CN111540195A - Regional traffic reachability evaluation method based on traffic big data - Google Patents
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Abstract
The invention discloses a method for evaluating regional traffic reachability based on traffic big data, which comprises the following steps: (1) determining the boundary of an evaluation area and the number of concentrated areas to be evaluated in the boundary; (2) determining the boundary of the peripheral area and the number of concentration areas participating in evaluation; (3) rasterizing all selected concentration areas; (4) setting a weight coefficient of the grid; (5) setting an evaluation operation parameter; (6) collecting road time data; (7) screening and processing the road time data; (8) and (4) calculating the regional traffic accessibility level of each grid and each concentration region in the evaluation region according to the data in the step (7). According to the method for evaluating the regional traffic reachability based on the traffic big data, the evaluation or estimation precision and the calculation efficiency can be considered, the regional traffic reachability evaluation or research in different regions can be evaluated or researched efficiently, comprehensively and consistently, and better public decisions can be made.
Description
Technical Field
The invention relates to comparison and evaluation of regional traffic reachability of different regions in a region, in particular to a regional traffic reachability evaluation method based on traffic big data.
Background
Regional transportation is a comprehensive transportation system that transports over a wide range of towns, counties, cities, provinces, and nationwide. In the past, the accessibility evaluation of regional traffic is more evaluation of a large scale range between cities, and the time inside the cities is not considered or roughly considered on the basis of the travel time of large traffic. For example, for a railway, the time between cities is the time from a railway station to a train, and at most, the average time from a key area inside the city to the railway station is added; for a highway, the time between cities is the time from the toll station to the toll station, plus the average time from the inside or key area of the city to the toll station. With the development of high-speed rail in China, the rough time-based assessment method is not suitable for the high-quality development of regional traffic based on the concept of 'travel as a service'. For example, jiaxing from shanghai to zhejiang province, from shanghai hong bridge station to jiaxing south station, the time for getting on high-speed rail is less than 30 minutes, most areas in shanghai city have more than 30 minutes to the hong bridge station, the time for jiaxing south station to get to the old city center in jiaxing city is about 30 minutes, and the urban traffic time of two places actually accounts for more than half of the whole trip time of regional traffic. Therefore, in the face of the acceleration development of regional traffic in a wide range and the expansion of the range of each regional area, the evaluation based on the station-to-station time only or the average station-to-station time of the city is not accurately reflected on the actual accessibility condition of the regional traffic.
The evaluation of rough time can reflect certain conditions due to the fact that the proportion of urban time to regional traffic time is not prominent because of the limitation of obtaining travel data and the fact that regional traffic speed is low. However, with the development of high-quality regional traffic and the appearance of travel big data, the travel time in cities and regions is easy to acquire, and a method for evaluating regional traffic by using the 'door-to-door' travel time is provided. Since regional traffic involves a wide geographical range, traveling time from actual "door to door" results in excessive data demand and even data loss, and such high position accuracy is not necessary from a regional perspective.
Therefore, it is highly desirable to design a new method for evaluating regional traffic reachability based on traffic big data, which can take into account both the precision and the calculation efficiency of evaluation or estimation, and simultaneously facilitate comprehensive and consistent comparison evaluation or research on regional traffic reachability in different areas in an area, and facilitate making better public decisions, for example, facilitate comprehensive consideration of the connection between city traffic and regional traffic and finding a feasible way for improving regional traffic reachability.
Disclosure of Invention
The invention aims to overcome the defects of the prior art that the prior art is lack, the precision and the efficiency can be considered, and meanwhile, a systematic method for evaluating or researching the regional traffic accessibility is provided.
