A travel scheduling optimization method based on residents' rigid demand

Abstract

The present invention discloses a travel scheduling optimization method based on the rigid demands of residents, comprising the following steps: S1: Obtain administrative division boundary data and the AMap_Adcode_citycode urban coding table of Amap; S2: Determine the required Point of Interest (POI) data based on administrative regions and urban coding; S3: Use Pandas to quickly preprocess the POI data; S4: Construct an average accessible distance model between regions; S5: Classify various types of POIs; S6: Process the obtained SDE results through ImageJ; S7: Use Matlab to construct a difference equation to calculate the equilibrium point; S8: Perform a weighted average on the equilibrium point values; S9: Determine a travel optimization scheduling plan for the result obtained from the weighted average. Based on more objective POI data of various types of transportation, life, and address facilities, the present invention conducts SDE analysis in a selected urban area, calculates the weight coefficient of the travel intensity generated by the rigid demands of residents in the research area, so as to formulate a travel scheduling optimization method suitable for the residents in the research area.

Description

Technical Field

[0001] The present invention relates to the field of computer application technologies, and particularly to an optimized method for travel scheduling based on the rigid demands of residents. Background Art

[0002] In recent years, the research on the travel scheduling of residents has become increasingly popular, especially the discussions on the intensity of residents' travel have become more and more intense. However, the research on the travel scheduling based on the rigid demands of residents is very scarce. Under the background of the epidemic, traditional data on travel intensity, such as the questionnaire survey method, has been greatly restricted. Paper questionnaire surveys cannot be carried out on a large scale, and even online questionnaire surveys are severely restricted. In addition, the results of questionnaire surveys are greatly affected by subjective factors and cannot objectively and truly reflect the travel intensity of residents' rigid demands.

[0003] To sum up, the existing travel scheduling methods based on the rigid demands of residents have the following deficiencies:

[0004] (1) Under the background of the epidemic, the traditional questionnaire survey method cannot be effectively carried out;

[0005] (2) The original survey method is affected by subjective judgment and cannot objectively reflect the real situation of residents' travel, and cannot bring us accurate and real POI data. Summary of the Invention

[0006] The purpose of the present invention is to provide an optimized method for travel scheduling based on the rigid demands of residents, so as to solve the problems raised in the above background art.

[0007] To achieve the above purpose, the present invention provides the following technical solutions:

[0008] An optimized method for travel scheduling based on the rigid demands of residents, comprising the following steps:

[0009] S1: Obtain administrative division boundary data and the AMap_Adcode_citycode city coding table of Amap;

[0010] S2: Determine the required Point of Interest (POI) data based on the administrative region and city code, and crawl the POI data of the target city through the Amap API;

[0011] S3: Use Pandas to quickly preprocess the POI data and screen out the data with time differences; for the processing of POI data, it should be judged whether some data are newly added or reduced data in a certain year, so as to endow this part of POI data with the attribute of time difference;

[0012] S4: Construct the average accessible distance model between regions to determine the travel intensity; construct the average accessible distance model between regions to determine the mathematical expression of the travel intensity generated by the rigid demand guidance.

[0013] S5: Classify various types of POIs, and calculate the service scope of various types of POIs through the Standard Deviational Ellipse (SDE) analysis; for the processing of POI data, it should be judged whether some data are newly added or reduced data in a certain year, so as to endow this part of POI data with the attribute of time difference.

[0014] S6: Obtain the change value of the travel intensity generated by the rigid demand guidance through processing the SDE results obtained by ImageJ.

[0015] S7: Use Matlab to construct a difference equation to calculate the equilibrium point, and this equilibrium point is the value of the travel intensity that has a strong correlation with the rigid demand of residents in the research area; use ImageJ to process the service scope results of the SDE analysis, and use the professional software Matlab to construct a difference equation for it to obtain the equilibrium point of the scope change, reducing the sensitivity of the POIs themselves from some data sources.

