City intelligent traffic public management system based on big data feedback

Abstract

The present application relates to the field of intelligent traffic technology, and discloses a city intelligent traffic public management system based on big data feedback, which comprises a data acquisition unit, a data analysis unit, a road condition evaluation unit, a safety evaluation unit, a traffic management unit, a time period analysis unit and an information display unit. The traffic analysis is detailed, the road condition evaluation is scientific, the traffic management corresponds to the countermeasures, the signal lamp is adjusted for light congestion, the induction is added for moderate congestion, and the regulation is increased for heavy congestion. The signal lamp timing is optimized according to the vehicle type, and the induced bypass route is accurately planned. The safety evaluation is combined with the vehicle speed and the visibility, and the safety management sets a warning or a no-entry sign according to the hidden danger. The time period analysis accurately distinguishes the peak and valley time periods. The information display unit can also enable all parties to know various traffic information in time. Overall, the traffic efficiency can be greatly improved, the congestion can be effectively relieved, the traffic safety guarantee can be enhanced, the rational allocation of traffic resources can be assisted, and the city traffic management can be promoted to be intelligent.

Description

Technical Field

[0001] This invention relates to the field of intelligent transportation technology, and more specifically to an urban intelligent transportation public management system based on big data feedback. Background Technology

[0002] Today, with rapid urbanization and an explosive growth in urban population and vehicle numbers, the transportation sector faces numerous severe challenges that traditional traffic management models can no longer adequately address.

[0003] Traffic congestion is extremely severe, placing an unbearable burden on limited urban road resources. During peak hours, main roads are teeming with traffic, with vehicles remaining stationary for extended periods, resulting in serious congestion. Existing traffic light timings are rigid and unable to adapt flexibly to real-time traffic flow, leading to excessive vehicle congestion on some sections while other sections remain underutilized. Furthermore, there is a lack of precise and effective vehicle detour guidance mechanisms for congested areas. Drivers struggle to obtain reliable alternative route information and are forced to blindly follow the flow of traffic, further exacerbating congestion. The large number of vehicles stranded also wastes energy and pollutes the environment. Traffic safety is equally concerning. Inconsistent vehicle speeds, diverse vehicle types, and environmental factors such as weather contribute to frequent traffic accidents. For example, in severe weather conditions such as dense fog or torrential rain, visibility decreases dramatically. If drivers fail to obtain timely road condition information and adjust their speed accordingly, the risk of collisions increases significantly. However, traditional traffic management systems have significant shortcomings in monitoring and analyzing abnormal vehicle speeds. They struggle to comprehensively consider and assess environmental factors and traffic safety, failing to provide early warnings and implement targeted safety management strategies. This undoubtedly poses a significant threat to the safe operation of urban traffic. Furthermore, traffic management departments often struggle to develop scientifically sound plans and allocate resources due to a lack of precise understanding of traffic flow patterns across different time periods. The inability to accurately differentiate between peak, off-peak, and normal traffic flow patterns leads to irrational infrastructure development, imbalanced allocation of human and material resources, and an inability to efficiently cope with traffic pressure at various times of day.

[0004] Based on this, a smart urban transportation public management system based on big data feedback is proposed. Summary of the Invention

[0005] The purpose of this invention is to provide an intelligent urban transportation public management system based on big data feedback, which solves the technical problems mentioned in the background art.

[0006] The objective of this invention can be achieved through the following technical solutions:

[0007] A smart urban transportation public management system based on big data feedback includes:

[0008] The data acquisition unit is used to collect vehicle traffic data using geomagnetic sensors installed on the road. The traffic data includes the number of vehicles passing through a specified road segment within a specified unit of time. It also collects the driving speed and type information of target vehicles using cameras. Furthermore, it collects environmental data using road weather sensors. The environmental data includes the visibility of a specified road segment within a specified unit of time.

[0009] The data analysis unit is used to perform traffic analysis on the results collected by the data acquisition unit.

[0010] The traffic condition assessment unit is used to assess traffic conditions based on the results obtained from the data analysis unit.

[0011] The safety assessment unit is used to conduct traffic safety assessments based on the results obtained from the data analysis unit.

[0012] The traffic management unit is used for congestion management based on the results of the road condition assessment unit, and also for safety management based on the results of the safety assessment unit.

[0013] The time period analysis unit is used to analyze peak and off-peak periods within multiple target time periods in a specified period;

[0014] The information display unit is used to present the results obtained from the data analysis unit, road condition assessment unit, safety assessment unit, time period analysis unit, and traffic management unit to relevant personnel.

