I. Technical Analysis of Cloud Computing and Edge Computing

Basic Principles of Cloud Computing

Cloud computing is an Internet – based computing model that provides computing resources (such as servers, storage, software, etc.) to users in the form of services through the network. Its basic principle is to distribute numerous computing tasks across multiple distributed computers for collaborative processing, rather than relying on a local computer or a single remote server. It’s like the shift from individual generators for power supply to centralized power generation in a power plant. Users can access the Internet to obtain the required computing power and services as conveniently as using water and electricity. Computing power can be obtained on – demand, similar to a commodity, and the cost is relatively low.

Application Advantages in Intelligent Transportation

Powerful Computing Capability: Intelligent transportation systems involve the processing of massive amounts of data, such as traffic flow monitoring data and vehicle driving trajectory data. The super – strong computing power of cloud computing can quickly process these data, enabling real – time traffic flow analysis and prediction, and providing strong support for traffic management decision – making. For example, by analyzing the traffic flow data of various intersections in a city in real – time, the cloud computing platform can promptly identify congested road sections and predict the development trend of congestion, thus providing decision – making suggestions such as optimizing traffic signal timing and implementing traffic control for traffic management departments.

Elastic Resource Allocation: The business load of intelligent transportation systems has obvious fluctuations. For example, the traffic data volume surges during peak hours in the morning and evening. The elastic resource allocation feature of cloud computing can automatically adjust the allocation of computing resources according to actual business needs. During peak hours, it automatically increases the number of virtual machines or upgrades the computing resource configuration to meet the data processing requirements; during off – peak hours, it reduces resource allocation to cut costs and improve resource utilization.

Data Storage and Sharing: Cloud computing provides large – capacity distributed storage, which can store the massive historical data generated by intelligent transportation systems. At the same time, through the cloud platform, different departments and institutions can easily share data, breaking down information silos and realizing the interconnection and interoperability of traffic data. For example, traffic management departments, bus companies, taxi companies, etc. can share vehicle operation data on the cloud computing platform to collaboratively optimize traffic operation management.

Cost Reduction: For the builders and operators of intelligent transportation systems, using cloud computing services eliminates the need for large – scale investment in building and maintaining local data centers, servers, and other hardware facilities. They only need to rent cloud resources on – demand, reducing initial construction costs and subsequent operation and maintenance costs. In addition, the resource – sharing feature of cloud computing also makes cost sharing more reasonable and improves the economic efficiency of resources.

II. Edge Computing

Edge computing is a computing model that performs data processing and analysis on the network edge side close to data sources or users. Its core principle is to offload some of the tasks originally completed by central cloud computing to edge nodes, reducing the data transmission distance and latency in the network and improving the real – time performance and efficiency of data processing. For example, in intelligent transportation, roadside cameras and vehicle sensors generate a large amount of data in real – time. If all this data is transmitted to the cloud for processing, it will not only consume a large amount of network bandwidth but also increase processing latency due to factors such as transmission distance and network congestion. Edge computing can perform preliminary processing on the data at the edge devices of cameras or vehicles, such as real – time identification of vehicles, pedestrians, and traffic signs, and only transmit the key processing results or abnormal data to the cloud, greatly reducing network transmission pressure and latency.

Edge computing realizes local data processing and analysis by deploying small – scale data centers or intelligent devices at the network edge. These edge nodes can make real – time decisions and responses to the collected data according to pre – set rules and algorithms. For example, in the scenario of autonomous driving, the edge computing devices on vehicles can analyze sensor data in real – time and quickly judge the surrounding traffic conditions, such as whether to perform emergency braking or evasive maneuvers, to ensure driving safety. At the same time, edge nodes can also work in collaboration with the cloud, uploading the locally processed results to the cloud for further analysis and storage, realizing global data sharing and in – depth mining.

Application Advantages of Edge Computing in Intelligent Transportation

Low Latency: For some application scenarios in intelligent transportation with extremely high real – time requirements, such as autonomous driving and vehicle collision warning, the low – latency feature of edge computing is of crucial importance. Since the data is processed locally without the need to be transmitted over a long distance to the cloud, the data processing and response time is greatly shortened. For example, in autonomous driving, when the vehicle sensor detects an obstacle suddenly appearing ahead, the edge computing device can make a braking or evasive decision within milliseconds to avoid an accident.

High Reliability: Edge computing realizes local data processing and storage. Even if the network fails or is interrupted, the edge nodes can still continue to work, ensuring the normal operation of some key functions of the intelligent transportation system. For example, in a traffic monitoring system, when the network fails, the edge node can continue to store the video data collected by the local camera and upload the data to the cloud after the network is restored, ensuring the integrity and continuity of the data.

Reduced Network Bandwidth Pressure: If all the large amount of data generated in the intelligent transportation system is transmitted to the cloud, it will consume a large amount of network bandwidth and cause network congestion. Edge computing processes data locally and only uploads the key processing results, greatly reducing the data transmission volume and relieving the pressure on network bandwidth. For example, for the large amount of video data collected by traffic cameras, real – time analysis is carried out at the edge nodes, and only the information related to detected traffic incidents (such as traffic accidents and violations) is uploaded to the cloud, effectively saving network bandwidth resources.

