A method for calculating the service area of ​​high-speed railway

Abstract

This invention discloses a method for calculating the service range of a high-speed railway, comprising: dividing the prefecture-level city / province to which the railway station k* to be studied belongs into traffic zones; calculating the generalized travel cost for passengers in traffic zone i to travel to destination j; selecting the zone with the lowest generalized travel cost from the railway station k* to destination j among all traffic zones; calculating the probability that passengers in the covered zone will choose to travel from the railway station k* to destination j; arranging the calculated travel probabilities from largest to smallest to determine the zone Q corresponding to the set quantile; calling the map API interface to calculate the travel time h from the centroid of zone Q to the railway station k* based on the actual road network; combining the actual road network to calculate the farthest travel distance from the railway station k* to each direction within the service radius time h for the corresponding mode of transportation; connecting all the farthest points to form a closed irregular area to constitute a traffic isochronous circle, thus forming the final station service range.

Description

Technical Field

[0001] The invention relates to the field of railway engineering, and in particular to a method for calculating the service range of a high-speed railway. Background Art

[0002] High-speed railway stations are distributed in the center, edge or suburbs of the city, playing a driving or service role in urban development. Some high-speed railway stations under construction have problems such as long station connection time and lower-than-expected passenger flow. In addition to unreasonable and asynchronous municipal transportation facilities, there are also reasons such as insufficient understanding of the service scope of high-speed railway stations. The service scope of the station is the basis for predicting high-speed railway passenger flow and laying out connection measures. At present, there is a lack of research on the service scope of high-speed railway stations. In actual production, most of them simply estimate their service radius based on spatial straight-line distance. From the current status of research on the service scope of buses, subways, railway stations and airports at home and abroad, it can be seen that the service radius of various stations is basically measured by distance. The more recognized station service radius is 400 meters for bus stations and 800 meters for subway stations. There is no unified opinion on the service scope of airports.

[0003] The service radius is defined and calculated based on the spatial distance to the station, and the service range is a circular area with the spatial distance as the radius. This definition and calculation method is relatively intuitive, but it does not take into account the relevant road network structure, station accessibility, and service level. It may cause deviations in the calculation of the station's service range and fail to accurately evaluate the station's service range, resulting in longer connection times at some stations and lower passenger flow than expected. Therefore, it is necessary to study a reasonable and effective method for determining the station's service range. Summary of the invention

[0004] The purpose of the present invention is to provide a method for calculating the service range of a high-speed railway in order to solve the problem that the service radius is directly estimated according to the spatial straight-line distance in the prior art, and the service range of a station cannot be accurately evaluated.

[0005] In order to achieve the above object, the technical solution adopted by the present invention is:

[0006] A method for calculating a high-speed railway service range comprises the following steps:

[0007] Step 1: Divide the prefecture-level city / province to which the railway station k* to be studied belongs into traffic zones;

[0008] Step 2: Search and analyze to obtain the railway stations, bus stations and aviation stations that can reach the destination j in the prefecture-level city / province of the railway station k* to be studied;

[0009] Step 3: Calculate the generalized travel cost of passengers in transportation area i to destination j;

[0010] When choosing public transportation, the generalized travel cost is:

[0011]

[0012] Where U i，j，k is the generalized travel cost from transportation community i to destination j via station k, AC i，k , AT i，k is the transportation cost and time for residents of transportation community i to travel to station k, F k，j is the average fare from station k to destination j, WT k The waiting time of passengers at station k, IT k，j is the passenger’s time on board, α is the adjustment parameter, and f k，j is the service frequency from station k to destination j, T k，business is the business hours of station k; vot is the average time value of passengers, vot = GDP / (population·worktime), GDP is the annual GDP of the prefecture-level city to which the railway station k* to be studied belongs, population is the total population of the prefecture-level city to which the railway station k* to be studied belongs, and worktime is the annual working hours of the prefecture-level city to which the railway station k* to be studied belongs;

[0013] When self-driving is chosen, k is 0, and the generalized travel cost is:

