CN116341944B - Urban public transportation sustainable suitable development area assessment method - Google Patents
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Abstract
The application discloses an urban public transport sustainable suitable development area assessment method, which comprises the following specific steps: and taking the grids as a space analysis unit, and obtaining the peak hour trip times between any two grids according to the mobile phone signaling data. And integrating actual travel distances among grid centers obtained by combining with the Goldmap path planning API, and then analyzing the sharing rate of ground buses under different distance sections according to resident travel investigation data, or called a bus sharing rate-distance curve. And then, according to the travel times of the peak hours among the grids and the sharing rate-distance curve, collecting the travel times of the buses of the peak hours among the grids, and correcting the travel times of the buses of the peak hours of the grids by combining the data of the bus IC card. The minimum passenger flow threshold value for providing ground public transport service is scientifically judged, and then the sustainable and proper development area of urban public transport is identified. The application can provide a method reference for the sustainable development of ground buses from the urban space level.
Description
Technical Field
The application relates to the field of traffic information technology assessment, in particular to an urban public transportation sustainable development area assessment method.
Background
The paris agreement emphasizes the importance of climate safety and proposes the goal of limiting the rise in global average surface temperature to below 2 ℃ (even 1.5 ℃) at pre-industrialisation levels. Many countries have begun taking relevant measures to mitigate the potential effects of climate change.
Compared with a car, the public transportation has the characteristics of large transportation capacity and low energy consumption, is generally considered as a sustainable transportation mode, can be used as a key tool for reducing dependence of the car, and in turn reduces negative effects caused by the car. In terms of environment, the carbon emission of a public transportation single person per kilometer is smaller in a reasonable travel range. It is predicted that an increase in U.S. mass transit occupancy of 9% in 2050 will reduce the carbon dioxide emissions of 76.6 ten thousand tons per year, and when occupancy increases to 25%, the cumulative carbon dioxide emissions of 6130 ten thousand tons will be reduced. In 2019, the average kilometer discharge factor of the Shenzhen urban traffic is 0.0812 (kgC 02/pkm), the average kilometer carbon discharge of the pure electric bus is 0.0543, and the subway is only 0.0345. Increasing the share of public transportation is also beneficial to reducing land consumption, improving transportation, improving economic efficiency and promoting economic development.
Traffic affects the "ecological footprint" of a city through an interactive relationship with land utilization. Spatial layout is positively correlated with traffic energy usage, and street structures suitable for walking and unsuitable for automotive traffic tend to exhibit lower energy usage. A compact and mixed use, the urban form of the human scale, especially the traffic mode, plays a significant role in the sustainable development of cities. The city should be turned to a space development form guided by buses. The existing research is focused on researching the operation efficiency and service level of the public transportation system from the space level, and as a result, the accessibility and the life quality improvement of the whole society cannot be ensured. Sustainability is the core of land utilization system and traffic system interactions, and research on public transportation space sustainability (The spatial sustainability of public transport) cannot be ignored.
Sustainability or sustainable development (sustainability or sustainable development) has been the subject of the era (the theme of our time) since 1970 s. Over the years, sustainability has now created a triple view (or triple bottom line), the environment, economy and society. While relatively few studies on space sustainability have been conducted, mainly in the area of regional economic research, or urban ecological footprint, or urban space structure, or from an economic geography perspective, the geographic environment of sustainable transition (sustainability transitions) has been studied. The definition of spatial sustainability (spatial sustainability) referred to herein is similar to that proposed by Nijkamp (1996), the implication of the latter being: the social and economic development and ecology compatibility in the space system are considered, and all the fields in the system conflict with each other and complement each other. Since public transportation has dual nature, defining the space sustainability (the spatial sustainability of public transport) of public transportation as the space allocation of public transportation lines and services can achieve a balance of social and economic benefits.
