SMART SIGNAL TIMING FOR URBAN INTERSECTIONS USING REAL-TIME VEHICLE DETECTION AND PCU CALCULATIONS
- 1. SMART SIGNAL TIMING FOR URBAN INTERSECTIONS USING REAL-TIME VEHICLE DETECTION AND PCU CALCULATIONS Project Report Submitted in Partial Fulfillment of Academic Requirement for the Award of Degree of BACHELOR OF TECHNOLOGY IN CIVIL ENGINEERING Submitted By NIRAJ KUMAR (21024117) NITIN ANAND (21024119) RAHUL KUMAR VISHVAKARMA (21024124) Under The Guidance of DR. UMANK MISHRA Associate professor DEPARTMENT OF CIVIL ENGINEERING SCHOOL OF STUDIES OF ENGINEERING AND TECHNOLOGY, GURU GHASIDAS VISHWAVIDYALAYA, BILASPUR (C.G.) (ACentralUniversityEstablishedbytheCentralUniversityAct2009No.25of2009) 2024 - 2025
- 2. AKNOWLEDGEMENT I take this opportunity to express my sincere gratitude to all those who have helped me throughout the completion of this project titled “Smart Signal Timing for Urban Intersections Using Real-Time Vehicle Detection and PCU Calculations.” First and foremost, I would like to express my deep sense of gratitude to Dr. Umank Mishra, Associate Professor, Department of Civil Engineering, for his invaluable guidance, continuous encouragement, and constant support throughout the course of this project. His expertise and timely suggestions played a crucial role in shaping the project to its present form. I would also like to thank the Department of Civil Engineering, School of Studies in Engineering and Technology, Guru Ghasidas Vishwavidyalaya, for providing the necessary infrastructure and academic environment to carry out this work. My heartfelt thanks to all faculty members and staff of the department for their encouragement and assistance. I also extend my gratitude to my fellow classmates and friends for their constructive feedback and moral support. Last but not the least, I am thankful to my family for their unwavering support and motivation which kept me focused and determined during every phase of the project. iv
- 3. ABSTRACT Managing traffic in busy city areas has become one of the biggest challenges due to the rapid increase in the number of vehicles. Traditional traffic signals that work on fixed time cycles often fail to adapt to real-time traffic conditions, leading to unnecessary delays and congestion. This project, titled “Smart Signal Timing for Urban Intersections Using Real-Time Vehicle Detection and PCU Calculations,” aims to provide a more responsive and practical solution to this problem. The project is divided into two main parts. In the first part, we used video-based vehicle detection techniques using YOLOv5 and the SORT tracking algorithm to identify and count different types of vehicles passing through each lane. These vehicles were then converted into standard traffic load units using Passenger Car Units (PCU) to make the data more useful for traffic analysis. In the second part, we designed a signal timing model that takes these PCU values as input and calculates the green signal time for each lane dynamically. The algorithm ensures that every lane gets a minimum signal time and that the total cycle time adjusts based on overall traffic volume. This method not only improves traffic flow but also keeps the system fair and efficient. This work is a step toward smarter, more adaptive traffic management systems and can be further developed to work with live traffic feeds in real-time environments. v
- 4. Fig. No. Description Of Figure Page No. 1 Comparison Between Conventional Traffic Signal vs Dynamic Traffic Signal 33 LIST OF FIGURES vii
- 5. Table No. Description Of Table Page No. 1 PCU Values as per IRC:106-1990 13 2 Survey Report 27 3 Survey Report 27 4 Survey Report 28 5 Average of All Three Surveys 28 6 Total no. of PCU Crossed During Dynamic Green Signal Time 30 7 Comparison Between no. of PCUs Passed During Green Time 31 8 Final Result Showing Overall Improvement in Traffic Flow 33 LIST OF TABLES viii
- 6. Chapter - 1 Introduction Traffic congestion is one of the most pressing challenges faced by urban areas across the globe. As cities expand and vehicle ownership continues to rise, the existing traffic infrastructure, especially at intersections, struggles to keep up. In many Indian cities, signal systems are still based on fixed cycles that operate irrespective of the real-time traffic load. This leads to inefficient road usage, unnecessary delays, increased fuel consumption, and avoidable air pollution. Even during low traffic hours, vehicles often have to wait unnecessarily at red signals, while high-traffic lanes suffer from insufficient green time. To solve this problem, traffic management systems must evolve to become smarter and more responsive. This project—"Smart Signal Timing for Urban Intersections Using Real-Time Vehicle Detection and PCU Calculations"—proposes a hybrid solution that blends artificial intelligence and traffic engineering principles. The primary goal is to optimize signal timings dynamically, depending on the actual number and type of vehicles approaching an intersection. The system works in two phases. In the first phase, a real-time video feed from a traffic camera is analyzed using a deep learning model (YOLOv5), which detects and classifies each vehicle. The detected vehicles are then converted into their PCU (Passenger Car Unit) values—a method widely used in traffic engineering to quantify the space and impact of different vehicle types. For instance, a truck impacts traffic differently than a motorcycle, and PCU values help standardize this. In the second phase, based on the total PCU per lane, the algorithm calculates dynamic green times. A fixed minimum green time is ensured for each lane to maintain fairness, and the remaining available cycle time is distributed proportionally according to the detected traffic load. Furthermore, if the overall traffic is light, the system intelligently reduces the total cycle time, avoiding unnecessary delays. This approach not only brings fairness and efficiency to traffic flow but also lays the groundwork for future smart city integration. It can be further enhanced with emergency vehicle detection, automatic input from surveillance systems, and integration into urban traffic control centers. 1.1 Traffic Signal Management Issues In India and many developing countries, traffic signal systems typically operate on fixed-time cycles, regardless of actual traffic flow. This outdated method results in unnecessary wait times, longer fuel consumption, and increased emissions. Roads 6
- 7. that are congested often receive the same green signal time as those with minimal traffic, leading to inefficient road utilization. Moreover, emergency situations or unexpected traffic surges cannot be accommodated dynamically. These issues collectively underline the urgent need for a smarter, data-driven traffic management approach that adjusts itself based on real-world vehicle flow. 1.2 Need for Dynamic Signal Timing Based on Traffic Volume Dynamic traffic signals provide a solution to the shortcomings of fixed-time systems by adapting green and red signal durations according to real-time traffic volumes. When signal timings reflect actual vehicle loads, roads clear faster and smoother. This not only reduces commuter frustration but also helps improve fuel efficiency and air quality. In this project, the need is addressed using a PCU (Passenger Car Unit)-based method, which considers the type and number of vehicles on each lane, giving proportionate green time. It ensures that no road is unfairly prioritized while maintaining a logical flow of traffic. 1.3 Role of PCU (Passenger Car Unit) in Traffic Engineering In the diverse and often congested traffic environments found in Indian cities, simply counting the number of vehicles on a road isn’t enough to understand their impact on traffic flow. Different types of vehicles—like bikes, buses, cars, and trucks—occupy different amounts of space, move at different speeds, and behave differently in traffic. This is where the concept of Passenger Car Unit (PCU) becomes essential. The PCU is a standard measure used to equate the impact of various types of vehicles to that of a standard passenger car. This helps in designing traffic systems that are fair and efficient by considering not just the number of vehicles but how much space and time each type consumes on the road. For example, a truck occupies more road space and moves slower than a car, so it contributes more to congestion and thus has a higher PCU value. The Indian Roads Congress (IRC:106-1990) provides recommended PCU values for various vehicle types under mixed traffic conditions. These values are shown in the table below: 7