The invention solves the technical problems by adopting the following technical scheme:
the invention provides an assessment method of regional traffic accessibility based on traffic big data, wherein a plurality of centralized areas such as cities and towns exist in a researched region, and the assessment method is characterized by comprising the following steps:
step one, determining an evaluation area boundary and the number of concentrated areas to be evaluated in the evaluation area boundary in a researched area;
determining the peripheral area boundary and the number of concentration areas participating in evaluation in the peripheral area boundary in a researched area;
performing rasterization processing on all selected concentration areas in the peripheral area and the evaluation area to form grids of each concentration area and determine the corresponding grid number;
setting weight coefficients of the grids;
setting evaluation operation parameters, wherein the operation parameters comprise a traffic time threshold and a traffic mode;
collecting road time data which starts from each grid in the evaluation area and respectively arrives at other grids in the grids through the traffic mode;
step seven, screening and processing the road time data to obtain road time data which is realized by adopting various traffic modes in the traffic modes independently and road time data which is realized by adopting a combined traffic mode;
and step eight, calculating the regional traffic reachability level of each grid in each concentration area in the evaluation area and the regional traffic reachability level of each concentration area in the evaluation area according to the data in the step seven.
The regional traffic accessibility level may be defined as a ratio of the number of grids of other concentration regions reachable within the traffic time threshold from the grid or all grids of the concentration region and their weighted values to the total number of grids of all other concentration regions and their weighted values. The ratio obtained from the starting of a single grid is the regional traffic accessibility level of the grid, and the average value of the ratios obtained from the starting of all the grids of the single concentrated region is the regional traffic accessibility level of the concentrated region.
According to some embodiments of the invention, the method of assessing further comprises:
step nine, checking the calculation result of the step eight, if the regional traffic accessibility level of any grid is 100% in the calculation of the regional traffic accessibility level of each grid in the evaluation area, returning to the step five, and reducing the value of the traffic time threshold; or returning to the step two, expanding the boundary of the peripheral area, and increasing the range of the existing concentration area participating in evaluation or adding a new concentration area participating in evaluation.
According to some embodiments of the invention, the method of assessing further comprises:
step nine, checking the calculation result of the step eight, if the regional traffic accessibility level of any one centralized region is 100% in the calculation of the regional traffic accessibility level of each centralized region in the evaluation region, returning to the step five, and reducing the value of the traffic time threshold; or returning to the step two, expanding the boundary of the peripheral area, and increasing the range of the existing concentration area participating in evaluation or adding a new concentration area participating in evaluation.
According to some embodiments of the invention, the fourth step further comprises: and setting the weight coefficient of each grid according to the regional characteristic information of each concentration region or each grid.
According to some embodiments of the invention, the regional characteristic information comprises a total number of average productions, a regional population density.
According to some embodiments of the invention, the step six of collecting the time-of-road data predicts the time-of-road data based on a known traffic model.
According to some embodiments of the invention, the step six of collecting the time-of-road data is obtained by collecting historical traffic big data.
According to some embodiments of the invention, the seventh step includes processing and obtaining the time-consuming number of time-of-road data implemented in the combined transportation means.
According to some embodiments of the present invention, all the grids formed by the rasterization process in the third step satisfy that the longest trip time in each grid is not greater than a preset time threshold corresponding to a transportation mode.
On the basis of the common knowledge in the field, the above preferred conditions can be combined randomly to obtain the preferred embodiments of the invention.
The positive progress effects of the invention are as follows:
according to the method for evaluating the regional traffic reachability based on the traffic big data, the evaluation or estimation precision and the calculation efficiency can be considered, the regional traffic reachability evaluation or research of different regions in the region can be efficiently, comprehensively and consistently performed, and better public decisions can be made, for example, the comprehensive consideration of the connection of urban traffic and regional traffic and the discovery of a feasible way for improving the regional traffic reachability can be facilitated. Therefore, the method has wide application prospect in planning and management of regional traffic, homeland space, urban traffic and the like.
Drawings
Fig. 1 is a flowchart illustrating a method for evaluating regional traffic reachability based on traffic big data according to a preferred embodiment of the present invention.
Detailed Description
The following detailed description of the preferred embodiments of the present invention, taken in conjunction with the accompanying drawings, is intended to be illustrative, and not restrictive, and any other similar items may be considered within the scope of the present invention.
In the following detailed description, directional terms, such as "left", "right", "upper", "lower", "front", "rear", and the like, are used with reference to the orientation as illustrated in the drawings. The components of various embodiments of the present invention can be positioned in a number of different orientations and the directional terminology is used for purposes of illustration and is in no way limiting.