At a social level, sustainable public transportation targets include: (1) social progress; (2) fairness; (3) sense. Fairness sense of public transportation is spatially embodied as spatial allocation of public transportation routes and services. The social rejection caused by unreasonable space allocation is why the vulnerable group (Vulnerable groups) is marginal in economic, social and citizen lives. In a highly mobile society and environment, a wealthy group cannot participate in the economic, political and social life of the community due to reduced accessibility to opportunities, services and social networks. With the development of computer technology and big data analysis, it has become relatively easy to evaluate public transportation reachability and fairness through objective and quantifiable metrics of potential demand, supply (quantity and quality), distance (in time and space), etc. However, the existing evaluation method often selects an evaluation index based on a specific study subject or study area, which results in that the study result is difficult to reflect actual values of a public transportation service level (service level) and an operation efficiency (operational efficiency), and the comparability and transferability of the result are damaged (comparability between places and transferability of findings).
On an economic level, a transportation system is considered as a facility or service provider that meets the demand for passenger and cargo transportation, and the cost of the transportation system is composed of fixed facility cost, transportation cost, and external cost. The sustainable public transportation system is required to meet the balance of cost-benefit, and is particularly embodied in that sufficient funds exist to maintain public transportation operation and expand the system, and meanwhile, the recovery of the system can be realized through fare income and the like. In general, fare revenues for public transportation are difficult to cover the total cost, this difference being only complemented by subsidies for public departments. How to balance the social benefits of traffic services and the subsidy burden of local governments has become an important issue. Traffic subsidy can improve overall social welfare and promote social fairness because it increases mobility of low-income groups and reduces spatial barriers to employment and consumption thereof. This is the "financial conflict (property conflict)" problem posed by Campbell, which exists between economic and social aspects. The existing research mainly analyzes space problems such as traffic jam, traffic accident, traffic stop and the like from the perspective of price and cost, focuses on the research on traffic problems caused by cars, and lacks attention on public transportation.
The purpose of public transportation is to provide the public with mobility through cities and to specific areas of the city. Its efficiency is based on transporting large numbers of people and achieving economies of scale. Due to variations in the socioeconomic characteristics of the population, particularly in employment and family, there is a greater likelihood of having a significant impact on public transportation. So previous research is directed to analyzing the relationship between the spatial distribution of urban population and public transportation network to evaluate whether public transportation network can meet population demand. Based on the social-economic perspective, the space sustainability of public transportation in Nanjing city in China is analyzed by utilizing data such as mobile phone signaling big data and resident trip investigation (the spatial sustainability of public transport), and a public transportation sustainable development area is identified (public transport sustainable development zones). First, the space sustainable development level (the spatial sustainability of bus) of ground buses is analyzed based on the number of bus trips in peak hours (the number of peak hour bus trips). Not all ground bus routes can realize profitability. Governments will secure the rights and interests of the residents in the border and low density areas by subsidizing the loss lines. Secondly, analyzing the space sustainable development level of the rail transit under a certain subsidy level based on the occupancy density (the spatial sustainability of rail transit). The construction of the rail transit plays a vital role in urban development, and can guide land utilization layout, industry and population gathering and the like. But the track traffic line has long construction period, high construction cost and difficult balance between operation income and cost, and subway operation enterprises are easy to sink into financial bare words, and usually require government subsidies. The analysis results of the two can respectively guide the layout planning of the network and the station of the ground public transportation and the rail transportation under the condition that a certain admission threshold of the living population is met.
Sustainable traffic is different from non-sustainable traffic in terms of social and economic contribution. The inconstant traffic such as private cars generates negative exteriority while obtaining convenience, and the costs of road congestion, environmental pollution and the like caused in the use process are commonly born by users in other various traffic modes. Users of non-motor vehicles and buses are not only exposed to risks of hazards, noise and pollutants, but also short plates in terms of space acquisition access, facility supply, and also time differences from private cars. The fair distribution of traffic emphasizes that individuals can obtain equal traffic opportunities if they wish, and that this opportunity is not sacrificed by increasing the level of accessibility of others. Therefore, under the sustainable development goal, fairness is considered, and the fairness of traffic investment, traffic planning and traffic management is tried to be realized, so that the intensified public transportation is advocated to the urban development.
