A method and system for county and rural transportation planning decisions based on road traffic index

Abstract

This invention discloses a method and system for county and rural transportation planning decision-making based on road traffic index. The method collects navigation API data, socio-economic data, and POI information related to county and rural transportation planning; processes the data, and then calculates route growth index, hourly travel time index, holiday impact index, dead-end road index, and planning simulation index. These are then weighted and fused to obtain the road traffic index. When calculating the road traffic index, an initial weight is assigned to each index, and an adaptive weight adjustment algorithm is used to dynamically adjust the weights of each index based on real-time traffic data and simulation results. County and rural transportation planning aims to reduce the road traffic index, and the difference in road traffic index before and after planning is used as the basis for decision-making. This invention effectively solves the problem of information asymmetry in grassroots traffic management and improves the accuracy of road network connectivity.

Description

Technical Field

[0001] This invention belongs to the field of intelligent transportation technology and relates to a method and system for county and rural transportation planning and decision-making based on road traffic index. Background Technology

[0002] County and rural transportation planning, as a crucial component of the regional transportation system, plays a vital role in promoting integrated urban-rural development, improving residents' travel conditions, and driving regional economic growth. However, current county and rural transportation planning faces numerous challenges. Counties and rural areas cover vast geographical areas with dispersed infrastructure. Traditional transportation planning relies heavily on manual surveys, requiring staff to conduct on-site visits to collect information such as road conditions and traffic flow. This method is not only costly in terms of manpower, resources, and time, but also struggles to obtain comprehensive and timely accurate data, resulting in insufficient timeliness and completeness. Furthermore, traditional planning methods often rely on a single data source, such as statistical data from the transportation department. This single-source analysis fails to comprehensively consider the impact of socio-economic factors, population distribution, and geographical environment on transportation, leading to biased analysis results that fail to accurately reflect the overall picture of the transportation system.

[0003] The transportation needs of counties and villages are characterized by dynamic changes. For example, traffic flow and travel demand can fluctuate significantly due to factors such as seasonal farming seasons, holiday travel peaks, and unforeseen events. However, traditional planning methods lack dynamic decision-making capabilities and struggle to adjust planning schemes in a timely manner based on real-time traffic conditions, leading to planning lags and inadequacies. Furthermore, county and village transportation has unique characteristics and needs that differ significantly from urban transportation. Poor connectivity between urban and rural roads and high road traffic indices are particularly evident. In some areas, dead-end roads and significant differences in road grades exist between urban and rural areas, resulting in poor connectivity. High road traffic indices between adjacent villages, towns, and county seats force residents to take longer routes, increasing travel time and costs. The transportation needs of counties and villages not only include daily commutes but also encompass various functions such as agricultural product transportation, rural tourism, and rural logistics. These needs are unevenly distributed in time and space and are significantly affected by seasonal and weather factors, requiring more refined planning methods to meet their demands. In addition, the construction of transportation infrastructure in counties and rural areas is relatively lagging behind, with problems such as low road grade, poor road conditions and insufficient supporting facilities being common. This makes it difficult to apply traditional transportation planning models directly, and optimization and adjustment are needed to suit the actual situation of transportation in counties and rural areas.

[0004] The shortcomings of existing technologies lie in the fact that most current traffic planning models are based on the traffic characteristics of large cities and are insufficiently adaptable to the special circumstances of county and rural traffic. These models cannot accurately reflect the actual traffic conditions and demands of county and rural roads, making it difficult to formulate traffic planning schemes that conform to local realities. Furthermore, traditional methods fail to fully tap the potential value behind the data, lacking scientific and precise decision-making basis. Therefore, there is an urgent need for a method and system that can integrate multi-source data, construct a scientific road traffic index model, and provide effective decision support to overcome the limitations of traditional county and rural traffic planning methods and meet the specific needs of county and rural traffic. Summary of the Invention

[0005] To address the problems of limited data, insufficient dynamic adaptability, and information asymmetry in existing county and township transportation planning methods, this invention provides a decision-making method and system for county and township transportation planning based on road traffic index. By integrating multi-source data, constructing a scientific road traffic index model, and combining a dynamic weight adjustment mechanism and visual decision support, it provides comprehensive and accurate technical support for county and township transportation planning.

