Bus route scheduling management method and system based on weather big data

Abstract

The invention relates to a bus route scheduling management method and system based on weather big data. The method comprises the following steps: obtaining and accessing a real-time meteorological data source in a bus route scheduling area; acquiring and accessing historical operation data and real-time operation data of bus lines in the bus line scheduling area; determining whether the current weather type in the bus route scheduling area is severe weather or not based on the real-time rainfall data; when it is determined that the weather is severe weather, whether the current real-time passenger flow link is in an increasing trend compared with the historical average passenger flow under the condition of no severe weather in the historical same period is judged according to the real-time passenger flow data of the bus line, and when it is judged that the current real-time passenger flow link is in the increasing trend, it is determined that the shift interval time of the bus line needs to be shortened. According to the invention, through integrating meteorological data information, intelligent scheduling management can be comprehensively and effectively carried out on the bus route and the operation of the bus, so that the service efficiency and the passenger experience are improved, and the adaptability and the flexibility of the bus system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of highway route optimization, and in particular to a bus route scheduling management method and system based on weather big data. Background Art

[0002] As the main means of transportation in the city, public transportation vehicles play an extremely important role in alleviating urban traffic pressure. The operation of public transportation vehicles is easily affected by bad weather. Although, with the development of science and technology, big data analysis technology has gradually been applied to public transportation operations and has become an important means of optimizing public transportation scheduling, there is still a lack of a method that can effectively schedule public transportation vehicles based on weather data (especially when in bad weather).

[0003] Therefore, in order to ensure the safety of public transportation and to effectively organize public transportation to transport passengers when the weather changes drastically, it is necessary to design a method for dispatching and managing bus routes and their vehicles based on weather data. Summary of the invention

[0004] In view of this, the present invention provides a bus route intelligent dispatching management method and system based on weather data, which integrates meteorological data with bus route operation data, and aims to integrate meteorological data information to comprehensively and effectively perform intelligent dispatching management on the operation of bus routes and their vehicles, so as to improve service efficiency and passenger experience, the adaptability and flexibility of the bus system, and at the same time ensure the safety of bus route operation.

[0005] The first aspect of the present invention discloses a bus route scheduling management method based on weather big data, comprising the following steps:

[0006] Acquire and access a real-time meteorological data source in the bus route dispatching area, the meteorological data source at least including real-time rainfall data;

[0007] Obtain and access historical operation data and real-time operation data of bus lines in the bus line dispatching area, the historical operation data at least includes historical passenger flow data, and the real-time operation data at least includes real-time passenger flow data;

[0008] Determine whether the current weather type in the bus route dispatch area is severe weather based on real-time rainfall data;

[0009] When it is determined to be bad weather, the real-time passenger flow data of the bus line is used to determine whether the current real-time passenger flow is increasing compared with the historical average passenger flow during the same period in history without bad weather. When it is determined to be increasing, it is determined that the bus line's interval time needs to be shortened.

[0010] The current time period is the date currently determined as bad weather plus N days before and after, where N is greater than or equal to zero. Preferably, N is 30 days, and the current time period is the date currently determined as bad weather plus 30 days before and after. The so-called historical period is the period corresponding to the current period determined in the previous year. The so-called historical average passenger flow is the average value of the total passenger flow in the historical period without bad weather in units of days, that is, the current real-time passenger flow is compared with the historical average passenger flow to determine whether the current real-time passenger flow is in an increasing state. When it is determined to be increasing, it is determined that the interval between buses in the current bus route needs to be shortened.

[0011] In the above implementation process, the implemented meteorological data and the real-time bus line operation data are integrated to judge the current weather type based on the real-time meteorological data. When the current weather type is judged to be severe weather, the real-time passenger flow data of the bus line is compared with the historical passenger flow data when there is no severe weather to judge whether the current real-time passenger flow has an increasing trend. When it is determined that the current real-time passenger flow is on an increasing trend, it is judged that the interval time of buses on the bus line needs to be shortened, that is, the interval time between the buses to be operated on the bus line needs to be shortened, so as to increase the passenger carrying capacity.

[0012] In the present invention, both the weather type and the changing trend of the current real-time passenger flow compared with the historical same period are taken into consideration. Both are indispensable and work together to judge whether it is necessary to shorten the interval between buses. This greatly reduces the possibility of misjudgment and improves the adaptability and flexibility of bus line operation to weather changes.

[0013] According to the bus route scheduling management method based on weather big data disclosed in the first aspect of the present invention, in the step of determining whether the current weather type in the bus route scheduling area is severe weather based on real-time rainfall data, it also includes: if it is determined to be severe weather, determining the impact level of the current severe weather on the bus route. The impact level is determined based on the comparison between the real-time rainfall data and a preset rainfall threshold; and when the impact level is determined to be the highest level, a message is issued to reduce or suspend the bus route.

