Urban intelligent traffic informatization management system based on Internet of Things

Abstract

The invention relates to the technical field of intelligent traffic management, and discloses an urban intelligent traffic informatization management system based on the Internet of Things, which comprises a data perception acquisition module, a network communication transmission module, an edge calculation and data processing module, an analysis module, an application service module and an operation and maintenance management module, the data sensing and collecting module is used for collecting multi-source traffic data in real time through various Internet of Things sensing devices arranged on urban traffic infrastructures; and the analysis module is used for carrying out total integration, modeling and deep analysis on the preprocessed data. The data storage and management unit builds a hierarchical storage system, stores various traffic data in a classified manner, provides efficient retrieval and calling services, and lays a solid data foundation for deep analysis. And the intelligent analysis engine unit operates various professional traffic analysis models to realize real-time evaluation of traffic states and short-time traffic flow prediction, thereby providing a quantitative basis for macroscopic traffic decision making.

Description

Technical Field

[0001] This invention relates to the field of intelligent traffic management technology, specifically to an urban intelligent traffic information management system based on the Internet of Things. Background Technology

[0002] With the acceleration of urbanization and the continuous growth of motor vehicle ownership, urban transportation systems are facing multiple challenges such as increased congestion, frequent safety accidents, and low management efficiency.

[0003] Currently, typical intelligent traffic management systems rely on a limited number of sensors, such as video surveillance, geomagnetic coils, and checkpoints, for data collection. Their system architecture is mostly centralized "edge-cloud." Existing systems primarily collect data on vehicle flow, speed, and identification, lacking effective monitoring of real-time road infrastructure health, meteorological parameters, and pedestrian and non-motorized vehicle flow, trajectories, and behaviors. Data silos and blind spots prevent traffic management departments from obtaining a comprehensive, multi-faceted traffic situation profile, hindering refined and human-centered integrated traffic governance. Furthermore, the systems suffer from low data integration, with multi-source, heterogeneous data difficult to effectively correlate and fuse. Data analysis methods are relatively simple, typically limited to static report statistics and visualization, lacking the ability to deeply mine and model traffic flow patterns. Therefore, this paper proposes an Internet of Things (IoT)-based urban intelligent traffic information management system. Summary of the Invention

[0004] The purpose of this invention is to provide an Internet of Things-based smart urban transportation information management system to solve the problems mentioned in the background.

[0005] To achieve the above objectives, the present invention provides the following technical solution: an Internet of Things-based smart urban transportation information management system, comprising a data sensing and acquisition module, a network communication transmission module, an edge computing and data processing module, an analysis module, an application service module, and an operation and maintenance management module; The data sensing and acquisition module is used to collect multi-source traffic data in real time through various Internet of Things (IoT) sensing devices deployed on urban transportation infrastructure. The network communication transmission module is used to construct a heterogeneous and integrated communication network; The edge computing and data processing module is used to perform local preprocessing, filtering and preliminary analysis on the raw sensing data. The analysis module is used for in-depth data integration, modeling analysis, and decision support. The application service module is used to provide specific traffic management and service functions to different users; The operation and maintenance management module is used to ensure the stable operation of the entire system; The data sensing and acquisition module is connected to the network communication transmission module, the network communication transmission module is connected to the edge computing and data processing module, the edge computing and data processing module is connected to the analysis module, the analysis module is connected to the application service module, and the application service module is connected to the operation and maintenance management module.

[0006] Preferably, the data sensing and acquisition module includes a vehicle information sensing unit, an infrastructure status sensing unit, an environmental information sensing unit, and a pedestrian and non-motorized vehicle sensing unit. The vehicle information sensing unit is connected to the infrastructure status sensing unit, the infrastructure status sensing unit is connected to the environmental information sensing unit, and the environmental information sensing unit is connected to the pedestrian and non-motorized vehicle sensing unit. The vehicle information sensing unit is used to acquire vehicle identity, location, speed, and traffic flow information; The infrastructure status sensing unit is used to monitor the operational status and health of transportation infrastructure. The environmental information sensing unit is used to collect outdoor environmental parameters that affect traffic operation; The pedestrian and non-motorized vehicle sensing unit is used to detect pedestrian and non-motorized vehicle traffic flow and behavior at intersections and key areas.

