A Big Data-Based Intelligent Traffic Management Method and System

Abstract

The present invention discloses a big-data-based intelligent traffic management method and system, which relates to the technical field of intelligent transportation. First, based on the starting point information of the target vehicle, multiple driving routes are generated. Then, based on the intersections, each driving route is divided into multiple sub-routes, and based on the average speed and route length of vehicle passing within a preset time period, the initial passing time of the target vehicle is calculated. Considering the influence of the driving environment, road conditions and the road itself on vehicle passing, the initial passing time of the target vehicle is adjusted multiple times, and finally the corrected passing time of the target vehicle is obtained. Based on the corrected passing time of the driving route, the optimal driving route of the target vehicle is screened out. By making full use of traffic historical data and traffic real-time data and considering the influence of the driving environment, road conditions and the road itself on vehicle driving, the present invention can provide a more accurate optimal driving route for the target vehicle.

Description

Technical Field

[0001] The present invention relates to the field of smart transportation technology, and in particular, to a smart transportation management method and system based on big data. Background Art

[0002] Smart transportation is a new transportation development model based on a combination of networks such as the Internet and the Internet of Things, with smart road networks, smart equipment, smart travel, and smart management as important contents. It has the basic characteristics of information connectivity, real-time monitoring, management coordination, and human-in-one. It is based on intelligent transportation and integrates high-tech IT technologies such as the Internet of Things, cloud computing, big data, and mobile Internet. It collects traffic information through high-tech and provides traffic information services under real-time traffic data. It makes extensive use of data processing technologies such as data models and data mining to realize the systematicness, real-timeness, interactivity of information exchange, and extensiveness of services of smart transportation.

[0003] Smart traffic management based on big data is a traffic management model that uses big data technology to optimize the operating efficiency, safety and sustainability of the traffic system, such as traffic congestion prediction and accident warning. However, the current smart traffic management system based on big data does not closely link traffic prediction, real-time traffic and route planning. For example, when planning a route, the route is planned based on the traffic volume, traffic light waiting time and vehicle travel time on the path within a certain period of time, without considering the impact of roads, weather and environment on vehicle traffic. When a traffic accident occurs on the planned route, most of the routes are re-planned based on the above data, without considering the radiation impact caused by the traffic accident. Therefore, there is an urgent need for a smart traffic management method and system based on big data, which can closely integrate traffic prediction, real-time traffic and route planning, so as to provide vehicles with more accurate optimal route planning. Summary of the invention

[0004] In order to solve the above-mentioned deficiencies in the prior art, the present invention provides a smart traffic management method based on big data, the method comprising:

[0005] Acquire the starting point information of the target vehicle, and generate multiple driving paths based on the starting point information;

[0006] Taking intersections as nodes, each driving path is divided into multiple sub-paths, and each node is included in a sub-path;

[0007] Acquire the real-time traffic data and the historical traffic data of all sub-paths, establish a first data set for storing the real-time traffic data, and establish a second data set for storing the historical traffic data;

[0008] Based on the first data set, obtain the average speed of the vehicle passing through a single sub-path within a preset time period, denoted as the first predicted speed of the target vehicle, and obtain the length of the single sub-path, denoted as the first distance;

[0009] Calculate the initial passing time of each driving path using the first formula, including:

[0010]

[0011] is the initial passing time of each driving path, is the first distance of the single sub-path in each driving path, is the first predicted speed of the target vehicle passing through the single sub-path in each driving path, is the number of sub-paths included in each driving path;

[0012] Based on the second data set, establish a curve graph of the average speed of the vehicle passing through a single sub-path changing with time under different environments, denoted as the first curve graph, and place all the first curve graphs in a curve graph set;

[0013] Based on the curve graph set and the first data set, perform a correction process on the first predicted speed of the target vehicle passing through the single sub-path to obtain the second predicted speed of the target vehicle passing through the single sub-path. Based on all the second predicted speeds and all the first distances, adjust the initial passing time of each driving path to obtain the first corrected passing time;

[0014] Based on the first data set, determine whether a traffic accident has occurred in all driving paths. If so, with the accident occurrence point as the center and a preset distance as the radius, delineate the accident impact area, and process the second predicted speed of the target vehicle in all sub-paths within the accident impact area to obtain the first processing result. Based on the first processing result and all the first distances, adjust the first corrected passing time to obtain the second corrected passing time. If not, then use the first corrected passing time as the second corrected passing time;

[0015] Based on the second data set, use a preset method to obtain the slope coefficient of the single sub-path to obtain the first acquisition result. Based on the first acquisition result, process the first distance of each sub-path to obtain the second processing result. Based on the first processing result and the second processing result, adjust the second corrected passing time to obtain the third corrected passing time;

[0016] Screen the driving path with the shortest third corrected passing time as the optimal driving path of the target vehicle.