[0014] In the present invention, the impact level of severe weather can be set to multiple levels. In one preferred embodiment, it can be set to three levels, namely level 1, level 2 and level 3, wherein level 1 is defined as the most severe and level 3 is defined as the least severe. Moreover, in a specific embodiment, the impact level can be determined based on the amount of rainfall, that is, it can be determined based on the real-time rainfall data in the weather data source. Of course, those skilled in the art can also understand that the impact level can also be determined based on other data in the weather data source, such as wind data, temperature data, visibility data, etc. These data can be combined with each other, and can also be combined with rainfall data alone or in combination with each other to determine the impact level.

[0015] According to the bus route scheduling management method based on weather big data disclosed in the first aspect of the present invention, when it is judged to be in an increasing trend, the step of determining that the bus route interval time needs to be shortened includes: according to the determined different impact levels, the interval time is shortened by different percentages. That is, taking the current interval time as the benchmark, when it is judged to be bad weather, the current interval time is multiplied by the percentage determined based on the impact level as the shortened interval time. In principle, the higher the impact level of the weather type, that is, the more serious, the smaller the interval time should be shortened, thereby reducing the waiting time of passengers. For example, when the impact level is 1, the interval time is shortened by 80%, when the impact level is 2, the interval time is shortened by 70%, and when the impact level is 3, the interval time is shortened by 60%. Of course, those skilled in the art can understand that when the weather type changes from bad weather to normal weather or the weather improves, the normal departure interval can be gradually restored according to the schedule and departure plan.

[0016] According to the bus route scheduling management method based on weather big data disclosed in the first aspect of the present invention, after the step of acquiring and accessing the historical operation data and real-time operation data of the bus routes in the bus route scheduling area, the method further includes:

[0017] Acquire and access map data corresponding to the bus route scheduling area, and divide the area map corresponding to the bus route scheduling area into a plurality of sub-area grids; in a preferred embodiment, the division of the sub-area grids can be performed based on a 1KM*1KM grid pattern;

[0018] Based on the map data, the real-time meteorological data source and the historical and real-time operation data of the bus routes are connected to multiple sub-regional grids to form a corresponding relationship based on geographical location;

[0019] In the step of determining whether the current weather type in the bus route scheduling area is severe weather based on the real-time rainfall data, determining whether the current weather type in each sub-regional grid is severe weather based on the corresponding relationship between the real-time meteorological data source and the sub-regional grid;

[0020] Among them, when it is determined that the current weather type in the sub-regional grid is severe weather, the step of: determining the bus routes that will pass through the corresponding sub-regional grid, and issuing severe weather warning notices to the dispatchers and / or drivers responsible for the corresponding bus routes.

[0021] According to the bus route dispatching management method based on weather big data disclosed in the first aspect of the present invention, after determining that the current weather type in the sub-region grid is severe weather, the method further includes the steps of:

[0022] Based on the real-time visibility data and / or real-time rainfall data in the real-time meteorological source data, the driving risk of the bus line is judged in the corresponding sub-regional grid where the weather type has been determined to be severe weather and the sub-regional grid adjacent to it at the first level; wherein the level of driving risk can be equivalent to the impact level of the weather type, and can also be judged based on one of the influencing factors such as temperature, rainfall, wind force and visibility, or a combination of at least two of them;

[0023] When it is determined that the travel risk of the bus line in the corresponding sub-regional grid whose weather type has been determined to be bad weather is high risk, at least one sub-regional grid whose travel risk of the bus line is lower than the high risk is selected from multiple sub-regional grids adjacent to it at the first level as a travel alternative sub-regional grid; wherein the first-level adjacent refers to a sub-regional grid that is directly adjacent to the corresponding sub-regional grid whose weather type has been determined to be bad weather in terms of geographical information, and according to the geographical location, there can be multiple sub-regional grids that are adjacent to the corresponding sub-regional grid whose weather type has been determined to be bad weather at the first level;

[0024] When the driving risk of the bus lines in the adjacent sub-regional grids of the first level is also high risk, the driving risk of the bus lines in the adjacent sub-regional grids of the next level is judged based on the current adjacent sub-regional grid, until at least one sub-regional grid with a driving risk lower than the high risk is obtained, and it is used as the driving replacement sub-regional grid;

[0025] The location information and distance information of the alternative sub-regional grid are sent together to the dispatcher and / or driver of the corresponding bus route of the sub-regional grid whose weather type has been determined to be severe weather on the original route.

[0026] With the help of the method of the present invention, a close linkage between weather data, bus route operation data and map data is achieved. When the weather is judged to be severe, the bus routes can be effectively dispatched based on the regional correspondence of the map data in a timely and effective manner, thereby improving the response capability of the bus system, and further improving the flexibility and stress capacity of the public transportation system, ensuring the safety of passengers and buses, and ensuring the provision of safer and more convenient bus operation services.