[0006] This invention is achieved through the following technical solution: A county and rural transportation planning decision-making method based on road traffic index, comprising the following steps:

[0007] S1: Collect navigation API data, socio-economic data, and POI information related to county and rural transportation planning;

[0008] S2: Clean and standardize the collected data;

[0009] S3: Match and integrate standardized data across time and space dimensions;

[0010] S4: Calculate the route growth index, hourly travel time index, holiday impact index, dead-end road index, and planning simulation index based on the integrated data, and perform weighted fusion to obtain the road traffic index. When calculating the road traffic index, assign an initial weight to each index and use an adaptive weight adjustment algorithm to dynamically adjust the weight of each index based on real-time traffic data and simulation results.

[0011] S5: Display the road traffic index and related data, and draw a heat map of the road traffic index. Carry out county and village transportation planning with the goal of reducing the road traffic index, and calculate the difference in road traffic index before and after the planning as the basis for decision-making.

[0012] Specifically, the route growth index , It is the straight-line distance between two points. It is the navigation distance between two points obtained through navigation API data;

[0013] Hourly usage index , , This provides the estimated travel time for different routes obtained through navigation API data. Theoretical travel time, Average speed;

[0014] Holiday Impact Index , It is the average route growth index or hourly travel time index during holidays. It is the average route growth index or hourly travel time index during non-holiday periods;

[0015] Dead-end road index , The total length of dead-end roads within the area; This represents the total length of roads within the area.

[0016] Planning Simulation Index , Current traffic indicators; To simulate traffic indicators after the planning scheme is implemented;

[0017] Road traffic index :

[0018] ;

[0019] Among them, w1, w2, w3, w4, and w5 are the weights of the route growth index, hourly travel time index, holiday impact index, dead-end road index, and planning simulation index, respectively.

[0020] Specifically, the adaptive weight adjustment algorithm steps are as follows:

[0021] Step 1: Adjusting index weights due to the impact of holidays:

[0022] ;

[0023] in, To adjust the impact of holidays on index weights, The intensity index is adjusted for holiday weighting. This is a function to indicate the status of holidays. This indicates that it is currently a holiday. This indicates that it is not a public holiday. This represents the average route growth index during holidays. This represents the average route growth index during non-holiday periods;

[0024] Step 2: Adjusting the weight of the dead-end road index:

[0025] ;

[0026] in, The revised dead-end road index weights. The normalized dead-end road index, ; This represents the maximum value of the dead-end road index. This represents the minimum value of the dead-end road index. Adjust the intensity index for dead-end roads;

[0027] Step 3: Weight Normalization

[0028] Calculate the corrected total weights:

[0029] ;

[0030] Then normalize:

[0031] ;

[0032] in, ;

[0033] in, This is the adjusted total weight sum. For the i-th corrected weight, For the i-th weight, The weight is the i-th normalized weight;

[0034] Step 4: Weighted iteration based on real-time data feedback:

[0035] ;

[0036] ;

[0037] in, Let be the weight matrix at time t. Let be the weight matrix at time t+1, and λ be the adaptive learning rate. It is the gradient vector of the road traffic index with respect to the weights.

[0038] Specifically, the complete process of calculating the road traffic index based on the adaptive weight adjustment algorithm is as follows:

[0039] Step 1: Initialize the weight matrix ;

[0040] Step 2: At time t:

[0041] Collect data and calculate various indices: ;

[0042] Based on whether it is a holiday or not and the dead-end road index, we perform the first and second steps to obtain the results. and .

[0043] The third step yields the normalized weight matrix. ;

[0044] Calculate the road traffic index ;

[0045] Calculate gradient .

[0046] The fourth step yields the weight matrix at time t+1. ;

[0047] Step 3: Convert the weight matrix at time t+1 As the initial weights for the next time step, repeat step 2.

[0048] This invention also provides a county and township transportation planning and decision-making system based on road traffic index, including a data acquisition module, a data preprocessing module, a multi-source data fusion module, a road traffic index calculation module, and a decision support system. The modules collaborate through data flow and logical connections, ensuring end-to-end technical support from data acquisition to decision output.