[0015] As a further aspect of the present invention, the traffic analysis method is as follows:

[0016] Step A1: Traffic Flow Analysis

[0017] Within the target time period t, the number of vehicles on a specified road segment within multiple specified time units is labeled as N. k，t k = 1, 2, ..., e, where e is the number of units of time contained in the target time period, and t represents the nth target time period;

[0018] Subsequently passed:

[0019]

[0020] In the formula, S represents the time interval of the target time period;

[0021] Calculate the average traffic flow Q of the specified road segment during the target time period. s，t ;

[0022] Step A2: Traffic Speed ​​Analysis

[0023] The speeds of multiple target vehicles within the target time period t are denoted as V. i，t i = 1, 2, ..., n;

[0024] Where n represents the number of target vehicles within the target time period t, and

[0025]

[0026] Subsequently passed:

[0027]

[0028] Calculate the average driving speed U of the specified road segment within the target time period t. s，t ;

[0029] Step A3: Vehicle Type Analysis

[0030] Within the target time period t, count the number of the same type of target vehicles on a specified road segment among all target vehicles, and label them as T. j，t j = 1, 2, ..., m, where m represents the number of target vehicle types;

[0031] Subsequently passed:

[0032]

[0033] Calculate the proportion P of different types of target vehicles among all target vehicles on the specified road segment. j，t .

[0034] As a further aspect of the present invention, the traffic condition assessment method is as follows:

[0035] Extract the average traffic flow Q within the target time period t. s，t Average driving speed U s，t And the proportion P of different types of target vehicles among all target vehicles on the designated road segment. j，t ;

[0036] Subsequently passed:

[0037]

[0038] Calculate the congestion coefficient D on the specified road segment. s ;

[0039] In the formula, β Q β V β P α is the preset weighting coefficient. j Correction coefficients preset to account for the impact of different vehicle types on congestion;

[0040] The congestion coefficient D on the designated road segment will then be... s Compare with preset congestion level thresholds Dy1, Dy2, and Dy3:

[0041] Where Dy1 < Dy2 < Dy3;

[0042] If D s If <Dy1, then the traffic conditions on the specified road segment are determined to be smooth;

[0043] If Dy1≤D s If <Dy2, then the traffic situation on the specified road segment is determined to be slightly congested;

[0044] If Dy2≤D s If Dy3 < Dy3, the traffic situation on the specified road segment is determined to be moderately congested;

[0045] If D s If the value is ≥Dy3, then the traffic situation on the specified road segment is determined to be severely congested.

[0046] As a further aspect of the present invention, the traffic management unit is structured as follows:

[0047] When a designated road segment is experiencing mild congestion, the traffic flow is determined based on the proportion of vehicle type P. j，t Adjust traffic light timing;

[0048] When a designated road section is moderately congested, the traffic light timing is adjusted first, and vehicles are guided to detour via a guidance screen.

[0049] When a designated road section is severely congested, in addition to adjusting traffic light timings and guiding vehicles to detour via information screens, temporary traffic control measures will also be implemented.

[0050] Temporary traffic control measures include restricting vehicles of certain types from entering designated road sections.

[0051] As a further aspect of the present invention, the traffic light timing adjustment method is as follows:

[0052] If the proportion of the corresponding vehicle type is greater than the preset type proportion threshold, the extended green light time is calculated by using the preset green light adjustment coefficient based on the vehicle type.

[0053] The corresponding formula is as follows:

[0054] GY j =λ j ×G0

[0055] In the formula, GY j To extend green light time for different vehicle types, λ j The green light adjustment coefficient is preset for different vehicle types, and G0 is the initial green light time;

[0056] GY j +G0 is used to adjust the green light time of a specified road segment under the current traffic conditions to adjust the traffic light timing.

[0057] As a further aspect of the present invention: when guiding vehicles to detour via the guidance screen, the optimal detour route is calculated based on the traffic flow and congestion conditions of the surrounding roads;

[0058] The specific method is as follows:

[0059] Extract all detour routes related to the specified road segment;

[0060] The number of road segments in each detour route is then obtained, the length of each road segment in each detour route is extracted, and the average traffic flow of each corresponding road segment in each detour route during the target time period is extracted.

[0061] Choose an alternative route;

[0062] pass:

[0063]

[0064] Calculate the detour coefficient RX for this detour route;

[0065] Among them, the smaller the detour coefficient RX value, the better the corresponding detour route;

[0066] In the formula, θ is a preset traffic flow weighting adjustment coefficient, used to calculate the traffic flow weighting coefficient Z in conjunction with the average traffic flow. f Q1 f LD represents the average traffic flow of each segment of the detour route during the target time period. f Let f be the length of each segment in the detour route, f = 1, 2, ..., h, where h is the number of segments in the detour route;

[0067] By analogy, the detour coefficients for all detour routes are calculated, and finally the detour route with the smallest detour coefficient value is selected as the optimal detour route, which is then displayed on the guidance screen.

