An Intelligent Traffic Control System using Edge-Cloud Computing and Deep Learning for Smart Cities

An Intelligent Traffic Control System using Edge- Cloud Computing and Deep Learning for Smart Cities

(1)Rafid Mohammed Khaleefah (2) Mohammed Dheyaa Algubili

Information and Communication Technology Centre. Information and Communication Technology Centre.

University of Basrah. University of Basrah. Basrah, Iraq. Basrah, Iraq.

Abstract– The rapid growth of urban populations has cities. Traditional traffic control systems, which rely on smart To address these limitations, the concept of Intelligent advancements in the Internet of Things (IoT), Artificial

conditions, resulting in increased congestion, higher fuel intelligent traffic control system that leverages the Internet of optimise traffic flow in real-time. The suggested system utilises computing units to quickly detect and classify vehicles using traffic analysis and making decisions on the fly. By generating AI-based model for adaptive signal control, the system times and reduce congestion. A case study based on the traffic urban traffic, is presented to frame the system’s design and demonstrate that the proposed system can significantly reduce compared to traditional fixed-time controllers, showcasing a contributing to sustainable urban Development.

This Urbanisation is a defining global trend, with an metropolitan areas [1]. This influx places immense strain on Vehicular congestion has become a chronic issue in major environmental pollution, and a decline in the quality of life therefore a paramount challenge in the Development of

Traditional traffic management systems typically employ to implement, these systems are inherently inefficient as they traffic volume [4]. This rigidity often results in scenarios

aims to make transportation safer, more efficient, and more approaches, including machine learning and deep learning, to utilise data from various sensors, including inductive loops,

Despite progress, many existing systems face challenges complexity of processing vast amounts of data in real time effective architecture that can integrate state-of-the-art citywide traffic optimisation. Such a system would improve sustainability and enhanced citizen well-being.

intelligent traffic control system based on an edge-cloud

• We propose a multi-layer system architecture that central cloud platform, ensuring real-time performance and bandwidth limitations often encountered with centralised raw solution.

model at the edge for highly accurate and real-time vehicle urban traffic scenarios.

module that generates real-time traffic density heatmaps and decisions for traffic signal control, aiming to minimise mechanism of this AI model is elaborated in Section III.

study based on Bucharest’s traffic patterns and supported by

practical applicability.

reviews related work in the field. Section III details the presents the results of our simulated evaluation and discusses

The field of intelligent traffic management is vast, with protocols, and control algorithms. This section reviews key

deployed AI-based systems to optimise traffic signals using

existing approaches, and highlights the motivations for our

ability to deploy interconnected sensors across a city has

I(2022) J Detection J Deployment J Jetson Nano

1. PROPOSED SYSTEM AND METHODOLOGY

   To address the challenges of dynamic traffic management and build upon the strengths while mitigating the weaknesses of existing approaches, we propose a multi-layer intelligent traffic control system. This section details the system architecture and the methodology for each of its key components.

   1. System Architecture

      The proposed system is based on a hierarchical edge-cloud architecture, as illustrated in Fig. 1. It consists of four main layers, designed for optimal performance, scalability, and responsiveness:

      • Perception Layer: This foundational layer comprises standard IP-based CCTV cameras strategically installed at intersections. These cameras are responsible for capturing continuous, high-resolution video streams of traffic flow, serving as the primary source of raw data for the entire system.
      • Edge Computing Layer: Located directly at each intersection, this layer is equipped with a low­ power edge computing device. Its core function is to execute a deep learning model to process the raw video feed from the associated camera in real-time. This localised processing performs immediate vehicle detection, classification, and extraction of relevant metadata (e.g., bounding box coordinates, vehicle types, and speed estimates). By processing video at the source, this layer drastically reduces the data volume requiring transmission to the cloud, thereby addressing critical bandwidth limitations and minimising processing latency. Edge devices, although efficient in processing raw video streams locally, face inherent limitations in terms of processing power and storage capacity. Our system mitigates these challenges by optim1smg the YOLOv5 model for edge deployment, which reduces the computational load and memory requirements. However, the limited storage on edge devices requires careful management of temporary data, ensuring only essential metadata is transmitted to the cloud. Future work will focus on improving the hardware capabilities of edge devices to handle more complex models and larger datasets.
      • Cloud Computing Layer: This centralised platform serves as an intelligent core, receiving only structured, lightweight metadata from all edge devices. Its responsibilities include comprehensive data aggregation, long-term storage for historical

        analysis, and executing the core traffic analysis and advanced AI-powered decision-making logic. This includes identifying citywide congestion patterns, detecting incidents, and optimising traffic flow on a global scale.

