An urban road planning and design system based on big data

Abstract

The present invention discloses an urban road planning and design system based on big data, which relates to the technical field of road planning and design, and includes an image acquisition module, wherein the image acquisition module performs image acquisition on the area to be planned to obtain the image of the area to be planned; the image preprocessing module performs frequency domain conversion on the image of the area to be planned by using a fast Fourier algorithm, and preprocesses the image of the area to be planned in the frequency domain. The present invention determines the risk prediction position by comprehensively analyzing the workplace location information, public facility location information, school location information and historical traffic data near the area to be planned, determines the vehicle braking displacement by calculating between the risk prediction position and the workplace location information, public facility location information and school location information, and sets reasonable speed limit information in combination with the driver's braking reaction time, which not only reduces the risk of accidents, but also avoids the problem of road congestion caused by unreasonable limit information.

Description

Technical Field

[0001] The present invention relates to the technical field of road planning and design, and in particular to an urban road planning and design system based on big data. Background Art

[0002] Urban roads refer to roads that connect various areas of a city, provide transportation and pedestrian use within the city, facilitate residents' lives, work, and cultural and entertainment activities, and connect with roads outside the city to carry external traffic. The main function of urban roads is transportation. Many large cities have rebuilt and built a large number of roads, which not only changed the appearance of the city, but also brought more opportunities for social and economic development.

[0003] When designing existing urban roads, comprehensive analysis of traffic data and buildings on both sides of the road was not conducted, and reasonable speed limits were not set at accident-prone points. The unreasonable speed limits cause vehicles to travel at too low speeds, causing road congestion. Summary of the invention

[0004] In order to solve the above technical problems, a city road planning and design system based on big data is provided. This technical solution solves the problem that the existing urban roads proposed in the above background technology are not designed with comprehensive analysis of traffic data and buildings on both sides of the road, and no reasonable speed limit is set for accident-prone points. The speed limit is unreasonable, resulting in vehicles traveling at too low a speed, causing road congestion.

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

[0006] An urban road planning and design system based on big data, comprising:

[0007] An image acquisition module, wherein the image acquisition module acquires images of the area to be planned and obtains images of the area to be planned;

[0008] An image preprocessing module, wherein the image preprocessing module performs frequency domain conversion on the image of the planned area by using a fast Fourier algorithm, and preprocesses the image of the planned area in the frequency domain, wherein the preprocessing includes image signal enhancement, image signal denoising, image signal restoration and image normalization processing;

[0009] A data storage module, the data storage module is used to store historical traffic data of the area to be planned;

[0010] A feature extraction module, wherein the feature extraction module extracts features from the preprocessed image of the area to be planned and the historical traffic data of the area to be planned to obtain geographic feature information and traffic feature information; wherein the geographic feature information includes workplace location information, public facility location information and school location information, and the traffic feature information includes travel information and accident information;

[0011] A trajectory curve drawing module, wherein the trajectory curve drawing module draws a curve according to the travel information to obtain a travel trajectory curve;

[0012] A risk prediction module, wherein the risk prediction module performs analysis and processing based on the travel trajectory curve and the accident information to obtain a risk prediction location, wherein the risk prediction location includes a high-risk location, a medium-risk location, and a low-risk location;

[0013] A road planning module, which designs roads in the planned area according to the geographical feature information and the risk prediction location and determines a road model;

[0014] A construction method design module performs construction analysis based on a road model and a travel trajectory curve to obtain a road construction method.

[0015] Preferably, the image acquisition module acquires images of the area to be planned, and obtaining the image of the area to be planned specifically includes the following steps:

[0016] Obtain location information of the area to be planned;

[0017] The two ends of the location information of the planned area are extended by using the ant colony algorithm. When the two ends of the location information of the planned area extend and the existing roads have the first intersection, the extension is stopped to determine the shortest merging path;

[0018] Use drones to collect images on both sides of the shortest confluence path to obtain images of the area to be planned.