[0068] As a further aspect of the present invention, the traffic safety assessment method is as follows:

[0069] The speed V of multiple target vehicles within the target time period t. i，t Compare with the preset vehicle speed threshold Vy:

[0070] V i，t If the speed is less than Vy, the vehicle speed is determined to be abnormal. The number of vehicles with abnormal speeds within the target time period t is then counted and marked as M.t ;

[0071] Subsequently passed:

[0072]

[0073] Calculate the number of vehicles with abnormal speeds within the target time period t in the range of N. t The percentage of target vehicles B t ;

[0074] At the same time, the visibility of the specified road segment during the target time period is recorded as W;

[0075] Then passed:

[0076]

[0077] Calculate the safety index A for the specified road segment;

[0078] In the formula, γ1 and γ2 are the corresponding preset weight coefficients;

[0079] Next, the safety index A on the specified road segment is compared with the preset safety thresholds Ay1, Ay2, and Ay3:

[0080] Among them, Ay1 > Ay2 > Ay3;

[0081] If A > Ay1, it means that the specified road segment is safe.

[0082] If Ay1≥A>Ay2, it indicates that the specified road section has a minor safety hazard, that is, the safety is average.

[0083] If Ay2≥A>Ay3, it indicates that the specified road section has a moderate safety hazard, that is, the safety is low.

[0084] If A < Ay3, it indicates that the specified road section has a serious safety hazard, that is, the safety is very low.

[0085] As a further aspect of the present invention, the security management method is as follows:

[0086] If a minor safety hazard exists in a designated road section, warning signs shall be set up in the designated road section.

[0087] If a designated road section has a moderate safety hazard, warning signs will be set up in the designated road section, and road patrols and monitoring will be strengthened.

[0088] If a designated road section has serious safety hazards, a no-passage sign will be set up on the designated road section.

[0089] As a further aspect of the present invention, the time period analysis unit is configured as follows:

[0090] Extract the average traffic flow on a specified road segment within adjacent target time periods of a specified period;

[0091] Subsequently passed:

[0092]

[0093] Calculate the rate of change R of the flow rate during the target time period t. t ;

[0094] In the formula, t-1 is the previous target time period adjacent to the t-th target time period;

[0095] Then, the rate of change R of the flow rate during the target time period t will be... t Compared with the preset change threshold R min and R max Comparison:

[0096] If R min ≤R t ≤R max If so, then the target time period t is determined to be a normal time period;

[0097] If R t <R min If so, then the target time period t is determined to be a low period;

[0098] If R t >R max If so, then the target time period t is determined to be the peak period.

[0099] The beneficial effects of this invention are:

[0100] Precise traffic analysis: The data collection unit collects data on various aspects such as vehicle flow, speed, vehicle type, and environmental visibility. The data analysis unit can perform detailed analysis of traffic flow, speed, and vehicle type, providing a comprehensive and accurate data foundation for subsequent assessment and management.

[0101] Scientific traffic condition assessment: Based on traffic analysis results, the road condition assessment unit uses a reasonable calculation formula to comprehensively consider factors such as average traffic flow, average driving speed, and the proportion of different types of vehicles. Combined with preset weighting coefficients and correction coefficients, it accurately assesses the congestion status of a designated road segment and distinguishes different levels such as smooth traffic, light congestion, moderate congestion, and heavy congestion, which helps traffic management departments to take targeted measures.

[0102] Effective traffic management measures: Traffic management units take corresponding measures according to different levels of congestion. For mild congestion, traffic light timings can be adjusted according to vehicle type ratios; for moderate congestion, in addition to adjusting traffic light timings, guidance screens are used to guide vehicles to detour; for severe congestion, temporary control measures are added on top of the above, such as restricting certain vehicle types from entering designated road sections, thereby effectively alleviating traffic congestion and improving road efficiency.

[0103] Reasonable adjustment of traffic light timing: Based on the comparison of the proportion of different vehicle types with preset thresholds, the green light time is extended by calculating the preset green light adjustment coefficient, thereby reasonably adjusting the traffic light timing to make the traffic light settings more in line with actual traffic needs, reduce vehicle waiting time, and further optimize traffic flow.

