Currently, the rapid expansion of sensor technologies, internet connectivity, and high-volume data generation has reshaped the landscape of intelligent systems, especially in the context of Internet of Things (IoT)-driven applications and smart cities. The continuous stream of real-time, heterogeneous data captured from diverse sensor sources has unlocked vast potential for real-world insights and decision-making. However, it also introduces new complexities related to the efficient integration, interpretation, and utilization of such multifaceted information (Ren et al., 2023). Extracting meaningful patterns from these rich datasets requires innovative approaches capable of merging varied data types and applying advanced learning techniques to uncover actionable intelligence. In this context, leveraging sensor-derived data from urban surveillance infrastructures, such as the smart city, presents a compelling opportunity to explore intelligent traffic solutions in dynamically evolving environments.

The Intelligent Transportation System (ITS) serve as a foundational element in the transformation of urban regions into smart cities by enabling adaptive and data-driven traffic solutions. The growing demand for accurate and timely traffic forecasting necessitates the integration of real-time inputs from diverse sensor modalities, including fixed and mobile surveillance units, environmental monitoring systems, and external contextual sources. Modern ITS frameworks capitalize on IoT to construct interconnected platforms that combine multi-source, multi-sensor, and multi-model data streams. In this context, our research leverages the integrated pipelined dataset obtained from heterogeneous features collected from smart city real time environment, combining traffic surveillance with exogenous features from weather sensing data to enable more precise traffic speed and congestion prediction through our proposed hybrid F-GGRU framework.

Traffic congestion arises from a range of exogenous contributing factors (Rehborn and Koller, 2014), including weather conditions, peak-hour load (Ali et al., 2021a), road maintenance, accidents, and adverse weather conditions (Romanowska and Budzyński, 2022a). Weather condition sensors are instrumental in monitoring atmospheric parameters such as rainfall, wind speed, temperature, humidity and visibility offering important inputs for both real-time and predictive traffic models (Du et al., 2022) (Vargas et al., 2021). Within this context, sensors form the technological backbone of the smart city, which goals to establish a smart, responsive and secure urban environment. The Safe City Islamabad, Pakistan (SCIP) network includes 2,758 cameras monitor high-definition surveillance radar-based cameras allowing near-total coverage of the SCIP (Khattak, 2025). Smart City authorities (Authors Anonymous, 2022) state that the initiative aims to ensure a digital record is maintained for every vehicle that enters the city. Additional by mobile patrol vehicles and surveillance drones, these sensor networks are linked to a centralized control hub, enabling continuous data collection. These extensive sensor deployments not only enhance urban smart city but also produce rich spatiotemporal datasets helping as a fundamental enabler of intelligent transportation research and congestion prediction.

There are two principal approaches to alleviating traffic congestion on urban road networks. The first involves expanding infrastructure by increasing the number of freeway lanes or constructing new roads. However, this solution demands significant land acquisition and financial investment, which may not be practical or sustainable in densely populated urban areas. Alternatively, a more efficient and scalable strategy is to implement intelligent traffic control mechanisms that optimize the use of existing road infrastructure. These control strategies depend heavily on accurate congestion prediction, empowering authorities to proactively manage traffic flow and mitigate congestion before it increases.

According to the United Nations, it is estimated that by 2050 nearly 68% of the global population will reside in urban regions, reflecting a significant shift toward urbanization (United Nation, 2023).

Country wise rapid pace of urbanization, coupled with exponential growth in vehicle ownership, has introduced serious challenges to traffic management, road safety, environmental sustainability, and overall urban livability. Pakistan population has grown tremendously, with census data indicating an increase from 132.35 million in 1998 to 241.49 million in 2023. Projections suggest this number will reach approximately 255.22 million by 2025 (Authors Anonymous, 2021). Alongside population growth, the number of people residing in urban areas has risen from 32.5% in 1998 to 38.82% in 2023, highlighting a marked demographic shift toward cities. This surge in urban population and vehicular density underscores the urgent need for intelligent, sensor-driven traffic solutions to address congestion and ensure sustainable urban mobility.