[0019] Preferably, the feature extraction module extracts features from the preprocessed image of the area to be planned and the historical traffic data of the area to be planned, and obtaining geographic feature information and traffic feature information specifically includes the following steps:

[0020] Using the spot detection algorithm, the pre-processed image of the planned area is extracted with workplaces, public facilities and schools as features to obtain geographic feature information;

[0021] A hash table is used to extract features from the historical traffic data of the planning area using vehicle travel and vehicle accidents as features to obtain traffic feature information.

[0022] Preferably, the trajectory curve drawing module draws a curve according to the travel information, and obtaining the travel trajectory curve specifically includes the following steps:

[0023] The Gaussian blur algorithm is used to blur the vehicles and pedestrians in the image of the planned area to obtain a blank image of the planned area;

[0024] According to the travel information, a curve is drawn in the blank image of the area to be planned to obtain the travel trajectory curve.

[0025] Preferably, the risk prediction module performs analysis and processing based on the travel trajectory curve and accident information, and obtaining the risk prediction position specifically includes the following steps:

[0026] Using accident information as features, fill in the blank image of the planned area with information to obtain the location where the accident occurred;

[0027] Analyze and process the accident location and travel trajectory curve to obtain risk data;

[0028] The risk data and set values ​​are judged and processed to obtain the risk prediction position.

[0029] Preferably, the step of judging and processing the risk data and the set value to obtain the risk prediction position specifically includes the following steps:

[0030] Make judgments on risk data and set values;

[0031] If the risk data is greater than or equal to the set first threshold, output the high-risk location;

[0032] If the risk data is less than the set first threshold, and the risk data is greater than or equal to the set second threshold, output the medium risk position;

[0033] If the risk data is less than the set second threshold, a low-risk location is output.

[0034] Preferably, the road planning module designs roads for the planned area according to the geographical feature information and the risk prediction location, and determines the road model specifically including the following steps:

[0035] Use drones to collect information at both ends of the shortest merging path to obtain information on the number of lanes;

[0036] Analyze and process the planned area according to the location information of the workplace, the location information of the school and the location information of the public facilities, determine the speed limit information, and obtain the vehicle waiting position and the corresponding passage path of the vehicle waiting position;

[0037] Warning signs are set up in the planned area according to the risk prediction location;

[0038] Three-dimensional modeling is performed based on lane quantity information, speed limit information, vehicle waiting positions, travel paths corresponding to vehicle waiting positions, and warning signs to obtain a road model.

[0039] Preferably, the analyzing and processing of the planned area according to the workplace location information, the school location information and the public facility location information, determining the speed limit information, and obtaining the vehicle waiting position and the passage path corresponding to the vehicle waiting position specifically comprises the following steps:

[0040] Calculate and process the risk prediction location and the location information of the workplace, school, and public facilities to obtain the braking displacement of the vehicle;

[0041] Obtain the driver's braking reaction time;

[0042] Analyze the vehicle's braking displacement and the driver's braking reaction time to determine the speed limit information;

[0043] The planned area is analyzed and processed according to the vehicle braking displacement and speed limit information to obtain the vehicle waiting position and the passage path corresponding to the vehicle waiting position.

[0044] Preferably, the construction method design module performs construction analysis according to the road model and the travel trajectory curve, and obtaining the road construction method specifically includes the following steps:

[0045] Analyze the road model based on the travel trajectory curve to determine the most traveled route;

[0046] The first construction goal is to have the most travel routes;

[0047] When the construction of the first target is underway, set up obvious isolation signs to prevent unauthorized personnel from entering the construction area;

[0048] When performing specific operations, construction workers should choose a safe location and maintain a correct construction posture.