[0049] The data acquisition module collects navigation API data, socio-economic data, and POI information related to county and rural transportation planning.

[0050] The data preprocessing module cleans and standardizes the collected data;

[0051] The multi-source data fusion module matches and integrates standardized data across time and space dimensions;

[0052] The road traffic index calculation module calculates the route growth index, hourly travel time index, holiday impact index, dead-end road index, and planning simulation index based on the integrated data, and performs weighted fusion to obtain the road traffic index. When calculating the road traffic index, an initial weight is assigned to each index, and an adaptive weight adjustment algorithm is used to dynamically adjust the weight of each index based on real-time traffic data and simulation results.

[0053] The decision support system displays the road traffic index and related data, and plots the road traffic index as a heat map.

[0054] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps in the county and rural transportation planning decision-making method based on road traffic index as described in Embodiment 1 above.

[0055] The present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps in the county and rural traffic planning decision-making method based on road traffic index as described in Embodiment 1 above.

[0056] This invention innovatively integrates multimodal data, including navigation API data, socioeconomic data, and POI information, enabling in-depth analysis of county and rural transportation from multiple perspectives. Existing decision-making algorithms often lack consideration for the unique geographical and transportation characteristics of counties and rural areas. This invention, however, designs a specialized road traffic index and dynamic weight adjustment mechanism tailored to the characteristics of counties and rural areas, taking into account factors such as holiday indices and the impact of dead-end roads, making the decision results more aligned with the actual needs of counties and rural areas. This combination of multimodal data fusion and targeted algorithms provides more accurate and comprehensive technical support for county and rural transportation planning and decision-making.

[0057] This invention has significant practical application effectiveness in county and township-level planning and decision-making. Firstly, it effectively solves the problem of information asymmetry in grassroots traffic management. Through multi-source data collection and fusion, managers can comprehensively and accurately grasp the actual traffic conditions in counties and townships, including road traffic conditions, population distribution, and industrial layout, providing strong data support for decision-making.

[0058] Secondly, it addresses the problem of poor road connectivity in the region. By analyzing various indices, it can accurately identify areas with poor road connectivity and propose targeted improvement suggestions, such as road widening and connecting dead-end roads, thereby improving the accuracy of road network connectivity.

[0059] Furthermore, this invention addresses the lack of quantitative data analysis. By establishing a scientific road traffic index, it quantitatively assesses traffic conditions, making the decision-making process more scientific and objective. These benefits effectively improve the efficiency of planning and decision-making at the county, township, and village levels, and promote the development of county, township, and village transportation. Attached Figure Description

[0060] Figure 1 This is a flowchart of the county and rural transportation planning decision-making method in Example 1.

[0061] Figure 2 This is a framework diagram of the county and rural transportation planning decision-making system in Example 2. Detailed Implementation

[0062] The present invention will be further explained in detail below with reference to the accompanying drawings and embodiments.

[0063] Example 1

[0064] like Figure 1As shown, a county and rural transportation planning decision-making method based on road traffic index includes the following steps:

[0065] S1: Collect navigation API data, socio-economic data, and POI information related to county and rural transportation planning;

[0066] S2: Clean and standardize the collected data;

[0067] S3: Match and integrate standardized data across time and space dimensions;

[0068] S4: Calculate the route growth index, hourly travel time index, holiday impact index, dead-end road index, and planning simulation index based on the integrated data, and perform weighted fusion to obtain the road traffic index. When calculating the road traffic index, assign an initial weight to each index and use an adaptive weight adjustment algorithm to dynamically adjust the weight of each index based on real-time traffic data and simulation results.

[0069] S5: Display the road traffic index and related data, and draw a heat map of the road traffic index. Carry out county and village transportation planning with the goal of reducing the road traffic index, and calculate the difference in road traffic index before and after the planning as the basis for decision-making.