[0017] The present invention is implemented through the following technical solutions: First, obtain the starting point information of the target vehicle. Generating multiple driving paths based on the starting point information can be achieved based on existing technologies (such as map software). Establish a first data set for storing real-time traffic data and a second data set for storing historical traffic data. To ensure the timeliness of data transmission, the first data set is selected to be stored at the edge device side, and the second data set is stored at the local terminal; through the average speed of the vehicle passing through a single sub-path within a preset time period (such as within the past 1 hour) in the first data set, and the distance of the single sub-path, the initial passing time of each driving path is obtained. However, at this time, the initial passing time is calculated based on real-time traffic data and does not consider the influence of historical traffic data (such as the influence of weather and time), road conditions (such as traffic accidents), and the road itself (gradient, curvature, etc.). Therefore, the following processing is performed on the initial passing time of each driving path:

[0018] First, based on the second data set, establish a first curve graph showing the variation of the average speed of vehicle passing through a single sub-path with time (such as within a day) in different environments, and place it in the curve graph set. Based on the curve graph set and the first data set, correct the first predicted speed of the target vehicle passing through the single sub-path, that is, use the curve graph set and the first data set to obtain the average driving speed of the target vehicle passing through the single sub-path in the same time period and the same environment in the past, and use this average driving speed to correct the first predicted speed to obtain the second predicted speed. Based on the second predicted speed, finally process to obtain the first corrected passing time of each driving path;

[0019] In addition, this solution also considers the radiation impact brought by traffic accidents. Determine whether a traffic accident has occurred in the planned path. If so, with the accident location as the center and a preset distance as the radius, determine the accident impact area. The closer to the center of the accident impact area, the greater the impact on vehicle passing. Then, correct the second predicted speed of the target vehicle in all sub-paths within the accident impact area, and finally process to obtain the second corrected passing time of each driving path. If not, do nothing and directly use the first corrected passing time as the second corrected passing time;

[0020] Then, this solution also considers the impact of the road itself on vehicle passing. Among paths with the same distance, the greater the gradient coefficient of the path, the greater the impact on vehicle passing. Therefore, based on the second data set, use a preset method to obtain the gradient coefficient of each sub-path, process each sub-path through the gradient coefficient, and finally process to obtain the third corrected passing time of each driving path;

[0021] Considering the influence of the driving environment, road conditions and the road itself on vehicle passage, the initial passage time of the target vehicle is corrected multiple times, and finally the third passage time of each driving path is obtained. Then, the driving path corresponding to the shortest third passage time is selected as the optimal driving path of the target vehicle.

[0022] As an alternative technical solution, obtaining the average speed of vehicle passage on a single sub-path within a preset time period includes:

[0023] Obtaining traffic monitoring data of a single sub-path within the preset time period, where the traffic monitoring data includes GPS trajectory data and object detection data of vehicles;

[0024] Based on all the traffic monitoring data within the preset time period, calculate the passage time of each vehicle passing through the single sub-path to obtain a first calculation result;

[0025] Based on the first calculation result, obtain the average speed of all vehicles passing through the single sub-path within the preset time period.

[0026] As an alternative technical solution, based on a second data set, establishing a curve graph of the average speed of vehicle passage on a single sub-path changing with time includes:

[0027] Obtain a single sub-path denoted as the first sub-path, and obtain the historical weather data of the first sub-path within one year;

[0028] Based on all the historical weather data, use a second formula to calculate the environmental parameters of the first sub-path, where the environmental parameters include multiple sub-environmental parameters;

[0029] The second formula includes:

[0030]

[0031] is the environmental parameter of the first sub-path, is the coefficient of a single sub-environmental parameter, is the data value of a single sub-environmental parameter, and r is the number of sub-environmental parameters;

[0032] Based on all the environmental parameters, classify the driving environment of the vehicle to obtain a third processing result;

[0033] Based on the third processing result, for each level of vehicle driving environment, obtain the average speed of the vehicle passing through the first sub-path within a day to obtain a second obtaining result;

[0034] Based on the second acquisition result, with 24h as the abscissa and the average speed of the vehicle passing through the first sub-path as the ordinate, a first curve graph is established for each level of vehicle driving environment.

[0035] As an alternative technical solution, the acquisition of the data value of a single sub-environment parameter includes:

[0036] Obtain any sub-environment parameter denoted as the first sub-environment parameter, and obtain all historical data of the first sub-environment parameter to obtain a third acquisition result;

[0037] Based on the third acquisition result, divide the data range of all historical data of the first sub-environment parameter, and assign values to each divided data range;

[0038] Based on the data range after assignment, obtain the data value corresponding to the first sub-environment parameter.

[0039] As an alternative technical solution, based on the curve graph set and the first data set, perform correction processing on the first predicted speed of the target vehicle passing through a single sub-path:

[0040] Based on the first data set, use the second formula to calculate the environmental parameters of the target vehicle when passing through the first sub-path, denoted as the first environmental parameter;

[0041] Based on the first environmental parameter, determine the level of the driving environment of the target vehicle to obtain a first determination result;

[0042] Based on the first determination result, match the corresponding first curve graph in the curve graph set, denoted as the second curve graph;

[0043] Based on the first predicted speed of the target vehicle, determine the start and end times of the target vehicle passing through the first sub-path in the second curve graph;

[0044] Based on the start and end times and the second curve graph, calculate the average speed of the vehicle within the start and end times, denoted as the first corrected speed;

[0045] Calculate the average value of the first corrected speed and the first predicted speed, and use the average value as the second predicted speed of the target vehicle.

[0046] As an alternative technical solution, process the second predicted speed of the target vehicle in all sub-paths within the accident impact area to obtain a first processing result, including:

[0047] Taking the center point of the accident impact area as the center of the circle, and based on the distance between any point in the accident impact area and the center of the circle, divide the entire accident impact area into multiple levels of sub-accident impact areas;

[0048] An influence coefficient is set for each sub-accident impact area, and the influence coefficient is proportional to the distance from the single sub-accident area to the center of the circle;

[0049] Obtain a single sub-path within the accident impact area, denoted as the second sub-path;

[0050] Calculate the distance between any point on the second sub-path and the center point of the accident impact area, denoted as the first distance;

[0051] Based on the first distance, obtain all corresponding sub-accident impact areas to get a fourth acquisition result;

[0052] Based on the fourth acquisition result, determine whether the second sub-path only contains a single sub-accident impact area. If so, obtain the influence coefficient of the corresponding sub-accident impact area, denoted as the first influence coefficient. Based on the first influence coefficient, process the second predicted speed of the target vehicle passing through the second sub-path to obtain the first processing result. If not, obtain the influence coefficients of all corresponding sub-accident areas, denoted as the second influence coefficients. Based on all the second influence coefficients, perform segmented processing on the second predicted speed of the target vehicle passing through the second sub-path to obtain the first processing result.