[0104] The guidance screen helps vehicles detour efficiently: When guiding vehicles to detour, it can comprehensively consider factors such as traffic flow and road length of surrounding roads. By calculating the detour coefficient of each detour route, it selects the best detour route with the smallest detour coefficient and displays it on the guidance screen. This helps drivers quickly find the optimal detour route, avoids traffic chaos caused by blind detours, and improves the overall traffic management effect.

[0105] Accurate traffic safety assessment: By comparing the target vehicle's speed with a preset speed threshold, counting the number of vehicles with abnormal speeds, and combining this with road visibility, a safety index is calculated using preset weighting coefficients. This accurately assesses the safety status of a designated road section and can distinguish between different situations such as good safety, minor, moderate, and severe safety hazards.

[0106] Targeted safety management measures: For safety hazards of different degrees, the safety management unit takes corresponding measures, such as setting up warning signs, strengthening road patrol and monitoring, or setting up no-entry signs, which effectively ensures road traffic safety and reduces the possibility of traffic accidents.

[0107] Precise Time Period Characteristics Analysis: By calculating the traffic flow change rate of the target time period and comparing it with a preset threshold, the time period analysis unit can accurately determine peak hours, off-peak hours, and normal hours. This helps traffic management departments to make advance preparations for traffic scheduling and resource allocation, and better cope with traffic pressure at different times.

[0108] In summary, the urban intelligent transportation public management system of the present invention can comprehensively and accurately analyze, evaluate, manage and regulate urban traffic, effectively improve traffic efficiency, ensure traffic safety, and enhance the level of intelligence in urban traffic management. Attached Figure Description

[0109] The invention will now be further described with reference to the accompanying drawings.

[0110] Figure 1This is a system block diagram of the urban intelligent transportation public management system based on big data feedback, as proposed in this invention. Detailed Implementation

[0111] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0112] Example 1

[0113] Please see Figure 1 As shown, this invention is an urban intelligent transportation public management system based on big data feedback, comprising:

[0114] The data acquisition unit is used to collect vehicle traffic data using geomagnetic sensors installed on the road. The traffic data includes the number of vehicles passing through a specified road segment within a specified unit of time.

[0115] And it collects information about the target vehicle's speed and type through cameras;

[0116] It also collects environmental data through road weather sensors, which includes visibility of a specified road section within a specified unit of time.

[0117] The data analysis unit is used to perform traffic analysis on the results collected by the data acquisition unit.

[0118] The specific method is as follows:

[0119] Step A1: Traffic Flow Analysis

[0120] Within the target time period t, the number of vehicles on a specified road segment within multiple specified time units is labeled as N. k，t k = 1, 2, ..., e, where e is the number of units of time contained in the target time period, and t represents the nth target time period;

[0121] Subsequently passed:

[0122]

[0123] In the formula, S represents the time interval of the target time period;

[0124] Calculate the average traffic flow Q of the specified road segment during the target time period. s，t ;

[0125] Step A2: Traffic Speed ​​Analysis

[0126] The speeds of multiple target vehicles within the target time period t are denoted as V. i，t i = 1, 2, ..., n;

[0127] Where n represents the number of target vehicles within the target time period t, and

[0128]

[0129] Subsequently passed:

[0130]

[0131] Calculate the average driving speed U of the specified road segment within the target time period t. s，t ;

[0132] Step A3: Vehicle Type Analysis

[0133] Within the target time period t, count the number of the same type of target vehicles on a specified road segment among all target vehicles, and label them as T. j，t j = 1, 2, ..., m, where m represents the number of target vehicle types;

[0134] In this embodiment, the vehicle type includes, for example, a small passenger car or a large truck.

[0135] Subsequently passed:

[0136]

[0137] Calculate the proportion P of different types of target vehicles among all target vehicles on the specified road segment. j，t ;

[0138] The traffic condition assessment unit is used to assess traffic conditions based on the results obtained from the data analysis unit.

[0139] The specific method is as follows:

[0140] Extract the average traffic flow Q within the target time period t. s，t Average driving speed U s，t And the proportion P of different types of target vehicles among all target vehicles on the designated road segment. j，t ;

[0141] Subsequently passed:

[0142]

[0143] Calculate the congestion coefficient D on the specified road segment. s ;

[0144] In the formula, β Q β V βP α is the preset weighting coefficient. j Correction coefficients preset to account for the impact of different vehicle types on congestion;

[0145] The congestion coefficient D on the designated road segment will then be... s Compare with preset congestion level thresholds Dy1, Dy2, and Dy3:

[0146] If D s If <Dy1, then the traffic conditions on the specified road segment are determined to be smooth;

[0147] If Dy1≤D s If <Dy2, then the traffic situation on the specified road segment is determined to be slightly congested;

[0148] If Dy2≤D s If Dy3 < Dy3, the traffic situation on the specified road segment is determined to be moderately congested;

[0149] If D s If the value is ≥Dy3, then the traffic situation on the specified road segment is determined to be severely congested.