      • Control Layer: This final layer comprises the standard traffic light controllers present at each intersection. They act as actuators, receiving dynamic commands from the cloud platform to adjust signal timings (e.g., extending green light phases, shortening red light durations, and modifying phase sequences) in real-time based on the cloud’s optimised decisions.

        The distributed architecture handles computationally intensive tasks (raw video processing) locally at the edge, ensuring low latency for immediate perception. Concurrently, strategic, data-intensive analysis and global optimisation are performed centrally in the cloud, leveraging its vast computational resources. This design inherently enhances scalability for citywide deployment and significantly improves data privacy by processing sensitive raw video data locally and transmitting only anonymised, aggregated metadata .

        Fig. 1: System Architecture. It illustrates the hierarchical architecture of the intelligent traffic control system, showing the data flow from the perception layer to the control layer. Each layer is defined, with data processed at the edge and higher-level analysis performed in the cloud.

   2. Vehicle Detection and Classification at the Edge

      For highly efficient and real-time vehicle detection and classification at the edge, we employ the YOLOv5 (You Only Look Once, version 5) model. YOLO is a cutting-edge,

      single-stage object detection algorithm renowned for its superior balance of speed and accuracy, making it exceptionally well-suited for deployment on resource­ constrained edge devices [2]. The YOLOv5 model was pre­ trained on a large-scale dataset, such as COCO, and then further fine-tuned on a custom traffic dataset comprising over 50,000 annotated images across various urban traffic scenarios. To address the issue of class imbalance, we applied data augmentation techniques, such as random scaling, cropping, and flipping, to ensure robust detection of all vehicle types. The method ensures it acquires generalised object recognition capabilities and achieves accurate recognition of specific vehicle classes relevant to urban traffic, such as ‘car’, ‘bus’, ‘truck’, and ‘motorcycle’. As illustrated in Fig. 2, the YOLOv5 model processes each frame of the video stream in near real time, outputting a list of detected vehicles. Each detection includes a precise bounding box outlining the vehicle, a classification label (e.g., ‘car’, ‘bus’), and a confidence score. This extracted structured data, which is orders of magnitude smaller than the raw video stream, is then securely transmitted to the cloud platform via a lightweight messaging protocol, such as MQTT (Message Queuing Telemetry Transport), chosen for its efficiency and suitability for IoT environments with limited bandwidth and intermittent connectivity.

      Fig. 2: YOLOv5 Detection Example. It demonstrates the real-time detection and classification of vehicles using the YOLOv5 model, where bounding boxes and confidence scores are utilised to categorise vehicles into classes such as ‘car’, ‘bus’, and ‘truck’. These visual aids are essential for understanding the system’s real-time functioning.

   3. Cloud-Based Traffic Analysis and Dynamic Signal Control

      The cloud platform serves as the central intelligence hub, aggregating structured metadata (vehicle counts, locations, types, and speeds) received from all edge devices across the urban network. This aggregation creates a comprehensive, real-time global view of the entire traffic network. The core of the cloud layer is the advanced AI-powered decision engine, which operates in two interdependent stages:

      1. Traffic Density Analysis: The system continuously processes the incoming vehicle count and location data from all intersections. This data is then used to dynamically generate and update traffic density heatmaps for each

         intersection and potentially for broader urban segments, as conceptually shown in Fig. 3. These heatmaps provide an intuitive and immediate visualisation of traffic loads and congestion levels in different lanes and approaches. By providing a real-time spatial representation of traffic volume, this analysis module enables the system to identify congestion hotspots, queue lengths, and underutilised lanes instantly, which is crucial for informed and adaptive signal control decisions.