[0053] As an optional technical solution, processing the second predicted speed of the target vehicle passing through the second sub-path based on the first influence coefficient includes:

[0054] Calculate the product of the first influence coefficient and the second predicted speed to obtain the predicted passing speed of the target vehicle passing through the second sub-path, denoted as the first processing result;

[0055] Processing the second predicted speed of the target vehicle passing through the second sub-path based on all the second influence coefficients includes:

[0056] Based on the level of the sub-accident areas included in the second sub-path, divide the second sub-path into multiple impact paths;

[0057] Obtain the second influence coefficient corresponding to each impact path, and calculate the product of the corresponding second influence coefficient and the second predicted speed to obtain the predicted passing speeds of the target vehicle passing through all the impact paths in the second sub-path, denoted as the first processing result.

[0058] As an optional technical solution, the method for obtaining the slope coefficient of a single sub-path by using a preset method includes:

[0059] Based on the second data set, obtain the planar image data and the corresponding point cloud image data of a single sub-path;

[0060] Determine a fitting surface model based on the ground morphology of a single sub-path, the planar image data, and the point cloud image data;

[0061] Determine a first set of feature points from the planar image data, and determine a second set of feature points based on the point cloud image data and the fitting surface model;

[0062] Based on the feature point coordinate data in the first set of feature points and the second set of feature points, determine a coordinate mapping relationship, which is used to characterize the correspondence between the two-dimensional coordinates of the feature points in the planar image data and the three-dimensional coordinates in the world coordinate system;

[0063] Based on the coordinate mapping relationship, determine the three-dimensional coordinates of each pixel point in the planar image data to obtain a second determination result;

[0064] Based on the second determination result, obtain the slope coefficient of a single sub-path.

[0065] As an optional technical solution, based on the first acquisition result, the processing of the first distance of each sub-path includes:

[0066] Calculate the product of the slope coefficient of a single sub-path and the corresponding first distance to obtain the second distance of the single sub-path, denoted as the second processing result.

[0067] To solve the deficiencies of the above-mentioned existing technologies, the present invention provides a big data-based intelligent transportation management system, which includes:

[0068] An initial unit, configured to obtain the starting point information of a target vehicle, and generate multiple driving paths based on the starting point information;

[0069] A division unit, configured to divide each driving path into multiple sub-paths with intersections as nodes, and each node is included in one sub-path;

[0070] A data set unit, configured to obtain the traffic real-time data and traffic historical data of all sub-paths, establish a first data set for storing the traffic real-time data, and establish a second data set for storing the traffic historical data;

[0071] An acquisition unit, configured to obtain the average speed of vehicle passing on a single sub-path within a preset time period, denoted as the first predicted speed of the target vehicle, based on the first data set, and obtain the length of the single sub-path, denoted as the first distance;

[0072] An initial travel time unit, configured to calculate the initial travel time of each driving path using a first formula, including:

[0073]

[0074] is the initial passing time of each driving path, is the first distance of a single sub-path in each driving path, is the first predicted speed of the target vehicle passing through a single sub-path in each driving path, is the number of segments of sub-paths included in each driving path;

[0075] The curve atlas unit is used to establish a curve graph of the average speed of vehicles passing through a single sub-path in different environments changing with time based on the second data set, denoted as the first curve graph, and place all the first curve graphs in the curve atlas;

[0076] The first corrected passing time unit is used to correct the first predicted speed of the target vehicle passing through a single sub-path based on the curve atlas and the first data set to obtain the second predicted speed of the target vehicle passing through a single sub-path, and adjust the initial passing time of each driving path based on all the second predicted speeds and all the first distances to obtain the first corrected passing time;

[0077] The second corrected passing time unit is used to judge whether a traffic accident occurs in all driving paths based on the first data set. If so, a traffic accident impact area is circled with the accident occurrence point as the center and a preset distance as the radius, and the second predicted speed of the target vehicle in all sub-paths within the accident impact area is processed to obtain a first processing result, and the first corrected passing time is adjusted based on the first processing result and all the first distances to obtain the second corrected passing time. If not, the first corrected passing time is used as the second corrected passing time;

[0078] The third corrected passing time unit is used to obtain the slope coefficient of a single sub-path by using a preset method based on the second data set to obtain a first obtaining result, process the first distance of each sub-path based on the first obtaining result to obtain a second processing result, and adjust the second corrected passing time based on the first processing result and the second processing result to obtain the third corrected passing time;

[0079] The optimal driving path unit is used to screen the driving path with the shortest third corrected passing time as the optimal driving path of the target vehicle.

[0080] One or more technical solutions provided by the present invention have at least the following technical effects or advantages:

[0081] The present invention discloses a big data-based intelligent traffic management method. First, based on the starting point information of the target vehicle, multiple driving routes are generated. Then, based on the intersections, each driving route is divided into multiple sub-routes, and based on the average speed and route length of vehicle passage within a preset time period, the initial passage time of the target vehicle is calculated. Considering the influence of the driving environment, road conditions, and the road itself on vehicle passage, the initial passage time of the target vehicle is adjusted three times, and finally the third corrected passage time of the target vehicle is obtained. Based on the third corrected passage time of the driving route, the optimal driving route of the target vehicle is selected. By closely integrating traffic prediction (traffic historical data), real-time traffic, and route planning, the present invention can provide a more accurate optimal driving route for the target vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, form a part of the present invention, and do not limit the embodiments of the present invention;

[0083] Figure 1 is a schematic flowchart of a big data-based intelligent traffic management method in the present invention;

[0084] Figure 2 is a schematic diagram of the composition of a big data-based intelligent traffic management system in the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0085] In order to more clearly understand the above objects, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the drawings and specific embodiments. It should be noted that, without conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other.