Furthermore, in the United States 3.3 Billion Gallons of fuel are wasted in 2022 due to the traffic congestion (U.S Department of Energy, 2025). Exogenous features (Weather conditions, peak hours, week days and weekends) (Ali et al., 2021a) can significantly influence traffic flow and congestion levels (Romanowska and Budzyński, 2022b; Agarwal et al., 2005; Lin et al., 2015). Rain (Mashros et al., 2014), fog (Ali et al., 2024), or snow often reduce visibility and road friction, leading to slower driving speeds and increased travel time. Severe weather may also cause accidents, lane closures, or disruptions in traffic signals, further compounding delays. Even mild weather changes can alter driver behavior, contributing to fluctuations in congestion patterns across urban roads.

In previous research work, for the traffic congestion prediction several Mobile Crowed Sensing (MCS) (Ali et al., 2021b), ML (YixinLi and Zhang, 2024; Yu and Xie, 2024) and DL (Zafar et al., 2022a; Lartey et al., 2021) techniques have been presented, including KNN, Support Vector Machines, RF, and deep networks such as GRUs, Convolutional Neural Network (CNN) and Long Short-Term Memory. Whereas these techniques have achieved varying accuracy, some of the techniques deal to adapt to real-world complexities such as temporal variations, missing data and sudden disturbances caused by external factors like weather. Traditional models often treat traffic data in isolation, supervising the impact of exogenous variables and failing to integrate heterogeneous data sources effectively. Furthermore, imbalanced class distributions and the lack of dynamic feature fusion methods limit their generalizability. However, their performance often declines in real-world situations due to issues such as imbalanced class distributions, missing or noisy data, temporal irregularities, and an inability to effectively integrate exogenous factors like weather conditions or peak hours and holidays. The existing techniques tend to deals with traffic data sources separately, lacking robust fusion strategies for heterogeneous and exogenous inputs. Our proposed F-GGRU framework overcomes these limitations through a novel hybrid pipelined combination of techniques: F-GGRU framework integrates fuzzy logic to fuse heterogeneous and exogenous features, use fuzzy logic-automatic labeling for classification, GANs for class balancing to balance the imbalanced class, and enhances temporal learning through a gated recurrent structure. This empowers the F-GGRU framework to capture more nuanced patterns in smart cities dynamic urban environments, contributing improved robustness, and predictive accuracy compared to existing approaches (Ali et al., 2021b; YixinLi and Zhang, 2024; Yu and Xie, 2024; Zafar et al., 2022a; Al-Qarafi et al., 2022; Yasir et al., 2022; Zafar et al., 2022b; Zhao et al., 2019; Zhong et al., 2024).

The integration of weather conditions into traffic congestion prediction models has garnered significant attention in recent years. Adverse weather events, such as fog, rain, snow, and humidity, have been empirically shown to influence traffic flow characteristics, including speed, capacity, and headways. For instance (Romanowska and Budzyński, 2022b) conducted a comprehensive study on a Polish expressway, revealing that average vehicle speeds could decrease by up to 19% and road capacity by 18% under adverse weather conditions, compared to normal conditions.

The study (Ali et al., 2021b) proposed MCS based dynamic traffic efficiency framework for traffic congestion prediction and avoidance. The real time vehicular traffic data collected through GetApp mobile application. The recommended MCS based dynamic traffic efficiency framework allocate the fasted available route with specific time slot to the commuter to follow and reached to the destination on time, but it does not explore other potential influencing factors in depth, such as weather conditions, which could also significantly impact congestion levels.