      2. Dynamic Signal Control Model: This This module constitutes the core intelligence for adaptive traffic signal optimisation. The Al-based decision-making system utilises an adaptive Deep Reinforcement Leaming (DRL) model, which was trained through extensive simulations designed to replicate real-world urban traffic conditions. This model continually learns and refines its strategy for optimising traffic signals through interactions with dynamic traffic data, aiming to minimise overall network delay and maximise throughput. The DRL agent is trained using traffic flow data, where the agent explores various traffic signal strategies to learn the most effective policies through trial and error. Performance metrics, including average waiting time and throughput, were used to evaluate the model’s success in real-world conditions. This agent is specifically designed to adjust the signal phases in response to real-time traffic conditions, ensuring efficient resource allocation, particularly during peak hours.

      In contrast to traditional fixed-time signal systems, the DRL model adapts to changing traffic patterns, dynamically adjusting green and red light durations based on observed congestion levels. This methodology allows the system to provide optimal signal timings across multiple intersections, improving traffic flow and reducing congestion. The model’s primary objective is to dynamically adjust traffic signal phases (e.g., extending green light duration for congested approaches, shortening red light duration for less busy ones, or re-sequencing phases) to minimise average vehicle waiting times and maximise overall vehicle throughput across the network.

      Fig. 3: Traffic Density Heatmap Visualization. We visualise a heatmap of traffic density, where different colour intensities signify the degree of congestion in various lanes or road segments.

   4. Case Study Context: Bucharest

      The conceptualisation and design of our intelligent traffic control system are framed by the persistent traffic challenges observed in major metropolitan areas, specifically using Bucharest, Romania, as a representative case study. With a population exceeding 2 million and a high density of registered vehicles (approaching 1 million in the metropolitan area), Bucharest frequently experiences severe traffic congestion, particularly during morning and evening peak hours [5], leading to significant delays and environmental impact. For instance, studies indicate that Bucharest residents spend an average of 100+ hours annually stuck in traffic, ranking among the highest globally. The city’s existing traffic infrastructure, although incorporating some modem elements, such as inductive loops, largely relies on fixed-time signal control mechanisms that struggle to manage highly dynamic and often unpredictable traffic flows efficiently. Our proposed system is specifically designed to be highly adaptable and potentially retrofittable into such an urban environment, leveraging existing camera infrastructure where possible to provide a more intelligent and responsive layer of control. This real-world context underscores the practical applicability, scalability, and substantial potential impact of our solution in enhancing urban mobility and quality oflife.

   5. Privacy Considerations and GDPR Compliance

      Privacy is a critical concern in the deployment of intelligent transportation systems (ITS), particularly in urban environments where real-time video surveillance and the transmission of metadata are involved. Our proposed architecture addresses this concern through the local edge processing of raw video data, where vehicle detection and classification occur directly at the intersection. Only anonymised metadata- such as vehicle counts, types, and estimated speeds- is transmitted to the cloud, minimising exposure to sensitive content. To further align with privacy regulations, such as the General Data Protection Regulation (GDPR), the system can incorporate privacy-by-design principles. Techniques such as face/license plate blurring, data anonymisation, and end-to-end encryption are integrated to ensure that no personally identifiable information (PII) is collected or transmitted. Moreover, adopting Privacy Impact Assessments (PIAs) and engineering frameworks, such as LINDDUN, allows for a structured evaluation of privacy risks throughout the system’s lifecycle. Future enhancements may also explore federated learning, allowing edge devices to collaboratively train models without centralising data, and blockchain integration for transparent, tamper-proof data management.

   6. DRL Agent Design

      The cloud-based decision engine employs a DeepQ­ Network (DQN) reinforcement learning model to optimise traffic signal timings dynamically. The neural network consists of two hidden layers, each with 36 and 24 neurons, respectively, utilising ReLU activations. The output layer maps to the discrete action space representing various traffic signal phases. An £-greedy exploration policy was adopted with initial £=1.0 and decayed to 0.05. The replay buffer size was set to 100,000 experiences, and the model was trained with a batch size of 64. The learning rate was

      initialised at a=0.001 and optimised using Adam. To stabilise learning, the target network was updated every 1000 steps. Training was conducted using the SUMO-RL framework integrated with OpenAI Gym to simulate traffic environments.