[0007] Preferably, the vehicle flow rate (q) in the vehicle information sensing unit is calculated using the following formula:

[0008] Where N is the number of vehicles passing through the detection section within the time interval T.

[0009] Preferably, the network communication transmission module includes a wired transmission unit and a wireless transmission unit; The wired transmission unit is used to connect to fixed core nodes with large data volumes; The wireless transmission unit is used to support data backhaul from mobile devices and distributed sensor nodes.

[0010] Preferably, the edge computing and data processing module includes a data cleaning and standardization unit, an edge analysis unit, and a data compression and aggregation unit; The data cleaning and standardization unit is used to improve data quality and consistency; The edge analysis unit is used for low-latency local response and decision-making; The data compression and aggregation unit is used to reduce network transmission bandwidth usage.

[0011] Preferably, the analysis module includes a data storage and management unit, an intelligent analysis engine unit, and a data fusion and mining unit; The data storage and management unit is connected to the intelligent analysis engine unit, and the intelligent analysis engine unit is connected to the data fusion and mining unit; The data storage and management unit is used to store and manage all traffic data; The intelligent analysis engine unit is used to run traffic analysis models and provide traffic condition assessment, prediction and macro decision support; The data fusion and mining unit is used to discover deep patterns and knowledge.

[0012] Preferably, the intelligent analysis engine unit includes a short-term traffic flow prediction function, and the prediction model formula used is as follows:

[0013] Where φ(B) and θ(B) are polynomials of the lag operator B, representing the autoregressive and moving average components respectively, d is the difference order, and y t For time series, ε t It is white noise.

[0014] Preferably, the application service module includes a traffic control unit, a travel information service unit, and an emergency command unit; The traffic control unit is connected to the travel information service unit, and the travel information service unit is connected to the emergency command unit. The traffic control unit is used to implement proactive traffic management; The travel information service unit is used to serve public travel needs; The emergency command unit is used to provide emergency response.

[0015] Preferably, in the traffic control unit, the path impedance cost (R) function for dynamic traffic guidance is:

[0016] Among them, TT a (t) represents the dynamic travel time, C a For fixed costs, D a (t) represents the dynamic penalty, and ω represents the weight.

[0017] Preferably, the operation and maintenance management module includes an equipment asset management unit and a user permission and security unit; The equipment asset management unit is connected to the user permissions and security unit; The equipment asset management unit is used to record the complete lifecycle information of equipment from procurement to scrapping; The user permissions and security unit is used to assign functional permissions and data permissions based on roles.

[0018] Compared with the prior art, the present invention, employing the above technical solution, has the following technical effects: I. By leveraging IoT sensing devices deployed throughout the city through data sensing and acquisition modules, a city-wide traffic data sensing network is constructed. Each sensing unit operates in an orderly and coordinated manner, synchronously collecting multi-dimensional data, covering vehicle identity, location, speed, and traffic flow; infrastructure status; outdoor environmental parameters; and pedestrian and non-motorized vehicle traffic flow and behavior. This fills the gap in slow-moving traffic data monitoring and provides comprehensive data support for traffic management.

[0019] Second, by monitoring the operational status and health of traffic facilities in real time, and by estimating the length of congestion queues, the scale of congestion queues on road sections can be predicted in advance. The forward-looking data collection and analysis enable traffic management departments to formulate response strategies in advance, effectively avoid the aggravation of traffic congestion, and improve the initiative of traffic management.

[0020] Third, during data transmission, the edge computing and data processing modules preprocess the raw data at edge nodes close to the data acquisition end. Data cleaning and standardization units filter outliers, reducing the impact of data quality on subsequent analysis; the edge analysis unit performs lightweight analysis for localized emergencies, achieving low-latency response and decision-making; and the data compression and aggregation unit eliminates redundant information, reducing network bandwidth usage and significantly alleviating the computational and bandwidth pressure on the core platform, thereby improving data processing efficiency.