In research study (Zafar et al., 2022a) the author presented LSTM-GRU model that combine heterogeneous data sources collected data from sensors sources, holiday data, tracking company, OSM road, Google, peak hour data and weather data. Weather data from weather APIs and Open Street map for mapping. The authors have provided an exploratory data analysis using GRU, LSTM, CNN and their hybrid integration. The combination of LSTM + GRU hybrid gave the finest output with 6.67% MAPE and 4.5% RMSE. As classification the LSTM-GRU model yields 95% accuracy.

This study (Pragalathan and Schramm, 2024) employs the Neural Prophet (NP) model to advance the prediction of urban traffic dynamics by incorporating exogenous variables such as meteorological conditions and public holidays. By integrating classical time-series analysis with neural network architectures, the NP model is capable of capturing non-linear and seasonally varying patterns in traffic flow. The model’s responsiveness to external influences particularly rain and calendric events demonstrates its robustness in urban forecasting contexts. These findings emphasize the necessity of including environmental and temporal factors in traffic prediction models, particularly in metropolitan areas where such variables exert a significant influence on vehicular movement.

In research study (Sun et al., 2021) the authors developed an online traffic flow prediction framework that integrates a Bi-LSTM framework with CNN. Real time data collected from the IoT sensors situated at intersection of Hongzehu Road and Qingnian Road, in Suqian City, Jiangsu Province, China. The dataset, structured as a time series, is managed by a Bi-LSTM network helping as the generator, while a CNN operates as the discriminator within the GAN model. To evaluate predictive performance, the study applies metrics including MSE, MAE, and binary entropy. According to the reported results, this GAN-based approach demonstrates improved accuracy compared to separate Bi-LSTM and ARIMA models. The study does not touch weather impact on traffic flow.

The study (Solanki et al., 2023) incorporates weather data alongside traffic and Twitter messages to enhance traffic flow predictions. The deep learning model Bi-directional LSTM Stacked Auto Encoder architecture is used, it aims to improve accuracy in predicting traffic congestion under varying weather conditions, contributing to effective traffic management. Twitter messages data and traffic and weather datasets are used.

The paper (Valarmathi and Dhanalakshmi, 2024) discusses using genetic algorithms for optimization and IoT for real time data collection of weather-adaptive traffic monitoring, enabling dynamic adjustments in traffic management strategies. This integration helps predict and mitigate traffic congestion by analyzing real-time weather data and optimizing traffic flow accordingly.

The paper (Dong et al., 2010) develops traffic estimation and prediction models that account for traffic response to extreme weather, enabling real-time traffic management systems to predict congestion and implement advisory and control strategies effectively, thus mitigating weather impacts on traffic flow. Loop detectors and roadside sensors data. Vehicle probes data for traffic conditions. Deficiency in current weather-responsive traffic management practices. Need for improved traffic estimation models for inclement weather.

The TransGTR-MCA model (Cui, 2024) incorporates weather factors, specifically precipitation, to enhance traffic flow predictions. By considering these external conditions, the model improves adaptability and accuracy in predicting traffic congestion, particularly in data-scarce urban environments. Limitations of the proposed work are insufficient optimization for long-term prediction accuracy and need for better adaptation to external factors.

The proposed Multilevel-Gated Recurrent Unit (MGRU) model (Sravani et al., 2024) incorporates weather conditions and vehicle numbers to enhance traffic congestion prediction accuracy, achieving a notable accuracy of 0.887 and a Mean Absolute Error of 82.34, outperforming existing methods like Conv-Bi-LSTM.

A Weather interaction-aware spatio-temporal attention network (WST-ANet) model (Zhong et al., 2024) proposed effectively predicts traffic flow by integrating weather factors, enhancing adaptability to varying weather conditions. It utilizes a spatio-temporal weather collaboration insight module, improving accuracy in forecasting traffic congestion under different weather scenarios.

The work in (Yasir et al., 2022) highlights that weather significantly impacts traffic congestion levels. By utilizing machine learning models MLP Regressor, Stacking Regressor, SVR used for predictions. Historical traffic volume data utilized for training and assessment, the study effectively predicts congestion dynamics, taking into account various weather parameters alongside time of day and holiday indicators to enhance forecasting accuracy. Through this model, congestion of a road can be predicted 1 week in advance with an average RMSE of 1.12. Consequently, this model can be used to take preventive measure beforehand.