[0150] Traffic management unit, used to manage congestion based on the results of road condition assessment unit;

[0151] The specific method is as follows:

[0152] When a designated road segment is experiencing mild congestion, the traffic flow is determined based on the proportion of vehicle type P. j，t Adjust traffic light timing;

[0153] The method is as follows:

[0154] If the proportion of the corresponding vehicle type is greater than the preset type proportion threshold, the extended green light time is calculated by using the preset green light adjustment coefficient based on the vehicle type.

[0155] The corresponding formula is as follows:

[0156] GY j =λ j ×G0

[0157] In the formula, GY j To extend green light time for different vehicle types, λ j The green light adjustment coefficient is preset for different vehicle types, and G0 is the initial green light time;

[0158] GY j +G0 is used to adjust the green light time of a specified road segment under the current road conditions to adjust the traffic light timing.

[0159] When a designated road section is moderately congested, the traffic light timing is adjusted first, and vehicles are guided to detour via a guidance screen.

[0160] When guiding vehicles to detour via the guidance screen, the optimal detour route is calculated based on the traffic flow and congestion conditions of the surrounding roads;

[0161] The specific method is as follows:

[0162] Extract all detour routes related to the specified road segment;

[0163] The number of road segments in each detour route is then obtained, the length of each road segment in each detour route is extracted, and the average traffic flow of each corresponding road segment in each detour route during the target time period is extracted.

[0164] Choose an alternative route;

[0165] pass:

[0166]

[0167] Calculate the detour coefficient RX for this detour route;

[0168] Among them, the smaller the detour coefficient RX value, the better the corresponding detour route;

[0169] In the formula, θ is a preset traffic flow weighting adjustment coefficient, used to calculate the traffic flow weighting coefficient Z in conjunction with the average traffic flow. f Q1 f LD represents the average traffic flow of each segment of the detour route during the target time period. f Let f be the length of each segment in the detour route, f = 1, 2, ..., h, where h is the number of segments in the detour route;

[0170] By analogy, the detour coefficients corresponding to all detour routes are calculated, and finally the detour route with the smallest detour coefficient value is selected as the optimal detour route, which is then displayed on the guidance screen.

[0171] When a designated road section is severely congested, in addition to adjusting traffic light timings and guiding vehicles to detour via information screens, temporary traffic control measures will also be implemented.

[0172] Temporary traffic control measures include restricting vehicles of certain types from entering designated road sections.

[0173] This embodiment utilizes a data acquisition unit with geomagnetic sensors, cameras, and road weather sensors to collect vehicle flow data, target vehicle speed and type information, and environmental visibility data. This provides a multi-dimensional, comprehensive, and accurate data foundation for subsequent traffic analysis, helping to more accurately grasp traffic conditions. The data analysis unit performs detailed analysis of the collected data on traffic flow, vehicle speed, vehicle type, and other aspects. It can accurately calculate key indicators such as average traffic flow, average speed, and the proportion of different vehicle types on a specified road segment within a target time period, providing strong support for traffic condition assessment and management. Based on the results of the data analysis unit, the road condition assessment unit uses reasonable calculations to... By calculating formulas and combining preset weighting and correction coefficients, the traffic conditions of a designated road segment can be accurately assessed, distinguishing between different levels such as smooth traffic, light congestion, moderate congestion, and heavy congestion. This allows traffic management departments to clearly understand the road congestion situation and take targeted measures. Based on the results of the road condition assessment unit, the traffic management unit takes corresponding measures for different levels of congestion. In cases of light congestion, traffic light timing can be adjusted according to the proportion of vehicle types to optimize traffic light settings and reduce vehicle waiting time. In cases of moderate congestion, in addition to adjusting traffic light timing, guidance screens are used to guide vehicles to detour, effectively easing traffic congestion. In cases of heavy congestion, temporary control measures are added on top of the above to further alleviate traffic pressure and improve road traffic efficiency.

[0174] Example 2

[0175] Please see Figure 1 As shown, as a second embodiment of the present invention, in specific implementation, the technical solution of this embodiment differs from that of embodiment one only in that, in addition to embodiment one, it also includes the following:

[0176] The safety assessment unit is used to conduct traffic safety assessments based on the results obtained from the data analysis unit.