[0021] Fourth, the analysis module performs full integration, modeling, and in-depth analysis of the preprocessed data. The data storage and management unit establishes a hierarchical storage system, classifies and stores various types of traffic data, and provides efficient retrieval and access services, laying a solid data foundation for in-depth analysis. The intelligent analysis engine unit runs multiple professional traffic analysis models to achieve real-time traffic condition assessment and short-term traffic flow prediction, providing quantitative basis for macro-level traffic decision-making. Attached Figure Description

[0022] Figure 1 This is a schematic diagram of the system of the present invention; Figure 2 This is a schematic diagram of the data sensing and acquisition module of the present invention; Figure 3 This is a schematic diagram of the network communication transmission module of the present invention; Figure 4 This is a schematic diagram of the edge computing and data processing module of the present invention; Figure 5 This is a schematic diagram of the analysis module of the present invention; Figure 6 This is a schematic diagram of the application service module of the present invention; Figure 7This is a schematic diagram of the operation and maintenance management module of the present invention. Detailed Implementation

[0023] Example Please see Figure 1-7 The present invention provides a technical solution: an Internet of Things-based smart urban transportation information management system, comprising a data sensing and acquisition module, a network communication transmission module, an edge computing and data processing module, an analysis module, an application service module, and an operation and maintenance management module; The data sensing and acquisition module is used to collect multi-source traffic data in real time through various Internet of Things (IoT) sensing devices deployed on urban transportation infrastructure. The data sensing and acquisition module includes a vehicle information sensing unit, an infrastructure status sensing unit, an environmental information sensing unit, and a pedestrian and non-motorized vehicle sensing unit. The vehicle information sensing unit is connected to the infrastructure status sensing unit, the infrastructure status sensing unit is connected to the environmental information sensing unit, and the environmental information sensing unit is connected to the pedestrian and non-motorized vehicle sensing unit. The vehicle information sensing unit is used to acquire vehicle identity, location, speed, and traffic flow information; it captures real-time vehicle identity information, such as license plate, location, speed, and traffic flow, providing a basis for traffic flow analysis. Infrastructure status sensing units are used to monitor the operational status and health of traffic infrastructure; monitor the operational status and health of facilities such as traffic lights, signs, roads, bridges, and tunnels in real time; and, by combining vehicle speed, stopping time, number of vehicles in queue, and congestion density, can proactively assess the degree of congestion at intersections or road sections, providing a basis for signal optimization.

[0024] In the infrastructure status sensing unit, the formula for estimating the predicted congestion queue length (L) is:

[0025] Where L is the queue length (meters), v is the vehicle approach speed, t is the average stopping time, N is the number of vehicles in the queue, and k is the congestion density.

[0026] The environmental information sensing unit is used to collect outdoor environmental parameters that affect traffic operation; it collects environmental parameters such as weather (rain, snow, fog), light, and road conditions to assess the impact of the external environment on traffic flow safety and efficiency.

[0027] The pedestrian and non-motorized vehicle sensing unit is used to detect the flow and behavior of pedestrians and non-motorized vehicles at intersections and key areas. It detects the flow, trajectory and behavior of pedestrians, bicycles and electric bicycles in key areas, completes the profile of urban traffic participants, and ensures the safety of slow traffic and the allocation of right-of-way.

[0028] In the vehicle information sensing unit, the formula for calculating vehicle flow (q) is as follows:

[0029] Where N is the number of vehicles passing through the detection section within the time interval T.

[0030] Network communication transmission module, used to build heterogeneous converged communication networks; The network communication transmission module includes a wired transmission unit and a wireless transmission unit; Wired transmission unit, used to connect fixed core nodes with large data volumes; The wireless transmission unit is used to support data backhaul for mobile devices and distributed sensor nodes.