[0014] U i，j，k =dis i，j cost per.km +time i，j ·vot+toll i，j ,

[0015] In the formula, dis i，j is the driving distance from traffic area i to destination j, cost per.km is the driving cost per kilometer; time i，j is the time it takes to drive from traffic zone i to destination j; toll i，j The highway toll for driving from traffic community i to destination j;

[0016] Step 4: Select the community with the lowest generalized travel cost from the railway station k* to the destination j from all the traffic communities, and select n coverage communities of the railway station k* to be studied:

[0017] Step 5: Based on the MNL discrete model, calculate the probability that passengers in n coverage cells choose to go to destination j via the railway station k* to be studied:

[0018]

[0019] In the formula, is the probability that a passenger in transportation zone i chooses to go to destination j via the railway station k* to be studied, is the generalized travel cost from transportation community i to destination j via the railway station k* to be studied, U i，j is the average generalized travel cost between places i and j, l is the total number of optional stations, k = 0, 1, 2, 3...l;

[0020] Step 6: Arrange the travel probabilities calculated in step 5 from large to small, determine the community Q corresponding to the set quantile, call the map API interface, and calculate the travel time h from the centroid of community Q to the railway station k* to be studied based on the actual road network. This time h is the service radius time of the railway station k* to be studied;

[0021] Step 7: Based on the actual road network, calculate the longest travel distance of the corresponding transportation mode from the railway station k* to be studied to each direction within the service radius time h, connect all the farthest points into a closed irregular area to form a traffic isochrone circle, and form the final station service range.

[0022] As a preferred technical solution, in the step 1: the fourth-level administrative division, i.e. town / village / street, is used to divide the traffic districts, and each traffic district takes the location of the local government / residents' committee as the centroid of the district.

[0023] As a preferred technical solution, in the step 2: searching and analyzing based on map software to obtain the railway stations, bus stations and aviation stations included in the prefecture-level city / province to which the railway station k* to be studied belongs.

[0024] As a preferred technical solution, in step three: use python to call the map API interface to obtain the transportation cost and time based on the actual road network.

[0025] As an optimal technical solution, in step four: there are m transportation communities, i = 1, 2, 3, ...n, ...m, and the generalized travel cost of a certain transportation community to reach the destination j via each optional station is calculated respectively according to step three. If the generalized travel cost of the transportation community via the railway station k* to be studied is less than that of other optional stations, then the community is considered to be the coverage community of the railway station k* to be studied.

[0026] As a preferred technical solution, in step 5: all calculation results form a selection probability matrix [P i，j，k ] (m×l)+1 ,like is the maximum value of the i-th row of the matrix, it means that the passengers of transportation community i will choose to travel through the railway station k* to be studied, otherwise the passengers of transportation community i will not choose to travel through the railway station k* to be studied.

[0027] As a preferred technical solution, in step six: the quantile is set to 75%.

[0028] As a preferred technical solution, in step seven: the modes of transportation include urban rail transit, buses and cars.

[0029] The present invention also discloses a high-speed railway service range calculation device, comprising at least one processor and a memory communicatively connected to the at least one processor; the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute any of the high-speed railway service range calculation methods.

[0030] The present invention also discloses a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of any of the high-speed railway service range calculation methods are implemented.

[0031] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0032] 1. The present invention proposes to first analyze and calculate the service radius from the perspective of time, and then combine the actual road network structure to convert the time radius into a spatial range to solve the service range of the high-speed railway station that meets the actual travel situation. The present invention first comprehensively considers factors such as transportation service quality, passenger characteristics, and the actual road network, and combines the MNL discrete model to establish a high-speed railway station service range calculation model to solve the service radius from the perspective of time; finally, combined with the concept of isochrone circles, an application idea of ​​applying the service range to engineering practice is given.

[0033] 2. The present invention solves the shortcomings of traditional railways, highways, airports and other transportation modes, such as single research ideas on the service scope of stations, extensive calculation process, poor accuracy of research results, lack of pertinence, etc., and fills the theoretical gap in high-speed railway planning and site selection. It is of great significance to improve the accuracy of passenger flow prediction, scientifically arrange transportation connection measures, enhance the attractiveness of stations, and give full play to the role of high-speed railways in driving regional economic development. It can also help determine the scale and boundaries of development, optimize the site selection plan of stations and the spatial structure of the area around stations, promote the reasonable layout of urban space along the high-speed railway and the optimization of urban spatial structure, and promote the benign interaction and organic coordination between high-speed railway construction and urban development. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 It is a schematic diagram of the process of the present invention.