[0027] The second aspect of the present invention discloses a bus route dispatching and management system based on weather big data, comprising:

[0028] A first acquisition and access unit, used to acquire and access a real-time meteorological data source in a bus route scheduling area, the meteorological data source at least including real-time rainfall data;

[0029] A second acquisition and access unit is used to acquire and access historical operation data and real-time operation data of bus lines in the bus line scheduling area, the historical operation data at least includes historical passenger flow data, and the real-time operation data at least includes real-time passenger flow data;

[0030] The weather type judgment unit is used to determine whether the current weather type in the bus line scheduling area is bad weather based on the real-time rainfall data; when it is determined to be bad weather, it is judged according to the real-time passenger flow data of the bus line whether the current real-time passenger flow is in an increasing trend compared with the historical passenger flow under the condition of no bad weather in history; when it is judged to be in an increasing trend, it is determined that the bus line interval needs to be shortened.

[0031] According to the bus route dispatching and management system based on weather big data disclosed in the second aspect of the present invention, the weather type judgment unit is further configured to determine the impact level of the current bad weather on the bus route when it is determined to be bad weather;

[0032] The impact level is determined based on the comparison between the real-time rainfall data and the preset rainfall threshold; when the impact level is determined to be the highest level, a message is issued to reduce or suspend the frequency of bus routes.

[0033] According to the second aspect of the present invention, the bus route dispatching and management system based on weather big data further includes:

[0034] A third acquisition and access unit acquires and accesses map data corresponding to the bus route scheduling area, and divides the area map corresponding to the bus route scheduling area into a plurality of sub-area grids;

[0035] A data correspondence generation unit is constructed to form a correspondence based on geographical location between the real-time meteorological data source and the historical and real-time operation data of the bus lines and the plurality of sub-regional grids based on the map data;

[0036] The weather type determination unit is further configured to determine whether the current weather type in each sub-regional grid is severe weather based on the corresponding relationship between the real-time meteorological data source and the sub-regional grids;

[0037] The warning unit is constructed so that, when it is determined that the current weather type in the sub-regional grid is severe weather, the bus routes that will pass through the corresponding sub-regional grid are determined, and a severe weather warning notice is issued to the dispatcher and / or driver responsible for the corresponding bus routes.

[0038] According to the second aspect of the present invention, the bus route dispatching and management system based on weather big data further includes:

[0039] A bus route travel risk judgment unit is configured to judge the bus route travel risk in the corresponding sub-region grid whose weather type has been determined to be severe weather and the sub-region grid adjacent to it at the first level based on the real-time visibility data and / or real-time rainfall data in the real-time meteorological source data;

[0040] When it is determined that the travel risk of the bus line in the corresponding sub-regional grid whose weather type has been determined to be severe weather is high risk, at least one sub-regional grid whose travel risk of the bus line is lower than the high risk is selected from the sub-regional grids adjacent to the first level as the travel alternative sub-regional grid;

[0041] When the driving risk of the bus lines in the adjacent sub-regional grids of the first level is also high risk, the driving risk of the bus lines in the adjacent sub-regional grids of the next level is judged based on the current adjacent sub-regional grid, until at least one sub-regional grid with a driving risk lower than the high risk is obtained, and it is used as the driving replacement sub-regional grid;

[0042] The early warning unit is also configured to send the location information and distance information of the alternative sub-regional grid to the dispatcher and / or driver of the corresponding bus line of the sub-regional grid whose weather type has been determined to be severe weather.

[0043] The third aspect of the present invention further discloses a computing device, which includes a processor and a memory, wherein the memory stores computer instructions, and the processor executes the computer instructions to implement the method disclosed according to the first aspect of the present invention.

[0044] The fourth aspect of the present invention further discloses a computer-readable storage medium storing computer instructions. When the computer instructions in the computer-readable storage medium are executed by a computing device, the computing device executes the method disclosed in the first aspect of the present invention, or the computing device implements the functions of the system disclosed in the second aspect of the present invention.

[0045] Beneficial effect: In the bus route intelligent dispatching management method and system based on weather data of the present invention, a close linkage between weather data, bus route operation data and map data is achieved, and when the weather is judged to be severe weather, the bus routes can be effectively dispatched based on the regional correspondence of map data in a timely and effective manner, thereby improving the response capability of the bus system, and further improving the flexibility and stress capacity of the public transportation system, ensuring the safety of passengers and buses, and ensuring the provision of safer and more convenient bus operation services.

[0046] The bus route intelligent dispatching management method and system based on weather data of the present invention will be disclosed in detail below in conjunction with the embodiments shown in the accompanying drawings and the accompanying drawing numbers. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 The flowchart of the steps of the bus route intelligent dispatching management method based on weather data of the present invention is shown.

[0048] Figure 2 A flow chart showing the steps of another embodiment of the method for intelligent dispatching and managing bus routes based on weather data of the present invention. DETAILED DESCRIPTION

[0049] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0050] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application may be combined with each other.