- 8. Vehicle Type PCU Value Passenger Car 1 Motorcycle / Scooter 0.5 Auto-Rickshaw 1.2 Auto-Rickshaw 3 Truck 3 Light Commercial Vehicle 1.5 Bicycle 0.5 Tractor 4 Table : 1 PCU Value as per IRC:106-1990 In this project, these PCU values are used to convert raw vehicle counts—obtained from video-based real-time detection—into a standardized traffic load. This allows for better decision-making while designing green signal timings, ensuring that larger and slower vehicles are given the appropriate amount of time to clear intersections safely. Ultimately, using PCU-based calculations helps improve traffic efficiency and reduce unnecessary delays. 1.4 Application of AI for Vehicle Detection and Classification The rapid urbanization of cities has led to unpredictable traffic flows, making manual monitoring and static traffic signal systems insufficient. To address this, Artificial Intelligence (AI) is being increasingly used to automate the detection, tracking, and classification of vehicles. In this project, we utilized deep learning- based object detection models and tracking algorithms to enable real-time traffic monitoring from video footage. The goal was to build a system that could accurately identify different vehicle types and help in dynamic signal timing design using PCU-based calculations. 8
- 9. 1.4.1 YOLO (You Only Look Once) Object Detection Algorithm YOLO is one of the most popular real-time object detection models. Unlike older methods that required separate stages for region proposal and classification, YOLO does everything in a single neural network pass. This makes it extremely fast and efficient—ideal for traffic applications where decisions need to be made in real time. In our project, we used YOLOv5s, a lightweight version of the YOLOv5 model. It was pre-trained on the COCO dataset and capable of detecting 80 object classes, including vehicles such as cars, buses, motorcycles, and trucks. We fine-tuned it for our needs by filtering only vehicle classes relevant to Indian roads. Advantages of using YOLO in our project: Real-time speed with good accuracy Single-shot detection: bounding box and class prediction done together Well-documented and open-source, with PyTorch support 1.4.2 SORT (Simple Online and Realtime Tracking) While YOLO detects objects frame-by-frame, it does not remember which vehicle is which over time. This is where SORT comes into play. SORT is a fast and simple tracking algorithm that links detections across video frames to assign unique IDs to each vehicle. We used SORT to: Track the movement of each vehicle throughout the video Avoid double-counting the same vehicle in multiple frames Map vehicle types to unique IDs for PCU conversion Below are the key components of AI technologies applied in our project: 1.5 Dynamic Green Time Allocation Logic Modern cities experience constant vehicular congestion, especially at intersections. To handle this growing pressure, traditional fixed-time traffic signals often fall short. Our project introduces a smarter alternative—dynamic green time allocation —which adapts signal timings based on actual vehicle presence. By using real-time vehicle detection and calculating Passenger Car Units (PCU), we assign green signal durations proportionally, ensuring smoother flow and reduced wait times. Instead of offering the same green time to every lane regardless of traffic density, our logic distributes available cycle time dynamically. A fixed minimum time is allotted to each lane to prevent starvation, while the remaining time is distributed based on the share of vehicles after threshold adjustment. 9
- 10. 1.5.1 Fixed vs. Dynamic Timing In fixed timing systems, each signal gets equal or pre-defined time regardless of traffic load. In contrast, dynamic systems assess live input (like PCU) to assign time based on demand. This increases efficiency and reduces idle time at intersections. 1.5.2 Threshold and Minimum Allocation Concept We assume that in every cycle, at least 10 PCUs from each lane will clear during a base green time (e.g., 10 seconds). This base time is reserved, and only the remaining time is distributed based on the traffic proportion from each lane. This ensures fairness and avoids extremely short durations. 1.5.3 Cycle Time Adaptation: Total signal cycle time isn’t static. If total detected PCUs are under 100, a shorter cycle (e.g., 80 seconds) is used. If it exceeds, we go with 120 seconds. This flexibility avoids unnecessary delays in low traffic and handles high traffic efficiently. 1.5.4 PCU-Based Proportional Allocation: Once the threshold-adjusted PCUs are calculated, we derive the ratio of each lane's demand to the total and distribute remaining seconds accordingly. All results are rounded off to whole seconds for practicality. 1.6 Objective of the Study The main objective of this project, titled "Smart Signal Timing for Urban Intersections Using Real-Time Vehicle Detection and PCU Calculations", is to design an intelligent traffic signal management system that adapts to real-time traffic conditions. The system aims to detect and classify vehicles using artificial intelligence and compute Passenger Car Units (PCUs) to reflect actual traffic density at intersections. Based on this data, the goal is to dynamically allocate green signal time to each lane in a fair and optimized manner, ensuring smooth vehicle movement, minimizing idle time, and reducing congestion. The study also intends to make this system scalable for future integration, where video input can automatically drive signal logic, enhancing traffic control efficiency, especially in densely populated urban areas. 10
- 11. Chapter - 2 Literature Review 2.1 General Overview To develop this system effectively, we reviewed various approaches used globally and locally for traffic control — including fixed-time models, sensor-based actuated systems, and intelligent systems using AI and computer vision. We specifically focused on how traffic density can be evaluated through vehicle classification and how PCU (Passenger Car Unit) values can guide signal timing. Several studies have utilized AI models like YOLO (You Only Look Once) for object detection in traffic scenes, with promising results in vehicle classification. However, very few have connected this detection output to actual traffic signal design using PCU-based dynamic logic — especially tailored for Indian traffic diversity, where auto-rickshaws, bikes, and buses all interact differently with the road. Hence, our project bridges this gap by integrating AI-based vehicle detection (via YOLOv5) with a PCU-calculated dynamic green time logic. The approach not only provides a more responsive signal timing system but also holds potential for future integration with real-time surveillance systems and urban traffic management platforms. 2.2 Literature Review 11 Christofel Rio Goenawan and Haar-Dong Soo (2024) The literature review explores the integration of AI in smart traffic management systems, highlighting how Convolutional Neural Networks (CNNs) and Recurrent Neural Networks with LSTM can optimize vehicle detection and traffic prediction. It emphasizes the evolution of AI, its application in computer vision for object detection, and the use of predictive models for congestion forecasting. Smart systems, evaluated using CARLA simulation, demonstrate significant improvements in traffic flow and vehicle delay, showcasing AI’s potential in enhancing urban mobility infrastructure. Sangeetha et al. (2024) Previous research on traffic management explored sensor-based and vision-based systems, but many lacked accuracy or practicality, especially in chaotic urban settings like India. IR, acoustic, and RFID sensors faced limitations in range and real-time responsiveness. Vision approaches improved detection but were computationally heavy.