[0070] Navigation API Data Collection: When calling the Gaode or Baidu Maps APIs, you first need to register as a developer on their open platform and obtain the corresponding API key. Taking obtaining navigation distance as an example, use the route planning interface in the API, passing in the latitude and longitude information of the starting point and destination, and specifying the mode of transportation (driving, cycling, or walking). In the request parameters, ensure that the latitude and longitude are in the correct format, and set other optional parameters according to the API documentation, such as whether to avoid toll roads. The call frequency needs to be controlled within the range allowed by the API to avoid request rejection due to frequent calls.

[0071] Socioeconomic data: Collaborate with local government statistical departments to obtain data on population distribution, industrial layout, and economic development level. This data is typically stored in tabular form and may include information such as population size, industry type, and output value for different administrative regions. Web scraping techniques can also be used to retrieve relevant data from publicly available government websites, statistical yearbooks, and other data sources. During the data collection process, attention should be paid to the timeliness and accuracy of the data, and cross-validation should be performed on data from different sources.

[0072] POI Information: Using the POI search interface provided by the map API, input keywords (such as schools, hospitals, shopping malls, factories, etc.) and the search scope (which can be a specific administrative region or latitude and longitude range) to obtain the corresponding POI information. POI information includes name, address, latitude and longitude. To ensure data integrity, multiple searches can be performed to expand the search scope or adjust keywords.

[0073] The cleaning process described in step S2 includes handling missing values, outlier values, and duplicate data.

[0074] Missing value handling: For missing values ​​in navigation API data, socioeconomic data, and POI information, imputation can be performed using the mean, median, or by prediction based on other relevant data. For example, if population data for a certain region is missing, it can be estimated and imputed using the average population density of the region's superior administrative region.

[0075] Outlier Handling: Outliers in the data are identified using methods such as box plots and scatter plots. For data that significantly deviates from the normal range, deletion, correction, or replacement can be used. For example, outliers such as an excessively large or small ratio between navigation distance and great circle distance can be corrected based on the overall distribution of the data.

[0076] Duplicate data processing: This involves removing duplicate data from the collected dataset to ensure its uniqueness. Duplicate records can be deleted based on key data information (such as latitude and longitude, name, etc.).

[0077] Multi-source data fusion: Spatiotemporal matching technology for traffic flow data with population distribution and industrial layout: First, traffic flow data, population distribution data, and industrial layout data are aligned according to time and spatial dimensions. In the time dimension, data can be divided into segments based on the same time period (e.g., daily, weekly); in the spatial dimension, data can be divided according to administrative regions or grids. Then, spatial analysis methods, such as buffer analysis and overlay analysis, are used to identify the correlation between traffic flow and population distribution and industrial layout. For example, the correlation between population density and traffic flow within each administrative region can be calculated, and the impact of industrial layout on traffic flow can be analyzed. Regression analysis and other methods can be used to establish mathematical models between traffic flow and population distribution and industrial layout to predict traffic flow changes in different regions.

[0078] For any two locations A and B, obtain the navigation distance by driving, cycling, or walking via API. Simultaneously, using latitude and longitude information, the Haversine formula is used to calculate the straight-line distance between the two points. The Haversine formula is:

[0079] ;

[0080] in, R is the straight-line distance between two points, and R is the Earth's radius. and These are the latitudes of the two points and the difference in latitude. Longitude difference , and These are the longitudes of the two points.

[0081] Route Growth Index , It is an intermediate variable in the Haversine formula, used to calculate the straight-line distance.

[0082] For calculating route growth indices between villages, townships, and county seats, statistics are compiled for all possible routes between different administrative levels. Taking village-to-village routes as an example, the latitude and longitude information of all villages is first determined. Then, the navigation distance and straight-line distance between any two villages are obtained via API, and the route growth index is calculated. The route growth indices for all village-to-village routes are summarized and statistically analyzed, and the average, median, and standard deviation are calculated to reflect the overall detour situation between villages. The same method is used to calculate the route growth indices for townships and county seats.

[0083] Based on the route growth index, village-to-village, township-to-town, and county-to-county routes are classified into four levels. Different thresholds can be set; for example, routes with a growth index less than 1.2 are classified as Level 1, those between 1.2 and 1.5 as Level 2, those between 1.5 and 2 as Level 3, and those greater than 2 as Level 4. Different levels reflect different degrees of detour, with Level 1 indicating the least detour and Level 4 indicating the most detour.