[0031] The collected raw data is transmitted back through a heterogeneous converged network constructed by the network communication transmission module. The wired transmission unit ensures stable and high-speed transmission of large amounts of data at fixed nodes such as traffic hubs and key intersections; the wireless transmission unit flexibly adapts to the communication needs of moving vehicles, distributed sensors and other nodes, ensuring that data can be transmitted "up, down, and up," providing a smooth information channel for subsequent processing.

[0032] The edge computing and data processing module is used to perform local preprocessing, filtering and preliminary analysis on the raw sensing data; the data first arrives at the edge computing and data processing module and is processed locally at the network edge close to the data source. The edge computing and data processing module includes a data cleaning and standardization unit, an edge analysis unit, and a data compression and aggregation unit; Data cleaning and standardization units are used to improve data quality and consistency; they automatically identify and filter outlier data points, and unify the format, precision, and timestamps of multi-source heterogeneous data, significantly improving data quality and consistency. In the data cleaning and standardization unit, outlier filtering is based on the 3σ principle or box plots; If data point x is marked as an anomaly, then

[0033] Where u is the mean of the data within the sliding time window, and σ is the size of the sliding window.

[0034] Edge analysis units are used for low-latency local response and decision-making; they perform latency-sensitive local analysis and decision-making, such as adaptive traffic light control based on real-time traffic flow at a single intersection and rapid accident detection, to achieve low-latency response.

[0035] The data compression and aggregation unit is used to reduce network transmission bandwidth consumption. It compresses and aggregates the cleaned data, significantly reducing the amount of data transmitted to the central cloud and the network bandwidth pressure, forming a highly efficient collaborative mode of "real-time edge processing and in-depth cloud analysis".

[0036] The analysis module is used for in-depth data integration, modeling analysis, and decision support; high-quality data after edge processing is aggregated into the analysis module for global and strategic-level in-depth analysis. The analysis module includes a data storage and management unit, an intelligent analysis engine unit, and a data fusion and mining unit; The data storage and management unit is connected to the intelligent analysis engine unit, and the intelligent analysis engine unit is connected to the data fusion and mining unit; The data storage and management unit is used to store and manage all traffic data; it utilizes a big data platform to classify, efficiently store, and manage massive amounts of historical and real-time traffic data, forming a traffic data resource pool.

[0037] The intelligent analysis engine unit is used to run traffic analysis models, providing traffic condition assessment, prediction, and macro-level decision support; it predicts short-term traffic flow and provides forward-looking decision support for macro-level traffic control.

[0038] The data fusion and mining unit is used to discover deep patterns and knowledge. Through machine learning and artificial intelligence algorithms, it performs in-depth fusion and correlation analysis on multi-source data to uncover deep knowledge and patterns such as travel patterns, causes of congestion, and accident risks, achieving a leap from "seeing" to "understanding".

[0039] The intelligent analysis engine unit includes a short-term traffic flow prediction function, and its prediction model formula is as follows:

[0040] Where φ(B) and θ(B) are polynomials of the lag operator B, representing the autoregressive and moving average components respectively, d is the difference order, and y t For time series, ε t It is white noise.

[0041] The application service module is used to provide specific traffic management and service functions to different users; The application service module includes a traffic control unit, a travel information service unit, and an emergency command unit; The traffic control unit is connected to the travel information service unit, and the travel information service unit is connected to the emergency command unit; Traffic control units are used to achieve proactive traffic management; by integrating real-time travel time, fixed costs, and dynamic penalties (such as congestion charges), they calculate and recommend optimal routes for drivers, and dynamically balance the load on the road network.

[0042] The travel information service unit is used to serve the public's travel needs; through public travel apps, variable message signs and other channels, it releases information such as real-time traffic conditions, predicted travel times, parking guidance, and event warnings to the public, thereby improving the travel experience.

[0043] The emergency command unit is used to provide emergency response. In special scenarios such as traffic accidents, severe weather, or large-scale events, it can quickly integrate data, locate events, assess impacts, generate response plans, and coordinate resources to achieve efficient cross-departmental collaborative command.

[0044] In the traffic control unit, the path impedance cost (R) function used for dynamic traffic guidance is:

[0045] Among them, TT a (t) represents the dynamic travel time, C a For fixed costs, D a (t) represents the dynamic penalty, and ω represents the weight.