[0016] S8: Perform weighted average on the equilibrium point values to further eliminate the sensitivity brought by some types of POIs, and obtain the weight coefficient of the travel intensity generated based on the rigid demand of residents in this area.

[0017] S9: For the result obtained by weighted average, determine the weight coefficient of the travel intensity generated by the rigid demand guidance, and make a judgment on the travel optimization scheduling plan.

[0018] A further improvement scheme of the present invention is that in S1, based on the obtained administrative division boundary data, complete the basic data processing through the professional software ArcGIS.

[0019] A further improvement scheme of the present invention is that in S2, use Python to crawl at least two years of POI data relying on the Gaode API and the target city code.

[0020] A further improvement scheme of the present invention is that in S3, use the Pandas data analysis package in Python to screen the crawled data and screen out the data with time differences.

[0021] A further improvement scheme of the present invention is that in S4, first construct the shortest distance model between regions to determine the travel intensity.

[0022] The shortest distance model between regions is shown in formula (1):

[0023]

[0024] where L i-j is the distance from the starting area i of the trip to the destination area j of the trip, and is the shortest reachable distance for the starting point i of the trip;

[0025] Based on formula (1), the average reachable distance between regions is calculated as shown in formula (2):

[0026]

[0027] where L average-i is the average reachable distance between regions, is the travel intensity weight generated by the rigid demand of the residents within the research area, and γ is the travel intensity index of the residents within the selected research area, together constituting the travel intensity of the residents in this area under rigid demand.

[0028] A further improvement of the present invention is that in S5, first, the crawled POIs are classified and data is summarized, and then the standard deviation ellipse tool in ArcGIS is used to analyze the service range of the data obtained in S3. In this step, it is necessary to first determine the SDE range suitable for the target area, that is, to construct the intersection of the service ranges generated by the θ i data of the reduced POIs and the β i data of the newly added POIs, as shown in formula (3), to reduce the sensitivity of some POIs.

[0029]

[0030] where the data points that existed in the previous year and disappeared in the following year among the selected types of POIs are θ i , and the data points that did not exist in the previous year and existed in the following year among the selected types of POIs are β i , S(θ i ∩β i ) is the intersection area of the service ranges generated by the θ i data of the reduced POIs and the β i data of the newly added POIs, and α1 is the change coefficient of the service range of the POIs guided by rigid demand screened out in this area.

[0031] A further improvement of the present invention is that in S6, the SDE map obtained in S5 is converted into an 8-bit image using ImageJ professional software, and pixel recognition is performed to quickly calculate the proportion of the SDE of traffic service type POIs in the SDEs of each of the life service type and address service type POIs.

[0032] A further improvement of the present invention is that in S7, the Matlab professional software is selected to construct a difference equation to calculate the balance point of the proportion of traffic service POIs in various POIs in S6, and the proportion of the travel intensity generated by the rigid demand of residents in the research area in the total travel intensity is obtained.

[0033] A further improvement of the present invention is that in S8, based on the proportion of various data types in S5, the result obtained in S7 is weighted and averaged to reduce the influence of the high sensitivity of some POIs.

[0034] A further improvement of the present invention is that in S9, according to the proportion of the travel intensity of residents' rigid demand in the research area, the travel scheduling plan of residents in the research area is determined.

[0035] The beneficial effects of the present invention are as follows:

[0036] Based on more objective POI data of various traffic, life and address facilities, the SDE analysis is carried out in the selected urban area, and the weight coefficient of the travel intensity generated by the rigid demand of residents in the research area is calculated to formulate an optimized method for residents' travel scheduling suitable for the research area. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is a flow block diagram of the present invention.

[0038] Figure 2 It is the processing result of the traffic facility service data in Huai'an City after screening the combined analysis in Embodiment 1. DETAILED DESCRIPTION OF THE INVENTION

[0039] The present invention will be further clarified below with reference to the drawings and specific embodiments.