[0084] Obtain estimated travel time for different routes using navigation API data. At the same time, the theoretical travel time is estimated based on the straight-line distance and average speed. Average speed can be set according to different modes of transportation (driving, cycling, walking) and road types. Hourly travel time index. , , This represents the average speed.

[0085] Similarly, routes are classified according to their hourly usage index, with different thresholds set. For example, routes with an hourly usage index less than 1.1 are classified as Level 1, routes between 1.1 and 1.3 are classified as Level 2, routes between 1.3 and 1.5 are classified as Level 3, and routes greater than 1.5 are classified as Level 4.

[0086] Collect historical traffic flow data and navigation distance data during holidays to analyze the impact of holidays on traffic conditions. It can calculate indicators such as average route growth index and hourly travel time index during different holidays, and compare them with data from non-holiday periods. Holiday Impact Index It can be calculated using the following formula:

[0087] ,

[0088] It is the average route growth index or hourly travel time index during holidays. It is the average route growth index or hourly travel time index during non-holiday periods.

[0089] Dead-end roads in county and township roads were identified through map data and field surveys. A dead-end road is a section of road where one end does not connect to any other road. Dead-end road index. The calculation can be based on factors such as the length of dead-end roads and the scope of their impact. For example, the ratio of the total length of dead-end roads to the total length of roads in each administrative region can be calculated as a dead-end road index. , The total length of dead-end roads in the area (km); This represents the total length of roads within the region (km).

[0090] Different traffic planning schemes are set up, such as road widening, new road construction, and traffic control. For each simulation scenario, indicators such as route growth index and hourly travel time index are recalculated and compared with the current indicators to obtain the planning simulation index. , Current traffic indicators (such as route growth index, hourly travel time index); This is used to simulate traffic indicators after implementing different planning schemes (such as road widening or new road construction). The planning simulation index can reflect the degree of improvement in traffic conditions brought about by different planning schemes.

[0091] Taking into account factors such as route growth index, hourly travel time index, holiday impact index, and dead-end road index, the road traffic index for each township is calculated. :

[0092] ;

[0093] Among them, w1, w2, w3, w4, and w5 are the weights of the route growth index, hourly travel time index, holiday impact index, dead-end road index, and planning simulation index, respectively. Based on the road traffic index, county and rural roads are ranked; the higher the index, the worse the road traffic conditions, and the more priority is given to improvement.

[0094] When calculating the road traffic index, initial weights are assigned to each index (route growth index, hourly travel time index, holiday impact index, dead-end road index, and planning simulation index). During holidays, the weights are dynamically adjusted based on the holiday impact index, increasing its weight to highlight the impact of holidays on traffic conditions. For areas with dead-end roads, the weight of the dead-end road index is increased to reflect the obstructive effect of dead-end roads on traffic. An adaptive weight adjustment algorithm can be used to dynamically adjust the weights of each index based on real-time traffic data and simulation results, making the road traffic index more accurately reflect traffic conditions.

[0095] The adaptive weight adjustment algorithm steps are as follows:

[0096] Step 1: Adjusting index weights due to the impact of holidays:

[0097] ;

[0098] in, To adjust the impact of holidays on index weights, The intensity index for adjusting the weight of holidays is set manually to control the magnitude of the adjustment. This is a function to indicate the status of holidays. This indicates that it is currently a holiday. This indicates that it is not a public holiday. This represents the average route growth index during holidays. This represents the average route growth index during non-holiday periods;

[0099] Step 2: Adjusting the weight of the dead-end road index:

[0100] ;

[0101] in, The revised dead-end road index weights. The normalized dead-end road index, ; This represents the maximum value of the dead-end road index. This represents the minimum value of the dead-end road index. Adjust the intensity index for dead-end roads;

[0102] Step 3: Weight Normalization

[0103] Calculate the corrected total weights:

[0104] ;

[0105] Then normalize:

[0106] ;

[0107] in, ;

[0108] in, This is the adjusted total weight sum. For the i-th corrected weight, For the i-th weight, The weight is the i-th normalized weight;

[0109] In this way, all ownership weights are normalized to a sum of 1.