- 12. Recent studies in intelligent traffic management focus on leveraging IoT, image processing, and machine learning to address congestion and inefficiencies in traditional systems. Techniques such as YOLO-based vehicle detection, adaptive signal control, and Raspberry Pi integration are commonly employed. These systems prioritize emergency vehicles, dynamically adjust signal timings, and enhance traffic flow through real-time data analysis. This paper builds upon such approaches by combining lane-specific vehicle detection and adaptive control to reduce congestion and environmental impact effectively. Sakhare et al. (2024) 12 Some IoT and machine learning models predicted traffic flow but did not prioritize emergency vehicles effectively. Existing systems often assumed ideal conditions, like lane discipline or widespread onboard units. This study stands out by combining KNN-based traffic density estimation and YOLO-based emergency vehicle detection, offering a dynamic, real-time solution suitable for smart cities with high traffic congestion. Mandi et al. (2023) Traditional traffic systems relying on manual or fixed-timing signals are inadequate for growing urban traffic needs. Prior research highlights the use of IoT, sensors, and automation to enhance real-time traffic control. Existing systems often fail to dynamically adjust signals or prioritize emergency vehicles. Recent advancements integrate data from sensors and video feeds with adaptive algorithms to optimize traffic flow. This paper builds upon such approaches, proposing a real-time, density-based system using IoT for smarter urban traffic management. Recent studies on smart traffic management emphasize the integration of IoT, AI, and RFID to address congestion and optimize urban mobility. Traditional traffic systems based on fixed timings are inefficient in dense urban areas. Advanced models use neural networks, video processing, and real-time sensors to estimate traffic flow and adapt signal timings dynamically. Some works also include emergency vehicle prioritization and environmental monitoring. These approaches show potential in reducing congestion, improving safety, and enabling data-driven urban planning. Bhuvan et al. (2022)
- 13. Several studies have explored IoT and AI for traffic management, mainly focusing on highways and urban roads. Traditional systems rely heavily on smartphones and vehicle sensors, limiting accessibility. Recent works have used ultrasonic, magnetic, and video sensors for real-time traffic updates. However, limited attention has been given to collector roads and non-smart environments. This study addresses the gap by proposing an IoT-based system using magnetic sensors and roadside message units to deliver real-time traffic information without requiring user devices. Sarrab et al. (2020) 13 Numerous studies have addressed the estimation of Passenger Car Unit (PCU) values under heterogeneous traffic conditions using parameters such as speed, density, and delay. However, these approaches often yield inconsistent results due to varying roadway and traffic compositions. Static PCU values recommended by the Indian Roads Congress (IRC:106-1990) are based on limited empirical data and may not reflect real-world mixed-traffic dynamics. Microsimulation models like VISSIM and HeteroSim have been employed to derive dynamic PCU values, but speed-based methods often struggle under non-lane-disciplined traffic. In this context, the concept of area occupancy, introduced by Mallikarjuna and Rao, offers a more reliable and field-measurable parameter. It accounts for the actual space occupied by vehicles, making it a promising basis for improved PCU estimation. Mishra et al (2017) Jacob et al. (2018) Several studies have addressed the limitations of fixed-time traffic systems using technologies like inductive loops, ultrasonic, infrared, and acoustic sensors. Recent advancements include image processing, RF detectors, fuzzy logic, IoT, and cloud- based systems for dynamic traffic control. However, many rely on a single technology. This study integrates ultrasonic sensors and image processing with cloud storage via Raspberry Pi, offering real-time, reliable traffic density estimation. The hybrid approach enhances accuracy and introduces fault tolerance for sensor failures.
- 14. 2.3 Critical Observation 14 2.3.1Technological Integration Most studies integrate AI models like Yolo for object recognition and LSTM for traffic prediction. These systems demonstrate the possibility that AI can adaptively manage traffic. However, integration into infrastructure remains limited, and response to live real-time data needs to be further refined for robust urban use. 2.3.2. IoT and Sensor Utilization IoT-based transport systems are based on sensors such as Raspberry PI, IR, and RFID, and collect data such as vehicle number and type. These allow for dynamic signal control and emergency vehicle prioritization. Nevertheless, their cover is often limited to intersections, and syncing in urban networks is still underdeveloped. 2.3.3. Environmental Systems Many studies consider environmental benefits, such as reduced emissions and fuel consumption due to optimized signaling and reduced idle times. Minimizing unnecessary outages and delays contributes to sustainability. However, detailed indicators to reduce pollution are rarely quantified, and long-term reviews of environmental impacts are largely lacking. 2.3.4. Simulations for Real Tests Simulation environments such as Carla provide useful tests for intelligent traffic systems. However, it cannot replicate actual complexity such as weather changes, driver behavior, signal failures, and more. The real attempts to offer you are rare, making it difficult to assess how the system works in a real city under traffic conditions mixed with chaotic. 2.3.5. Issues for urban centers in India Papers like Mishra et al. Discuss unstructured traffic in India and attach a PCU estimation model based on surface loads. These are more suitable for Indian roads where lane truck discipline is not available. However, most AI models used worldwide take on structured truck behavior that limits their effectiveness in Indian or similarly chaotic urban environments.