      This agent was trained offline using traffic patterns derived from Bucharest to ensure convergence before deployment. Once deployed, it operates in real-time by adjusting signal durations based on continuously updated vehicle density metadata received from the edge layer.

2. RESULTS AND DISCUSSION

   To rigorously evaluate the performance of the proposed intelligent traffic control system, we conducted a comprehensive simulation-based study. We modelled a typical four-way intersection, a standard configuration in urban areas, using traffic data patterns representative of Bucharest to ensure realistic conditions. The performance of our Al-based system was then directly compared against a traditional fixed-time controller, which serves as the established baseline in many cities globally.

   1. Simulation Setup

      The simulation was meticulously conducted using SUMO (Simulation of Urban Mobility), a widely recognised open-source microscopic traffic simulator that allows detailed modelling of individual vehicle movements and traffic light interactions. Auxiliary Python libraries, such as Traci, were extensively utilised to enable programmatic interactions with the SUMO environment, facilitating real­ time data exchange and dynamic control adjustments. A detailed four-way intersection model was constructed, featuring four lanes in each direction to represent the typical complexity of urban intersections accurately. While a visual representation is not included, the setup ensured a realistic flow. Traffic demand profiles were generated based on empirical traffic data patterns observed in Bucharest, incorporating a realistic mix of various vehicle types, including cars, buses, trucks, and motorcycles. We tested the system during both busy and quiet periods to assess its performance under various traffic conditions. Each simulation run spanned 60 minutes of simulated time, sufficient to capture both transient initial conditions and steady-state traffic behaviour. The proposed Al-based innovative traffic signal system dynamically adjusts green times and phase sequencing based on real-time traffic density information, which is effectively collected from simulated edge devices (representing vehicle counts, waiting times, and traffic flow rates). This dynamic approach was then benchmarked against a static, traditional fixed-time controller, whose timings remained constant throughout the simulation.

   2. Development Environment and Frameworks

      The proposed intelligent traffic control system was developed using Python 3.8 and integrated with the SUMO traffic simulator (version 1.16.0) via the TraCI API for real­ time communication. Object detection tasks were implemented using YOLOv5 (Ultralytics repository), built on PyTorch 1.12, and fine-tuned using a custom traffic dataset. Preprocessing video frames, including resizing and colour space conversion, was handled using the OpenCV library. The reinforcement learning agent was trained using

      the PyTorch deep learning framework with GPU acceleration. Simulations and training were conducted on a workstation equipped with an NVIDIA RTX 3090 Ti GPU and 64 GB of RAM, ensuring real-time processing capabilities and efficient model optimisation.

   3. Performance Metrics

      We rigorously evaluated the systems based on two universally recognised and critical categories of urban traffic management:

      • Average Vehicle Waiting Time: Defined as the average duration a vehicle spends completely stopped at the intersection before proceeding. This metric directly quantifies the efficiency of traffic flow and significantly reflects driver frustration, fuel consumption, and economic losses due to delays. Minimising this metric is a primary objective of intelligent traffic control.
      • Vehicle Throughput: Represents the total number of vehicles that successfully pass through the intersection per hour. This metric is a direct measure of the intersection’s capacity utilisation and its overall efficiency in moving traffic. Maximising throughput is crucial for mitigating congestion and enhancing urban mobility.

   4. Comparative Analysis

      The simulation was conducted for two distinct traffic scenarios: peak hours, characterised by high and often imbalanced traffic volumes, and off-peak hours, characterised by lower and more uniform traffic volumes. The results are summarised below, and they are visually represented in Fig. 4 and Fig. 5 .

      From Fig. 4, illustrating the average vehicle waiting time, it is evident that the proposed Al-based system significantly outperforms the traditional fixed-time controller. During peak hours, the traditional controller resulted in an average waiting time of 120 seconds. In stark contrast, the proposed system reduced this duration to approximately 75 seconds, representing a 37.5% reduction. During off-peak hours, the traditional controller recorded an average waiting time of 45 seconds, while our Al-based system achieved a waiting time of just 30 seconds, representing a 33.3% improvement. These reductions highlight the system’s ability to adapt to dynamic traffic loads and manage queues efficiently.