For purposes of understanding the following description and description of exemplary embodiments of the invention, the terms, variables and expressions which have been employed are now defined or explained below, with some of the variables being defined solely for the purpose of facilitating an understanding of the formulae which are referred to in the following description. Also, it is to be understood that the following definitions or descriptions are to be regarded as illustrative rather than restrictive.
1) A grid is understood to be a grid representing the location of the origin and the arrival. The size of the grid affects the position accuracy and is generally determined according to the size of an evaluation area, so that the travel time difference caused by different actual travel positions in the grid can be ignored compared with the whole travel time. This time difference relates to the time of the trip in the grid, which can be controlled with a trip duration threshold corresponding to the mode of transportation;
2) the centralized region can refer to a region where people or industrial activities are centralized, and generally refers to a town, an industrial and mining area or a scenic spot where large-scale people flow activities are intensive;
3) rasterization, which can mean covering a concentration area with a grid;
4) t, traffic time threshold, refers to the maximum time of travel specified to start from one concentration grid and arrive at another concentration grid. During this time, some grids may arrive and some may not. According to the scale of the evaluation area and the peripheral area, the specified maximum journey time can be set according to hours or minutes, and the transportation mode can be an airplane, a high-speed rail, a train, a track, an automobile, a bicycle or a ship and the like, and can also be a combination of various transportation modes;
5) the evaluation area can refer to an area needing to be evaluated, concentrated areas needing to be evaluated are arranged in the boundary of the evaluation area, and the concentrated areas are rasterized to form a concentrated area grid of the evaluation area;
6) s, representing an evaluation area;
7)Msthe total number of the concentrated areas needing to be evaluated in the S area is represented;
8) j represents the sequence number of the concentration area in the S area, j is 1,2,3s；
9)NjExpressing the number of grids in the jth concentration area in the S area;
10) i, representing the grid number associated with the jth concentration zone in the S zone, i ═ 1,2,3j；
11)
The arrival state variable of the ith grid of the jth concentration area in the S area in the T time is represented, wherein the arrival time is 1, and the non-arrival time is 0;
12) the peripheral area can refer to an area range outside the evaluation area, concentrated areas participating in evaluation are arranged in the range, and the concentrated areas are rasterized to form a concentrated area grid of the peripheral area;
13) r, represents a peripheral region;
14)MRrepresenting the total number of concentration areas in which the R area participates in evaluation;
15) l denotes the number of the concentration region where R region participates in evaluation, and l is 1,2,3R；
16)NlThe number of grids of the first concentration area in the R area is represented;
17) k denotes a grid number associated with the l-th concentration region in the R region, k is 1,2,3, …, Nl；
18)
The arrival state variable of the kth grid of the first concentration area of the R area in the traffic time threshold T is represented, wherein the arrival time is 1, and the non-arrival time is 0;
19)wijand wklThe grid weight coefficients of the evaluation region S and the peripheral region R, respectively. In practical applications, population density, average local output value, and the number of people or goods arriving from other centralized areas can be used as weighting factors.
As shown in fig. 1, an evaluation method of regional traffic reachability based on traffic big data according to a preferred embodiment of the present invention, where a plurality of concentration areas exist in an area under study, wherein the evaluation method includes:
step one, determining an evaluation area boundary and the number of concentrated areas to be evaluated in the evaluation area boundary in a research area, wherein the number of concentrated areas belonging to the evaluation area is recorded as Ms；
Step two, determining the periphery area boundary and the number of concentration areas participating in evaluation in the periphery area boundary in the researched area, wherein the number of the concentration areas belonging to the periphery area is recorded as MR；
Performing rasterization processing on all selected concentration areas in the peripheral area and the evaluation area to form grids of each concentration area and determine the corresponding grid number;
setting weight coefficients of the grids;
setting evaluation operation parameters, wherein the operation parameters comprise a traffic time threshold T and a traffic mode;
collecting road time data which starts from each grid in the evaluation area and respectively arrives at other grids in the grids through the traffic mode;
step seven, screening and processing the road time data to obtain road time data which is realized by adopting various traffic modes in the traffic modes independently and road time data which is realized by adopting a combined traffic mode;
and step eight, calculating the regional traffic reachability level of each grid in each concentration area in the evaluation area and the regional traffic reachability level of each concentration area in the evaluation area according to the data in the step seven.