[0040] Embodiment 1: As Figure 1-2 shown, in this embodiment, Huai'an City, Jiangsu Province is taken as an example. First, the administrative division boundary of Huai'an City and the corresponding AMap_Adcode_citycode urban code of Gaode Map are obtained. The urban code of Huai'an City is 320800.

[0041] Based on the administrative region and urban code of Huai'an City, the required POI data is determined, and the POI data of the target city for at least 2 years is crawled through the Gaode API. In this invention, the POI data of Huai'an City in 2019 and 2020 is taken as an example.

[0042] Use Pandas to quickly preprocess the POI data and screen out the data with time differences, as shown in Table 1.

[0043] Table 1 Screening Results of POI Data in Huai'an City

[0044]

[0045] Build the average accessible distance model between regions and determine the mathematical expression of the travel intensity generated by rigid demand guidance.

[0046] The shortest distance model between regions is shown in formula (1):

[0047]

[0048] Where L i-j is the distance from the origin region i of the trip to the destination region j of the trip, and is the shortest accessible distance for the trip origin i.

[0049] Based on formula (1), calculate the average accessible distance between regions, as shown in formula (2):

[0050]

[0051] Where, L average-i is the average accessible distance between regions, is the weight of the travel intensity generated by the rigid demand of urban residents in this area, and γ is the travel intensity index of residents in this area, Together, they constitute the calculation of the travel intensity of residents in this area under rigid demand.

[0052] Classify various types of POIs, as shown in Table 2.

[0053] Table 2 Classification of POI data in Huai'an City

[0054]

[0055] Use the SDE tool of ArcGIS professional software to calculate the service scope of various types of POIs, as shown in Table 3.

[0056] Table 3 Superposition analysis of the SDE service scope generated by various types of POIs

[0057]

[0058]

[0059] Take the traffic facility service data of Huai'an City in 2019 and 2020 after screening as an example for the processing results, as Figure 2 shown.

[0060] Obtain the change value of the travel intensity generated by rigid demand guidance through the SDE results processed by ImageJ, and obtain the result shown in formula (4).

[0061]

[0062] Using MATLAB, the equilibrium point is obtained by calculating the difference equation of formula (6), that is, the service range change index with great relevance of traffic service POIs in Huai'an City in 2019 and 2020 among POIs such as life service and address service. At the same time, the equilibrium point matrix of SDE with great relevance can objectively describe the intensity of residents' travel demand in this area (the final result is rounded to two decimal places).

[0063]

[0064] To make the travel intensity more general, and considering that some values are derived from POIs that are more sensitive to the external environment, we choose to perform weighted average calculation to reduce the sensitivity of some types of POIs, that is, perform weighted average through 22 different points of interest of 3 major categories of POIs in Table 2, and finally obtain the weighted average coefficient of the travel demand intensity of urban residents in Huai'an City.

[0065]

[0066] Finally, according to the proportion of the travel intensity of residents' rigid demand in this area, it can be determined whether the travel scheduling plan for residents in the research area is feasible. When the material supply in this area can meet the rigid demand of residents, the travel scheduling plan for residents can be formulated under the background of external shocks.

[0067] The above implementation manners are only for explaining the technical concept and characteristics of the present invention, and the purpose is to enable those who are familiar with this technology to understand the content of the present invention and implement it accordingly, and it cannot be used to limit the protection scope of the present invention. Any equivalent transformation or modification made according to the spirit and essence of the present invention should be covered within the protection scope of the present invention.