[0086] In the following description, many specific details are set forth in order to fully understand the present invention. However, the present invention can also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited by the specific embodiments disclosed below.

[0087] Embodiment 1

[0088] Please refer to Figure 1 , Figure 1 which is a schematic flowchart of a big data-based intelligent traffic management method in the present invention. The method includes:

[0089] Obtain the starting point information of the target vehicle, and based on the starting point information, generate multiple driving routes;

[0090] Taking the intersections as nodes, divide each driving route into multiple sub-routes, and each node is included in a sub-route;

[0091] Obtain the real-time traffic data and traffic history data of all sub-paths, establish a first data set for storing the real-time traffic data, and establish a second data set for storing the traffic history data;

[0092] Based on the first data set, obtain the average speed of vehicle passing on a single sub-path within a preset time period as the first predicted speed of the target vehicle, and obtain the length of a single sub-path as the first distance;

[0093] Use the first formula to calculate the initial passing time of each driving path, including:

[0094]

[0095] is the initial passing time of each driving path, is the first distance of a single sub-path in each driving path, is the first predicted speed of the target vehicle passing through a single sub-path in each driving path, is the number of sub-paths included in each driving path;

[0096] Based on the second data set, establish a curve graph showing the change of the average speed of vehicle passing on a single sub-path with time under different environments, denoted as the first curve graph, and place all the first curve graphs in a curve graph set;

[0097] Based on the curve graph set and the first data set, perform a correction process on the first predicted speed of the target vehicle passing through a single sub-path to obtain the second predicted speed of the target vehicle passing through a single sub-path. Based on all the second predicted speeds and all the first distances, adjust the initial passing time of each driving path to obtain the first corrected passing time;

[0098] Based on the first data set, determine whether a traffic accident has occurred in all driving paths. If so, with the accident occurrence point as the center and a preset distance as the radius, delineate the accident impact area, and process the second predicted speed of the target vehicle in all sub-paths within the accident impact area to obtain a first processing result. Based on the first processing result and all the first distances, adjust the first corrected passing time to obtain the second corrected passing time. If not, then use the first corrected passing time as the second corrected passing time;

[0099] Based on the second data set, use a preset method to obtain the slope coefficient of a single sub-path to obtain a first acquisition result. Based on the first acquisition result, process the first distance of each sub-path to obtain a second processing result. Based on the first processing result and the second processing result, adjust the second corrected passing time to obtain the third corrected passing time;

[0100] Screen the driving route with the shortest third corrected passing time as the optimal driving route of the target vehicle.

[0101] The specific embodiments of the present invention are as follows: including calculating the initial passing time of each driving route of the target vehicle and making multiple adjustments to the initial passing time;

[0102] Calculate the initial passing time of each driving route of the target vehicle:

[0103] Obtain the starting point information of the target vehicle, and generate multiple driving routes based on the starting point information; use intersections as nodes to divide each driving route into multiple sub-routes, and each node is included in one sub-route. The reason for dividing sub-routes is to more accurately provide the optimal driving route for the vehicle; then establish a first data set for storing real-time traffic data (such as traffic flow, environment, video, etc. data), and a second data set for storing traffic historical data (with the same data type as the real-time data); based on the first data set, obtain the average speed of vehicle passing on a single sub-route in the past hour as the first predicted speed of the target vehicle, and obtain the length of each sub-route as the first distance;

[0104] Calculate the initial passing time of each driving route using the first formula, including:

[0105]

[0106] is the initial passing time of each driving route, is the first distance of a single sub-route in each driving route, is the first predicted speed of the target vehicle passing through a single sub-route in each driving route, is the number of sub-routes included in each driving route;

[0107] Among them, the initial passing time of each driving route can be accurately calculated through the first formula.

[0108] Obtaining the average speed of vehicle passing on a single sub-route within a preset time period includes:

[0109] Obtain the traffic monitoring data of a single sub-route within the preset time period, and the traffic monitoring data includes the GPS trajectory data and object detection data of the vehicle;

[0110] Based on all the traffic monitoring data within the preset time period, calculate the passing time of each vehicle passing through a single sub-route to obtain the first calculation result;

[0111] Based on the first calculation result, obtain the average speed of all vehicles passing through a single sub-route within the preset time period.

[0112] Process the initial passing time to obtain the first corrected passing time:

[0113] Based on the second data set, establish a curve graph showing the variation of the average speed of vehicles passing through a single sub-path in different environments, including:

[0114] Obtain a single sub-path and denote it as the first sub-path, and obtain the historical weather data of the first sub-path within one year;

[0115] Based on all the historical weather data, use the second formula to calculate the environmental parameters of the first sub-path, and the environmental parameters include multiple sub-environmental parameters;

[0116] The second formula includes:

[0117]

[0118] is the environmental parameter of the first sub-path, is the coefficient of a single sub-environmental parameter, is the data value of a single sub-environmental parameter, and r is the number of sub-environmental parameters;

[0119] Based on all the environmental parameters, classify the driving environment of the vehicle to obtain the third processing result;

[0120] Based on the third processing result, for each level of vehicle driving environment, obtain the average speed of the vehicle passing through the first sub-path within one day to obtain the second acquisition result;

[0121] Based on the second acquisition result, with 24h as the abscissa and the average speed of the vehicle passing through the first sub-path as the ordinate, establish the first curve graph under each level of vehicle driving environment.