Public welfare of public transportation is superior to economy, passengers are guaranteed to obtain equal traffic opportunities, and low-fare public products with high quality are provided for the passengers. The line is divided into a hot line and a cold line, the hot line refers to a public transport line with more passengers and higher benefits, and the cold line is opposite. The cold line is less likely to have financial loss due to passenger traffic, but the loss is generally subsidized by government under the guidance of public welfare, thereby realizing fairness of public transportation and covering more areas with lower population density.
In terms of accessibility allocation fairness, minimum accessibility should be provided, while the vulnerable group is prioritized, minimum accessibility criteria can be set and secured by policies. The bus support zone not only takes economy into account, but also includes fairness. In order to meet the fairness of the bus mode of the residents in space and reduce the difference between the bus accessibility and the bus accessibility, public transportation service can be standardized by using population density supporting bus development, and the travel demands of the residents are met. The conventional public transportation supporting area has potential public transportation trip amount, and the trip amount exceeds a certain threshold value, so that the area for developing public transportation service can be supported.
CN 109670671A discloses a bus network evaluation method and device, and obtains a plurality of travel demand information, wherein each travel demand information comprises a user travel starting point, a user travel ending point and travel times; calculating path planning indexes corresponding to the travel demand information according to traffic data of a public transportation network and a subway network and combining the travel demand information; the path planning indexes corresponding to the travel demand information comprise one or more of walking time, walking distance, waiting time, transfer times, transfer time and riding time; and evaluating and analyzing the travel efficiency of the public transportation network according to the path planning index corresponding to each travel demand information. The method only evaluates the travel efficiency of the public transportation network according to indexes such as walking time, walking distance, waiting time, transfer times, transfer time, riding time and the like of the public transportation network. Accurate evaluation cannot be made for the proper development area of the buses in the area.
CN 114298880A discloses a method for determining the scale of urban land based on dominant travel distance in public transportation mode, comprising the steps of: obtaining the relationship characteristics of the specific weight of the resident traveling in different traffic modes along with the traveling distance according to the resident traveling characteristics obtained by pre-investigation; based on the mobile phone signaling big data, a grid starting and ending distribution map is manufactured, and frequency maps of travel people in different distance sections are further obtained; multiplying the proportion of each traffic mode of different travel distance sections by the travel times of the different travel distance sections to obtain travel frequencies of the different traffic modes in each distance section and curves thereof; and finally, taking a distance section with dominant public transport travel as a basis for determining reasonable city scale. The application combines resident investigation data and mobile phone signaling big data, and provides basis for scientifically making reasonable scale of public transportation guiding type city through travel distance quantitative analysis and dominant distance judgment under traffic view angle. The emphasis is on providing theoretical basis on the scale and the type of public transportation, but the theoretical basis for sustainable development of ground public transportation cannot be provided.
Disclosure of Invention
In order to solve the defects in the prior art, the application provides an evaluation method for sustainable and suitable development areas of urban buses, which is compatible with sociality and economy.