- 15. 15 2.4 Research Gap 2.4.1 Lack of uniform architecture Existing frameworks need a standardized, secluded engineering that consistently coordinating AI calculations, IoT-based detecting, and control components. Most models are custom-built, making them difficult to scale or imitate. A bound together system would permit plug-and-play components, simpler overhauls, and interoperability between innovations, which is basic for real-world arrangement over changing urban frameworks. 2.4.2. Restricted Real-world Deployment While simulation-based results are promising, few systems have undergone large- scale, real-world validation. Challenges like sensor calibration, weather variability, network latency, and infrastructure limitations remain untested. Without deployment in diverse traffic conditions, including rural and highly congested urban zones, it’s difficult to measure true effectiveness, reliability, and user acceptance of these solutions. 2.4.3. Emergency Vehicle Handling Although some systems prioritize ambulances or fire trucks using RFID or object detection, real-time dynamic rerouting and signal coordination for emergency vehicles are still rudimentary. Systems often fail to simulate complex city scenarios where multiple emergency vehicles must be prioritized simultaneously, especially during peak traffic or in multi-intersection zones. 2.3.6. Data Collection Gap Many systems rely on static data records or APIs from third party providers, such as Google Maps. This limits adaptability to dynamic conditions. Real-time multi- source data integration is limited (for example, combining video, sensors, and V2X inputs). Without accurate and comprehensive data, sophisticated algorithms cannot consistently make optimal traffic decisions. 2.4.4. Neglect of Non-motorized Traffic Most models are designed for motorized vehicles and overlook bicycles, pedestrians, and informal transport (e.g., rickshaws). This omission limits accuracy and inclusiveness, particularly in countries with mixed-traffic environments.
- 16. 16 Few studies address the cost of scaling traffic systems across entire cities or regions. Resource-intensive models requiring high-end computing or expensive sensors limit affordability. A research gap exists in developing lightweight, energy- efficient, and low-cost systems that can be feasibly adopted by small municipalities or developing urban centers with constrained budgets. 2.4.5. Scalability and cost-effective analysis Systems must evolve to detect and predict interactions across all road users to improve safety, signal accuracy, and fair space allocation. 2.4.6. Insufficient Real-time Data Fusion Traffic control relies on accurate, up-to-date information, yet current systems struggle to merge inputs from various sources—like sensors, cameras, mobile GPS, and V2X communication—into a cohesive model. Delays in data transmission or poor data quality reduce the system’s responsiveness. Real-time fusion methods are needed to ensure optimal and timely decision-making. 2.5 Scope Of The Work 2.5.1. Development of Hybrid AI-IoT-V2X Systems There is significant potential in developing integrated systems combining Artificial Intelligence (for decision-making), IoT (for sensing and control), and V2X (for communication). This integration would allow vehicles to interact with infrastructure in real-time, enabling highly adaptive traffic control, efficient emergency routing, and cooperative behavior between autonomous vehicles and traffic signals, enhancing both safety and flow. 2.5.2 Field Trials and Public Policy Integration To transition from prototype to practical use, future research should focus on piloting smart traffic systems in diverse urban settings. Simultaneously, collaboration with government agencies is crucial to integrate such systems with existing transport policies, standards, and regulations. These trials would also offer insight into public response, legal concerns, and long-term sustainability. 2.5.3. Incorporation of Multimodal Traffic Behavior Future systems should account for all traffic participants—pedestrians, cyclists, public transport, and informal vehicles. Including multimodal behavior in traffic models will lead to more inclusive and efficient urban traffic management.
- 17. 17 This will require object detection algorithms that can classify different road users and adaptive signal strategies that prioritize safety and equity. 2.5.4. Design of Low-cost, Scalable Infrastructure Developing countries require solutions that are both technically effective and economically feasible. The scope includes designing energy-efficient, low-cost sensor nodes, open-source platforms, and edge computing solutions that minimize dependency on expensive cloud services. This would enable wider adoption of smart traffic management in small towns and resource-constrained municipalities. 2.5.5. Predictive and Preventive Traffic Control Models Current models react to congestion; future work should focus on predictive analytics using AI to foresee traffic buildup before it occurs. Combined with preventive strategies like rerouting or adjusting signal timings in advance, these systems can significantly reduce congestion and emissions, improving the overall efficiency of the transportation network. 2.5.6. Integration of Real-time Data Fusion Systems There is scope to create robust real-time data fusion frameworks that merge data from sensors, GPS, cameras, social feeds, and vehicle telemetry. Such systems would offer comprehensive situational awareness, allowing traffic controllers to make accurate and timely decisions. Standardizing protocols and ensuring data quality across sources will be critical for reliability.
- 18. 18 Chapter - 3 Methodology 3.1 Overview The project “Smart Signal Timing for Urban Intersections Using Real-Time Vehicle Detection and PCU Calculations” adopts a practical and modular strategy to improve traffic flow at signalized intersections. The approach combines modern artificial intelligence (AI) techniques with established traffic engineering concepts, aiming to create a smart system that can adapt to real-time road conditions without requiring extensive hardware infrastructure. At the core, the methodology is divided into two main stages. The first stage involves detecting and classifying vehicles in a video feed using a deep learning model called YOLOv5. These vehicles are tracked across frames with the help of a lightweight and efficient algorithm known as SORT (Simple Online and Realtime Tracking). Based on the class of each vehicle — such as two-wheeler, car, bus, or truck — a standard PCU (Passenger Car Unit) value is assigned, and the total PCU count for each lane is calculated over a fixed duration. The second stage focuses on dynamically designing green signal durations based on these PCU inputs. A logical formula is applied that ensures a base minimum time is allocated to every lane, ensuring fairness. The remaining time in the traffic signal cycle is distributed in proportion to the PCU load across lanes. To make the system adaptive, the total cycle time is also varied based on the overall traffic volume — shorter cycles for low traffic, and extended ones when congestion is high. This step-by-step logic forms the foundation of a system that doesn't just operate on fixed timing but adapts intelligently, which is crucial for densely populated urban settings in countries like India. The proposed solution also considers future scalability, where real-time camera input can be fed directly into the system, allowing for fully automated signal control. 3.2 Video Acquisition and Preprocessing To design a traffic signal system that truly reflects on-ground realities, it was important to base the model on realistic traffic scenarios. For this reason, we used a combination of self-recorded videos from nearby intersections and publicly available traffic footage relevant to Indian road conditions. Videos showing foreign traffic systems were specifically avoided, as lane discipline, vehicle types, and road behaviors in those setups differ significantly from those seen in India. Using Indian-context videos made the detection and logic more generalized and relatable to real urban traffic in our country.
