Real-Time Bengaluru City Traffic Congestion Prediction Using Deep Learning Models

Detecting and anticipating traffic congestion is the primary goal for enhancing the transportation network. The deep learning application dealing with traffic congestion detection and prediction in reinforcement learning, classification of congestion, variable sign prediction, and recurring and non-recurring traffic congestion achieved an accuracy ranging from 80% to 83%. Some of the issues faced by this work include identifying the state-of-the-art model, quantifying the traffic variable, varying prediction horizon lengths, and measuring performance [1].

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Figure 1. Heavy traffic (dark red spot) in Bengaluru cities

For urban planning, solving the traffic flow problem is critical. This research provides a traffic flow prediction method based on graph convolution and LSTM network models in reinforcement learning. Reinforcement Learning (RL) is a machine learning technique that relies on agents and environments [2]. The intelligent transportation system improves traffic congestion by providing emergency alerts and traffic information; the system also recommends machine learning approaches such as feedforward neural networks and radial basis function neural networks for traffic flow prediction. The FFNN and RBFNN are prediction schemes that use average waiting time, street pairs, and days of the week. Vehicle moments include hours, holidays, and seasonal pricing. The FFNN approach produces predictions with an accuracy of 97.6% [3].

Utilizing sophisticated information from the traveler system, intelligent transportation systems assist individuals in making sensible travel arrangements. In this research, a novel way to design a congestion matrix on area traffic utilizing relative positions is presented. Next, the convolutional long short-term network employs support vector machines (SVR), random forests (RF), long short-term memory (LSTM), linear regression (LR), and autoregressive integrated moving averages (ARIMA) to anticipate traffic congestion in any area [4]. In urban areas, traffic congestion is steadily growing, posing serious problems for a rising number of people and causing time wastage. The author offers a sophisticated traffic management system that uses hybrid Xception support vector machines to predict short-term traffic congestion and classifies traffic using convolution and ReLU based on Google Maps comprised snapshots of Bangalore, and the accuracy of the result achieved 97.16% [5].

The analysis and prediction of urban traffic for effective intelligent transportation systems, as well as the development of a framework to support local decision-making. The author presented a Faster R-CNN model for generating vehicle counts and measuring traffic conditions, as well as investigating the evaluation metrics and performance of statistical-based models (SARIMAX), RF, and deep learning models (LSTM) [6]. The traffic flow problem is critical for urban planning and administration in today's globe, as the population is steadily expanding. Consider the urban traffic network and present a traffic flow prediction approach for smart cities using GCN, LSTM, and reinforcement learning [7].

The continued development of urban population has resulted in traffic congestion, which has an impact on life, time, safety, and air quality. The incorporation of traffic congestion prediction using a pattern is critical. The author presents a machine learning technique for predicting traffic congestion, selecting the best way for predicting traffic using an ensemble tree-based algorithm, and comparing deep learning methods [8]. With the world's large population, accidents are becoming more common, as are deviant human acts such as stealing, aggression, and traffic accidents. This study uses CNNs to detect aberrant activity in films acquired by video traffic surveillance cameras (VTSC). This study effectively detected traffic accidents in the traffic monitoring system with an accuracy of 82% [9].

Road safety, air quality, commute time, and life quality are all negatively impacted by traffic congestion, which is a persistent problem as the metropolitan population grows. Research on traffic congestion prediction for the development of intelligent transportation systems included enhanced Internet of Things sensor technology, machine learning, and artificial intelligence. Relative and absolute assessment metrics from classification and regression techniques are discussed using a variety of TCP approaches [10]. The problem of traffic flow estimate is presented in this study utilizing the faster R-CNN model [11] to categorize vehicles by direction and count them based on the data.

With the world's population growing rapidly, traffic congestion is a major issue that contributes to increasing accident rates, fuel consumption, air pollution, longer travel times, and health problems. This research problem may evolve as traffic prediction improves. In order to address traffic issues, the author focuses on artificial intelligence-based traffic prediction and various data kinds [12]. Due to the population's rapid expansion and growth as well as the ongoing rise in the number of vehicles, traffic congestion in metropolitan areas has gotten worse, which affects both public safety and traffic efficiency. Using an object detection method, the author looks into emergency lane detection and traffic congestion prediction. The author predicts traffic congestion using the multi-object detection algorithm's Byte Track and the object detection model YOLOv11 [13]. The urban road network crossings have a difficulty; Yang proposes a machine vision control mode procedure to regulate traffic lights based on deep learning, employing the Yolov3 model to obtain a real-time video image of the road. Tain offers university campus vehicle recognition using the YOLOv11 model to identify vehicles on campus and improve traffic efficiency [14].