[0122] The acquisition of the data value of a single sub-environmental parameter includes:

[0123] Obtain any sub-environmental parameter and denote it as the first sub-environmental parameter, and obtain all the historical data of the first sub-environmental parameter to obtain the third acquisition result;

[0124] Based on the third acquisition result, divide all the historical data of the first sub-environmental parameter into data intervals, and assign values to each divided data interval;

[0125] Based on the data intervals after assignment, obtain the data value corresponding to the first sub-environmental parameter.

[0126] Based on the curve graph set and the first data set, correct the first predicted speed of the target vehicle passing through a single sub-path:

[0127] Based on the first data set, use the second formula to calculate the environmental parameters when the target vehicle passes through the first sub-path, denoted as the first environmental parameters;

[0128] Based on the first environmental parameters, determine the level of the driving environment of the target vehicle to obtain a first determination result;

[0129] Based on the first determination result, match the corresponding first curve graph in the curve graph set, denoted as the second curve graph;

[0130] Based on the first predicted speed of the target vehicle, determine the start and end times when the target vehicle passes through the first sub-path in the second curve graph;

[0131] Based on the start and end times and the second curve graph, calculate the average speed of the vehicle within the start and end times, denoted as the first corrected speed;

[0132] Calculate the average value of the first corrected speed and the first predicted speed, and use the average value as the second predicted speed of the target vehicle.

[0133] Among them, in this embodiment, by obtaining historical weather data, the driving environment of the vehicle is represented by environmental parameters, and the second formula is used to calculate the environmental parameters. All environmental parameters obtained based on historical weather data are statistically analyzed, and the driving environment of the vehicle is classified. Then, for each level of driving environment, a first curve graph showing the change of the average speed of a single sub-path vehicle passing with time (24h) is established and placed in the curve graph set; then, based on the first data set, the current environmental parameters of the target vehicle are obtained. The environmental parameters represent the current driving environment of the target vehicle. The data interval where the current environmental parameters of the target vehicle are located is judged, so as to obtain the corresponding level of driving environment. In the curve graph set, the first curve graph corresponding to a single sub-path of this level of driving environment is obtained, denoted as the second curve graph; based on the first predicted speed of the target vehicle passing through a single sub-path, the start and end times are determined in the second curve graph, and then the average speed of the vehicle passing within the start and end times is calculated by mathematical software such as MATLAB, denoted as the first corrected speed. By calculating the average value of the first corrected speed and the first predicted speed, it is used as the second predicted speed of the target vehicle. Finally, based on the second predicted speed and the first distance corresponding to the sub-path, the first corrected passing time is processed. By fusing traffic historical data and traffic real-time data, the second predicted speed of the target vehicle obtained can more accurately predict the speed of the target vehicle passing through the corresponding sub-path.

[0134] Process the first corrected passing time to obtain the second corrected passing time:

[0135] Based on the first data set, determine whether a traffic accident has occurred in all driving paths. If so, with the traffic accident occurrence point as the center and a preset distance as the radius, demarcate the accident impact area, process the second predicted speed of the target vehicle in all sub-paths within the accident impact area to obtain a first processing result, and adjust the first corrected travel time based on the first processing result and all first distances to obtain a second corrected travel time. If not, then use the first corrected travel time as the second corrected travel time;

[0136] Processing the second predicted speed of the target vehicle passing through the second sub-path based on the first influence coefficient includes:

[0137] Calculate the product of the first influence coefficient and the second predicted speed to obtain the predicted passing speed of the target vehicle passing through the second sub-path, denoted as the first processing result;

[0138] Processing the second predicted speed of the target vehicle passing through the second sub-path based on all second influence coefficients includes:

[0139] Based on the level of the sub-accident area included in the second sub-path, divide the second sub-path into multiple affected paths;

[0140] Obtain the second influence coefficient corresponding to each affected path, and calculate the product of the corresponding second influence coefficient and the second predicted speed to obtain the predicted passing speed of the target vehicle passing through all affected paths in the second sub-path, denoted as the first processing result.

[0141] Among them, this embodiment fully considers the impact of traffic accidents on vehicle traffic and further differentiates this impact, that is, the closer to the accident occurrence point, the greater the impact on vehicle traffic. For example, if a traffic accident occurs at point A, a preset distance B is used as the radius to define the accident impact area. The accident impact area is further subdivided. Based on the distance from any point in the accident impact area to point A, the entire accident impact area is divided into 5 sub-accident impact areas (i.e., concentric circles). The impact coefficients of the 5 sub-accident impact areas from point A outwards are C1, C2, C3, C4, and C5 respectively, and C1 > C2 > C3 > C4 > C5. If there is a second sub-path D in the accident impact area in the driving path and the second predicted speed of the target vehicle passing through the second sub-path D is E, when the second sub-path D only contains a single sub-accident impact area, such as only containing the third sub-accident area from point A outwards, then the predicted passing speed of the target vehicle through the second sub-path is equal to the product of E·C3; when the second sub-path D contains multiple sub-accident impact areas, such as containing the fourth sub-accident area and the fifth sub-accident area from point A outwards, then the second sub-path D needs to be segmented. The section containing the fourth sub-accident area is denoted as D1, and the section containing the fifth sub-accident area is denoted as D2. The predicted passing speed of the target vehicle through section D1 is equal to the product of E·C4, and the predicted passing speed of the target vehicle through section D2 is equal to the product of E·C5. It should be noted that the above data is only for explanatory purposes and can be adjusted according to the actual situation.