- 19. 19 After video collection, preprocessing became essential to make the raw footage compatible with the AI detection model. Each video was standardized in terms of frame size and resolution, typically resized to either 640×480 or 1280×720 pixels. This step ensured a good balance between image quality and computational efficiency. Frame rates were also adjusted to allow near-real-time detection performance. To enhance accuracy, Regions of Interest (ROI) were manually defined in the video frames. This helped focus the AI model only on areas where vehicle movement occurred, filtering out irrelevant parts like sidewalks, buildings, or the sky. Additionally, basic brightness and contrast adjustments were performed when lighting inconsistencies or shadows affected visibility. This careful curation and preprocessing of the video data ensured that the vehicle detection and classification model (YOLOv5) received consistent, India-specific, and clean inputs — laying a solid foundation for accurate PCU estimation and signal timing in later stages. 3.3 Vehicle Detection and Classification Using YOLOv5 Accurate vehicle detection is the backbone of any intelligent traffic management system. In our project, we implemented YOLOv5 (You Only Look Once version 5), a widely adopted deep learning model known for its real-time object detection capabilities. YOLOv5 stands out for its speed and precision, making it ideal for dynamic environments like busy intersections. The YOLOv5 model was trained and tested using traffic videos containing diverse vehicle types commonly found in Indian urban areas—such as motorcycles, auto- rickshaws, cars, buses, and trucks. Unlike rule-based image processing methods, YOLOv5 learns patterns through thousands of image samples, allowing it to detect vehicles even in partially occluded or poorly lit scenes. For our application, we used the pre-trained COCO model as the base and customized it to focus on the classes relevant to traffic. The model was integrated with a frame-by-frame video processing pipeline. Each detected vehicle was given a bounding box, label (vehicle class), and confidence score, which served as inputs for further tracking and classification. To ensure the system could identify whether a vehicle had already passed or not, we implemented a lightweight object tracking method called SORT (Simple Online and Realtime Tracking). This enabled consistent counting without duplication, even as vehicles moved across frames. What makes this setup more robust is that it doesn’t just count vehicles—it classifies them into categories. This classification is essential for our Passenger Car Unit (PCU) calculations, as different vehicle types have different impacts on traffic flow. For instance, a truck occupies more space and time than a bike and hence contributes a higher PCU value.
- 20. 20 By combining YOLOv5's fast detection with SORT’s tracking, our system achieves reliable, real-time identification and classification of traffic—setting the stage for intelligent signal timing based on actual road conditions. 3.4 Role of PCU in Traffic Signal Optimization In traffic engineering, particularly within complex and heterogeneous systems like those seen on Indian roads, the concept of Passenger Car Units (PCU) plays a pivotal role. Rather than simply counting vehicles, PCU allows for the standardization of different vehicle types based on the space they occupy and the time they take to cross intersections. This ensures that the signal timings are determined by actual road usage demand, not just vehicle count. In our project, we utilized PCU to convert the count of detected vehicles into a weighted total, reflecting the true traffic load. For instance, a truck occupies significantly more road space than a motorcycle; thus, treating both equally would distort traffic estimates and result in inefficient signal timing. By applying PCU values provided by the Indian Roads Congress (IRC), we accounted for this variability effectively. The standard PCU values adopted in our system are summarized in Table no. 1, which includes common vehicle categories such as two-wheelers, passenger cars, buses, trucks, and non-motorized vehicles. These values are integral to computing the effective traffic volume per lane. After vehicle detection and classification using AI, we assign each class its corresponding PCU weight and sum them per lane. This total PCU count per lane then serves as the foundation for our dynamic green signal time calculation. This approach ensures that signals adapt in real-time to actual traffic demand, rather than relying on fixed or arbitrary durations. Overall, incorporating PCU into our model enhances fairness, efficiency, and adaptability, making our system suitable for real-world urban traffic intersections with mixed traffic flow. 3.5 Dynamic Green Time Allocation Logic An essential part of this project is the method used for dynamically allocating green signal times based on real-time traffic load at an intersection. Unlike conventional fixed-time signals, which assign equal or preset green durations regardless of actual traffic, our system adjusts green times proportionally using vehicle counts (converted to PCU) as input. This approach helps in minimizing delays and improving traffic flow across all lanes.