The regional traffic accessibility level may be defined as a ratio of the number of grids of other concentration regions reachable within the traffic time threshold from the grid or all grids of the concentration region and their weighted values to the total number of grids of all other concentration regions and their weighted values. And the average value of the values obtained after starting from all the grids in the single concentration area is the regional traffic accessibility level of the concentration area.
According to some preferred embodiments, as shown in fig. 1, the evaluation method further comprises:
step nine, checking the calculation result of the step eight, if the regional traffic accessibility level of any grid in the evaluation area is 100% in the calculation of the regional traffic accessibility level of each grid in the evaluation area, returning to the step five, and reducing the value of the traffic time threshold T; or returning to the step two, expanding the boundary of the peripheral area, and increasing the range of the existing concentration area participating in evaluation or adding a new concentration area participating in evaluation.
According to further alternative preferred embodiments, the evaluation method further comprises:
step nine, checking the calculation result of the step eight, if the regional traffic accessibility level of any one centralized region is 100% in the calculation of the regional traffic accessibility level of each centralized region in the evaluation region, returning to the step five, and reducing the value of the traffic time threshold T; or returning to the step two, expanding the boundary of the peripheral area, and increasing the range of the existing concentration area participating in evaluation or adding a new concentration area participating in evaluation.
According to some preferred embodiments of the present invention, the fourth step further comprises: and setting the weight coefficient of each grid according to the regional characteristic information of each concentration region or each grid.
Further preferably, the regional characteristic information includes a total average production value and a regional population density.
As described above, the evaluation model used in the evaluation method is a quantitative evaluation method. In principle, the evaluation model used is constructed on the basis of a basic principle that, starting from a certain location in a certain concentration area in the evaluation area within a specified time, the wider or more important the area that can reach other concentration areas, the better the regional traffic accessibility of the starting area. In other words, with the grid representing position and only the grid of the evaluation area as the starting point, the accessibility of the starting grid is higher as the number of grids of other concentration areas that can be reached by one or more combined transportation methods is larger from a certain grid of a certain concentration area of the evaluation area within a predetermined travel time, for example, 2 hours. When the importance of the grid needs to be considered, each grid is given a grid weight coefficient. Thus, the more high-weight grids that arrive at other concentration areas from a certain grid of the evaluation area, the higher the accessibility of the departure grid to the important area.
Expressing the concept by formula, and setting the sequence number of the centralized area of the departure place of the S area as j0，j0∈Ms. Within the traffic time threshold T, the j th traffic mode from the S area in one or more combinations0Starting from the ith grid of the concentration area, the number of grids which can reach other concentration areas is
Namely, it is
Wherein j ≠ j0And is and
in the formula (1), the first term on the right side of the first equation is the number of grids that can be reached by the S area, j ≠ j0Excluding the starting place concentration j therein0A grid of (a); the second term is the number of grids that the R region can reach.
And
and the arrival state variables of the S-zone grid and the R-zone grid respectively are 1 if the grid can arrive and 0 if the grid can not arrive through one or more combined transportation modes within the transportation time threshold T.
Considering that the total number of grids in the S region + R region is not changed, the difference in the number of grids in each concentration region in the S region results in the change in the total number of grids outside the concentration regions. Thus, the concentration region with a large number of grids has a small number of grids outside the concentration region, and vice versa. Thus, the number of grids from one concentration to another
Can be directly used for the accessibility comparison between S area concentration areas, but the relative value of the proportion of the number of the grids reaching outside the S area to the total number of the grids reaching outside the S area is used as the accessibility evaluation index. That is, the number of grids reaching other concentration areas from ith grid of jth concentration area of S area within the traffic time threshold T
Ratio of total number of grids in other concentration areas
As an index of the accessibility of the grid, that is,
wherein j ≠ j0In the formula (2), the first term of the denominator is the S regionTotal number of grids, j ≠ j0Excludes the origin j0A grid of concentration zones; the second term is the total number of R region grids; the sum of the two terms is the total number of grids in other concentration areas. Based on the principle that the larger the area from the departure place to the area outside the concentration area, the higher the accessibility, the indexes obtained from one grid
The higher the external accessibility. When in use
In time, the traffic time threshold T represents that the target can reach the concentrated region j from a grid0Except for all grids.