[0142] Process the second corrected passing time to obtain the third corrected passing time:

[0143] Based on the second data set, use a preset method to obtain the slope coefficient of a single sub-path to obtain a first acquisition result. Based on the first acquisition result, process the first distance of each sub-path to obtain a second processing result. Based on the first processing result and the second processing result, adjust the second corrected passing time to obtain the third corrected passing time;

[0144] Using a preset method to obtain the slope coefficient of a single sub-path includes:

[0145] Based on the second data set, obtain the plane image data and the corresponding point cloud image data of a single sub-path;

[0146] Based on the ground form of a single sub-path, the plane image data and the point cloud image data, determine the fitting surface model;

[0147] Determine the first feature point set from the plane image data, and determine the second feature point set based on the point cloud image data and the fitting surface model;

[0148] Based on the feature point coordinate data in the first set of feature points and the second set of feature points, determine a coordinate mapping relationship, which is used to represent the correspondence between the two-dimensional coordinates of the feature points in the planar image data and the three-dimensional coordinates in the world coordinate system;

[0149] Based on the coordinate mapping relationship, determine the three-dimensional coordinates of each pixel point in the planar image data to obtain a second determination result;

[0150] Based on the second determination result, obtain the slope coefficient of a single sub-path.

[0151] Based on the first obtained result, the processing of the first distance of each sub-path includes:

[0152] Calculate the product of the slope coefficient of a single sub-path and the corresponding first distance to obtain the second distance of the single sub-path, denoted as the second processing result.

[0153] Among them, by obtaining the planar image data and point cloud image data of a single sub-path, determine a fitting surface model, and by means of feature point matching, determine the three-dimensional coordinates of each pixel point in the planar image data. Calculate the slope coefficient (i.e., slope data) of the sub-path through these three-dimensional coordinates. For example, if the slope coefficient of a single sub-path is F1 and the first distance of the single sub-path is H1, then the second distance of the single sub-path is equal to the product of F1·H1.

[0154] Further, based on the first processing result and the second processing result, this embodiment adjusts the second corrected passing time to obtain a third corrected passing time, and screens the driving path with the shortest third corrected passing time as the optimal driving path of the target vehicle, including:

[0155] Obtain a sub-path in any driving path, denoted as sub-path G. The second predicted speed of the target vehicle passing through sub-path G is I, the first distance of sub-path G is M, and the slope coefficient of sub-path G is N. Then the first corrected passing time of the target vehicle passing through sub-path G is the ratio of M / I. If sub-path G is located in the accident impact area and the impact coefficients of the corresponding sub-accident areas are K1 and K2 respectively, and the corresponding first distance M is divided into M1 and M2, then the second corrected passing time of the target vehicle passing through sub-path G at this time is the value of (K1·M1 / I + K2·M2 / I), and the third corrected passing time of passing through sub-path G is the value of (K1·NM1 / I + K2·NM2 / I). It should be noted that the above data is only for explanatory purposes and can be adjusted according to the actual situation.

[0156] Embodiment 2

[0157] Please refer to Figure 2 , Figure 2It is a schematic diagram of the composition of an intelligent transportation management system based on big data in the present invention. The initial unit is used to obtain the starting point information of the target vehicle, and based on the starting point information, generate multiple driving paths;

[0158] The division unit is used to take intersections as nodes, divide each driving path into multiple sub-paths, and each node is included in at most one sub-path;

[0159] The data set unit is used to obtain the real-time traffic data and traffic historical data of all sub-paths, establish a first data set for storing the real-time traffic data, and establish a second data set for storing the traffic historical data;

[0160] The acquisition unit is used to obtain the average speed of vehicle passing on a single sub-path within a preset time period based on the first data set, denoted as the first predicted speed of the target vehicle, and obtain the length of a single sub-path, denoted as the first distance;

[0161] The initial passing time unit is used to calculate the initial passing time of each driving path by using the first formula, including:

[0162]

[0163] is the initial passing time of each driving path, is the first distance of a single sub-path in each driving path, is the first predicted speed of the target vehicle passing through a single sub-path in each driving path, is the number of sub-path segments included in each driving path;

[0164] The curve atlas unit is used to establish a curve graph of the average speed of vehicle passing on a single sub-path changing with time under different environments based on the second data set, denoted as the first curve graph, and place all the first curve graphs in the curve atlas;

[0165] The first corrected passing time unit is used to correct the first predicted speed of the target vehicle passing through a single sub-path based on the curve atlas and the first data set to obtain the second predicted speed of the target vehicle passing through a single sub-path, and adjust the initial passing time of each driving path based on all the second predicted speeds and all the first distances to obtain the first corrected passing time;

[0166] The second corrected travel time unit is configured to determine whether a traffic accident has occurred in all travel routes based on the first data set. If so, a traffic accident impact area is demarcated with the traffic accident occurrence point as the center and a preset distance as the radius. The second predicted speed of the target vehicle in all sub-routes within the traffic accident impact area is processed to obtain a first processing result. Based on the first processing result and all the first distances, the first corrected travel time is adjusted to obtain a second corrected travel time. If not, the first corrected travel time is used as the second corrected travel time;

[0167] The third corrected travel time unit is configured to obtain a slope coefficient of a single sub-route by using a preset method based on the second data set to obtain a first acquisition result. Based on the first acquisition result, each first distance of the sub-routes is processed to obtain a second processing result. Based on the first processing result and the second processing result, the second corrected travel time is adjusted to obtain a third corrected travel time;

[0168] The optimal travel route unit is configured to screen the travel route with the shortest third corrected travel time as the optimal travel route of the target vehicle.