[0051] Figure 1 The following is a flow chart showing the steps of the bus route intelligent dispatching management method based on weather data of the present invention. Figure 1 As shown, the first aspect of the present invention discloses a bus route scheduling management method based on weather big data, comprising the following steps:

[0052] Acquire and access real-time meteorological data sources within the bus route dispatching area, where the meteorological data sources include real-time rainfall data; the meteorological data sources may also include rainfall, wind speed, temperature, visibility and other data, as well as future forecasted weather information, extreme weather warning information, etc.;

[0053] Acquire and access historical operation data and real-time operation data of bus lines in the bus line dispatching area, where the historical operation data at least includes historical passenger flow data, and the real-time operation data includes real-time passenger flow data; the real-time operation data can also include multiple data such as bus GPS positioning, vehicle status monitoring, passenger flow statistics, bus departure plan, shift schedule, and shift interval time;

[0054] Determine whether the current weather type in the bus route dispatch area is severe weather based on real-time rainfall data;

[0055] When it is determined to be bad weather, the real-time passenger flow data of the bus line is used to determine whether the current real-time passenger flow is increasing compared with the historical passenger flow during the same period in history without bad weather. When it is determined to be increasing, it is determined that the bus line interval needs to be shortened.

[0056] In the above implementation process, the implemented meteorological data and the real-time bus line operation data are integrated to judge the current weather type based on the real-time meteorological data. When the current weather type is judged to be severe weather, the real-time passenger flow data of the bus line is compared with the historical passenger flow data when there is no severe weather to judge whether the current real-time passenger flow has an increasing trend. When it is determined that the current real-time passenger flow is on an increasing trend, it is judged that the interval time of buses on the bus line needs to be shortened, that is, the interval time between the buses to be operated on the bus line needs to be shortened, so as to increase the passenger carrying capacity.

[0057] In the aforementioned embodiment of the method of the present invention, the weather type and the change trend of the current real-time passenger flow compared with the historical same period are taken into consideration at the same time. Both are indispensable and work together to judge whether it is necessary to shorten the interval between buses. This greatly reduces the possibility of misjudgment and improves the adaptability and flexibility of bus line operation to weather changes.

[0058] In the aforementioned embodiment of the method of the present invention, in the step of determining whether the current weather type in the bus route scheduling area is severe weather based on the real-time rainfall data, it further includes: in the case of determining that it is severe weather, determining the impact level of the current severe weather on the bus route. The impact level is determined based on the comparison between the real-time rainfall data and a preset rainfall threshold; and when the impact level is determined to be the highest level, a message is issued to reduce or suspend the frequency of the bus route.

[0059] In the present invention, the impact level of severe weather can be set to multiple levels. In one preferred embodiment, it can be set to three levels, namely level 1, level 2 and level 3, wherein level 1 is defined as the most severe and level 3 is defined as the least severe. Moreover, in a specific embodiment, the impact level can be determined based on the amount of rainfall, that is, it can be determined based on the real-time rainfall data in the weather data source. Of course, those skilled in the art can also understand that the impact level can also be determined based on other data in the weather data source, such as wind data, temperature data, visibility data, etc. These data can be combined with each other, and can also be combined with rainfall data alone or in combination with each other to determine the impact level.

[0060] In the aforementioned embodiment of the method of the present invention, when it is judged to be increasing, the step of determining that the bus line interval time needs to be shortened includes: shortening the bus interval time by different percentages according to the determined different impact levels. In principle, the higher the impact level of the weather type, that is, the more severe it is, the smaller the bus interval time should be shortened, thereby reducing the waiting time for passengers. Of course, those skilled in the art can understand that when the weather type changes from bad weather to normal weather or the weather improves, the normal departure interval can be gradually restored according to the schedule and departure plan.

[0061] Figure 2 A flowchart showing another embodiment of the bus route intelligent dispatching management method based on weather data of the present invention is shown. Figure 1 A further solution based on the illustrated embodiment is not another solution that is completely different or parallel in principle. Figure 2 In the embodiment shown, after the step of acquiring and accessing the historical operation data and the real-time operation data of the bus lines in the bus line scheduling area, the following steps are further included:

[0062] Acquire and access map data corresponding to the bus route scheduling area, and divide the area map corresponding to the bus route scheduling area into a plurality of sub-area grids; in a preferred embodiment, the division of the sub-area grids can be performed based on a 1KM*1KM grid pattern;

[0063] Based on the map data, the real-time meteorological data source and the historical and real-time operation data of the bus routes are connected to multiple sub-regional grids to form a corresponding relationship based on geographical location;

[0064] In the step of determining whether the current weather type in the bus route scheduling area is severe weather based on the real-time rainfall data, determining whether the current weather type in each sub-regional grid is severe weather based on the corresponding relationship between the real-time meteorological data source and the sub-regional grid;

[0065] Among them, when it is determined that the current weather type in the sub-regional grid is severe weather, the step of: determining the bus routes that will pass through the corresponding sub-regional grid, and issuing severe weather warning notices to the dispatchers and / or drivers responsible for the corresponding bus routes.