The grid accessibility evaluation index is a value between 0 and 1 or 0% to 100%, and the higher the value, the better the accessibility.
Considering that grids are located in different regions, the importance of the connection is different, and the weight coefficient is introduced to reflect the difference. Setting the weight coefficient of the ith grid of the jth concentration area in the S area as wij，i＝1，2，3，...，Nj，j＝1，2，3，...，Ms(ii) a The weight coefficient of the kth grid of the first concentration area of the R area is wkl，k＝1，2，3，...，Nl，l＝1，2，3，...，MR. From equation (1), after grid weighting, from zone S, j within traffic time threshold T0Starting from the ith grid of the concentration area, the weighted sum of the grids reaching other concentration areas is
Wherein j ≠ j0And is and
in equation (3), the first term on the right of the first equation is the weighted sum of the S-zone arrival grids, j ≠ j0Excluding the starting concentration j0A grid of (a); the second term is the weighted sum of the arrival of the R region at the grid. The rest is the same as formula (1).
Thus, within the traffic time threshold T, from zone j0Starting from the ith grid of the concentration area, the ratio of the weighted sum of the grids reaching other concentration areas to the weighted sum of all grids in other concentration areas
Reachability evaluation index weighted for grid, i.e.
Wherein j ≠ j0In the formula (4), the first term of the denominator is the sum of the weights of all other grids in the S area, and j ≠ j0Excluding the starting concentration j0A grid of (a); the second term of the denominator is the sum of the weights of all grids in the R region.
The value of the reachability evaluation index representing the regional influence of the grid is between 0 and 1 or 0 to 100 percent, and the higher the value of the index is, the better the reachability to the high-weight region is.
It is easy to understand that when in the formulas (3) and (4), wijAnd wklIn the case of both 1, the expressions (3) and (4) are equal to the expressions (1) and (2), respectively, so that the expressions (1) and (2) are special forms of the expressions (3) and (4) in which the weight coefficients both take 1.
According to actual needs, evaluating a secondary concentration area j in the S area0Overall out-of-contact reachability level for all grid departures
The value is calculated by equation (5)
In formula (5), a concentration region j in the S region0The overall external contact accessibility level is the average value of external contact accessibility indexes of all grids.
The value of the reachability evaluation index is between 0 and 1 or between 0% and 100%, and the higher the value is, the better the reachability is.
Therefore, the departure place traverses all grids of the concentration areas in the S area according to different traffic time thresholds T and traffic modes, the accessibility levels of the concentration areas and the grids can be obtained, and the advantages and the defects of external connection of the concentration areas and the internal grid plots of the concentration areas can be evaluated by comparing the accessibility levels with each other.
At the same time, two evaluation cases should be noted. When T is large enough, certain concentration area of the S area appears in the reachability evaluation of the concentration area of the S area
I.e. from concentration j0From any one grid, all grids of all other concentration zones can be reached within the traffic time threshold T. If two such concentration zones are present in the S zone, the two concentration zones lose their mutual comparison. Therefore, the value of the traffic time threshold T should not be so large that all the grids in any one concentration area in the S area can reach all the grids in all other concentration areas, that is, the traffic time threshold T should satisfy the formula (5)
Similarly, in the reachability evaluation of the grids in the S area, in order to compare the reachability among the grids, the value of the traffic time threshold T should not be so large that any one of the concentration areas in the S area has all grids that can reach all other concentration areas, that is, the traffic time threshold T should be such that the formula (4) satisfies
The expressions (6) and (7) are used for checking the suitability of the maximum value of the traffic time threshold T. When the requirement is not met, two adjusting methods are provided, namely, the traffic time threshold value T is reduced, and the scale of the peripheral area concentration area is increased, wherein the scale of the peripheral area concentration. Then, reachability calculation is performed again. This process proceeds until the traffic time threshold T satisfies the requirement, and a calculation result of the reachability is finally obtained.
According to some preferred embodiments of the present invention, the time-of-road data collected in the sixth step is predicted based on a known traffic model to obtain the time-of-road data.