[0110] Step 4: Weighted iteration based on real-time data feedback:

[0111] ;

[0112] ;

[0113] in, Let be the weight matrix at time t. Let be the weight matrix at time t+1, and λ be the adaptive learning rate. It is the gradient vector of the road traffic index with respect to the weights, calculated as the partial derivative of the road traffic index with respect to the weights, and is actually equal to the values ​​of each index. The modulus of a vector (Euclidean norm).

[0114] The complete process of calculating the road traffic index using the adaptive weight adjustment algorithm is as follows:

[0115] Step 1: Initialize the weight matrix W0 (e.g., by equal distribution, or by setting it based on experience).

[0116] Step 2: At time t:

[0117] Collect data and calculate various indices: ;

[0118] Based on whether it is a holiday or not and the dead-end road index, we perform the first and second steps to obtain the results. and .

[0119] The third step yields the normalized weight matrix. ;

[0120] Calculate the road traffic index ;

[0121] Calculate gradient .

[0122] The fourth step yields the weight matrix Wt+1 at time t+1.

[0123] Step 3: Use the weight matrix Wt+1 at time t+1 as the initial weight for the next time step, and repeat step 2.

[0124] Furthermore, the road traffic index of townships is displayed on the map in the form of a heat map. Different colors are used to represent different traffic index ranges, such as red indicating a high traffic index (poor traffic conditions) and green indicating a low traffic index (good traffic conditions). The heat map can intuitively show the distribution of road traffic conditions in townships, providing a visual basis for traffic planning decisions.

[0125] Impact Analysis of Road Widening Schemes on Route Growth Index Prediction: First, select the road sections requiring widening and determine the width and length of the widening. Then, use a traffic simulation model to simulate the traffic flow distribution and route selection after road widening. Based on the simulation results, recalculate the route growth index and compare it with the current route growth index to analyze the impact of the road widening scheme on the route growth index. Multiple simulations can be used to consider different traffic demand scenarios and widening schemes to obtain more accurate prediction results.

[0126] Comparison of indices before and after improvement: Before and after implementing improvement measures such as road widening, connecting dead-end roads, utilizing low-grade routes, and improving local congestion points, indicators such as route growth index, hourly travel time index, and average operating speed are calculated and compared. For example, the difference in average route growth index, average hourly travel time index, and average operating speed before and after improvement are calculated. These differences can intuitively show the effectiveness of the improvement measures.

[0127] Heatmap Comparison: Before and after the implementation of improvement measures, heatmaps of road traffic index were drawn and compared. The changes in color on the heatmaps were observed to analyze the impact of the improvement measures on road traffic conditions in different areas. For example, did the red areas (poor traffic conditions) decrease, and did the green areas (good traffic conditions) increase?

[0128] Efficiency Improvement Analysis: In addition to index comparisons and heatmap comparisons, the impact of improvement measures on traffic efficiency can also be analyzed. For example, indicators such as the percentage increase in the number of vehicles passing through per unit time and the percentage reduction in travel time can be calculated to assess the effectiveness of the improvement measures in improving traffic efficiency.

[0129] In a certain region of Jiangxi Province, adjacent counties, townships, and villages were selected, and their road traffic index was calculated and optimized in a tiered manner. The indices involved included route growth index, hourly travel time index, holiday impact index, dead-end road index, and planning simulation index. Initially, the road traffic index for this region was 2.1; through a series of optimization measures, it was successfully reduced to 1.5.

[0130] First, actual navigation distances for driving, cycling, and walking are obtained using navigation API data. Simultaneously, socioeconomic data and POI information are combined to analyze population distribution, industrial layout, and traffic flow within the region. The route growth index is obtained by calculating the ratio of driving / cycling / walking navigation distance to straight-line distance.

[0131] Different classification and statistical algorithms were designed for adjacent counties, townships, and villages. The route growth index was calculated using a weighted average method based on the characteristics of different regions. The hourly travel time index was determined based on the ratio of actual travel time to theoretical travel time. The holiday impact index was set by analyzing traffic flow changes during holidays. The dead-end road index was assessed based on the number and length of dead-end roads within the region. The planning simulation index was based on predictions of future transportation planning and construction projects.