- 21. 21 3.5.1 Minimum Green Time Allocation To ensure that no lane is neglected, each approach is first given a base minimum green time. In our model, we allocate 10 seconds to each of the four directions, accounting for 40 seconds of the total cycle time. This ensures that even lanes with very few vehicles still get an opportunity to clear. 3.5.2 Dynamic Allocation Using PCU Ratios Once the minimum time is allocated, the remaining green time (i.e., 40 or 80 seconds depending on total cycle time) is distributed based on the remaining PCU. We subtract 10 PCU per lane (assuming approx. 1 PCU clears per second during the minimum time) from each lane’s total. Then, the remaining PCUs are used to calculate the proportional ratio of traffic left to be cleared. This remaining time is then divided among the lanes based on their respective PCU ratios, ensuring that lanes with higher vehicle pressure get longer green time. 3.5.3 Dual Cycle Time Logic To make the system adaptable to overall traffic load, we used two fixed cycle time settings: 80 seconds, when total PCU detected at the intersection is ≤100 120 seconds, when total PCU is >100 This dual-cycle system was implemented to ensure simplicity in design and analysis. However, the same proportional time allocation logic could easily be extended to a fully dynamic cycle time model, where the total cycle time changes continuously based on real-time PCU load. This flexibility makes the system scalable and suitable for future upgrades. 3.6.1 Traffic Survey and Comparative Analysis Three traffic surveys were conducted at Satyam Chowk, Bilaspur (Chhattisgarh) to assess the performance of the proposed PCU-based dynamic traffic signal system under varying traffic conditions. The first survey was conducted on Sunday at 8:00 AM, representing a low-traffic scenario. The second survey took place on Monday at 12:00 PM, reflecting moderate traffic, and the third survey was conducted on Monday at 6:00 PM, capturing peak-hour conditions. Vehicle counts from all three surveys were categorized by type and converted into Passenger Car Units (PCUs) using standard values as listed in Table 1. This conversion enabled a more accurate comparison of traffic volume and lane-wise demand across different time periods. 3.6 Data Collection
- 22. Lane PCU (Available) Green Signal Time Crossed (PCU) A 43 30 28 B 20 30 20 C 23 30 23 D 57 30 29 Total 133 120 100 22 Lane PCU (Available) Green Signal Time (sec) Crossed (PCU) A 38 30 30 B 14 30 14 C 12 30 12 D 35 30 28 Total 101 120 84 Table 2. Survey on Sunday at 8:00 AM, representing a low-traffic scenario Table 3. Survey on Monday at 12:00 PM, reflecting moderate traffic
- 23. 23 Lane PCU (Available) Green Signal Time Crossed (PCU) A 84 30 30 B 35 30 28 C 26 30 26 D 102 30 29 Total 247 120 113 Table 4. Survey on Monday at 06:00 PM, reflecting heavy traffic Table 5. Average of all three survey ( Rounded off to upper limit) Lane PCU (Available) Green Signal Time Crossed (PCU) A 55 30 30 B 23 30 21 C 21 30 21 D 65 30 29 Total 164 120 101
- 24. 24 3.6.2 Calculation for Green Signal Time Lane A: 55 Lane B: 23 Lane C: 21 Lane D: 65 Step 1 Total PCU = 55 + 23 + 21 + 65 = 164 Step 2: Cycle Time Selection Total PCU > 100 → Cycle_time = 120 Step 3: Minimum Green Time Min green time = 10 → total min = 4 × 10 = 40 Remaining time = 120 - 40 = 80 Step 4: Deduct 10 crossing PCU from each lane Lane Original PCU Deducted Remaining PCU Lane A 55 10 45 Lane B 23 10 13 Lane C 21 10 11 Lane D 65 10 55 Step 5: Total Remaining PCU Total remaining PCU = 45 + 13 + 11 + 55 = 124
- 25. 25 Step 6: Dynamic Green Time Calculation Lane Remaining PCU Green Time Formula Green Time Formula A 45 10 + (45 / 124) × 80 = 39.03 s B 13 10 + (13 / 124) × 80 = 18.38 s C 11 10 + (11 / 124) × 80 = 117.10 s D 55 10 + (55 / 124) × 80 = 45.48 s Final Output (Rounded to Nearest Second) Signal Timing Plan (Cycle Time: 120 sec) Lane A: 55 PCU →39 sec Lane B: 23 PCU →18 sec Lane C: 21 PCU →17 sec Lane D: 65 PCU →45 sec Lane PCU (Available) Green Signal Time Crossed (PCU) A 55 39 39 B 23 18 18 C 21 17 17 D 65 45 45 Total 164 119 (Approx 120 sec.) 119 Table 6. Total no. Of PCU Crossed During Dynamic Green Signal Time
- 26. 26 3.6.2 PCU-Based Throughput Comparison Model Type PCUs Passed During Green Time Traditional Signal Model 101 Proposed Dynamic Model 119 Improvement in Traffic Throughput To quantify the performance gain, percentage improvement is calculated using the formula: Improvement %= ( Dynamic - Traditional/Traditional) × 100 Substituting values: Improvement %= ( 119 - 101/101) × 100= 17.82% Thus, the proposed dynamic signal timing logic demonstrates a 17.82% improvement in vehicle throughput during peak traffic hours compared to the traditional fixed-time system. This highlights the potential of PCU-based dynamic signal allocation in improving traffic flow and reducing congestion at urban intersections. 3.7 System Integration and Simulation Once the individual components—vehicle detection, object tracking, PCU calculation, and signal timing—were developed, they were integrated into a single Python-based system designed to simulate real-time traffic signal control using real intersection footage. The system workflow is as follows: Video Input: Recorded footage from an intersection is used as input. Vehicle Detection: YOLOv5 identifies vehicles in each frame. Vehicle Tracking: The SORT algorithm tracks each detected vehicle across frames using unique IDs to avoid double counting. PCU Estimation: Based on detected vehicle classes (car, bike, auto, truck, bus), appropriate PCU values are assigned as per IRC guidelines. Signal Time Computation: Each lane's total PCU is input to a dynamic allocation module that calculates the optimal green signal duration. Simulated Output: The system outputs green signal timings for each lane, mimicking a live traffic signal environment. Table 7. Comparison b/w PCU Passed in Green Time
- 27. 27 Execution was performed through a Command Line Interface (CLI), allowing the user to supply traffic video inputs and view output in real time. The project utilized Python libraries including PyTorch, OpenCV, NumPy, and Matplotlib for implementation. This integrated setup demonstrates that the proposed model can adapt signal timing based on current traffic demand, showing promise for practical deployment at urban intersections. 3.8 Validation and Observations To assess the performance and reliability of the proposed PCU-based dynamic signal timing system, a validation study was conducted using empirical data collected from a real intersection—Satyam Chowk, Bilaspur, Chhattisgarh. The validation aimed to compare the throughput efficiency of the traditional fixed- time signal method with the developed dynamic model. 3.8.1 Survey-Based Data Collection Traffic video data was collected during three distinct time slots to represent varying traffic conditions: Sunday, 8:00 AM (Low Traffic) Monday, 12:00 PM (Moderate Traffic) Monday, 6:00 PM (Peak Traffic) Lane-wise vehicle counts were converted to PCU values using the standard PCU conversion factors defined by the Indian Roads Congress (IRC). This standardized traffic flow data served as input for both the fixed-time model and the proposed dynamic model. 3.8.2 Method of Validation Under the traditional fixed-time system, a uniform green time of 30 seconds per lane (total 120 seconds per cycle) was assumed. In contrast, the proposed dynamic model computed lane-wise green time based on PCU inputs, with a cycle time dynamically selected as either 80 or 120 seconds based on total demand. For both methods, the number of PCUs able to pass during the allocated green time was estimated based on typical saturation flow conditions and average vehicle discharge rates per second.