[0066] In another embodiment of the method according to the present invention, after determining that the current weather type in the sub-region grid is severe weather, the method further includes the steps of:

[0067] Based on the real-time visibility data and / or real-time rainfall data in the real-time meteorological source data, the travel risk of the bus line is judged in the corresponding sub-regional grid where the weather type has been determined to be severe weather and the sub-regional grid adjacent to it at the first level;

[0068] When it is determined that the travel risk of the bus line in the corresponding sub-regional grid whose weather type has been determined to be severe weather is high risk, at least one sub-regional grid whose travel risk of the bus line is lower than the high risk is selected from the sub-regional grids adjacent to the first level as the travel alternative sub-regional grid;

[0069] When the driving risk of the bus lines in the adjacent sub-regional grids of the first level is also high risk, the driving risk of the bus lines in the adjacent sub-regional grids of the next level is judged based on the current adjacent sub-regional grid, until at least one sub-regional grid with a driving risk lower than the high risk is obtained, and it is used as the driving replacement sub-regional grid;

[0070] The location information and distance information of the alternative sub-regional grid are sent together to the dispatcher and / or driver of the corresponding bus route of the sub-regional grid whose weather type has been determined to be severe weather on the original route.

[0071] With the help of the method of the present invention, a close linkage between weather data, bus route operation data and map data is achieved. When the weather is judged to be severe, the bus routes can be effectively dispatched based on the regional correspondence of the map data in a timely and effective manner, thereby improving the response capability of the bus system, and further improving the flexibility and stress capacity of the public transportation system, ensuring the safety of passengers and buses, and ensuring the provision of safer and more convenient bus operation services.

[0072] Additionally, in the present invention, the step of acquiring and accessing a real-time meteorological data source in a bus route scheduling area can be achieved by the following steps:

[0073] Use map tile technology to slice the data map, generate map tiles and obtain tile numbers;

[0074] Based on the geographic information of the regional surface of the bus line operation area, all map tiles covering the regional surface are obtained;

[0075] The weather forecast data corresponding to the geographical location of the map tiles within the regional surface is acquired and accessed in real time to update the weather forecast data corresponding to the map tiles within the regional surface in real time.

[0076] The map tile technology is implemented by existing technology, such as the Mercator tile technology to be mentioned below. According to the Mercator tile technology, the numbering of the Mercator tiles formed by online map slices is fixed, which can also be implemented by existing technology and will not be repeated here. The so-called data map refers to an online map, which can be derived from an existing map database, such as Baidu Map, Gaode Map or Tencent Map, etc., and can also be other commercial or non-commercial map databases for the public. In addition, the so-called bus line operation area can be selected and set as needed. Taking Chongqing as an example, the bus line operation area here can be selected and set as the area covered by the main urban area of ​​Chongqing; wherein the area surface is determined by the geographical location of the administrative division as the boundary. Regarding the access to weather forecast data corresponding to the geographical location of the map tile within the area surface, it can be achieved by writing data to an external database (such as a meteorological real-time database), which can be achieved through existing programming or calculation methods, and will not be repeated here.

[0077] The second aspect of the present invention discloses a bus route dispatching and management system based on weather big data, comprising:

[0078] A first acquisition and access unit, used to acquire and access a real-time meteorological data source in a bus route scheduling area, the meteorological data source at least including real-time rainfall data;

[0079] A second acquisition and access unit is used to acquire and access historical operation data and real-time operation data of bus lines in the bus line scheduling area, the historical operation data at least includes historical passenger flow data, and the real-time operation data at least includes real-time passenger flow data;

[0080] The weather type judgment unit is used to determine whether the current weather type in the bus line scheduling area is bad weather based on the real-time rainfall data; when it is determined to be bad weather, it is judged according to the real-time passenger flow data of the bus line whether the current real-time passenger flow is in an increasing trend compared with the historical passenger flow under the condition of no bad weather in history; when it is judged to be in an increasing trend, it is determined that the bus line interval needs to be shortened.

[0081] According to the bus route dispatching and management system based on weather big data disclosed in the second aspect of the present invention, the weather type judgment unit is further configured to determine the impact level of the current bad weather on the bus route when it is determined to be bad weather;

[0082] The impact level is determined based on the comparison between the real-time rainfall data and the preset rainfall threshold; when the impact level is determined to be the highest level, a message is issued to reduce or suspend the frequency of bus routes.

[0083] According to the second aspect of the present invention, the bus route dispatching and management system based on weather big data further includes:

[0084] A third acquisition and access unit acquires and accesses map data corresponding to the bus route scheduling area, and divides the area map corresponding to the bus route scheduling area into a plurality of sub-area grids;

[0085] A data correspondence generation unit is constructed to form a correspondence based on geographical location between the real-time meteorological data source and the historical and real-time operation data of the bus lines and the plurality of sub-regional grids based on the map data;

[0086] The weather type determination unit is further configured to determine whether the current weather type in each sub-regional grid is severe weather based on the corresponding relationship between the real-time meteorological data source and the sub-regional grids;

[0087] The warning unit is constructed so that, when it is determined that the current weather type in the sub-regional grid is severe weather, the bus routes that will pass through the corresponding sub-regional grid are determined, and a severe weather warning notice is issued to the dispatcher and / or driver responsible for the corresponding bus routes.