      Fig. 4: Comparison of Average Vehicle Waiting Time between the proposed system and a traditional controller across different traffic scenarios.

      Correspondingly, Correspondingly, Fig. 5 depicts the vehicle throughput. The proposed Al-based system consistently demonstrated higher throughput. During peak hours, the traditional controller managed a throughput of approximately 1800 vehicles per hour. Our system, however, achieved a remarkable 2500 vehicles per hour, marking an increase of approximately 38.9%. For off-peak hours, the traditional controller’s throughput was 800 vehicles per hour, whereas the proposed system improved the throughput to 1000 vehicles per hour, a 25% increase. These results highlight the efficiency gains, indicating that more vehicles can pass through the intersection within the same timeframe, directly alleviating congestion.

      Fig. 5: Comparison of Vehicle Throughput between the proposed system and a traditional controller across different traffic scenarios.

   5. Discussion

      The simulation results strongly suggest that an adaptive, AI­ driven approach to traffic signal control, implemented through an edge-cloud architecture, is significantly superior to static, pre-programmed systems. The key advantage of our proposed system is its ability to perceive and respond dynamically to real-time traffic conditions. The YOLOv5 model, used at the edge, gave very accurate and quick counts and types of vehicles, which were essential for analysing traffic density in real-time. The Deep Reinforcement Leaming (DRL) agent in the cloud-based

      decision model can then effectively utilise this detailed, current data to develop innovative traffic signal control strategies based on what it has learned. This adaptive capability allows for efficient resource allocation (green light time) in the most congested lanes, thereby minimising delays and maximising overall traffic flow.

      While the current simulation results were conducted under standard traffic conditions, future work will focus on simulating real-world factors such as adverse weather conditions (e.g., heavy rain, snow) and traffic incidents (e.g., accidents). These factors can significantly impact traffic flow and the system’s ability to optimise signal timings. For example, during heavy rainfall or fog, signal control may need to be more conservative to ensure safety. Future simulations will model these scenarios to evaluate the system’s robustness under such conditions.

      To evaluate the performance improvements achieved by the new system, we employed a two-sample t-test to compare the average waiting times and throughput of the Al-based system with those of the traditional fixed-time controller. The results show statistically significant improvements in both average waiting time (p<0.05) and throughput (p<0.01), indicating that the proposed system is more effective in managing traffic flow under diverse conditions. One of the biggest challenges we faced while developing and testing the system was managing the diverse types of data from various sensors and clearly defining the reward function and state-action space for the DRL model to ensure it learns effectively. Another significant challenge in real­ world deployment is ensuring robust data privacy and security, particularly as the system handles sensitive video data [10]. Our proposed edge-cloud architecture inherently helps mitigate this concern by processing raw video locally on edge devices and only transmitting anonymous, aggregated metadata to the central cloud. This design choice reduces the risk associated with centralising raw video feeds. Additionally, obtaining the public’s approval and gradually integrating these innovative systems into current city setups are important social and technical factors to consider [11]. Future work will include conducting real-

      1. Emergency Scenarios and System Responsiveness

      In In addition to optimising routine traffic conditions, a critical capability of intelligent traffic control systems is their responsiveness to emergency events, such as accidents, road blockages, or security alerts. We designed the proposed system with this flexibility in mind.

      When an emergency is detected-either through video analysis at the edge (e.g., a vehicle stopped abnormally or an accident identified via deep learning classifiers) or through external alerts (e.g., police/fire dispatch)- the system

      initiates a priority override protocol. This allows the cloud­ based decision engine to dynamically reassign signal phases, granting extended green time to lanes used by emergency responders or redirecting traffic flow from congested or hazardous zones.

      world pilot deployments to address these practical challenges and gather empirical data from live traffic.

   6. Statistical Significance of Results

   To confirm that the new intelligent traffic control system works better than the old fixed-time controller, we performed a statistical analysis using a two-sample paired t­ test. The test was applied to key performance metrics, including average vehicle waiting time and vehicle throughput, across identical traffic conditions.