[0049] Compared with the prior art, the present invention provides an urban road planning and design system based on big data, which has the following beneficial effects:

[0050] The present invention determines the risk prediction location by comprehensively analyzing the workplace location information, public facility location information, school location information and historical traffic data near the planned area, determines the vehicle braking displacement by calculating between the risk prediction location and the workplace location information, public facility location information and school location information, and sets reasonable speed limit information in combination with the driver's braking reaction time, which not only reduces the risk of accidents, but also avoids the problem of road congestion caused by unreasonable limit information. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 This is a structural block diagram of an urban road planning and design system based on big data proposed by the present invention;

[0052] Figure 2 A schematic diagram of the process of obtaining an image of an area to be planned proposed by the present invention;

[0053] Figure 3 A schematic diagram of the process of obtaining geographic feature information and traffic feature information proposed by the present invention;

[0054] Figure 4 A schematic diagram of the process of obtaining a travel trajectory curve proposed by the present invention;

[0055] Figure 5 A schematic diagram of the process of obtaining risk prediction position proposed by the present invention;

[0056] Figure 6 A schematic diagram of the process of obtaining risk prediction position proposed by the present invention;

[0057] Figure 7 A schematic diagram of a process for determining a road model proposed by the present invention;

[0058] Figure 8 A schematic diagram of a process for obtaining a vehicle waiting position and a passage path corresponding to the vehicle waiting position proposed by the present invention;

[0059] Fig. 9 This is a schematic flow chart of the road construction method proposed in the present invention. DETAILED DESCRIPTION

[0060] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art may think of other obvious variations.

[0061] Reference Figure 1 As shown, a city road planning and design system based on big data includes:

[0062] An image acquisition module, wherein the image acquisition module acquires images of the area to be planned and obtains images of the area to be planned;

[0063] An image preprocessing module, wherein the image preprocessing module performs frequency domain conversion on the image of the planned area by using a fast Fourier algorithm, and preprocesses the image of the planned area in the frequency domain, wherein the preprocessing includes image signal enhancement, image signal denoising, image signal restoration and image normalization processing;

[0064] A data storage module, the data storage module is used to store historical traffic data of the area to be planned;

[0065] A feature extraction module, wherein the feature extraction module extracts features from the preprocessed image of the area to be planned and the historical traffic data of the area to be planned to obtain geographic feature information and traffic feature information; wherein the geographic feature information includes workplace location information, public facility location information and school location information, and the traffic feature information includes travel information and accident information;

[0066] A trajectory curve drawing module, wherein the trajectory curve drawing module draws a curve according to the travel information to obtain a travel trajectory curve;

[0067] A risk prediction module, wherein the risk prediction module performs analysis and processing based on the travel trajectory curve and the accident information to obtain a risk prediction location, wherein the risk prediction location includes a high-risk location, a medium-risk location, and a low-risk location;

[0068] A road planning module, which designs roads in the planned area according to the geographical feature information and the risk prediction location and determines a road model;

[0069] A construction method design module, wherein the construction method design module performs construction analysis according to a road model and a travel trajectory curve to obtain a road construction method;

[0070] Those skilled in the art will appreciate that when planning and designing roads, it is necessary to design them in conjunction with areas with high traffic volume, such as schools, public facilities, and office buildings on both sides of the road. In addition, it is necessary to analyze the historical traffic data of the road to determine the accident-prone locations on the road, and set reasonable speed limit information based on the distances between the accident-prone locations and schools, public facilities, and office buildings. This can not only reduce the accident rate, but also avoid road congestion and allow vehicles to pass quickly.

[0071] Reference Figure 2 As shown, the image acquisition module acquires images of the area to be planned, and obtaining the image of the area to be planned specifically includes the following steps:

[0072] Obtain location information of the area to be planned;

[0073] The two ends of the location information of the planned area are extended by using the ant colony algorithm. When the two ends of the location information of the planned area extend and the existing roads have the first intersection, the extension is stopped to determine the shortest merging path;

[0074] Use drones to collect images on both sides of the shortest incoming path to obtain images of the area to be planned;

[0075] In this embodiment, the road design in the area to be planned needs to merge into the existing road. Therefore, the location information of the area to be planned needs to be extended. When the location information of the area to be planned is extended to an intersection with the existing road, the first intersection point is selected as the road convergence point, and the distance from the location information of the area to be planned to the first intersection point is the shortest convergence path. Image collection is performed on both sides of the shortest convergence path to determine the workplace, public facilities and schools in the location information of the area to be planned, and road planning and design are performed.