[0035] Figure 2 It is the service range isochronous circle of Shanghai Hongqiao Station (urban rail transit) calculated by the present invention.

[0036] Figure 3 It is the isochronous circle (urban rail transit) of the service range of Xuzhou East Station calculated by the present invention.

[0037] Figure 4 It is the isochronous circle (urban rail transit) of the service range of Kunshan South Station calculated by the present invention. DETAILED DESCRIPTION

[0038] The present invention will be described in detail below in conjunction with the accompanying drawings.

[0039] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0040] Example 1

[0041] The service scope of a station can be defined as the area involved in attracting passenger flow after the station is put into operation, that is, the potential passenger flow attraction area, which is determined according to the wishes of the passengers themselves. When passengers travel, they will choose the travel mode that makes them most satisfied according to their psychological needs. In this decision-making process, passengers will comprehensively consider factors such as travel time, travel costs, comfort, safety, punctuality, etc. of different modes of transportation, and choose the best travel mode according to their own characteristics and travel preferences. Considering the diversity of services provided by railway stations (going to different destinations), the starting points of passengers traveling through the station at different travel distances are quite different, so each service (each destination) of the station should have a corresponding service scope. For the convenience of analysis, the station service scope discussed below is for a certain destination (that is, a certain destination j).

[0042] like Figure 1 As shown, a method for calculating the service range of a high-speed railway comprises the following steps:

[0043] Step 1: Divide the area of ​​the railway station k* to be studied into traffic zones based on administrative divisions.

[0044] Usually, for provincial / municipality-level high-speed railway stations, traffic zones can be divided according to the province / municipality to which they belong, and for prefecture-level / county-level high-speed railway stations, traffic zones can be divided according to the prefecture-level city to which they belong. Of course, the research scope of prefecture-level / county-level high-speed railway stations can also be expanded to the province / municipality to which they belong according to actual conditions. That is, the local area / wide area can be calculated according to actual conditions.

[0045] Usually, the service area of ​​a high-speed railway station is relatively large, so the division of traffic zones does not need to be too detailed. However, the urban travel time of each traffic zone to different stations needs to be considered during the calculation process. Therefore, the traffic zones can be divided according to the fourth-level administrative division, that is, town / township / street, and the location of the local government / residents' committee is used as the centroid of each traffic zone. Of course, if the computing power is sufficient, the fifth-level administrative division, that is, village / community, can also be used to divide the traffic zones, and the location of the local government / residents' committee is used as the centroid of each traffic zone.

[0046] Step 2: Search and analyze to obtain the transportation stations (railway stations, bus stations and aviation stations) that can reach the destination j in the prefecture-level city / province to which the railway station k* to be studied belongs.

[0047] Usually, a search and analysis based on map software can be performed to obtain the railway stations, bus stations and aviation stations included in the prefecture-level city / province to which the railway station k* to be studied belongs.

[0048] Step 3: Calculate the generalized travel cost of passengers in transportation area i to destination j;

[0049] When choosing public transportation, the generalized travel cost is:

[0050]

[0051] Where U i，j，k is the generalized travel cost from transportation community i to destination j via station k, AC i，k , AT i，k is the transportation cost and time for residents of transportation community i to travel to station k, F k，j is the average fare from station k to destination j, WT k is the waiting time of passengers at station k, IT k，j is the passenger’s time on board, α is an adjustment parameter, which is generally 0.1 to 0.2, and f k，j is the service frequency from station k to destination j, T k，business is the business hours of station k; vot is the average time value of passengers, vot = GDP / (population·worktime), GDP is the annual GDP of the prefecture-level city to which the railway station k* to be studied belongs, population is the total population of the prefecture-level city to which the railway station k* to be studied belongs, and worktime is the annual working hours of the prefecture-level city to which the railway station k* to be studied belongs.