   We used simulation data from multiple independent runs to model both peak and off-peak hours. For average waiting time, the traditional system recorded 120 seconds during peak hours, while our Al-based system achieved a reduced average of75 seconds. Our system was able to handle 2500 vehicles per hour, while the conventional controller was only able to handle 1800 vehicles per hour.

   The results of the t-tests are summarised in Table II, The p­ values obtained were <0.001 in both metrics, indicating statistically significant differences with 95% confidence.

   These results confirm that the improvements in traffic flow and efficiency observed are not due to random variations but reflect the effectiveness of the proposed system.

   Table II: Statistical Significance of Performance Improvements.

   Metric	T rad1.t.10na l System	propose d System	T- va1u e	p– value	Signiflcanc e
   Avg. Waiting Time (sec)	120	75	8.21	<0.00 1	Significant
   Vehicle Throughpu t (/hr)	1800	2500	7 .92	<0.00 1	Significant

   evacuation routes, ensure minimal disruption at surrounding intersections, and maintain operational stability even under atypical conditions.

   Future work will include integration with V2X (vehicle-to­ everything) systems, enabling emergency vehicles to communicate directly with intersection controllers and further improving response times and coordination during crises.

   The underlying Deep Reinforcement Leaming (DRL) model is trained with simulated emergency events to identify and respond effectively. This enables the system to prioritise

   Fig. 6: Comparison of T-test results between the traditional system and the smart system.

   Fig. 7: Emergency response flowchart for intelligent traffic control system.

3. CONCLUSION AND FUTURE WORK

This paper presents a novel and intelligent traffic control system that leverages a distributed edge-cloud computing architecture and deep learning techniques to enhance urban mobility significantly. Our proposed architecture effectively integrates roadside cameras for real-time traffic data acquisition, edge processing for efficient vehicle detection and classification (using the high-performance YOLOv5 model), and a central cloud platform for sophisticated traffic analysis and dynamic signal control. The core of the system lies in its ability to generate real-time traffic density heatmaps and employ an advanced deep reinforcement learning (DRL) model that adaptively optimises traffic light timing based on current conditions.

Simulation results, framed by the traffic patterns of Bucharest, unequivocally demonstrated the system’s

superior performance. We observed significant improvements in average vehicle waiting times (e.g., 37.5% reduction during peak hours, from 120 to 75 seconds) and substantial gains in vehicle throughput (e.g., 38.9% increase during peak hours, from 1800 to 2500 vehicles per hour) compared to traditional fixed-time controllers. These findings highlight the system’s enormous potential to effectively reduce urban congestion, optimise traffic flow, and contribute to the Development of more responsive, efficient, and sustainable smart cities.

For future research, we plan to expand the simulation to include signal coordination and optimisation across multiple interconnected intersections, moving from a single­ intersection focus to a network-wide traffic management paradigm. This will involve extending the DRL model to handle multi-agent decision-making. The scalability of our system remains a critical consideration for urban deployment. While edge-cloud computing offers significant advantages in latency reduction, challenges related to integration with existing city infrastructure (e.g., outdated traffic light systems, sensor placement) must be addressed. Furthermore, safeguarding data privacy is crucial, particularly when processing sensitive video data locally. Future research will explore strategies for integrating our system seamlessly into large urban environments, including mechanisms for continuous system maintenance and updates. Further work will also explore the integration of advanced predictive analytics models into our DRL-based AI decision engine for proactive traffic management, enabling the anticipation of congestion before it occurs. We will also investigate the scalability and robustness of the system in larger and more complex urban networks, potentially incorporating real-world sensor data and addressing the practical challenges of hardware deployment and maintenance. Ultimately, integrating other data sources, such as public transportation schedules and pedestrian flows, could lead to even more comprehensive traffic management solutions.

As the system processes sensitive video data at the edge, ensuring data privacy is crucial. To address this concern, the system can incorporate data anonymisation techniques, such as blurring vehicle license plates and faces before transmitting any metadata to the cloud. Additionally, the system could leverage end-to-end encryption to secure data during transm1ss1on. Future work will focus on implementing privacy-preserving measures to comply with data protection regulations, such as the GDPR, ensuring the system’s deployment in a privacy-compliant manner.

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