In order to achieve the purpose of the application, the technical scheme adopted by the application is as follows:
a city bus sustainable development area suitable evaluation method comprises the following steps:
step 1), acquiring travel people number data through mobile phone signaling, using grids as space statistics units, identifying residence places and working places of each ID according to the mobile phone signaling data, and further counting to obtain travel people number t of peak hours between any two grids ij ；
Step 2) obtaining travel distance between travel starting and ending points of each time based on a Goldpath planning API according to travel starting and ending point information in resident travel survey data;
step 3) obtaining ground bus sharing rates under different distance sections, namely a bus sharing rate-distance curve, after normalization processing according to travel mode data in resident travel survey data;
step 4) according to the bus sharing rate-distance curve of the step 2) and the step 3), obtaining the travel distance between grid centers based on the Goodyear path planning API, determining the bus travel sharing rate between any two grids, and multiplying the bus travel sharing rate by the travel times t between any two grids in the step 1) ij Obtaining the bus travel amount between any two grids;
step 5) respectively calculating the bus travel times of getting on the bus from the grid i and the bus travel times from other grids to the grid i, and summarizing to obtain the total quantity Q of the up-down passenger flow of the grid i i
Q i ＝∑ j ((t ij ·f(d ij ))+∑ j ((t ji ·f(d ij )) (1)
In the formula (1),
t ij peak hour travel times for grids i through j
f(d ij ): bus sharing rate-distance curve corresponding to ground bus departure from grid i to grid jLine sharing rate
t ij ·f(d ij ) Representing the number of bus trips from grid i to grid j
∑ j ((t ij ·f(d ij ) The summation result is the number of people on bus in the grid i, and passengers go to other grids
Similarly, sigma j ((t ji ·f(d ij ) A) represents the number of passengers getting off the bus in grid i, and passengers come from other grids
Q i The number of people getting on or off buses in the grid i in peak hours;
step 6) correcting Q of each grid according to the bus IC card data i Obtaining the bus travel times of each grid of the corrected peak hours
K in the formula (2) is a correction coefficient based on bus IC card data, as follows:
m, total bus card swiping times (based on bus IC card swiping data);
step 7) determining a lowest threshold value for providing bus travel times in the grid peak hours;
step 8) identifying a set of grids with the lowest threshold value of the bus travel times in each peak hour in the single grid, namely the suitable development area of the bus, wherein the bus travel times in the peak hours in the single grid are more than or equal to those in step 7).
Because only one operator provides mobile phone signaling data, the result is smaller than the actual value, so that the bus card swiping data is used for carrying out sample expansion processing on the calculation result, and the error is reduced as much as possible.
Further, the specific steps in the step 1) are as follows: according to the operator's handThe machine signaling data identifies the residence and the workplace of each ID, so as to construct an OD matrix of each grid, and peak hour travel times t between any two grids are obtained after further statistics ij 。
Further, the grid in the step 1) uses 500m×500m as a space statistics unit.
Further, the method for calculating the sharing rate of the ground buses in Nanjing city in different distance sections in the step 3) comprises the following steps: ground bus sharing rate under a certain distance section=ground bus travel times/total travel times under the distance section 100%.
Further, the bus trip sharing rate f (d) in the step 4) ij ) The specific method comprises the following steps: inquiring the corresponding ground bus sharing rate obtained in the step 3) according to the travel distance between any two grids obtained in the step 2).
Further, from the perspective of social or fairness and public welfare, bus service is required to be provided even if passenger flow is low; from the economical point of view, the passenger flow is low, and the bus operation loss is serious. Therefore, considering the sustainability of the social and economical properties of buses, it is assumed that the minimum criteria for providing bus services are: in the normal departure interval of 10-15 minutes, each station has at least 1 bus taking person (including getting on and off); and obtaining the lowest threshold value of 10 people, which is provided by 500m x 500m grid peak hours, of bus travel times according to the bus network density and the bus station spacing required by the related specifications.
The peak in the application refers to the time period with highest daily traffic volume, and usually has five to seven points from seven to nine points of the early peak and five to seven points of the late peak.
Compared with the prior art, the method has the advantages that the method can obtain the relatively accurate travel times of the ground buses in the area in each peak hour, further can accurately grasp the operation of the ground buses in the area, avoids the limitation that the evaluation of the travel times is only carried out on a single bus station or a single bus line in the prior art, and can provide theoretical basis and reference for sustainable development of the ground buses and consideration of fairness (sociality) and economy.
Drawings
Fig. 1 is a flow chart of the present application.
Fig. 2 is a road map of the number of people on the bus trip at peak hours of each grid.
Fig. 3 is a graph showing travel distance in the tokyo city in this embodiment.
Fig. 4 is a graph showing the distribution of the number of people on the bus trip at peak hours of each grid.