[0046] The operation and maintenance management module is used to ensure the stable operation of the entire system; The operation and maintenance management module includes an equipment asset management unit and a user permissions and security unit; The device asset management unit is connected to the user permissions and security unit; it performs full lifecycle management of all IoT devices, servers, network devices, etc. in the system, including deployment, status monitoring, maintenance, and disposal, to ensure the stability and reliability of the physical layer.

[0047] The equipment asset management unit is used to record complete lifecycle information of equipment from procurement to disposal. The User Permissions and Security Unit is used to assign functional and data permissions based on roles, establish a strict role-based access control mechanism, allocate differentiated functional and data permissions to traffic managers, maintenance personnel, and public users, and implement network security protection to ensure system operation standards and data security.

[0048] The data sensing and acquisition module is connected to the network communication and transmission module, the network communication and transmission module is connected to the edge computing and data processing module, the edge computing and data processing module is connected to the analysis module, the analysis module is connected to the application service module, and the application service module is connected to the operation and maintenance management module.

[0049] Working principle: The data sensing and acquisition module constructs a full-area traffic data sensing network through various Internet of Things (IoT) sensing devices deployed on urban roads, intersections, parking lots and other traffic infrastructure. Each sensing unit is linked sequentially according to a predetermined link to achieve synchronous collection of multi-dimensional data. The vehicle information sensing unit collects information on the identity, real-time location, speed, and traffic flow per unit time of passing vehicles through license plate recognition, vehicle positioning terminals, radar, and other equipment. The infrastructure status sensing unit connected to the vehicle information sensing unit monitors the operation status and health of traffic facilities such as traffic lights, road guardrails, bridges, and tunnels in real time. At the same time, it uses congestion queue length estimation to predict the scale of congestion queues on road sections in advance, providing basic data for subsequent management and control. The infrastructure status sensing unit is linked with the environmental information sensing unit to collect outdoor environmental parameters that affect traffic operation, such as rainfall, snowfall, visibility, and road surface temperature and humidity. The environmental information sensing unit is further connected to the pedestrian and non-motorized vehicle sensing unit, which uses video surveillance, infrared sensors and other equipment to detect pedestrian and non-motorized vehicle traffic flow and behavior in key areas such as intersections, business districts, and schools, filling the gap in slow traffic data monitoring.

[0050] After completing multi-source data acquisition, the network communication transmission module receives the output from the data sensing and acquisition module. It achieves stable data transmission through a heterogeneous converged communication network of "wired + wireless". The raw data transmitted by the network first enters the edge computing and data processing module, where preprocessing is completed at the edge node close to the data acquisition end, reducing the computing and bandwidth pressure on the core platform. The data cleaning and standardization unit first optimizes the quality of the raw data, identifying and filtering outliers based on the 3σ principle or box plot. The standardized data is then sent to the edge analysis unit. This unit runs a lightweight analysis model for local emergencies such as intersection congestion and traffic light malfunctions, achieving low-latency local response and decision-making. Finally, the data compression and aggregation unit compresses the processed data, removes redundant information, and then transmits the aggregated core data upward to the analysis module, significantly reducing network transmission bandwidth consumption.

[0051] The preprocessed data enters the analysis module, where it integrates, models, and performs in-depth analysis of the entire dataset, providing decision support for upper-level applications. The data storage and management unit establishes a hierarchical storage system, classifying and storing all traffic data, including perceived data, processed data, and analysis results. It also provides efficient data retrieval and retrieval services, laying the data foundation for in-depth analysis. Connected to the storage unit, the intelligent analysis engine unit runs various professional traffic analysis models. On one hand, it performs real-time traffic status assessment; on the other hand, it uses the ARIMA model to predict short-term traffic flow, providing quantitative evidence for macro-level traffic decisions. The intelligent analysis engine unit, in conjunction with the data fusion and mining unit, integrates multi-dimensional data from vehicles, facilities, the environment, and pedestrians through multi-source data fusion algorithms. This allows it to mine deeper knowledge such as the spatiotemporal distribution patterns of traffic flow, causes of congestion, and travel characteristics, providing data insights for system function optimization.