According to some preferred embodiments of the present invention, the step six of collecting the time-of-road data is obtained by collecting historical traffic big data.
According to some preferred embodiments of the present invention, the seventh step comprises processing and obtaining the several time-of-road data that takes the shortest time to implement in the combined transportation mode.
According to some preferred embodiments of the present invention, all the grids formed by the rasterization process in the third step satisfy that the longest trip time in each grid is not greater than a preset time threshold corresponding to a transportation mode. Thus, the rasterized region can be relatively more accurately and equivalently compared to assess the reachability of regional traffic.
According to the method for evaluating the regional traffic accessibility based on the traffic big data, which is disclosed by the invention, the evaluation or estimation accuracy and the calculation efficiency can be considered, the regional traffic accessibility in different regions can be efficiently, comprehensively and consistently evaluated or researched in a comparison mode, and better public decisions can be made, for example, the method can help comprehensively consider the connection of urban traffic and regional traffic and find a feasible way for improving the regional traffic accessibility. The method has wide application prospect in planning and management of regional traffic, homeland space, urban traffic and the like.
While specific embodiments of the invention have been described above, it will be appreciated by those skilled in the art that these are by way of example only, and that the scope of the invention is defined by the appended claims. Various changes and modifications to these embodiments may be made by those skilled in the art without departing from the spirit and scope of the invention, and these changes and modifications are within the scope of the invention.
Claims (9)
1. An assessment method of regional traffic accessibility based on traffic big data, wherein a plurality of concentration areas exist in a research region, characterized in that the assessment method comprises:
step one, determining an evaluation area boundary and the number of concentrated areas to be evaluated in the evaluation area boundary in a researched area;
determining the peripheral area boundary and the number of concentration areas participating in evaluation in the peripheral area boundary in a researched area;
performing rasterization processing on all selected concentration areas in the peripheral area and the evaluation area to form grids of each concentration area and determine the corresponding grid number;
setting weight coefficients of the grids;
setting evaluation operation parameters, wherein the operation parameters comprise a traffic time threshold and a traffic mode;
collecting road time data which starts from each grid in the evaluation area and respectively arrives at other grids in the grids through the traffic mode;
step seven, screening and processing the road time data to obtain road time data which is realized by adopting various traffic modes in the traffic modes independently and road time data which is realized by adopting a combined traffic mode;
and step eight, according to the data of the step seven, calculating and obtaining the regional traffic accessibility level of each grid in each concentration area in the evaluation area and the regional traffic accessibility level of each concentration area in the evaluation area, wherein the regional traffic accessibility level is defined as the number of grids of other concentration area grids reachable from the grid or all the grids of the concentration areas within the traffic time threshold and the weighted value of the grids, and the ratio of the total number of the grids of all the other concentration areas and the weighted value of the grids is the ratio, wherein the ratio obtained by starting from a single grid is the regional traffic accessibility level of the grid, and the average value of the ratios obtained by starting from all the grids of the concentration areas is the regional traffic accessibility level of the concentration areas.
2. The evaluation method of claim 1, further comprising:
step nine, checking the calculation result of the step eight, if the regional traffic accessibility level of any grid is 100% in the calculation of the regional traffic accessibility level of each grid in the evaluation area, returning to the step five, and reducing the value of the traffic time threshold; or returning to the step two, expanding the boundary of the peripheral area, and increasing the range of the existing concentration area participating in evaluation or adding a new concentration area participating in evaluation.
3. The evaluation method of claim 1, further comprising:
step nine, checking the calculation result of the step eight, if the regional traffic accessibility level of any one centralized region is 100% in the calculation of the regional traffic accessibility level of each centralized region in the evaluation region, returning to the step five, and reducing the value of the traffic time threshold; or returning to the step two, expanding the boundary of the peripheral area, and increasing the range of the existing concentration area participating in evaluation or adding a new concentration area participating in evaluation.
4. The evaluation method of claim 1, wherein said step four further comprises: and setting the weight coefficient of each grid according to the regional characteristic information of each concentration region or each grid.
5. The evaluation method of claim 4, wherein the regional characteristic information comprises a total number of average productions, a regional population density.
6. The evaluation method according to claim 1, wherein the step six of collecting the time-of-road data predicts the time-of-road data based on a known traffic model.