Fig. 5 is a schematic diagram of a conventional bus development support area in an embodiment.
Fig. 6 is a diagram of a conventional bus service level in a bus support area in an embodiment.
Detailed Description
The technical scheme of the application is further described below with reference to the accompanying drawings and examples. The following examples are only for more clearly illustrating the technical aspects of the present application, and are not intended to limit the scope of the present application.
The research range of the embodiment is in the urban area of Nanjing, and Nanjing is operated by three enterprises of Jiangnan public transportation, yangzi public transportation and Jiang Ning public transportation by 2017, wherein the total number of operation lines is 620, and the passenger transportation times in 2017 are 85931 ten thousand, and the operation data are all from the capital performance evaluation report of financial subsidy special for the cost regulation of Nanjing public transportation enterprises in 2017. Specific operational data are shown in table 1 below.
Table 1 summary of operational data of public transportation enterprises in 2017 in Nanjing city
By 2017, the motorized travel sharing rate of public transportation in the central urban area of Nanjing city reaches 63.1%, and the coverage rate of public transportation sites is 100%.
As shown in fig. 1 and 2, the present embodiment uses a 500×500m grid as a spatial statistics unit. The method comprises the following specific steps: step 1), acquiring travel number data through mobile phone signaling, using 500 x 500m grids as a space statistics unit, identifying residence and work places of each ID according to the mobile phone signaling data, and further counting to obtain peak hour travel number t between any two grids ij ；
Step 2) obtaining travel distance between travel starting and ending points of each time based on a Goldpath planning API according to travel starting and ending point information in resident travel survey data;
step 3) obtaining ground bus sharing rates under different distance sections, namely bus sharing rate-distance curves, according to travel mode data in resident travel survey data after normalization processing, wherein the ground bus sharing rate-distance curves of Nanjing city are shown in fig. 3;
step 4) according to the bus sharing rate-distance curve of the step 2) and the step 3), obtaining the travel distance between grid centers based on the Goodyear path planning API, determining the bus travel sharing rate between any two grids, and multiplying the bus travel sharing rate by the travel times t between any two grids in the step 1) ij Obtaining the bus travel amount between any two grids;
step 5) respectively calculating the bus travel times of getting on the bus from the grid i and the bus travel times from other grids to the grid i, and summarizing to obtain the total quantity Q of the up-down passenger flow of the grid i i
Q i ＝∑ j ((t ij ·f(d ij ))+∑ j ((t ji ·f(d ij )) (1)
In the formula (1),
t ij peak hour travel times for grids i through j
f(d ij ): bus sharing rate-distance curve corresponding to ground bus travel sharing rate from grid i to grid j
t ij ·f(d ij ) Representing the number of bus trips from grid i to grid j
∑ j ((t ij ·f(d ij ) The summation result is the number of people on bus in the grid i, and passengers go to other grids
Similarly, sigma j ((t ji ·f(d ij ) A) represents the number of passengers getting off the bus in grid i, and passengers come from other grids
Q i The number of people getting on or off buses in the grid i in peak hours;
step 6) correcting Q of each grid according to the bus IC card data i Obtaining the bus travel times of each grid of the corrected peak hours
K in the formula (2) is a correction coefficient based on bus IC card data, as follows:
m, total bus card swiping times (based on bus IC card swiping data);
step 7) determining a lowest threshold value for providing bus travel times in the grid peak hours;
step 8) identifying a set of grids with the lowest threshold value of the bus travel times in each peak hour in the single grid, namely the suitable development area of the bus, wherein the bus travel times in the peak hours in the single grid are more than or equal to those in step 7).
The final distribution is shown in fig. 4. The overall distribution of the number of bus trips in the peak hours shows the situation that the number of bus trips is high from inside to outside, the area with higher number of bus trips is mainly distributed in a main urban area, and the area is secondly a mountain area at the southwest side of the main urban area, and the new areas in the northwest are distributed in a punctiform and banded manner.