[0052] The decision results and data insights from the analysis module are ultimately transformed into specific functions for different users through the application service module. Based on the prediction and evaluation results from the analysis module, the traffic control unit achieves proactive traffic management, calculates the optimal guidance path, and dynamically adjusts traffic light timing and variable lane directions to maximize road network traffic efficiency. The traffic control unit, in conjunction with the travel information service unit, releases information such as real-time traffic conditions, optimal travel routes, bus arrival times, and parking space availability to the public through channels such as mobile apps and roadside guidance screens, providing guidance for public travel and improving the travel experience. The travel information service unit connects with the emergency command unit, and in the event of emergency scenarios such as traffic accidents, severe weather, or public safety incidents, it quickly retrieves traffic data and monitoring footage from the affected area and automatically generates emergency evacuation plans.

[0053] In summary, by leveraging IoT sensors deployed throughout the city through the data sensing and acquisition module, a city-wide traffic data sensing network is constructed. The various sensing units work in an orderly and coordinated manner to synchronously collect multi-dimensional data, covering vehicle identity, location, speed, and traffic flow; infrastructure status; outdoor environmental parameters; and pedestrian and non-motorized vehicle traffic flow and behavior. This fills the gap in slow-moving traffic data monitoring and provides comprehensive data support for traffic management.

[0054] By monitoring the operational status and health of traffic facilities in real time, and estimating congestion queue lengths, the scale of congestion queues on road sections can be predicted in advance. This forward-looking data collection and analysis allows traffic management departments to develop response strategies in advance, effectively preventing the aggravation of traffic congestion and improving the initiative of traffic management.

[0055] During data transmission, the edge computing and data processing modules preprocess the raw data at edge nodes close to the data acquisition end. Data cleaning and standardization units filter outliers, reducing the impact of data quality on subsequent analysis. The edge analysis unit performs lightweight analysis for localized emergencies, enabling low-latency response and decision-making. The data compression and aggregation unit removes redundant information, reducing network bandwidth usage and significantly alleviating the computational and bandwidth pressure on the core platform, thus improving data processing efficiency.

[0056] The analysis module performs full integration, modeling, and in-depth analysis of the preprocessed data. The data storage and management unit establishes a hierarchical storage system, classifying and storing various types of traffic data, and providing efficient retrieval and access services, laying a solid data foundation for in-depth analysis. The intelligent analysis engine unit runs multiple professional traffic analysis models to achieve real-time traffic condition assessment and short-term traffic flow prediction, providing quantitative evidence for macro-level traffic decision-making.

[0057] Those skilled in the art will understand that the features described in the various embodiments and / or claims of the present invention can be combined or combined in various ways, even if such combinations or combinations are not explicitly described in the present invention. In particular, the features described in the various embodiments and / or claims of the present invention can be combined or combined in various ways without departing from the spirit and teachings of the present invention. All such combinations and / or combinations fall within the scope of the present invention.

Claims

1. A smart urban transportation information management system based on the Internet of Things, characterized in that, It includes a data sensing and acquisition module, a network communication and transmission module, an edge computing and data processing module, an analysis module, an application service module, and an operation and maintenance management module; The data sensing and acquisition module is used to collect multi-source traffic data in real time through various Internet of Things (IoT) sensing devices deployed on urban transportation infrastructure. The network communication transmission module is used to construct a heterogeneous and integrated communication network; The edge computing and data processing module is used to perform local preprocessing, filtering and preliminary analysis on the raw sensing data. The analysis module is used for in-depth data integration, modeling analysis, and decision support. The application service module is used to provide specific traffic management and service functions to different users; The operation and maintenance management module is used to ensure the stable operation of the entire system; The data sensing and acquisition module is connected to the network communication transmission module, the network communication transmission module is connected to the edge computing and data processing module, the edge computing and data processing module is connected to the analysis module, the analysis module is connected to the application service module, and the application service module is connected to the operation and maintenance management module.