[0076] Reference Figure 3 As shown, the feature extraction module extracts features from the preprocessed image of the area to be planned and the historical traffic data of the area to be planned, and obtains geographic feature information and traffic feature information, specifically including the following steps:

[0077] Using the spot detection algorithm, the pre-processed image of the planned area is extracted with workplaces, public facilities and schools as features to obtain geographic feature information;

[0078] Gaussian filters are used to enhance the edge features of workplaces, public facilities, and schools, making feature extraction of images of planned areas more accurate.

[0079] Using a hash table, the historical traffic data of the planning area is extracted with vehicle travel and vehicle accidents as features to obtain traffic feature information;

[0080] In this embodiment, it is necessary to accurately locate the workplace, public facilities and schools in the image of the planned area, determine the geographic feature information, and design the road based on the geographic feature information. In addition, it is necessary to combine the historical traffic data of the planned area to determine the accident-prone locations in the planned area and mark the accident-prone locations, so that the accident rate can be reduced when the road is built.

[0081] Reference Figure 4 As shown, the trajectory curve drawing module draws the curve according to the travel information, and obtaining the travel trajectory curve specifically includes the following steps:

[0082] The Gaussian blur algorithm is used to blur the vehicles and pedestrians in the image of the planned area to obtain a blank image of the planned area;

[0083] Draw a curve in the blank image of the area to be planned according to the travel information to obtain the travel trajectory curve;

[0084] Those skilled in the art can understand that the curve can intuitively reflect the distribution of data. Therefore, by drawing a travel trajectory curve based on travel information, the vehicle driving conditions can be intuitively seen. In order to improve the accuracy of road design, the travel trajectory curve is drawn in the image of the area to be planned. At the same time, in order to avoid interference of other factors in the image of the area to be planned on the travel trajectory curve, the image of the area to be planned is blurred, and the vehicle features and pedestrian features in the area to be planned are blurred.

[0085] Reference Figure 5 As shown, the risk prediction module analyzes and processes the travel trajectory curve and accident information, and obtains the risk prediction location, which specifically includes the following steps:

[0086] Using accident information as features, fill in the blank image of the planned area with information to obtain the location where the accident occurred;

[0087] Analyze and process the accident location and travel trajectory curve to obtain risk data;

[0088] Make judgments and process risk data and set values ​​to obtain risk prediction positions;

[0089] It can be understood by those skilled in the art that there is a range of injuries at the accident location. Therefore, the accident location and the travel trajectory curve are analyzed in combination. The center point close to the accident location is a high-risk location, while the center point far from the accident location is a medium- and low-risk location. The first threshold, the second threshold and the third threshold are set according to the distance between the travel trajectory curve and the accident location.

[0090] Reference Figure 6 As shown, the risk data and the set value are judged and processed, and the risk prediction position is obtained, which specifically includes the following steps:

[0091] Make judgments on risk data and set values;

[0092] If the risk data is greater than or equal to the set first threshold, output the high-risk location;

[0093] If the risk data is less than the set first threshold, and the risk data is greater than or equal to the set second threshold, output the medium risk position;

[0094] If the risk data is less than the set second threshold, a low-risk location is output.

[0095] Reference Figure 7 As shown in the figure, the road planning module designs roads for the planned area according to the geographical feature information and the risk prediction location, and determines the road model, which specifically includes the following steps:

[0096] Use drones to collect information at both ends of the shortest merging path to obtain information on the number of lanes;

[0097] Analyze and process the planned area according to the location information of the workplace, the location information of the school and the location information of the public facilities, determine the speed limit information, and obtain the vehicle waiting position and the corresponding passage path of the vehicle waiting position;

[0098] Warning signs are set up in the planned area according to the risk prediction location;

[0099] Three-dimensional modeling is performed based on lane quantity information, speed limit information, vehicle waiting positions, travel paths corresponding to vehicle waiting positions, and warning signs to obtain a road model.