[0177] The specific method is as follows:

[0178] The speed V of multiple target vehicles within the target time period t. i，t Compare with the preset vehicle speed threshold Vy:

[0179] V i，t If the speed is less than Vy, the vehicle speed is determined to be abnormal. The number of vehicles with abnormal speeds within the target time period t is then counted and marked as M. t ;

[0180] Subsequently passed:

[0181]

[0182] Calculate the number of vehicles with abnormal speeds within the target time period t in the range of N. tThe percentage of target vehicles B t ;

[0183] At the same time, the visibility of the specified road segment during the target time period is recorded as W;

[0184] Then passed:

[0185]

[0186] Calculate the safety index A for the specified road segment;

[0187] In the formula, γ1 and γ2 are the corresponding preset weight coefficients;

[0188] Next, the safety index A on the specified road segment is compared with the preset safety thresholds Ay1, Ay2, and Ay3:

[0189] If A > Ay1, it means that the specified road segment is safe.

[0190] If Ay1≥A>Ay2, it indicates that the specified road section has a minor safety hazard, that is, the safety is average.

[0191] If Ay2≥A>Ay3, it indicates that the specified road section has a moderate safety hazard, that is, the safety is low.

[0192] If A < Ay3, it means that the specified road section has a serious safety hazard, that is, the safety is very low;

[0193] The traffic management unit is also used for safety management based on the results of the safety assessment unit;

[0194] The method is as follows:

[0195] If a minor safety hazard exists in a designated road section, warning signs shall be set up in the designated road section.

[0196] If a designated road section has a moderate safety hazard, warning signs will be set up in the designated road section, and road patrols and monitoring will be strengthened.

[0197] If a designated road section has serious safety hazards, a no-passage sign will be set up on the designated road section.

[0198] This embodiment adds a safety assessment unit to the first embodiment, enabling traffic safety assessment based on the results of the data analysis unit. By comparing the target vehicle's speed with a preset speed threshold, counting the number of vehicles with abnormal speeds, and combining factors such as road visibility, a safety index is calculated using preset weighting coefficients. This accurately assesses the safety status of a designated road segment and can distinguish between different levels of safety hazards, such as good safety, minor, moderate, and severe safety hazards. The traffic management unit takes corresponding safety management measures based on the results of the safety assessment unit. Warning signs are set up for minor safety hazards; warning signs are set up and road patrols and monitoring are strengthened for moderate safety hazards; and no-entry signs are set up for severe safety hazards, effectively ensuring road traffic safety and reducing the possibility of traffic accidents.

[0199] Example 3

[0200] Please see Figure 1 As shown, as a third embodiment of the present invention, in specific implementation, compared with embodiments one and two, the technical solution of this embodiment is to combine the solutions of embodiments one and two. The difference between the technical solution of this embodiment and embodiments one and two is only that this embodiment also includes:

[0201] The time period analysis unit is used to analyze peak and off-peak periods within multiple target time periods in a specified period;

[0202] The method is as follows:

[0203] Extract the average traffic flow on a specified road segment within adjacent target time periods of a specified period;

[0204] Subsequently passed:

[0205]

[0206] Calculate the rate of change R of the flow rate during the target time period t. t ;

[0207] In the formula, t-1 is the previous target time period adjacent to the t-th target time period;

[0208] Then, the rate of change R of the flow rate during the target time period t will be... t Compared with the preset change threshold R min and R max Comparison:

[0209] If R min ≤R t ≤R max If so, then the target time period t is determined to be a normal time period;

[0210] If R t <Rmin If so, then the target time period t is determined to be a low period;

[0211] If R t >R max If so, then the target time period t is determined to be the peak period.

[0212] This embodiment adds a time-period analysis unit to the existing embodiments one and two. It can analyze the average traffic flow on a specified road segment within a specified period of adjacent target time periods, calculate the traffic flow change rate, and compare it with a preset threshold to accurately determine peak, off-peak, and normal time periods. This helps traffic management departments to prepare traffic scheduling and resource allocation in advance, better cope with traffic pressure at different times, and improve the scientific nature and effectiveness of traffic management.

[0213] Example 4

[0214] Please see Figure 1 As shown, as Embodiment 4 of the present invention, in specific implementation, compared with Embodiments 1, 2, and 3, the difference between this embodiment and Embodiments 1, 2, and 3 is only that, based on the above embodiments, this embodiment further includes:

[0215] The information display unit is used to present the results obtained from the data analysis unit, road condition assessment unit, safety assessment unit, time period analysis unit, and traffic management unit to relevant personnel.