[0132] Based on the calculation results, targeted improvement measures will be developed. For areas with a high route growth index, consideration will be given to widening roads, connecting dead-end roads, or optimizing traffic light settings. For areas significantly affected by holidays, public transportation capacity will be increased or traffic control measures will be implemented. Through the implementation of these measures, the road traffic index will be gradually reduced.

[0133] Before and after the implementation of the optimization measures, various indices were statistically analyzed and compared. The changes in these indices before and after the improvements are presented in a table, including multi-dimensional indicators such as average operating speed, heatmap comparisons, and efficiency improvements. After a period of implementation, the road traffic index decreased from 2.1 to 1.5, indicating that the optimization measures achieved significant results.

[0134]

[0135] Different parameter configuration strategies are needed for mountainous counties and plain counties with different geographical environments to ensure the effectiveness and adaptability of the system.

[0136] Mountainous counties have complex terrain, with steep slopes, numerous curves, and relatively low traffic volume. Data collection needs to increase monitoring of topography and road conditions, such as slope and curvature. In calculating the road traffic index, the weighting of the route growth index and hourly travel time index should be appropriately increased to reflect the actual traffic conditions on mountainous roads. Regarding the dead-end road index, due to the limitations of mountainous terrain, there may be many dead-end roads; therefore, greater attention needs to be paid to improving their connectivity. In the road optimization process, priority should be given to constructing infrastructure such as tunnels and bridges to improve traffic conditions in mountainous areas.

[0137] Pingyuan County has flat terrain, good road conditions, and relatively high traffic volume. Data collection focuses on changes in traffic flow and population distribution. In calculating the road traffic index, the weighting of the holiday impact index and the planning simulation index is appropriately increased to address traffic pressure during holidays and future development needs in Pingyuan County. Regarding the dead-end road index, given the relative ease of road construction in the plains area, the existence of dead-end roads should be minimized. In the road optimization process, emphasis is placed on optimizing traffic light settings, increasing public transportation capacity, and building intelligent transportation systems to improve traffic efficiency in Pingyuan County.

[0138] By employing different parameter configuration strategies, the system can better adapt to the characteristics of mountainous and plain counties, providing more precise support for county and township transportation planning decisions in different regions.

[0139] Example 2

[0140] like Figure 2 As shown, this invention also provides a county and township transportation planning and decision-making system based on road traffic index, including a data acquisition module, a data preprocessing module, a multi-source data fusion module, a road traffic index calculation module, and a decision support system. The modules collaborate through data flow and logical connections, ensuring full-chain technical support from data acquisition to decision output.

[0141] The data acquisition module collects navigation API data, socio-economic data, and POI information related to county and rural transportation planning.

[0142] The data preprocessing module cleans and standardizes the collected data;

[0143] The multi-source data fusion module matches and integrates standardized data across time and space dimensions;

[0144] The road traffic index calculation module calculates the route growth index, hourly travel time index, holiday impact index, dead-end road index, and planning simulation index based on the integrated data, and performs weighted fusion to obtain the road traffic index. When calculating the road traffic index, an initial weight is assigned to each index, and an adaptive weight adjustment algorithm is used to dynamically adjust the weight of each index based on real-time traffic data and simulation results.

[0145] The decision support system displays the road traffic index and related data, and plots the road traffic index as a heat map.

[0146] Example 3

[0147] This embodiment provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps in the county and rural transportation planning decision-making method based on road traffic index as described in Embodiment 1 above.

[0148] Example 4

[0149] This embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps in the county and rural traffic planning decision-making method based on road traffic index as described in Embodiment 1 above.