7. The evaluation method according to claim 1, wherein the time-of-road data collected in the sixth step is obtained by collecting historical traffic volume data.
8. The assessment method according to claim 1, wherein said seventh step comprises processing and obtaining a number of time-of-transit data that take the shortest time to implement in a combined transportation means.
9. The method of claim 1, wherein all grids formed by the rasterizing process in step three satisfy that the longest travel time in each grid is not greater than a preset time threshold corresponding to transportation means.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113034329A (en) * | 2021-04-26 | 2021-06-25 | 上海同济城市规划设计研究院有限公司 | Method for evaluating regional traffic accessibility of traffic mode with time table |
| CN113409570A (en) * | 2021-04-27 | 2021-09-17 | 上海同济城市规划设计研究院有限公司 | Method for evaluating physical traffic accessibility |
| CN113988484A (en) * | 2021-12-23 | 2022-01-28 | 广州市交通规划研究院 | Traffic accessibility index optimization method applied to territorial space total planning |
| CN114971085A (en) * | 2022-07-13 | 2022-08-30 | 湖南省交通科学研究院有限公司 | Method and system for predicting accessibility of bus station and storage medium |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105389631A (en) * | 2015-11-16 | 2016-03-09 | 广州地理研究所 | Transportation accessibility analysis method for area with multiple cross-sea channels |
| US10200249B1 (en) * | 2016-12-09 | 2019-02-05 | Amazon Technologies, Inc. | Network traffic management for virtualized graphics devices |
| CN109784225A (en) * | 2018-12-28 | 2019-05-21 | 南京大学 | A kind of geographical space mode identification method based on reachable tree |
| CN110705848A (en) * | 2019-09-18 | 2020-01-17 | 交通运输部公路科学研究所 | Comprehensive traffic service evaluation method and device based on individual trip chain |
| CN110796337A (en) * | 2019-09-20 | 2020-02-14 | 江苏大学 | System for evaluating accessibility of urban bus station service |
-
2020
- 2020-03-23 CN CN202010206974.0A patent/CN111540195B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105389631A (en) * | 2015-11-16 | 2016-03-09 | 广州地理研究所 | Transportation accessibility analysis method for area with multiple cross-sea channels |
| US10200249B1 (en) * | 2016-12-09 | 2019-02-05 | Amazon Technologies, Inc. | Network traffic management for virtualized graphics devices |
| CN109784225A (en) * | 2018-12-28 | 2019-05-21 | 南京大学 | A kind of geographical space mode identification method based on reachable tree |
| CN110705848A (en) * | 2019-09-18 | 2020-01-17 | 交通运输部公路科学研究所 | Comprehensive traffic service evaluation method and device based on individual trip chain |
| CN110796337A (en) * | 2019-09-20 | 2020-02-14 | 江苏大学 | System for evaluating accessibility of urban bus station service |
Non-Patent Citations (3)
| Title |
|---|
| 单晓晨等: "基于多元数据的城市区域可达性评估模型", 《计算机应用研究》 * |
| 孙一帆等: "公路网对外交通可达性评价――以广东省为例", 《长沙理工大学学报(自然科学版)》 * |
| 顾秋丽等: "中国区域综合交通可达性研究关注点与方法评述", 《云南地理环境研究》 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113034329A (en) * | 2021-04-26 | 2021-06-25 | 上海同济城市规划设计研究院有限公司 | Method for evaluating regional traffic accessibility of traffic mode with time table |
| CN113409570A (en) * | 2021-04-27 | 2021-09-17 | 上海同济城市规划设计研究院有限公司 | Method for evaluating physical traffic accessibility |
| CN113988484A (en) * | 2021-12-23 | 2022-01-28 | 广州市交通规划研究院 | Traffic accessibility index optimization method applied to territorial space total planning |
| CN113988484B (en) * | 2021-12-23 | 2022-03-04 | 广州市交通规划研究院 | Traffic accessibility index optimization method applied to territorial space total planning |
| CN114971085A (en) * | 2022-07-13 | 2022-08-30 | 湖南省交通科学研究院有限公司 | Method and system for predicting accessibility of bus station and storage medium |
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