2. The urban intelligent transportation information management system based on the Internet of Things according to claim 1, characterized in that, The data sensing and acquisition module includes a vehicle information sensing unit, an infrastructure status sensing unit, an environmental information sensing unit, and a pedestrian and non-motorized vehicle sensing unit. The vehicle information sensing unit is connected to the infrastructure status sensing unit, the infrastructure status sensing unit is connected to the environmental information sensing unit, and the environmental information sensing unit is connected to the pedestrian and non-motorized vehicle sensing unit. The vehicle information sensing unit is used to acquire vehicle identity, location, speed, and traffic flow information; The infrastructure status sensing unit is used to monitor the operational status and health of transportation infrastructure. The environmental information sensing unit is used to collect outdoor environmental parameters that affect traffic operation; The pedestrian and non-motorized vehicle sensing unit is used to detect pedestrian and non-motorized vehicle traffic flow and behavior at intersections and key areas.

3. The urban intelligent transportation information management system based on the Internet of Things according to claim 2, characterized in that, The vehicle flow rate (q) in the vehicle information sensing unit is calculated using the following formula: ； Where N is the number of vehicles passing through the detection section within the time interval T.

4. The urban intelligent transportation information management system based on the Internet of Things according to claim 1, characterized in that, The network communication transmission module includes a wired transmission unit and a wireless transmission unit; The wired transmission unit is used to connect to fixed core nodes with large data volumes; The wireless transmission unit is used to support data backhaul from mobile devices and distributed sensor nodes.

5. The urban intelligent transportation information management system based on the Internet of Things according to claim 1, characterized in that, The edge computing and data processing module includes a data cleaning and standardization unit, an edge analysis unit, and a data compression and aggregation unit. The data cleaning and standardization unit is used to improve data quality and consistency; The edge analysis unit is used for low-latency local response and decision-making; The data compression and aggregation unit is used to reduce network transmission bandwidth usage.

6. The urban intelligent transportation information management system based on the Internet of Things according to claim 1, characterized in that, The analysis module includes a data storage and management unit, an intelligent analysis engine unit, and a data fusion and mining unit; The data storage and management unit is connected to the intelligent analysis engine unit, and the intelligent analysis engine unit is connected to the data fusion and mining unit; The data storage and management unit is used to store and manage all traffic data; The intelligent analysis engine unit is used to run traffic analysis models and provide traffic condition assessment, prediction and macro decision support; The data fusion and mining unit is used to discover deep patterns and knowledge.

7. The urban intelligent transportation information management system based on the Internet of Things according to claim 6, characterized in that, The intelligent analysis engine unit includes a short-term traffic flow prediction function, and its prediction model formula is as follows: ； Where φ(B) and θ(B) are polynomials of the lag operator B, representing the autoregressive and moving average components respectively, d is the difference order, and y t For time series, ε t It is white noise.

8. The urban intelligent transportation information management system based on the Internet of Things according to claim 1, characterized in that, The application service module includes a traffic control unit, a travel information service unit, and an emergency command unit. The traffic control unit is connected to the travel information service unit, and the travel information service unit is connected to the emergency command unit. The traffic control unit is used to implement proactive traffic management; The travel information service unit is used to serve public travel needs; The emergency command unit is used to provide emergency response.

9. A smart urban transportation information management system based on the Internet of Things according to claim 8, characterized in that, In the traffic control unit, the path impedance cost (R) function used for dynamic traffic guidance is: ； Among them, TT a (t) represents the dynamic travel time, C a For fixed costs, D a (t) represents the dynamic penalty, and ω represents the weight.

10. A smart urban transportation information management system based on the Internet of Things according to claim 1, characterized in that, The operation and maintenance management module includes an equipment asset management unit and a user permission and security unit; The equipment asset management unit is connected to the user permissions and security unit; The equipment asset management unit is used to record the complete lifecycle information of equipment from procurement to scrapping; The user permissions and security unit is used to assign functional permissions and data permissions based on roles.

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