[0052] Given the destination and the departure station, the ticket price, journey time, service frequency and other data can be obtained through the relevant ticket website. For the long-distance bus station, the ticket price F k，j , IT time in the car k，jTake the average of all trains, waiting time WT k Take 40 minutes. For high-speed rail stations, F k，j Take the average fare of second-class seats on all trains, and the train time IT k，j Take the average travel time of all trains and the waiting time WT k Take 30 minutes. For ordinary railway stations, the fare is F k，j Take the average of all hard seat / hard sleeper ticket prices for all trains, and the IT k，j Take the average travel time of all trains and the waiting time WT k Take 40 minutes. For airports, fare F k，j Take the average fare and travel time of the past 30 days k，j Take the average travel time of all flights in a day, and the waiting time WT k Take 60 minutes, service frequency f k，j Take the average number of flights per flight over the 30 days.

[0053] When self-driving is chosen, k is 0, and the generalized travel cost is:

[0054] U i，j，k =dis i，j cost per.km +time i，j ·vot+toll i，j (2)

[0055] In the formula, dis i，j is the driving distance from traffic area i to destination j, cost per.km is the driving cost per kilometer; time i，j is the time it takes to drive from traffic zone i to destination j; toll i，j is the highway toll for driving from traffic community i to destination j.

[0056] The preferred solution is to use artificial intelligence algorithms and Python to call the map API interface to obtain the transportation costs and time based on the actual road network. For example, for self-driving travel, obtain travel data based on the actual road network, the path planning strategy is "fastest speed", the car consumes 7L of gasoline per 100 kilometers, and the oil price is 7.5 yuan / L, then the cost per kilometer is 0.525 yuan / km.

[0057] Step 4: Select the community with the lowest generalized travel cost from the railway station k* to be studied to the destination j from all the traffic communities, and select n coverage communities of the railway station k* to be studied.

[0058] Suppose there are m transportation communities, i = 1, 2, 3, ...n, ...m. According to the step three, the generalized travel cost of a certain transportation community to reach the destination j via each optional station is calculated respectively. If the generalized travel cost of the transportation community via the railway station k* to be studied is less than that of other optional stations, then the community is considered to be the coverage community of the railway station k* to be studied.

[0059] All calculation results form a selection probability matrix [P i，j，k ] (m×l)+1 ,like is the maximum value of the i-th row of the matrix, it means that the passengers of transportation community i will choose to travel through the railway station k* to be studied, otherwise the passengers of transportation community i will not choose to travel through the railway station k* to be studied.

[0060] Step 5: Based on the MNL discrete model, in order to eliminate the serious expansion of the result difference caused by exponential growth, the generalized travel cost is averaged, and the probability of passengers in n covered communities choosing to go to destination j via the railway station k* to be studied is calculated:

[0061]

[0062] In the formula, is the probability that a passenger in transportation zone i chooses to go to destination j via the railway station k* to be studied, is the generalized travel cost from transportation community i to destination j via the railway station k* to be studied, U i，j is the average generalized travel cost between places i and j, l is the total number of optional stations, k = 0, 1, 2, 3...l.

[0063] Step 6: Arrange the travel probabilities calculated in step 5 from large to small, determine the community Q corresponding to the 75% percentile, call the map API interface, and calculate the travel time h from the centroid of community Q to the railway station k* to be studied based on the actual road network. This time h is the service radius time of the railway station k* to be studied.

[0064] Step 7: Based on the actual road network, calculate the longest travel distance of the corresponding transportation modes (urban rail transit, buses, cars, etc.) from the railway station k* to be studied to each direction within the service radius time h, connect all the farthest points into a closed irregular area to form a traffic isochrone circle, and form the final station service range.

[0065] Calculation example:

[0066] my country has a vast territory, and the topography, economic development level, urban form, population density and high-speed rail network density of different regions are quite different. In order to facilitate analysis, this project selected some stations in seven major regions of my country, namely Northeast China, North China, East China, South China, Central China, Northwest China and Southwest China, for relevant research.