Claims

1. A travel scheduling optimization method based on the rigid needs of residents, characterized in that It includes the following steps: S1: Obtain administrative division boundary data and the AMap_Adcode_citycode city coding table of Amap; S2: Determine the required Point of Interest (POI) data based on administrative regions and city codes, and crawl the POI data of the target city through the Amap API; S3: Use Pandas to quickly preprocess the POI data and filter out data with time differences; S4: Build an average accessible distance model between regions to determine the travel intensity; S5: Classify various types of POIs and calculate the service scope of various types of POIs through SDE; S6: Use ImageJ to process the SDE results obtained to get the change value of travel intensity generated by rigid demand guidance; S7: Use Matlab to build a difference equation to calculate the equilibrium point, and this equilibrium point is the travel intensity value that has a strong correlation with the rigid demand of residents in this area; S8: Perform weighted average on the equilibrium point values to further eliminate the sensitivity brought by some types of POIs and obtain the travel intensity weight coefficient generated by the rigid demand of residents in this area; S9: Determine the weight coefficient of the travel intensity generated by rigid demand guidance for the result obtained by weighted average and judge the travel optimization scheduling plan.

2. The travel scheduling optimization method based on residents' rigid demands according to claim 1, characterized in that: In S1, based on the obtained administrative division boundary data, complete the basic data processing through professional software ArcGIS.

3. The travel scheduling optimization method based on residents' rigid demands according to claim 1, characterized in that: In S2, rely on the Amap API and the target city code through Python to crawl POI data for at least two years.

4. The travel scheduling optimization method based on the rigid needs of residents according to claim 1, characterized in that: In S3, use the Pandas data analysis package in Python to filter the crawled data and filter out data with time differences.

5. The optimization method for travel scheduling based on the rigid needs of residents according to claim 1, wherein: In S4, first build the shortest distance model between regions to determine the travel intensity; The shortest distance model between regions is shown in formula (1): Among which L i-j is the distance from the starting area i of the trip to the destination area j, and is the shortest reachable distance for the starting point i of the trip; Based on formula (1), calculate the average accessible distance between regions, as shown in formula (2): Among them, L average-i is the average accessible distance between regions, is the travel intensity weight generated by the rigid demand of residents within the selected research area, and γ is the travel intensity index of residents within the research area, which together constitute the calculation of the travel intensity of residents within the research area under rigid demand.

6. The travel scheduling optimization method based on residents' rigid demands according to claim 1, characterized in that: In S5, first classify and summarize the crawled POIs, and then use the standard deviation ellipse tool in ArcGIS to analyze the service area of the data obtained in S3. In this step, it is necessary to first determine the SDE range suitable for the target area, that is, construct θ for reducing POIs i The data and β of the newly added POIs i The intersection of the service areas generated by the data, as shown in formula (3), is used to reduce the sensitivity of some POIs; Among them, the data points that exist in the previous year and disappear in the following year in the selected type of POI are θ i , and the data points that do not exist in the previous year and exist in the following year in the selected type of POI are β i , S(θ i ∩β i ) is the intersection area of the service scope generated by the θ i data of the reduced POI and the β i data of the newly added POI. α1 is the change coefficient of the service scope of the POI guided by rigid demand screened out in this area.

7. The travel scheduling optimization method based on residents' rigid demand according to claim 1, characterized in that: In S6, use the professional software ImageJ to convert the SDE map obtained in S5 into an 8-bit image, perform pixel recognition, and quickly calculate the proportion of the SDE of traffic service POIs in the SDEs of life service POIs and address service POIs.

8. The optimized travel scheduling method based on residents' rigid demands according to claim 1, characterized in that: In S7, select the professional software Matlab to build a difference equation to calculate the equilibrium point of the proportion of traffic service POIs in various types of POIs in S6, and obtain the proportion of the travel intensity generated by the rigid demand of residents in the study area in the total travel intensity.

9. The travel scheduling optimization method based on residents' rigid demands as described in claim 1, wherein: In S8, perform weighted average processing on the results obtained in S7 according to the proportion of various data types in S5 to reduce the influence of the high sensitivity of some POIs.

10. The travel scheduling optimization method based on the rigid demand of residents according to claim 1, characterized in that: In S9, judge the travel scheduling plan of residents in the study area according to the proportion of the travel intensity of residents' rigid demand in the study area.

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