[0100] Reference Figure 8 As shown, the following steps are specifically included to analyze and process the planned area according to the workplace location information, school location information, and public facility location information, determine the speed limit information, and obtain the vehicle waiting position and the passage path corresponding to the vehicle waiting position:

[0101] The specific path includes the following steps:

[0102] Calculate and process the risk prediction location and the location information of the workplace, school, and public facilities to obtain the braking displacement of the vehicle;

[0103] Obtain the driver's braking reaction time;

[0104] Analyze the vehicle's braking displacement and the driver's braking reaction time to determine the speed limit information;

[0105] Analyze and process the planned area according to the vehicle braking displacement and speed limit information to obtain the vehicle waiting position and the passage path corresponding to the vehicle waiting position;

[0106] Those skilled in the art can understand that workplaces, schools and public facilities need to be far away from accident locations, however, there is no guarantee that workplaces, schools and public facilities are far away from accident locations. When workplaces, schools and public facilities are close to accident locations, speed limits need to be imposed on roads. Therefore, the workplaces, schools and public facilities are calculated from the accident location to determine the distance between them, and reasonable speed limit information is set for the roads in combination with the driver's braking reaction time. Reasonable speed limit information can not only reduce the accident rate, but also avoid road congestion. In addition, a special vehicle waiting position is set according to the vehicle braking displacement. The passage is the road for pedestrians from workplaces, schools and public facilities to the vehicle waiting position. This road is for pedestrians only.

[0107] Reference Fig. 9As shown, the construction method design module performs construction analysis based on the road model and travel trajectory curve, and obtains the road construction method, which specifically includes the following steps:

[0108] Analyze the road model based on the travel trajectory curve to determine the most traveled route;

[0109] The first construction goal is to have the most travel routes;

[0110] When the construction of the first target is underway, set up obvious isolation signs to prevent unauthorized personnel from entering the construction area;

[0111] When carrying out specific operations, construction workers should choose a safe position and maintain a correct construction posture;

[0112] In this embodiment, the denser the travel trajectory curves are, the more the driver likes the area. Therefore, the most traveled route is taken as the first construction target. When the construction of the first construction target is completed, it can be used immediately, which can reduce the driver's annoyance with road construction.

[0113] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. The present invention may be subject to various changes and improvements without departing from the spirit and scope of the present invention. These changes and improvements fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the attached claims and their equivalents.

Claims

1. An urban road planning and design system based on big data, characterized in that: include: An image acquisition module, wherein the image acquisition module acquires images of the area to be planned and obtains images of the area to be planned; An image preprocessing module, wherein the image preprocessing module performs frequency domain conversion on the image of the planned area by using a fast Fourier algorithm, and preprocesses the image of the planned area in the frequency domain, wherein the preprocessing includes image signal enhancement, image signal denoising, image signal restoration and image normalization processing; A data storage module, the data storage module is used to store historical traffic data of the area to be planned; A feature extraction module, wherein the feature extraction module extracts features from the preprocessed image of the area to be planned and the historical traffic data of the area to be planned to obtain geographic feature information and traffic feature information; wherein the geographic feature information includes workplace location information, public facility location information and school location information, and the traffic feature information includes travel information and accident information; A trajectory curve drawing module, wherein the trajectory curve drawing module draws a curve according to the travel information to obtain a travel trajectory curve; A risk prediction module, wherein the risk prediction module performs analysis and processing based on the travel trajectory curve and the accident information to obtain a risk prediction location, wherein the risk prediction location includes a high-risk location, a medium-risk location, and a low-risk location; A road planning module, which designs roads in the planned area according to the geographical feature information and the risk prediction location and determines a road model; The road planning module designs roads in the planned area according to the geographical feature information and the risk prediction location, and determines the road model, which specifically includes the following steps: Use drones to collect information at both ends of the shortest merging path to obtain information on the number of lanes; Analyze and process the planned area according to the location information of the workplace, the location information of the school and the location information of the public facilities, determine the speed limit information, and obtain the vehicle waiting position and the corresponding passage path of the vehicle waiting position; Warning signs are set up in the planned area according to the risk prediction location; Perform three-dimensional modeling based on lane number information, speed limit information, vehicle waiting positions, traffic paths corresponding to vehicle waiting positions, and warning signs to obtain a road model; The steps of analyzing and processing the planned area according to the workplace location information, the school location information and the public facility location information, determining the speed limit information, and obtaining the vehicle waiting position and the passage path corresponding to the vehicle waiting position specifically include the following steps: Calculate and process the risk prediction location and the location information of the workplace, school, and public facilities to obtain the braking displacement of the vehicle; Obtain the driver's braking reaction time; Analyze the vehicle's braking displacement and the driver's braking reaction time to determine the speed limit information; Analyze and process the planned area according to the vehicle braking displacement and speed limit information to obtain the vehicle waiting position and the passage path corresponding to the vehicle waiting position; A construction method design module performs construction analysis based on a road model and a travel trajectory curve to obtain a road construction method.