[0088] According to the second aspect of the present invention, the bus route dispatching and management system based on weather big data further includes:

[0089] A bus route travel risk judgment unit is configured to judge the bus route travel risk in the corresponding sub-region grid whose weather type has been determined to be severe weather and the sub-region grid adjacent to it at the first level based on the real-time visibility data and / or real-time rainfall data in the real-time meteorological source data;

[0090] When it is determined that the travel risk of the bus line in the corresponding sub-regional grid whose weather type has been determined to be severe weather is high risk, at least one sub-regional grid whose travel risk of the bus line is lower than the high risk is selected from the sub-regional grids adjacent to the first level as the travel alternative sub-regional grid;

[0091] When the driving risk of the bus lines in the adjacent sub-regional grids of the first level is also high risk, the driving risk of the bus lines in the adjacent sub-regional grids of the next level is judged based on the current adjacent sub-regional grid, until at least one sub-regional grid with a driving risk lower than the high risk is obtained, and it is used as the driving replacement sub-regional grid;

[0092] The early warning unit is also configured to send the location information and distance information of the alternative sub-regional grid to the dispatcher and / or driver of the corresponding bus line of the sub-regional grid whose weather type has been determined to be severe weather.

[0093] The third aspect of the present invention further discloses a computing device, which includes a processor and a memory, wherein the memory stores computer instructions, and the processor executes the computer instructions to implement the method disclosed according to the first aspect of the present invention.

[0094] The fourth aspect of the present invention further discloses a computer-readable storage medium storing computer instructions. When the computer instructions in the computer-readable storage medium are executed by a computing device, the computing device executes the method disclosed in the first aspect of the present invention, or the computing device implements the functions of the system disclosed in the second aspect of the present invention.

[0095] Taking Chongqing as an example, the specific application and implementation of the method and system of the present invention may include the following processes:

[0096] 1. Integration of real-time weather and bus dispatch data: In the method and system of the present invention, the authoritative meteorological data source of the meteorological department is accessed through the API interface to obtain real-time and future forecast weather information including rainfall, wind speed, temperature, visibility, and extreme weather warning information. These data are crucial for predicting weather events (such as heavy rain, fog, ice and snow) that may affect bus operations. Multiple data sources such as bus GPS positioning, vehicle status monitoring, passenger flow statistics, bus departure plans, shift plans, and shift intervals are integrated, and big data processing technology is used to integrate data to form a unified data processing mechanism.

[0097] 2. Dynamic dispatch strategy: Automatically adjust the bus route's departure intervals based on connected weather data and other dispatch operation parameters (such as shift plans, personnel arrangements, intervals, departure times, passenger flow, and vehicle locations).

[0098] Specific methods include:

[0099] (1) Connect to the weather data interface of Chongqing Meteorological Bureau to obtain real-time rainfall, wind speed, temperature, visibility data, and whether there is extreme weather in the area;

[0100] (2) Access to real-time bus route operation data, such as bus schedule, departure schedule, bus interval, real-time vehicle positioning, and real-time passenger flow;

[0101] (3) Establish the impact level corresponding to the weather type. For example, if the rainfall exceeds a certain level, it is considered as severe weather. Severe weather is divided into three levels according to the degree of rainfall (level 3, level 2, and level 1, with level 3 being the least severe and level 1 being the most severe). This triggers a dynamic scheduling strategy and adjusts the departure intervals of bus routes in real time.

[0102] (4) In normal bad weather, the real-time passenger flow on the current route is used to determine whether the passenger flow is increasing compared to the historical period without bad weather. The interval between buses is shortened by different percentages according to the different levels (in principle, the worse the weather, the shorter the interval), thus reducing the waiting time for passengers. When the weather improves, the normal departure interval is gradually restored according to the schedule and departure plan.

[0103] (5) In extremely severe weather conditions, based on the type of extreme weather, the bus schedule, departure schedule, bus intervals, and real-time vehicle positioning for the day will be reduced or the dispatcher will be notified directly to suspend bus services to ensure the safety of vehicles and personnel.

[0104] 3. Dispatch emergency response: According to the real-time weather conditions, if severe weather occurs, the system can trigger the early warning mechanism to notify the dispatch center and the driver to take preventive measures. The specific methods of early warning are as follows:

[0105] (1) Connect to the weather data interface of Chongqing Meteorological Bureau to obtain real-time rainfall, wind speed, temperature, visibility data, and whether there is extreme weather in the area;

[0106] (2) Establish impact thresholds corresponding to weather types. For example, if the rainfall exceeds a certain level, it is considered severe weather. Severe weather is divided into three levels according to the degree of rainfall (level 3, level 2, and level 1, with level 3 being the least severe and level 1 being the most severe);

[0107] (3) Divide the main urban area map into 1 km*1 km grid areas to determine whether the actual weather type in the area reaches the impact threshold;

[0108] (4) If the weather in the area reaches the impact level, determine which bus routes pass through these areas. If so, issue an early warning notification in the dispatch system for the dispatchers responsible for dispatching these routes. A pop-up window will inform the dispatchers which route and the adjacent roads in which area have severe weather conditions. The dispatchers will decide whether to adjust the route or the frequency based on the current situation. At the same time, the vehicle equipment connected to the dispatch system will remind the driver of which area of ​​the route has severe weather conditions. The voice reminder includes slowing down, keeping a safe distance, and paying attention to flooded sections.

