CN114066001B true CN114066001B (en) | 2024-03-19 |
Family
ID=80233949
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111124961.XA Active CN114066001B (en) | 2021-09-25 | 2021-09-25 | Traffic service optimization method, system, terminal and medium based on travel intrinsic |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114066001B (en) |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106934107A (en) * | 2017-02-20 | 2017-07-07 | 北京百度网讯科技有限公司 | Traffic trip scenario building method, device, equipment and storage medium |
| CN110570660A (en) * | 2019-11-06 | 2019-12-13 | 深圳市城市交通规划设计研究中心有限公司 | real-time online traffic simulation system and method |
| CN112183904A (en) * | 2020-11-19 | 2021-01-05 | 北京清研宏达信息科技有限公司 | Bus route optimization method based on resident travel OD |
| CN112556717A (en) * | 2021-02-20 | 2021-03-26 | 腾讯科技（深圳）有限公司 | Travel mode screening method and travel route recommending method and device |
| CN113191029A (en) * | 2021-06-30 | 2021-07-30 | 深圳坤湛科技有限公司 | Traffic simulation method, program, and medium based on cluster computing |
| CN113191028A (en) * | 2021-06-30 | 2021-07-30 | 深圳坤湛科技有限公司 | Traffic simulation method, system, program, and medium |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US11663378B2 (en) * | 2019-07-16 | 2023-05-30 | Here Global B.V. | Method, apparatus, and system for providing traffic simulations in a smart-city infrastructure |
-
2021
- 2021-09-25 CN CN202111124961.XA patent/CN114066001B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106934107A (en) * | 2017-02-20 | 2017-07-07 | 北京百度网讯科技有限公司 | Traffic trip scenario building method, device, equipment and storage medium |
| CN110570660A (en) * | 2019-11-06 | 2019-12-13 | 深圳市城市交通规划设计研究中心有限公司 | real-time online traffic simulation system and method |
| CN112183904A (en) * | 2020-11-19 | 2021-01-05 | 北京清研宏达信息科技有限公司 | Bus route optimization method based on resident travel OD |
| CN112556717A (en) * | 2021-02-20 | 2021-03-26 | 腾讯科技（深圳）有限公司 | Travel mode screening method and travel route recommending method and device |
| CN113191029A (en) * | 2021-06-30 | 2021-07-30 | 深圳坤湛科技有限公司 | Traffic simulation method, program, and medium based on cluster computing |
| CN113191028A (en) * | 2021-06-30 | 2021-07-30 | 深圳坤湛科技有限公司 | Traffic simulation method, system, program, and medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114066001A (en) | 2022-02-18 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Sider et al. | Land-use and socio-economics as determinants of traffic emissions and individual exposure to air pollution | |
| Fontes et al. | Are HOV/eco-lanes a sustainable option to reducing emissions in a medium-sized European city? | |
| Zahabi et al. | Spatio-temporal analysis of car distance, greenhouse gases and the effect of built environment: A latent class regression analysis | |
| Kou et al. | Multiobjective optimization model of intersection signal timing considering emissions based on field data: A case study of Beijing | |
| Mahmod et al. | Reducing local traffic emissions at urban intersection using ITS countermeasures | |
| Gaduh et al. | Life in the slow lane: Unintended consequences of public transit in Jakarta | |
| CN111291321A (en) | Urban traffic model construction method based on big data | |
| CN110021161B (en) | Traffic flow direction prediction method and system | |
| Bandeira et al. | Exploring the potential of web based information of business popularity for supporting sustainable traffic management | |
| Fernandes et al. | A macroscopic approach for assessing the environmental performance of shared, automated, electric mobility in an intercity corridor | |
| Low et al. | Quantifying bus travel time variability and identifying spatial and temporal factors using Burr distribution model | |
| CN114298493A (en) | Road operation monitoring system, method, terminal and storage medium | |
| Dogaroglu et al. | Investigation of car park preference by intelligent system guidance | |
| Beimborn et al. | Transit accessibility and connectivity impacts on transit choice and captivity | |
| CN114066001B (en) | Traffic service optimization method, system, terminal and medium based on travel intrinsic | |
| Lu et al. | Creating an agent-based long-haul freight transport model for Germany | |
| Barlovic et al. | Online traffic simulation with cellular automata | |
| Banik et al. | Corridor level mobility analysis using GPS data | |
| Breiland et al. | Operational effect of single-occupant hybrid vehicles in high-occupancy vehicle lanes | |
| Asmael et al. | Prediction origin-destination matrix of freight travel demand in Baghdad City | |
| Viton | Will mixed logit change urban transport policies? | |
| CN113689715B (en) | Information pushing method, device, electronic equipment and computer readable storage medium | |
| Zwick et al. | Ride-pooling efficiency in large, medium-sized and small towns: Simulation assessment in the Munich metropolitan region | |
| Norhisham et al. | Assessing Bus Performance Rating in Kajang, Selangor | |
| Livingston et al. | Forecasting a future with automated vehicles in Switzerland: Exploring the urban-rural divide and system effects |
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 | ||
| GR01 | Patent grant |