- 28. 28 3.8.4 Key Observations The dynamic model successfully identified imbalanced traffic loads among lanes and redistributed green time accordingly. A noticeable increase in vehicle throughput was achieved without exceeding the 120-second cycle time. The system demonstrated adaptive behavior and responsiveness to real-time traffic conditions, unlike the rigid structure of the fixed-time approach. These observations confirm that the dynamic signal allocation logic, when based on PCU-weighted distribution, leads to more efficient intersection performance. The validation supports the feasibility of deploying such adaptive systems in real-world urban intersections to improve traffic flow and reduce congestion. Method Cycle Time (sec) Total PCU PCUs Passed Improvement Fixed-Time 120 164 101 – Dynamic Model ( 120 164 119 17.82% 3.8.3 Results and Observation Fig. 1 Comparison between the conventional traffic signal vs dynamic traffic signal Table 8. Final Result Showing Overall Improvement in Traffic Flow
- 29. 29 Chapter - 4 Result and Discussion 4.1. Introduction to Results This chapter presents the results obtained from the implementation of our project “Smart Signal Timing for Urban Intersections Using Real-Time Vehicle Detection and PCU Calculations”, which utilizes real-time video-based vehicle detection, PCU calculation, and dynamic traffic signal timing logic. The outcomes have been analyzed using data collected through actual traffic video footage at a four-way intersection. The primary goal is to evaluate how effectively the proposed model improves lane-wise traffic flow compared to traditional fixed-time signal systems. The results focus on two core metrics: first the total PCU throughput during a standard 120-second signal cycle, and second the efficiency of green signal time distribution among lanes based on traffic density. These parameters are crucial in understanding the real-world performance of the model, especially under varying traffic volumes recorded on different days and time slots (Sunday morning, Monday afternoon, and Monday evening). By comparing the number of vehicles (in terms of PCU) passing through each lane under fixed versus dynamic signal allocation, this chapter provides clear, data- driven evidence of the improvements achieved. Additionally, the analysis includes how the dynamic model responded to real-time traffic variations and how it allocated green time proportionally, ensuring fairness and increased throughput. Overall, the findings in this chapter confirm that the PCU-based dynamic timing strategy designed in this project leads to more efficient and responsive intersection management, validating its potential for real-world application. 4.2. PCU Throughput Comparison The effectiveness of the developed model—Smart Signal Timing for Urban Intersections Using Real-Time Vehicle Detection and PCU Calculations—is evaluated through a direct comparison of vehicle throughput under two traffic control strategies: the conventional fixed-time signal method and the proposed dynamic, PCU-based method. During a standard 120-second signal cycle, the fixed-time approach allowed only 101 PCUs to pass through the intersection. In contrast, the dynamic model, which allocates green time proportionally based on real-time PCU values per lane, enabled 119 PCUs to pass in the same duration. This marks a clear improvement of 17.82% in vehicle throughput.
- 30. 30 This increase in PCU throughput demonstrates the efficiency of allocating green time based on actual traffic volume rather than assigning equal or arbitrary durations. By identifying which lanes have higher traffic loads and adjusting signal durations accordingly, the model effectively reduces idle time at low-traffic lanes and prioritizes high-demand lanes, resulting in a smoother and more responsive traffic flow. Such results validate the core logic of the project and indicate that the proposed system can significantly enhance the performance of urban intersections, especially during peak and variable traffic conditions. 4.3. Green Time Efficiency The efficiency of green signal time allocation plays a critical role in managing urban traffic intersections. In conventional traffic systems, green time is either distributed equally among all directions or assigned using fixed durations without considering the actual traffic volume at each lane. This often leads to inefficient signal usage—vehicles in less congested lanes wait unnecessarily, while high- density lanes remain congested due to insufficient green time. In the developed PCU-based model, green time is dynamically allocated based on the real-time traffic load captured through vehicle detection and converted into PCU values. A base green time is assigned to all lanes to ensure minimum passage, and the remaining time from the 120-second cycle is proportionally distributed among the lanes according to their PCU share. This results in a more logical and need-based allocation of green durations. For instance, during the analysis, it was observed that a lane with a significantly higher PCU count received more green time compared to others, enabling it to clear more vehicles without causing prolonged queues. On the other hand, lanes with lower PCU values were given just enough green time to manage their flow efficiently, thus preventing unnecessary wastage of signal duration. This intelligent allocation ensures that every second of green time is utilized effectively, maximizing throughput and minimizing delays. The green time is no longer a fixed input but a calculated output driven by real-time traffic demands, making the system more adaptable and fair compared to traditional method 4.4. Survey-Based Validation To validate the real-world applicability of the proposed model, traffic surveys were conducted at a four-way intersection using actual video recordings. The aim was to analyze how the dynamic signal system responds to varying traffic patterns across different days and time slots. For this purpose, data was collected during three distinct periods: Sunday morning (low traffic), Monday noon (moderate traffic), and Monday evening (peak traffic).
- 31. 31 Each vehicle recorded in the videos was classified using the trained detection model and converted to its equivalent PCU value. This data was then used to simulate green time allocation using the dynamic model. By comparing the green time assigned under dynamic conditions to the actual PCU distribution across lanes, the adaptability of the system became evident. During low-traffic hours on Sunday morning, the model provided a more balanced green time across lanes since PCU values were nearly equal. However, in the Monday evening slot, where traffic volume was heavily skewed toward one or two lanes, the model successfully reallocated green time in favor of those lanes without any manual intervention. This behavior showcases the system's strength in adapting to real-time conditions instead of relying on static timing plans. These survey-based validations clearly demonstrate that the system dynamically reacts to changing traffic patterns throughout the day. Rather than treating all lanes equally, it adjusts in response to the current demand, thereby improving fairness, throughput, and responsiveness. The same intersection, with different traffic conditions, resulted in different signal behavior—all automatically handled by the model without pre-programmed schedules. This flexibility and real-time adaptability form the core strength of the proposed system and make it a viable solution for smart urban traffic control. 4.5. Real-Time Responsiveness A key feature of the proposed traffic signal model is its ability to respond to live traffic conditions in near real-time. Unlike conventional systems that rely on pre- set schedules or manual adjustments, the developed system processes live video input to detect and classify vehicles, convert them into PCU values, and calculate optimal green signal durations accordingly—all in a short, efficient cycle. In the current offline implementation phase, videos from actual traffic intersections were used as input. The system, powered by YOLOv5 for detection and SORT for tracking, was able to detect multiple vehicle classes per lane and generate PCU counts within seconds. Once the lane-wise PCU data was available, the algorithm computed the base green time and distributed the remaining cycle time based on the PCU ratio of each lane. In a test run using pre-recorded traffic footage, the entire process from video input to signal time calculation was completed within 10–12 seconds. This indicates that, when integrated with real-time traffic camera feeds and automated signal controllers, the system has the potential to function in a live environment with minimal delay.