[0109] (5) At the same time, the system will provide emergency plan suggestions in the dispatching system, such as mobilizing spare vehicles (temporarily mobilizing vehicles from other routes, or reusing vehicles that are not in operation on that day) to ensure the continuity of line operations. Or when visibility is extremely low or rainfall is too heavy (severe waterlogging on the road), it is judged that the routes passing through these weather areas are at high risk, and suggestions for temporary detours or suspension of operation are provided.

[0110] 4. Extreme weather warning: Automatically warn bus drivers based on extreme weather conditions. For example, when the system receives an extreme weather warning for a certain area, such as heavy rain or snowstorm, it will automatically analyze which routes are affected by the extreme weather in the area through the dispatching system, and send weather warning messages to buses on these routes, reminding drivers to drive safely or stop temporarily to avoid the weather.

[0111] 5. Passenger information service: Through public transportation service announcements, mobile applications and other channels, the system will release real-time notifications of temporary adjustments to public transportation routes affected by weather to the public, helping passengers plan their trips and reduce travel inconveniences caused by weather.

[0112] 6. Data analysis and optimization: Long-term collection and analysis of correlation data between weather and bus departure schedules. Based on operational feedback and data analysis results, continuous optimization of reasonable parameters for warning thresholds, scheduling adjustment suggestions, and notification mechanisms is carried out to improve the accuracy and timeliness of warnings, so as to achieve optimal allocation of resources and improve overall operational efficiency and service quality.

[0113] In the present invention, through meteorological data analysis, it is possible to achieve close linkage between bus scheduling and weather changes, predict the impact of weather changes on traffic flow and passenger travel demand, formulate corresponding scheduling strategies, improve the response ability of the bus system, enhance the flexibility and adaptability of the public transportation system, ensure the safety of passengers and buses, and provide safer and more convenient bus operation services.

[0114] For ease of description, spatially relative terms such as "above", "above", "on the upper surface of", "above", etc. may be used here to describe the spatial positional relationship between a device or feature and other devices or features as shown in the figure. It should be understood that spatially relative terms are intended to include different orientations of the device in use or operation in addition to the orientation described in the figure. For example, if the device in the accompanying drawings is inverted, the device described as "above other devices or structures" or "above other devices or structures" will be positioned as "below other devices or structures" or "below other devices or structures". Thus, the exemplary term "above" can include both "above" and "below". The device can also be positioned in other different ways (rotated 90 degrees or in other orientations), and the spatially relative descriptions used here are interpreted accordingly.

[0115] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0116] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein, for example. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0117] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

[0118] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

Claims

1. A bus route scheduling management method based on weather big data, characterized in that: The following steps are involved: Acquire and access a real-time meteorological data source in a bus route dispatching area, wherein the meteorological data source includes at least real-time rainfall data; Acquire and access historical operation data and real-time operation data of bus lines in a bus line dispatching area, wherein the historical operation data at least includes historical passenger flow data, and the real-time operation data at least includes real-time passenger flow data; Determine whether the current weather type in the bus route dispatch area is severe weather based on real-time rainfall data; When it is determined to be bad weather, the real-time passenger flow data of the bus line is used to determine whether the current real-time passenger flow is increasing compared with the historical average passenger flow during the same period in history without bad weather. When it is determined to be increasing, it is determined that the bus line's interval time needs to be shortened.

2. The bus route dispatching management method based on weather big data according to claim 1 is characterized in that: The step of determining whether the current weather type in the bus route scheduling area is severe weather based on the real-time rainfall data also includes: if it is determined to be severe weather, determining the impact level of the current severe weather on the bus route.

3. The bus route dispatching management method based on weather big data according to claim 2 is characterized in that: When it is determined that the impact is in an increasing trend, the step of determining that the bus line interval time needs to be shortened includes: shortening the bus line interval time by different percentages according to the determined different impact levels.

4. The bus route dispatching management method based on weather big data according to claim 2 is characterized in that: The impact level is determined based on the comparison of real-time rainfall data with a pre-set rainfall threshold; When the impact level is determined to be the highest level, a message is issued to reduce or suspend the frequency of bus routes.

5. The bus route dispatching management method based on weather big data according to any one of claims 1 to 4, characterized in that: After the step of acquiring and accessing the historical operation data and real-time operation data of the bus lines in the bus line scheduling area, the method further includes: Acquire and access map data corresponding to the bus route scheduling area, and divide the area map corresponding to the bus route scheduling area into a plurality of sub-area grids; Based on the map data, the real-time meteorological data source and the historical and real-time operation data of the bus routes are connected to multiple sub-regional grids to form a corresponding relationship based on geographical location; In the step of determining whether the current weather type in the bus route scheduling area is severe weather based on the real-time rainfall data, determining whether the current weather type in each sub-region grid is severe weather based on the corresponding relationship between the real-time meteorological data source and the sub-region grid; Among them, when it is determined that the current weather type in the sub-regional grid is severe weather, the step of: determining the bus routes that will pass through the corresponding sub-regional grid, and issuing severe weather warning notices to the dispatchers and / or drivers responsible for the corresponding bus routes.