[0067] Taking Shanghai Hongqiao Station in East China as an example, its service radius is calculated as follows:

[0068] Shanghai Hongqiao Railway Station is located in Minhang District, Shanghai, China. It is connected to the Beijing-Shanghai High-Speed ​​Railway and the Shanghai-Wuhan-Chengdu High-Speed ​​Railway at the north end, and to the Shanghai-Kunming High-Speed ​​Railway and the Shanghai-Hangzhou-Ningbo Passenger Dedicated Line at the south end. It is a component of the Shanghai Hongqiao Comprehensive Transportation Hub and the most important and largest railway passenger hub in East China.

[0069] For Shanghai Hongqiao Station, the research destination is selected from Shanghai to Beijing as the calculation basis, and Shanghai is divided into traffic communities according to the fourth-level administrative division, that is, town / township / street. Each community is located at the location of the local government / residential committee as the community centroid, and a total of 264 traffic communities are divided. The search found 9 transportation stations in Shanghai and surrounding areas that can go to Beijing (considering railway, long-distance bus, and aviation stations), and the detailed information of the stations is shown in Table 1. The average time value of residents in the city in 2018 was 63.92 yuan / hour.

[0070] Table 1 Shanghai and surrounding areas - Beijing station information

[0071]

[0072]

[0073] Combining the above data and taking into account the direct driving situation, using the high-speed railway station service range calculation model, it is found that 222 of the 264 communities have the lowest generalized cost of traveling through Shanghai Hongqiao Station (that is, the generalized cost of reaching Beijing via Shanghai Hongqiao Station in these 222 communities is the lowest compared to other stations). These 222 communities are the coverage of Shanghai Hongqiao Station. The 222 communities are arranged from large to small according to the strength of their willingness to choose Shanghai Hongqiao Station (the probability of choosing Shanghai Hongqiao Station for travel), and the arrival time corresponding to the 75% quantile is taken as the service radius of Shanghai Hongqiao Station. The final local service radius of Shanghai Hongqiao Station is 1.2h.

[0074] Using the same algorithm, the calculation range is expanded from the city to the city and surrounding areas, and the wide-area service radius of Shanghai Hongqiao Station is obtained to be 1.75h.

[0075] The calculation results for the seven major regions of Northeast China, North China, East China, South China, Central China, Northwest China and Southwest China are summarized in Tables 2, 3 and 4.

[0076] Table 2 Summary of calculation results for provincial high-speed railway stations

[0077]

[0078]

[0079] Table 3 Summary of calculation results of prefecture-level city high-speed railway stations

[0080]

[0081] Table 4 Summary of calculation results of high-speed railway stations in county-level cities

[0082]

[0083]

[0084] Traffic isochronous circles refer to the area boundary lines drawn by travel time. The travel time from any point on this closed line to the designated center is equal. Isochronous circles can clearly show the land area that can be reached within the specified travel time starting from a certain attraction point in the city. For the station distribution problem, isochronous circles can be drawn based on the wide-area service range (such as Figure 2 , 3 , 4), record the towns covered along the line under the station distribution plan and the arrival time of the towns and other data. It is understandable that if Figure 2 , 3 , 4 show the wide-area service range and local-area service range of the station for urban rail transit. Of course, if other modes of transportation are used, such as buses, cars, etc., isochrone circles for the corresponding modes of transportation can also be drawn.

[0085] Example 2

[0086] A high-speed railway service range calculation device includes at least one processor and a memory communicatively connected to the at least one processor; the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the high-speed railway service range calculation method as described in Example 1.

[0087] Example 3

[0088] A computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of the high-speed railway service range calculation method as described in Example 1 are implemented.