[0169] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications once they learn the basic creative concept. Therefore, the appended claims are intended to be construed to include the preferred embodiments as well as all changes and modifications falling within the scope of the present invention.

[0170] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. A smart traffic management method based on big data, characterized in that: The method comprises: Acquire the starting point information of the target vehicle, and generate multiple driving paths based on the starting point information; Taking intersections as nodes, each driving path is divided into multiple sub-paths, and each node is included in a sub-path; Acquire the real-time traffic data and the historical traffic data of all sub-paths, establish a first data set for storing the real-time traffic data, and establish a second data set for storing the historical traffic data; Based on the first data set, an average speed of vehicles passing through a single sub-path within a preset time period is obtained and recorded as a first predicted speed of the target vehicle, and a length of the single sub-path is obtained and recorded as a first distance; The first formula is used to calculate the initial travel time of each driving path, including: is the initial travel time of each driving path, is the first distance of a single-segment sub-path in each driving path, is the first predicted speed of the target vehicle passing through a single sub-path in each driving path, is the number of segments containing sub-paths in each driving path; Based on the second data set, a curve graph of the average speed of vehicles passing through a single sub-path under different environments over time is established, recorded as a first curve graph, and all first curve graphs are placed in a curve graph set; Based on the curve atlas and the first data set, a first predicted speed of the target vehicle passing through the single-segment sub-path is corrected to obtain a second predicted speed of the target vehicle passing through the single-segment sub-path, and based on all the second predicted speeds and all the first distances, an initial travel time of each driving path is adjusted to obtain a first corrected travel time; Based on the first data set, determine whether a traffic accident occurs in all driving paths; if so, define the accident impact area with the traffic accident point as the center and a preset distance as the radius, process the second predicted speed of the target vehicle in all sub-paths within the accident impact area to obtain a first processing result; based on the first processing result and all first distances, adjust the first corrected travel time to obtain a second corrected travel time; if not, use the first corrected travel time as the second corrected travel time; Based on the second data set, a preset method is used to obtain a slope coefficient of a single sub-path to obtain a first acquisition result; based on the first acquisition result, a first distance of each sub-path is processed to obtain a second processing result; based on the first processing result and the second processing result, the second corrected travel time is adjusted to obtain a third corrected travel time; The driving path with the shortest third corrected travel time is selected as the optimal driving path of the target vehicle.

2. According to the big data-based intelligent traffic management method of claim 1, it is characterized in that: Obtaining the average speed of vehicles passing through a single sub-path within a preset time period includes: Acquire traffic monitoring data of a single-segment sub-path within the preset time period, wherein the traffic monitoring data includes GPS trajectory data of the vehicle and object detection data; Based on all traffic monitoring data within the preset time period, the travel time of each vehicle passing through a single sub-path is calculated to obtain a first calculation result; Based on the first calculation result, an average speed of all vehicles passing through the single-segment sub-path within the preset time period is obtained.

3. The intelligent traffic management method based on big data according to claim 1 is characterized in that: Based on the second data set, a curve chart showing the average speed of vehicles passing through a single sub-path under different environments over time is established, including: Obtain a single-segment sub-path as a first sub-path, and obtain historical weather data of the first sub-path within one year; Based on all historical weather data, an environmental parameter of the first sub-path is calculated using a second formula, wherein the environmental parameter includes a plurality of sub-environmental parameters; The second formula includes: is the environmental parameter of the first subpath, is the coefficient of a single sub-environment parameter, is the data value of a single sub-environment parameter, and r is the number of sub-environment parameters; Based on all environmental parameters, the driving environment of the vehicle is graded to obtain a third processing result; Based on the third processing result, for each level of vehicle driving environment, an average speed of the vehicle passing through the first sub-path in one day is obtained to obtain a second acquisition result; Based on the second acquisition result, a first curve graph under each level of vehicle driving environment is established with 24 hours as the horizontal coordinate and the average speed of the vehicle passing through the first sub-path as the vertical coordinate.

4. The intelligent traffic management method based on big data according to claim 3 is characterized in that: The data value acquisition of a single sub-environment parameter includes: Obtain any sub-environment parameter and record it as a first sub-environment parameter, and obtain all historical data of the first sub-environment parameter to obtain a third acquisition result; Based on the third acquisition result, all historical data of the first sub-environment parameter are divided into data intervals, and a value is assigned to each divided data interval; Based on the assigned data interval, a data value corresponding to the first sub-environment parameter is obtained.

5. The intelligent traffic management method based on big data according to claim 3 is characterized in that: Based on the curve atlas and the first data set, a first predicted speed of the target vehicle passing through a single-segment sub-path is corrected: Based on the first data set, the second formula is used to calculate the environmental parameters when the target vehicle passes through the first sub-path, recorded as the first environmental parameters; Based on the first environmental parameter, determining the level of the target vehicle's driving environment to obtain a first determination result; Based on the first determination result, a corresponding first curve graph is obtained by matching in the curve graph set, and recorded as a second curve graph; Based on the first predicted speed of the target vehicle, determining the start and end times of the target vehicle passing through the first sub-path in the second curve graph; Based on the start and end times and the second curve graph, an average speed of the vehicle within the start and end times is calculated and recorded as a first corrected speed; An average of the first corrected speed and the first predicted speed is calculated, and the average is used as a second predicted speed of the target vehicle.