[0216] This embodiment adds an information display unit to the above embodiment, which can display the results obtained by the data analysis unit, road condition assessment unit, safety assessment unit, time period analysis unit and traffic management unit to relevant personnel. This makes it convenient for traffic management departments, drivers and other relevant personnel to understand the traffic conditions, safety situation, time period characteristics and management measures taken in a timely and comprehensive manner, so as to make reasonable decisions.

[0217] Example 5

[0218] Please see Figure 1 As shown, as a fifth embodiment of the present invention, in specific implementation, compared with embodiments one, two, three and four, the technical solution of this embodiment is to combine the solutions of embodiments one, two, three and four.

[0219] This embodiment combines the solutions from Embodiments 1, 2, 3, and 4, thus integrating the beneficial effects of each embodiment, including comprehensive and accurate data collection, detailed traffic analysis, scientific traffic condition assessment, effective congestion management measures, traffic safety assessment, targeted safety management measures, precise analysis of time-period characteristics, and centralized information display. It enables comprehensive, multi-level analysis, assessment, management, and information sharing of urban traffic, effectively improving traffic efficiency, ensuring traffic safety, and enhancing the intelligence level of urban traffic management.

[0220] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters and thresholds in the formulas are set by those skilled in the art according to the actual situation.

[0221] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A smart urban transportation public management system based on big data feedback, characterized in that: include: The data acquisition unit is used to collect vehicle traffic data, target vehicle speed and target vehicle type information, and at the same time collect road environmental data. Traffic data includes the number of vehicles passing through a specified road segment within a specified unit of time, and environmental data includes the visibility of the specified road segment within a specified unit of time. The data analysis unit performs traffic analysis on the results collected by the data acquisition unit, and then transmits the analysis results to the road condition assessment unit, safety assessment unit, and time period analysis unit. The traffic condition assessment unit is used to assess traffic conditions based on the results obtained from the data analysis unit, and then transmits the assessment results to the traffic management unit. The safety assessment unit is used to conduct traffic safety assessments based on the results obtained from the data analysis unit, and then transmits the assessment results to the traffic management unit. The traffic management unit is used for congestion management based on the results of the road condition assessment unit, and also for safety management based on the results of the safety assessment unit. The time period analysis unit is used to analyze peak and off-peak periods in multiple target time periods within a specified period, and then transmit the analysis results to the information display unit. The information display unit is used to present the results obtained from the data analysis unit, road condition assessment unit, safety assessment unit, time period analysis unit, and traffic management unit to relevant personnel.

2. The urban intelligent transportation public management system based on big data feedback according to claim 1, characterized in that, Traffic analysis is performed as follows: Step A1: Traffic Flow Analysis Within the target time period t, the number of vehicles on a specified road segment within multiple specified time units is labeled as N. k，t k = 1, 2, ..., e, where e is the number of units of time contained in the target time period, and t represents the nth target time period; Subsequently passed: In the formula, S represents the time interval of the target time period; Calculate the average traffic flow Q of the specified road segment during the target time period. s，t ; Step A2: Traffic Speed ​​Analysis The speeds of multiple target vehicles within the target time period t are denoted as V. i，t , i = 1, 2, ..., n; Where n represents the number of target vehicles within the target time period t, and Subsequently passed: Calculate the average driving speed U of the specified road segment within the target time period t. s，t ; Step A3: Vehicle Type Analysis Within the target time period t, count the number of the same type of target vehicles on a specified road segment among all target vehicles, and label them as T. j，t j = 1, 2, ..., m, where m represents the number of target vehicle types; Subsequently passed: Calculate the proportion P of different types of target vehicles among all target vehicles on the specified road segment. j，t .

3. The urban intelligent transportation public management system based on big data feedback according to claim 2, characterized in that, The traffic condition assessment method is as follows: Extract the average traffic flow Q within the target time period t. s，t Average driving speed U s，t And the proportion P of different types of target vehicles among all target vehicles on the designated road segment. j，t ; Subsequently passed: Calculate the congestion coefficient D on the specified road segment. s ; In the formula, β Q β V β P α is the preset weighting coefficient. j Correction coefficients preset to account for the impact of different vehicle types on congestion; Then, the congestion coefficient D on the designated road segment will be calculated. s Compare with preset congestion level thresholds Dy1, Dy2, and Dy3: If D s If <Dy1, then the traffic conditions on the specified road segment are determined to be smooth; If Dy1≤D s If <Dy2, then the traffic situation on the specified road segment is determined to be slightly congested; If Dy2≤D s If Dy3 < Dy3, the traffic situation on the specified road segment is determined to be moderately congested; If D s If the value is ≥Dy3, then the traffic situation on the specified road segment is determined to be severely congested.