6. The bus route dispatching management method based on weather big data according to claim 5 is characterized in that: When it is determined that the current weather type in the sub-region grid is severe weather, the following steps are also included: Based on the real-time visibility data and / or real-time rainfall data in the real-time meteorological source data, the travel risk of the bus line is judged in the corresponding sub-regional grid where the weather type has been determined to be severe weather and the sub-regional grid adjacent to it at the first level; When it is determined that the travel risk of the bus line in the corresponding sub-regional grid whose weather type has been determined to be severe weather is high risk, at least one sub-regional grid whose travel risk of the bus line is lower than the high risk is selected from the sub-regional grids adjacent to the first level as the travel alternative sub-regional grid; When the driving risk of the bus lines in the adjacent sub-regional grids of the first level is also high risk, the driving risk of the bus lines in the adjacent sub-regional grids of the next level is judged based on the current adjacent sub-regional grid, until at least one sub-regional grid with a driving risk lower than the high risk is obtained, and it is used as the driving replacement sub-regional grid; The location information and distance information of the alternative sub-regional grid are sent together to the dispatcher and / or driver of the corresponding bus route of the sub-regional grid whose weather type has been determined to be severe weather on the original route.

7. The bus route dispatching and management system based on weather big data is characterized by: include: A first acquisition and access unit, used to acquire and access a real-time meteorological data source in a bus route scheduling area, wherein the meteorological data source at least includes real-time rainfall data; A second acquisition and access unit, used to acquire and access historical operation data and real-time operation data of bus lines in the bus line scheduling area, wherein the historical operation data at least includes historical passenger flow data, and the real-time operation data at least includes real-time passenger flow data; A weather type judgment unit, used to determine whether the current weather type in the bus route scheduling area is severe weather based on real-time rainfall data; When it is determined to be bad weather, the real-time passenger flow data of the bus line is used to determine whether the current real-time passenger flow is on an increasing trend compared with the historical passenger flow under conditions without bad weather. When it is determined to be on an increasing trend, it is determined that the bus line interval needs to be shortened.

8. The bus route dispatching and management system based on weather big data according to claim 7 is characterized in that: The weather type determination unit is further configured to determine the impact level of the current severe weather on the bus route when severe weather is determined to be severe weather; The impact level is determined based on the comparison between the real-time rainfall data and a preset rainfall threshold; when the impact level is determined to be the highest level, a message is issued to reduce or suspend the frequency of the bus line.

9. The bus route dispatching and management system based on weather big data according to claim 8 is characterized in that: The system further comprises: A third acquisition and access unit acquires and accesses map data corresponding to the bus route scheduling area; A grid division unit, which is used to divide the area map corresponding to the bus route scheduling area into a plurality of sub-area grids; A data correspondence generation unit is constructed to form a correspondence based on geographical location between the real-time meteorological data source and the historical and real-time operation data of the bus lines and the plurality of sub-regional grids based on the map data; The weather type determination unit is further configured to determine whether the current weather type in each sub-regional grid is severe weather based on the corresponding relationship between the real-time meteorological data source and the sub-regional grids; The warning unit is constructed so that, when it is determined that the current weather type in the sub-regional grid is severe weather, the bus routes that will pass through the corresponding sub-regional grid are determined, and a severe weather warning notice is issued to the dispatcher and / or driver responsible for the corresponding bus routes.

10. The bus route dispatching and management system based on weather big data according to claim 9 is characterized in that: The system further comprises: A bus route travel risk judgment unit is configured to judge the bus route travel risk in the corresponding sub-regional grid whose weather type has been determined to be severe weather and the sub-regional grid adjacent to it at the first level based on the real-time visibility data and / or real-time rainfall data in the real-time meteorological source data; When it is determined that the travel risk of the bus line in the corresponding sub-regional grid whose weather type has been determined to be severe weather is high risk, at least one sub-regional grid whose travel risk of the bus line is lower than the high risk is selected from the sub-regional grids adjacent to the first level as the travel alternative sub-regional grid; When the driving risk of the bus lines in the adjacent sub-regional grids of the first level is also high risk, the driving risk of the bus lines in the adjacent sub-regional grids of the next level is judged based on the current adjacent sub-regional grid, until at least one sub-regional grid with a driving risk lower than the high risk is obtained, and it is used as the driving replacement sub-regional grid; The early warning unit is also configured to send the location information and distance information of the alternative sub-regional grid to the dispatcher and / or driver of the corresponding bus line of the sub-regional grid whose weather type is determined to be severe weather on the original route.

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