500m grid is taken as a basic research unit, and the density of a public bus network constructed in public buses and cities is 3 km/square km, so that the length of a public bus line in a single grid is 0.75km. The distance between bus stops is generally 300m-500m, and in this embodiment, 400m is taken as the average distance between bus stops for calculation, so that the number of bus stops in a single grid is 1.875. Under fairness or public welfare principles, public transportation should meet the needs of all people under ideal conditions. When the number of people in a region exceeds a certain value, bus stops and routes are correspondingly arranged, and the aim of simultaneously serving core passengers and edge passengers is fulfilled, so that spatial fairness is revealed. The bus departure interval of the urban buses in China is 10-15 minutes, and an average value is taken, in the embodiment, the public welfare threshold value is 5 times of the number of passengers at one bus stop in a peak hour (namely, at least one bus is taken in each bus), and the travel time of the public welfare lowest threshold value in each peak hour in a single grid (25 hectares) is 5 times of 1.875 and about 10 times.
As shown in figure 5, the support area for the development of the conventional buses with more than 10 persons in the grid is developed in a bulk form between the main urban area of the south of the Yangtze river and the Xianlin and east mountain connecting sheets, the finger development 'palm' is larger than the range of the main urban area, and the 'finger' boundary is unclear; the Jiangbei area is in a strip shape, has a longer length, extends and develops from the north side to the six-in area, and the six-in area development support area is radial. In the figure, the northwest band-shaped and southward bulk-shaped areas are connected with each other near the Yangtze river bridge of Nanjing, and the reason is probably that the number of traveling people is more in the hours of the peak of two banks of the Yangtze river near the area, and certain deviation occurs when the population of the grid is converted according to the range of the mobile phone base station. The areas where the conventional bus lines pass are not all areas where the development of buses should be supported, and some bus lines are mainly distributed in peripheral areas, such as Jiang Ning, longpao, pukou and the like, which shows that the minimum passenger threshold considered when the bus lines are set in Nanjing is smaller than the public welfare minimum threshold of the study, and the adaptive bus lines are set for remote areas.
Under the social view, the overlapping distribution of the service range of the conventional bus stop 500m and the bus development support area is shown in fig. 6, and the uncovered range of the conventional bus belongs to the bus support area but the uncovered range of the service range of the bus stop 500 m. The areas with public transportation demands and public transportation unserviceable are mainly distributed on two sides of a northwest banded development shaft in the south New City, zhong Shan area periphery, tianbao subway station periphery, jiangxin African area, long Lu subway station periphery and Jiangbei area in the south of Qinhuai. The south New City area in south Beijing is under development and construction, and multiple stations of track traffic No. 5, no. 6 and No. 10 in the second period are under construction in the land, so that public transportation service is lacking. The public transportation stations are arranged around Zhong Shan because of natural geographic conditions and development restrictions, so that the public transportation in partial areas is not covered. The periphery of the Tianbao subway station is positioned on the south side of the fish mouth and is currently used as a village construction land such as Shi Cun, so that buses do not provide service. The reason that the river-center continent area has larger bus travel demands and smaller service range is that the bus operation is difficult to support due to the particularity of the geographical position of the river-center continent, the area of the continent is smaller, and the bus connection between the river-center isolation and the main urban area is inconvenient. The long-reed subway station is located in a six-in area, the periphery of the long-reed subway station is a construction land for a Yangzi chemical garden and a village, the city traffic service belongs to internal traffic, and bus stations are required to be arranged at entrances and exits of the garden to meet requirements. Overall, the public transport service level in the south Beijing city is higher, and the proportion of the conventional public transport service range to the public transport supporting area is more than 80%. Besides the analysis area, no public traffic service exists in the local punctiform area, so that the public traffic coverage rate is improved in space, and the public service level is improved.