6. The method for intelligent traffic management based on big data according to claim 1 is characterized in that: Processing the second predicted speed of the target vehicle in all sub-paths in the accident impact area to obtain a first processing result includes: Taking the center point of the accident impact area as the center of the circle, and based on the distance between any point in the accident impact area and the center of the circle, dividing the entire accident impact area into multiple levels of sub-accident impact areas; An influence coefficient is set for each sub-accident impact area, and the influence coefficient is proportional to the distance from the single sub-accident area to the center of the circle; Obtain a single-segment sub-path within the accident impact area, recorded as a second sub-path; Calculate the distance between any point in the second sub-path and the center point of the accident impact area, and record it as the first distance; Based on the first distance, all corresponding sub-accident impact areas are acquired to obtain a fourth acquisition result; Based on the fourth acquisition result, determine whether the second sub-path contains only a single sub-accident impact area. If so, obtain the influence coefficient of the corresponding sub-accident impact area and record it as the first influence coefficient. Based on the first influence coefficient, process the second predicted speed of the target vehicle through the second sub-path to obtain the first processing result. If not, obtain the influence coefficient corresponding to all sub-accident areas and record it as the second influence coefficient. Based on all the second influence coefficients, perform segmented processing on the second predicted speed of the target vehicle through the second sub-path to obtain the first processing result.

7. The method for intelligent traffic management based on big data according to claim 6 is characterized in that: Processing a second predicted speed of the target vehicle passing through the second sub-path based on the first influence coefficient includes: Calculate the product of the first influence coefficient and the second predicted speed to obtain the predicted passing speed of the target vehicle passing through the second sub-path, which is recorded as the first processing result; Processing the second predicted speed of the target vehicle passing through the second subpath based on all the second influence coefficients includes: Based on the level of the sub-accident area included in the second sub-path, dividing the second sub-path into a plurality of impact paths; The second influence coefficient corresponding to each influencing path is obtained, and the product of the corresponding second influence coefficient and the second predicted speed is calculated to obtain the predicted passing speed of the target vehicle through all the influencing paths in the second sub-path, which is recorded as the first processing result.

8. The intelligent traffic management method based on big data according to claim 1 is characterized in that: The preset method to obtain the slope coefficient of a single-segment sub-path includes: Based on the second data set, obtaining plane image data and corresponding point cloud image data of a single-segment sub-path; Based on the ground morphology of the single-segment sub-path, the plane image data and the point cloud image data, a fitting surface model is determined; Determine a first feature point set from the plane image data, and determine a second feature point set based on the point cloud image data and the fitted surface model; Determine a coordinate mapping relationship based on the feature point coordinate data in the first feature point set and the second feature point set, wherein the coordinate mapping relationship is used to represent the correspondence between the two-dimensional coordinates of the feature points in the plane image data and the three-dimensional coordinates in the world coordinate system; Based on the coordinate mapping relationship, determine the three-dimensional coordinates of each pixel point in the plane image data to obtain a second determination result; Based on the second determination result, the slope coefficient of the single-segment sub-path is obtained.

9. The intelligent traffic management method based on big data according to claim 1 is characterized in that: Based on the first acquisition result, processing the first distance of each sub-path includes: The product of the slope coefficient of the single-segment sub-path and the corresponding first distance is calculated to obtain the second distance of the single-segment sub-path, which is recorded as the second processing result.

10. A smart traffic management system based on big data, characterized in that: The system comprises: An initialization unit, used to obtain the starting point information of the target vehicle, and generate multiple driving paths based on the starting point information; A division unit is used to divide each driving path into multiple sub-paths with the intersection as a node, and each node is included in a sub-path; A data set unit, used to obtain the real-time traffic data and the historical traffic data of all sub-paths, establish a first data set for storing the real-time traffic data, and establish a second data set for storing the historical traffic data; An acquisition unit, configured to acquire, based on the first data set, an average speed of vehicles passing through a single-segment sub-path within a preset time period and record it as a first predicted speed of the target vehicle, and acquire a length of the single-segment sub-path and record it as a first distance; The initial travel time unit is used to calculate the initial travel time of each driving path using the first formula, including: is the initial travel time of each driving path, is the first distance of a single-segment sub-path in each driving path, is the first predicted speed of the target vehicle passing through a single sub-path in each driving path, is the number of segments containing sub-paths in each driving path; A curve graph set unit, used to establish a curve graph of the average speed of vehicles passing through a single sub-path under different environments over time based on the second data set, recorded as a first curve graph, and put all the first curve graphs into the curve graph set; A first corrected travel time unit is used to correct the first predicted speed of the target vehicle passing through the single-segment sub-path based on the curve atlas and the first data set to obtain a second predicted speed of the target vehicle passing through the single-segment sub-path, and to adjust the initial travel time of each driving path based on all the second predicted speeds and all the first distances to obtain a first corrected travel time; A second corrected travel time unit is used to determine whether a traffic accident occurs in all driving paths based on the first data set; if so, the accident impact area is defined with the traffic accident point as the center and a preset distance as the radius, and the second predicted speed of the target vehicle in all sub-paths within the accident impact area is processed to obtain a first processing result; based on the first processing result and all first distances, the first corrected travel time is adjusted to obtain a second corrected travel time; if not, the first corrected travel time is used as the second corrected travel time; a third modified travel time unit, configured to obtain a slope coefficient of a single sub-path by a preset method based on the second data set to obtain a first acquisition result, process a first distance of each sub-path based on the first acquisition result to obtain a second processing result, and adjust the second modified travel time based on the first processing result and the second processing result to obtain a third modified travel time; The optimal driving path unit is used to select the driving path with the shortest third corrected travel time as the optimal driving path of the target vehicle.

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