2. The urban road planning and design system based on big data according to claim 1 is characterized in that: The image acquisition module acquires images of the area to be planned, and obtaining the image of the area to be planned specifically includes the following steps: Obtain location information of the area to be planned; The two ends of the location information of the planned area are extended by using the ant colony algorithm. When the two ends of the location information of the planned area extend and the existing roads have the first intersection, the extension is stopped to determine the shortest merging path; Use drones to collect images on both sides of the shortest confluence path to obtain images of the area to be planned.

3. The urban road planning and design system based on big data according to claim 1 is characterized in that: The feature extraction module extracts features from the preprocessed image of the area to be planned and the historical traffic data of the area to be planned, and obtains geographic feature information and traffic feature information, specifically including the following steps: Using the spot detection algorithm, the pre-processed image of the planned area is extracted with workplaces, public facilities and schools as features to obtain geographic feature information; A hash table is used to extract features from the historical traffic data of the planning area using vehicle travel and vehicle accidents as features to obtain traffic feature information.

4. The urban road planning and design system based on big data according to claim 1 is characterized in that: The trajectory curve drawing module draws a curve according to the travel information, and obtaining the travel trajectory curve specifically includes the following steps: The Gaussian blur algorithm is used to blur the vehicles and pedestrians in the image of the planned area to obtain a blank image of the planned area; According to the travel information, a curve is drawn in the blank image of the area to be planned to obtain the travel trajectory curve.

5. The urban road planning and design system based on big data according to claim 1 is characterized in that: The risk prediction module analyzes and processes the travel trajectory curve and accident information to obtain the risk prediction position, which specifically includes the following steps: Using accident information as features, fill in the blank image of the planned area with information to obtain the location where the accident occurred; Analyze and process the accident location and travel trajectory curve to obtain risk data; The risk data and set values ​​are judged and processed to obtain the risk prediction position.

6. The urban road planning and design system based on big data according to claim 5 is characterized in that: The determination and processing of the risk data and the set value to obtain the risk prediction position specifically includes the following steps: Make judgments on risk data and set values; If the risk data is greater than or equal to the set first threshold, output the high-risk location; If the risk data is less than the set first threshold, and the risk data is greater than or equal to the set second threshold, output the medium risk position; If the risk data is less than the set second threshold, a low-risk location is output.

7. The urban road planning and design system based on big data according to claim 1 is characterized in that: The construction method design module performs construction analysis based on the road model and the travel trajectory curve, and obtains the road construction method, which specifically includes the following steps: Analyze the road model based on the travel trajectory curve to determine the most traveled route; The first construction goal is to have the most travel routes; When the construction of the first target is underway, set up obvious isolation signs to prevent unauthorized personnel from entering the construction area; When performing specific operations, construction workers should choose a safe location and maintain a correct construction posture.

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