While the applicant has described and illustrated the embodiments of the present application in detail with reference to the drawings, it should be understood by those skilled in the art that the above embodiments are only preferred embodiments of the present application, and the detailed description is only for the purpose of helping the reader to better understand the spirit of the present application, and not to limit the scope of the present application, but any improvements or modifications based on the spirit of the present application should fall within the scope of the present application.
Claims (5)
1. The method for the proper development area of the bus is characterized by comprising the following steps of:
step 1), acquiring travel people number data through mobile phone signaling, using grids with a certain fixed size as a space statistics unit, identifying residence and work places of each ID according to the mobile phone signaling data, and further counting to obtain peak hour travel people number t between any two grids ij ；
Step 2) obtaining travel distance between travel starting and ending points of each time based on a Goldpath planning API according to travel starting and ending point information in resident travel survey data;
step 3) obtaining ground bus sharing rates under different distance sections, namely a bus sharing rate-distance curve, after normalization processing according to travel mode data in resident travel survey data;
step 4) according to the bus sharing rate-distance curve of the step 2) and the step 3), obtaining the travel distance between grid centers based on the Goodyear path planning API, determining the bus travel sharing rate between any two grids, and multiplying the bus travel sharing rate by the travel times t between any two grids in the step 1) ij Obtaining the bus travel amount between any two grids;
step 5) respectively calculating the bus travel times of getting on the bus from the grid i and the bus travel times from other grids to the grid i, and summarizing to obtain the total quantity Q of the up-down passenger flow of the grid i i
Q i ＝∑ j ((t ij ·f(d ii ))+∑ j ((t ji ·f(d ij )) (1)
In the formula (1),
t ij peak hour travel times for grids i through j
f(d ij ): bus sharing rate-distance curve corresponding to ground bus travel sharing rate from grid i to grid j
t ij ·f(d ij ) Representing the number of bus trips from grid i to grid j
∑ j ((t ij ·f(d ij ) The summation result is the number of people on bus in the grid i, and passengers go to other grids
Similarly, sigma j ((t ji ·f(d ij ) A) represents the number of passengers getting off the bus in grid i, and passengers come from other grids
Q i The number of people getting on or off buses in the grid i in peak hours;
step 6) correcting Q of each grid according to the bus IC card data i Obtaining the bus travel times of each grid of the corrected peak hours
K in the formula (2) is a correction coefficient based on bus IC card data, as follows:
m, total bus card swiping times;
step 7) determining a lowest threshold value for providing bus travel times in the grid peak hours; the lowest threshold determining method comprises the following steps: assuming that the lowest standard for providing the public transport service is within a normal departure interval of 10-15 minutes, each station has at least 1 public transport riding number of passengers, and then obtaining the lowest threshold for providing public transport riding number of passengers for 10 people in 500m x 500m grid peak hours according to the public transport network density and the public transport station spacing required by relevant specifications;
step 8) identifying a set of grids with the lowest threshold value of the bus travel times in each peak hour in the single grid, namely the suitable development area of the bus, wherein the bus travel times in the peak hours in the single grid are more than or equal to those in step 7).
2. A method of mass transit suitable development area as in claim 1 wherein: the specific steps in the step 1) are as follows: the residence place and the working place of each ID are identified according to the mobile phone signaling data provided by an operator, so that an OD matrix of each grid is constructed, and peak hour travel times t between any two grids are obtained after further statistics ij 。
3. A method of mass transit suitable development area according to claim 1 or 2, characterized in that: the grid in the step 1) takes 500m by 500m as a space statistics unit.
4. A method of mass transit suitable development area as in claim 3 wherein: the method for calculating the sharing rate of the ground buses under different distance sections in the step 3) comprises the following steps: ground bus sharing rate under a certain distance section=ground bus travel times/total travel times under the distance section 100%.
5. A method of mass transit suitable development area as in claim 3 wherein: the bus trip sharing rate f (d) in the step 5) ij ) The specific method comprises the following steps: inquiring the corresponding ground bus sharing rate obtained in the step 3) according to the travel distance between any two grids obtained in the step 2).
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