Data Privacy Protection: In intelligent transportation, a large amount of personal privacy data is involved, such as vehicle driving trajectories and driver identity information. Edge computing processes data locally, reducing data transmission in the network and lowering the risk of data leakage, thus better protecting users’ data privacy. For example, the edge computing devices on vehicles encrypt the locally collected data before analysis, and only authorized data is transmitted to the cloud, ensuring data security.

III. Collaborative Principles and Architecture of Cloud Computing and Edge Computing in Intelligent Transportation

The collaboration between cloud computing and edge computing in intelligent transportation focuses on realizing hierarchical data processing and rational task allocation to give full play to their respective advantages and improve the overall performance of the intelligent transportation system.

Hierarchical Data Processing

In an intelligent transportation system, devices such as sensors and cameras generate massive amounts of data. These data are processed hierarchically according to their real – time requirements, importance, and processing needs. For data with extremely high real – time requirements that need immediate response, such as emergency braking signals during vehicle driving and data on suddenly detected obstacles ahead, they are quickly processed locally by edge computing nodes. The edge computing nodes are close to the data sources and can respond to this data within milliseconds, realizing immediate vehicle control and ensuring driving safety. For some historical data, statistical analysis data, and data that need to be deeply mined, such as long – term traffic flow data and comprehensive analysis data of traffic accidents, they are transmitted to the cloud computing center for processing. The cloud computing center has powerful storage and computing capabilities and can store, analyze, and model these large – scale data to provide decision – making support for traffic planning and policy – making.

Rational Task Allocation

Tasks are rationally allocated to cloud computing and edge computing according to their characteristics and resource requirements. Edge computing is mainly responsible for processing local and real – time – sensitive tasks, such as real – time control of traffic lights, real – time monitoring and identification of vehicles, and optimization of traffic flow in local areas. Take traffic light control as an example. Edge computing devices can collect the traffic flow data at intersections in real – time and dynamically adjust the signal light duration according to preset algorithms and rules to optimize the traffic flow. Cloud computing, on the other hand, undertakes complex and global tasks, such as overall urban traffic planning, traffic situation prediction, and cross – regional traffic data fusion analysis. By comprehensively analyzing the traffic data of multiple urban areas, the cloud computing platform can predict the development trend of traffic congestion, formulate traffic guidance plans in advance, and feedback relevant information to edge computing nodes and traffic management departments to achieve collaborative traffic control.

IV. Collaborative Application Scenarios

In intelligent traffic collaborative control, cloud computing and edge computing play an indispensable role. They work together to achieve efficient traffic management.

Cloud Computing: Global Control and In – Depth Analysis

The cloud computing platform is like the “super brain” of intelligent transportation. It is responsible for collecting traffic data from every corner of the city, including data collected by road sensors, vehicle sensors, traffic lights, and other intelligent transportation devices. After these data converge in the cloud computing center, with the help of powerful computing capabilities and big data analysis technology, it conducts comprehensive and real – time monitoring and in – depth analysis of the city’s traffic conditions. For example, by analyzing the traffic flow data of different road sections over a period of time, the cloud computing platform can draw a detailed traffic flow map, clearly showing the busy areas and time periods of urban traffic, predicting the development trend of traffic congestion, and providing a strong basis for traffic management departments to formulate scientific and reasonable traffic control strategies.

Edge Computing: Local Response and Fast Processing

Edge computing devices are distributed at various nodes of the traffic network, such as roadside intelligent cameras and traffic light control boxes. They are closer to the data sources and can quickly process and analyze traffic data locally. When an intelligent camera detects a traffic accident on a certain road section, the edge computing device can immediately analyze the image of the accident scene, identify key information such as the severity of the accident and the number of vehicles involved, and quickly upload this information to the cloud computing platform. At the same time, the edge computing device can also adjust the traffic lights locally according to pre – set rules to guide vehicles to detour and avoid serious traffic congestion around the accident scene.

Collaborative Work: Efficient Linkage and Precise Control

Cloud computing and edge computing achieve efficient linkage in traffic control through close collaboration. The cloud computing platform sends the comprehensively analyzed traffic event information and optimized traffic control strategies to the edge computing devices. The edge computing devices precisely control the local traffic facilities according to the received instructions. During peak traffic hours, the cloud computing platform predicts that several intersections may become congested based on real – time traffic flow data. It then sends the optimized signal light timing scheme to the edge computing devices at these intersections. The edge computing devices immediately execute the scheme, extending the green light time and shortening the red light time, allowing vehicles to pass through the intersections quickly and alleviating traffic congestion. This collaborative working mode of cloud computing and edge computing greatly improves the efficiency and precision of traffic control and ensures the smooth operation of urban traffic.