[0150] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of hardware embodiments, software embodiments, or embodiments combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0151] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0152] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A county and rural transportation planning decision-making method based on road traffic index, characterized in that, Includes the following steps: S1: Collect navigation API data, socio-economic data, and POI information related to county and rural transportation planning; S2: Clean and standardize the collected data; S3: Match and integrate standardized data across time and space dimensions; S4: Calculate the route growth index based on the integrated data. Hourly usage index Holiday Impact Index Dead-end road index and planning simulation index The road traffic index is obtained by weighted fusion. In calculating the road traffic index At that time, an initial weight is assigned to each index, and an adaptive weight adjustment algorithm is used to dynamically adjust the weight of each index based on real-time traffic data and simulation results. The adaptive weight adjustment algorithm steps are as follows: Step 1: Adjusting index weights due to the impact of holidays: ； in, The revised weighting of the holiday impact index is shown in w3. The intensity index is adjusted for holiday weighting. This is a function to indicate the status of holidays. This indicates that it is currently a holiday. This indicates non-holidays; It is the average route growth index or hourly travel time index during holidays. It is the average route growth index or hourly travel time index during non-holiday periods; Step 2: Adjusting the weight of the dead-end road index: ； in, Here are the corrected dead-end road index weights, and w4 represents the weight of the dead-end road index. The normalized dead-end road index, ; This represents the maximum value of the dead-end road index. This represents the minimum value of the dead-end road index. Adjust the intensity index for dead-end roads; Step 3: Weight Normalization Calculate the corrected total weights: ； Among them, w1, w2, and w5 are the weights of the route growth index, the hourly travel time index, and the planning simulation index, respectively. Then normalize: ； ； in, This is the adjusted total weight sum. For the i-th corrected weight, For the i-th weight, The weight is the i-th normalized weight; Step 4: Weighted iteration based on real-time data feedback: ； ； in, Let be the weight matrix at time t. Let be the weight matrix at time t+1, and λ be the adaptive learning rate. It is the gradient vector of the road traffic index with respect to the weights; S5: Display the road traffic index and related data, and draw a heat map of the road traffic index. Carry out county and village transportation planning with the goal of reducing the road traffic index, and calculate the difference in road traffic index before and after the planning as the basis for decision-making.

2. The county and rural transportation planning decision-making method according to claim 1, characterized in that the route... Growth Index , It is the straight-line distance between two points. It is the navigation distance between two points obtained through navigation API data; Hourly usage index , , This provides the estimated travel time for different routes obtained through navigation API data. Theoretical travel time, Average speed; Holiday Impact Index ; Dead-end road index , The total length of dead-end roads within the area; This represents the total length of roads within the area. Planning Simulation Index , Current traffic indicators; To simulate traffic indicators after the planning scheme is implemented; Road traffic index : 。 3. The county and rural transportation planning decision-making method according to claim 2, characterized in that, The complete process of calculating the road traffic index using the adaptive weight adjustment algorithm is as follows: Step 1: Initialize the weight matrix ; Step 2: At time t: Collect data and calculate various indices: ; Based on whether it is a holiday or not and the dead-end road index, we perform the first and second steps to obtain the results. and ; The third step yields the normalized weight matrix. ; Calculate the road traffic index ; calculate ; The fourth step yields the weight matrix at time t+1. ; Step 3: Convert the weight matrix at time t+1 As the initial weights for the next time step, repeat step 2.

4. A system for implementing the county and rural transportation planning decision-making method according to any one of claims 1-3, characterized in that, It includes a data acquisition module, a data preprocessing module, a multi-source data fusion module, a road traffic index calculation module, and a decision support system; The data acquisition module collects navigation API data, socio-economic data, and POI information related to county and rural transportation planning. The data preprocessing module cleans and standardizes the collected data; The multi-source data fusion module matches and integrates standardized data across time and space dimensions; The road traffic index calculation module calculates the route growth index, hourly travel time index, holiday impact index, dead-end road index, and planning simulation index based on the integrated data, and performs weighted fusion to obtain the road traffic index. When calculating the road traffic index, an initial weight is assigned to each index, and an adaptive weight adjustment algorithm is used to dynamically adjust the weight of each index based on real-time traffic data and simulation results. The decision support system displays the road traffic index and related data, and plots the road traffic index as a heat map.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps in the county and rural transportation planning decision-making method based on road traffic index as described in any one of claims 1-3.

6. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps in the county and rural transportation planning decision-making method based on road traffic index as described in any one of claims 1-3.

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