[0089] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for calculating the service range of a high-speed railway, characterized in that: The following steps are involved: Step 1: Divide the prefecture-level city / province to which the railway station k* to be studied belongs into traffic zones; Step 2: Search and analyze to obtain the railway stations, bus stations and aviation stations that can reach the destination j in the prefecture-level city / province of the railway station k* to be studied; Step 3: Calculate the generalized travel cost of passengers in transportation area i to destination j; When choosing public transportation, the generalized travel cost is: Where U i,j,k is the generalized travel cost from transportation community i to destination j via station k, AC i,k , AT i,k is the transportation cost and time for residents of transportation community i to travel to station k, F k,j is the average fare from station k to destination j, WT k The waiting time of passengers at station k, IT k,j is the passenger’s time on board, α is the adjustment parameter, and f k,j is the service frequency from station k to destination j, T k,business is the business hours of station k; vot is the average time value of passengers, vot = GDP / (population·worktime), GDP is the annual GDP of the prefecture-level city to which the railway station k* to be studied belongs, population is the total population of the prefecture-level city to which the railway station k* to be studied belongs, and worktime is the annual working hours of the prefecture-level city to which the railway station k* to be studied belongs; When self-driving is chosen, k is 0, and the generalized travel cost is: U i,j,k =dis i,j ·cost per.km +time i,j ·vot+toll i,j , In the formula, dis i,j is the driving distance from traffic area i to destination j, cost per.km is the driving cost per kilometer; time i,j is the time it takes to drive from traffic zone i to destination j; toll i,j The highway toll for driving from traffic community i to destination j; Step 4: Select the community with the lowest generalized travel cost from the railway station k* to the destination j from all the traffic communities, and select n coverage communities of the railway station k* to be studied; Step 5: Based on the MNL discrete model, calculate the probability that passengers in n coverage cells choose to go to destination j via the railway station k* to be studied: In the formula, is the probability that a passenger in transportation zone i chooses to go to destination j via the railway station k* to be studied, is the generalized travel cost from transportation community i to destination j via the railway station k* to be studied, U i,j is the average generalized travel cost between places i and j, l is the total number of optional stations, k = 0, 1, 2, 3…l; Step 6: Arrange the travel probabilities calculated in step 5 from large to small, determine the community Q corresponding to the set quantile, call the map API interface, and calculate the travel time h from the centroid of community Q to the railway station k* to be studied based on the actual road network. This time h is the service radius time of the railway station k* to be studied; Step 7: Based on the actual road network, calculate the longest travel distance of the corresponding transportation mode from the railway station k* to be studied to each direction within the service radius time h, connect all the farthest points into a closed irregular area to form a traffic isochrone circle, and form the final station service range.

2. A method for calculating the service range of a high-speed railway according to claim 1, characterized in that: In the step 1, the fourth-level administrative division, i.e., town / village / street, is used to divide the traffic communities, and the location of the local government / residents' committee is used as the centroid of each traffic community.

3. A high-speed railway service range calculation method according to claim 1, characterized in that: In the step 2: searching and analyzing based on map software to obtain the railway stations, bus stations and aviation stations included in the prefecture-level city / province to which the railway station k* to be studied belongs.

4. A high-speed railway service range calculation method according to claim 1, characterized in that: In the step three: use python to call the map API interface to obtain the transportation cost and time based on the actual road network.

5. A method for calculating the service range of a high-speed railway according to claim 1, characterized in that: In the step 4: there are m transportation cells, i = 1, 2, 3, ... n, ... m. According to the step 3, the generalized travel cost of a certain transportation cell to reach the destination j via each optional station is calculated respectively. If the generalized travel cost of the transportation cell via the railway station k* to be studied is less than that of other optional stations, then the cell is considered to be the coverage cell of the railway station k* to be studied.

6. A method for calculating the service range of a high-speed railway according to claim 5, characterized in that: In step 5, all calculation results form a selection probability matrix [P i,j, k ] (m×l)+1 ,like is the maximum value of the i-th row of the matrix, it means that the passengers of transportation community i will choose to travel through the railway station k* to be studied, otherwise the passengers of transportation community i will not choose to travel through the railway station k* to be studied.

7. A method for calculating the service range of a high-speed railway according to claim 1, characterized in that: In step six: the quantile is set to 75%.

8. A method for calculating the service range of a high-speed railway according to any one of claims 1 to 7, characterized in that: In the step 7: the modes of transportation include urban rail transit, buses and cars.

9. A high-speed railway service range calculation device, characterized in that: comprising at least one processor, and a memory communicatively connected to the at least one processor; The memory stores instructions that can be executed by at least one processor, and the instructions are executed by at least one processor so that the at least one processor can execute the high-speed railway service range calculation method as described in any one of claims 1-8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the high-speed railway service range calculation method as described in any one of claims 1 to 8 are implemented.

Images