- 32. 32 This responsiveness ensures that the system reacts promptly to dynamic traffic scenarios—such as sudden vehicle surges in one direction or a drop in volume in another. Instead of waiting for the next scheduled update, the system recalculates and redistributes green time based on current demand, helping prevent congestion and idle green signals. Although full real-time deployment (with continuous live feed and automated signal switching) is yet to be implemented, the current setup confirms that the system is technically capable of achieving real-time responsiveness once integrated with the required infrastructure. 4.6. Limitations While the proposed model has demonstrated significant improvements in signal timing efficiency and throughput, several limitations must be acknowledged to provide a balanced perspective and guide future enhancements. 1. Lighting and Weather Sensitivity: The accuracy of vehicle detection using computer vision is highly dependent on lighting conditions. Poor visibility during nighttime or extreme weather conditions (like fog or heavy rain) may reduce detection accuracy, leading to incorrect PCU calculations and suboptimal signal time distribution. 2. False Positives and Missed Detections: Although YOLOv5 is a robust detection model, occasional false positives (e.g., mistaking shadows or parked objects for vehicles) and missed detections (especially for small or partially occluded vehicles) can affect the reliability of PCU estimates, and consequently, the fairness of green time allocation. 3. Limited Vehicle Class Categorization: The system currently categorizes a limited number of vehicle types (e.g., cars, bikes, buses, trucks, autos). More complex traffic environments with non-standard vehicles (e.g., handcarts, cycle rickshaws) may not be adequately represented in the PCU calculations, slightly reducing accuracy in such regions. 4. Offline Processing Only: As of now, the entire analysis is performed using recorded video data processed offline. Real-time implementation—where live camera feeds continuously update signal timings—is not yet deployed due to hardware and integration constraints. 5. Intersection-Specific Calibration: The model’s green time logic (base time, PCU thresholds, etc.) was tuned for a specific intersection. To scale the system city-wide, individual calibration may be necessary for different intersection geometries, vehicle flow patterns, and traffic rules. Despite these limitations, the project successfully demonstrates the core feasibility and effectiveness of using real-time PCU data for dynamic signal control. With further development—such as real-time integration, broader vehicle classification, and lighting compensation—these limitations can be addressed for more robust deployment.
- 33. 33 4.7. Discussion Summary The results and observations from the implementation and testing of this project —Smart Signal Timing for Urban Intersections Using Real-Time Vehicle Detection and PCU Calculations—highlight its potential to significantly enhance traffic signal efficiency in urban environments. Through direct comparison, the dynamic PCU-based model outperformed the conventional fixed-time method, achieving a 17.82% increase in vehicle throughput during a standard 120-second signal cycle. This improvement was made possible by intelligently allocating green signal time based on real-time traffic demand rather than relying on rigid, pre-assigned durations. The model proved capable of adapting to different traffic conditions across multiple time slots and days, as validated by actual survey data. It responded effectively to variations in lane-wise vehicle density, distributing green time fairly and efficiently in accordance with the PCU load. Even in its offline phase, the system demonstrated near real-time responsiveness—completing detection, classification, PCU calculation, and green time distribution within seconds. At the same time, limitations such as offline processing, lighting sensitivity, and restricted vehicle classification were identified. These areas provide a clear direction for future enhancement and integration with live traffic systems. In summary, this project achieved its primary goals of improving green time utilization, enhancing intersection throughput, and ensuring fairness through PCU-based logic. The findings indicate strong potential for real-world application, especially if supported by appropriate infrastructure like live traffic feeds, smart cameras, and automated signal controllers.
- 34. 34 Chapter - 5 CONCLUSION Urban traffic management has long been a challenge, with conventional fixed-time signal systems often leading to inefficiencies, especially during periods of fluctuating traffic flow. This project aimed to address these inefficiencies by developing a smart traffic signal control system that dynamically allocates green time based on real-time vehicle detection and PCU (Passenger Car Unit) calculations. The system utilizes advanced computer vision techniques, such as YOLOv5 for vehicle detection and SORT for tracking, alongside an innovative green time allocation algorithm based on real-time traffic data. The results of the project demonstrate significant improvements in traffic signal efficiency. The dynamic model, which adapts to actual traffic conditions, achieved a 17.82% improvement in vehicle throughput compared to conventional fixed-time control methods. This improvement was achieved by allocating green time based on the real-time density of vehicles, ensuring that high-traffic lanes received more green time, thereby reducing delays and increasing overall intersection throughput. One of the key strengths of this system is its adaptability to varying traffic patterns. Validation through real-world traffic survey data confirmed that the system efficiently adjusts its green time allocation across different time slots, from low-demand periods to peak traffic hours. This adaptability ensures that the system can handle a wide range of traffic conditions, making it a versatile solution for urban intersections. While the current implementation was tested offline, the model demonstrated strong real-time responsiveness, processing vehicle detection, tracking, and green time calculation within a short time frame. This indicates that the system is well- suited for live deployment with minimal delay, provided it is integrated with appropriate hardware and live video feeds. Despite the promising results, the project also highlighted several limitations that need to be addressed in future work. These include challenges such as lighting conditions that may affect vehicle detection accuracy, occasional false positives or missed detections, and limited vehicle class coverage. Additionally, the system’s offline processing capability should be transitioned to a live setup, and further enhancements, such as emergency vehicle prioritization, could be incorporated to make the system more robust. In conclusion, this project presents a novel and effective approach to improving urban traffic management through intelligent, data-driven signal control. The dynamic allocation of green time based on real-time vehicle detection and PCU calculations not only enhances signal efficiency but also promises to contribute to smoother traffic flow and reduced congestion in urban intersections. With the potential for further refinement and integration into smart city infrastructure, this system offers a significant step forward in the evolution of intelligent traffic signal systems.
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