Deep learning employs traffic flow prediction [15] based on massive datasets such as traffic speed and volume to underpin deep neural networks to minimize traffic flow. This model can be used to anticipate future traffic circumstances, hence reducing congestion, optimizing traffic conditions, and improving traffic safety. Road accidents and traffic congestion are major issues in metropolitan regions [16]. Road safety has a direct impact on daily life in metropolitan areas; thus, the author develops a RF model to monitor crash rates and provide safe route recommendations to drivers in order to reduce traffic congestion and obtain 78% predictive capability.

The deep learning model-based end-to-end framework for anomaly detection is compared to the well-known LSTM network model and EVT-LSTM for real-world data sets [17]. As the number of vehicles on the road continues to rise, pollution and traffic congestion become serious problems. In order to address this issue, the author reviews recent decades and proposes the creation of an intelligent transportation system that makes use of cutting-edge technologies to analyze traffic patterns and improve decision-making for drivers [18]. Predicting traffic congestion at various times and spaces is improved by the deep learning technology [19]. The lengthy short-term model and recurrent neural networks consistently predict traffic conditions and provide notable enhancements.

Detecting abnormalities in roads, such as traffic jams and accidents, can help users manage road networks by optimizing routes, shifting traffic flows, and reducing congestion. The author recently conducted research on several histories based on traffic prediction, utilizing a deep learning model to handle traffic tasks [20]. Real-time vehicle tracking on highways, roads, and streets provides valuable data for infrastructure designers and traffic management. This study examines computer vision and advanced neural networks for object recognition and classification. The author offers picture detection and categorization using the YOLOv3 and YOLOv4 models [21].

Traffic flow forecasting is vital for smart transportation systems and traffic management. This work fully analyzes deep learning algorithms for traffic forecasting systems that use various assessment metrics [22]. This paper proposed real-time event-driven road traffic monitoring using CCTV video, accident and non-accident classification using the YOLO model, and event summarization using the DCNN model, providing 82.3% accuracy on test data, reaching 56.7% due to insufficient dataset availability [23]. Traffic congestion forecasts play an important role in future decision-making. This study focuses on traffic congestion and considers meteorological conditions, temperature, and humidity. The author presents a deep learning model and assesses it in the New Delhi traffic conditions, achieving an average RMSE of 1.12 [24, 25].

With the increasing number of vehicles on the road, traffic congestion monitoring systems have become one of the most critical components in ensuring optimal traffic flow and mobility. The vehicle and drivers require consistent and accurate real-time traffic information [26]. The approach provided to solve the traffic problem in vehicle cloud computing using IOT sensors. The distance between ultrasonic sensors and barriers using ultrasonic wave duration and speed to prevent road accidents, as acquired by sensor and camera data using image processing algorithms.

Traffic management is improved in smart cities utilizing machine learning and deep learning technologies to ease daily travel and improve productivity. However, traffic management suffers from numerous issues, such as lack of traffic flow management. Poor traffic congestion forecasting, public transportation optimization, and emergency management. This article [27] explains the disadvantages and proposes the best solution using matched learning and deep learning techniques in Traffic Management Systems (ITS).

This study demonstrates the application of deep learning models including convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and support vector regression (SVR) for effective traffic congestion prediction identification in Bengaluru as shown in map. A comparative evaluation shows the CNN-based approach achieves the highest predictive accuracy (74.48% by RMSE), outperforming LSTM (58.83%) and SVR (66.93%) in modelling traffic congestion using real urban data. The research validates these models through robust statistical metrics, including RMSE, MSE, and R², ensuring transparent and meaningful assessment of prediction performance. The integration with high-availability systems underscores the model’s practical readiness for real-time deployment, providing congestion maps and best-route recommendations for end users. The currently identified the limitations primarily rely on conventional traffic data sources (e.g., sensors, basic crowd-sourced inputs) rather than rich real-time video or image feeds, possibly limiting predictive precision. Geographical coverage and data diversity may be restricted, as the focus is mainly on specific routes like KR Market to Shivajinagar; thus, performance in more complex or varied traffic scenarios remains less explored. The study’s predictive accuracies indicate that while performance is promising, there is considerable room for improvement, particularly for real-time and adaptive congestion monitoring.

The future work will explore the expanding data sources to include real-time image and video feeds using advanced computer vision techniques to capture more dynamic traffic patterns. Enhanced deep learning architectures, such as hybrid models combining CNNs and LSTMs or novel transformer-based networks, will be explored to boost predictive performance and generalizability. Integration with city-wide intelligent traffic management systems to enable automated interventions, such as dynamic signal control and predictive rerouting. Rigorous evaluation across varied temporal and spatial scales, incorporating edge-computing frameworks to ensure both accuracy and high availability for live city deployments. Collaborative work with urban planners and authorities to translate model recommendations into practical strategies that improve urban mobility and reduce congestion city-wide.