4. The urban intelligent transportation public management system based on big data feedback according to claim 3, characterized in that, The traffic management unit is structured as follows: When a designated road segment is experiencing mild congestion, the traffic flow is determined based on the proportion of vehicle type P. j，t Adjust traffic light timing; When a designated road section is moderately congested, the traffic light timing is adjusted first, and vehicles are guided to detour via a guidance screen. When a designated road section is severely congested, in addition to adjusting traffic light timings and guiding vehicles to detour via information screens, temporary traffic control measures will be implemented.

5. The urban intelligent transportation public management system based on big data feedback according to claim 4, characterized in that, The traffic light timing adjustment method is as follows: If the proportion of the corresponding vehicle type is greater than the preset type proportion threshold, the extended green light time is calculated by using the preset green light adjustment coefficient based on the vehicle type. The corresponding formula is as follows: GY j =λ j ×G0 In the formula, GY j To extend green light time for different vehicle types λj The green light adjustment coefficient is preset for different vehicle types, and G0 is the initial green light time; GY j +G0 is used to adjust the green light time of a specified road segment under the current traffic conditions to adjust the traffic light timing.

6. The urban intelligent transportation public management system based on big data feedback according to claim 4, characterized in that, When guiding vehicles to detour via the guidance screen, the optimal detour route is calculated based on the traffic flow and congestion conditions of the surrounding roads; The specific method is as follows: Extract all detour routes related to the specified road segment; The number of road segments in each detour route is then obtained, the length of each road segment in each detour route is extracted, and the average traffic flow of each corresponding road segment in each detour route during the target time period is extracted. Choose an alternative route; pass: Calculate the detour coefficient RX for this detour route; In the formula, θ is a preset traffic flow weighting adjustment coefficient, used to calculate the traffic flow weighting coefficient Z in conjunction with the average traffic flow. f Q1 f LD represents the average traffic flow of each segment of the detour route during the target time period. f Let f be the length of each segment in the detour route, f = 1, 2, ..., h, where h is the number of segments in the detour route; Similarly, the detour coefficients for all detour routes are calculated, and finally the detour route with the smallest detour coefficient value is selected as the optimal detour route, which is then displayed on the guidance screen.

7. The urban intelligent transportation public management system based on big data feedback according to claim 4, characterized in that, Temporary traffic control measures include restricting vehicles of certain types from entering designated road sections.

8. The urban intelligent transportation public management system based on big data feedback according to claim 2, characterized in that, The traffic safety assessment methods are as follows: The speed V of multiple target vehicles within the target time period t. i，t Compare with the preset vehicle speed threshold Vy: V i，t If the speed is less than Vy, the vehicle speed is determined to be abnormal. The number of vehicles with abnormal speeds within the target time period t is then counted and marked as M. t ; Subsequently passed: Calculate the number of vehicles with abnormal speeds within the target time period t in the range of N. t The percentage of target vehicles B t ; At the same time, the visibility of the specified road segment during the target time period is recorded as W; Then passed: Calculate the safety index A for the specified road segment; In the formula, γ1 and γ2 are the corresponding preset weight coefficients; Next, the safety index A on the specified road segment is compared with the preset safety thresholds Ay1, Ay2, and Ay3: If A > Ay1, it means that the specified road segment is safe. If Ay1≥A>Ay2, it indicates that there is a minor safety hazard in the specified road section; If Ay2≥A>Ay3, it indicates that there is a moderate safety hazard in the specified road section; If A < Ay3, it indicates that there is a serious safety hazard in the specified road section.

9. The urban intelligent transportation public management system based on big data feedback according to claim 8, characterized in that, The security management methods are as follows: If a minor safety hazard exists in a designated road section, warning signs shall be set up in the designated road section. If a designated road section has a moderate safety hazard, warning signs will be set up in the designated road section, and road patrols and monitoring will be strengthened. If a designated road section has serious safety hazards, a no-passage sign will be set up on the designated road section.

10. The urban intelligent transportation public management system based on big data feedback according to claim 2, characterized in that, The time-period analysis unit is structured as follows: Extract the average traffic flow on a specified road segment within adjacent target time periods of a specified period; Subsequently passed: Calculate the rate of change R of the flow rate during the target time period t. t ; In the formula, t-1 is the previous target time period adjacent to the t-th target time period; Then, the rate of change R of the flow rate during the target time period t will be... t Compared with the preset change threshold R min and R max Comparison: If R min ≤R t ≤R max If so, then the target time period t is determined to be a normal time period; If R t <R min If so, then the target time period t is determined to be a low period; If R t >R max If so, then the target time period t is determined to be the peak period.

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