Recent advances in traffic prediction have demonstrated the strengths and limitations of different modeling approaches. Early studies such as (Wu et al., 2018) and (Polson and Sokolov, 2017) focused primarily on temporal patterns of traffic flow using deep neural networks and recurrent models. While these methods achieved promising accuracy for short-term prediction, they lacked the ability to incorporate exogenous factors, making them less reliable under unusual or disruptive conditions.

Subsequent works introduced weather and accident information to improve robustness. For example, (Zhong et al., 2024), highlighted that rainfall and visibility significantly affect congestion, while Gu et al. (2016) emphasized the role of accident reports in traffic disruption. Although these studies improved predictions (Wang et al., 2016) under specific scenarios, they were limited by narrow boundary considerations and often struggled with the data imbalance problem, where rare events such as severe accidents or extreme weather were underrepresented.

The study (Yixinli and Zhang, 2024) investigates the relationship between various traffic modes and congestion levels, establishing a high-fidelity prediction model that analyzes multi-modal traffic and congestion data across different time frames. It employs machine learning techniques, including decision tree, logistic regression, KNN and random forest (RF) models. The RF model achieved accuracy of 99.88% after optimization. The study primarily focuses on traffic volume as the most important predictor of congestion, but it does not explore other potential influencing factors in depth, such as weather conditions, road infrastructure, or driver behavior, which could also significantly impact congestion levels. The paper does not specifically address the impact of weather conditions on traffic congestion prediction. The paper establishes a robust prediction model using various machine learning techniques; it does not address the addition of real-time data sources or application of the model in dynamic traffic management systems, which could enhance the practical utility of the findings in real-world scenarios.

The study (Hazarika et al., 2024) introduced an edge machine learning framework for adaptive traffic signal control in intelligent transportation systems. Their method utilized lightweight object detection models deployed at the edge to monitor vehicle density in real time and dynamically adjust traffic light phases. While their approach significantly reduced delays at intersections and improved traffic coordination, the study did not explicitly consider exogenous influences such as weather variability or special events, which often disrupt flow patterns. This highlights a key research direction, as integrating environmental conditions into traffic prediction frameworks can complement edge-based management systems by providing more resilient and context-aware congestion forecasting.

Despite the fact that various of the above studies on vehicular traffic congestion prediction presents valuable insights and robust models, these also has certain limitation like it does not explore other potential influencing factors in depth, such as weather conditions, which could also significantly impact congestion levels that should be acknowledged. These limitations indicate areas for future research and improvement, highlighting the need for a broader framework to understanding and predicting traffic congestion effectively on the basis of heterogeneous and exogenous data. There are some limitations of widely used models such as LSTM, Bi-LSTM and CNN-based hybrids. The LSTMs often suffer from gradient vanishing, while Conv-bi-LSTM has high computational cost. These shortcomings motivated the development of our FG-GGRU model, which integrates fuzzy logic with gated recurrent units to address these issues.

To effectively capture the sequential and temporal dynamics inherent in traffic flow data, this study employs a Gated Recurrent Unit (GRU)–based neural architecture at the core of the predictive framework. GRUs are a refined variant of traditional Recurrent Neural Networks (RNNs), specifically designed to preserve relevant information over long sequences while discarding irrelevant signals through an efficient gating mechanism. Compared to Long Short-Term Memory (LSTM) networks, which utilize three gates (input, forget, and output) and a memory cell, GRUs reduce architectural complexity by combining the forget and input mechanisms into a single update gate and introducing a reset gate. This simplification results in fewer parameters, reduced computational overhead, and faster training making GRUs well-suited for time-sensitive and resource-limited applications such as traffic congestion prediction.

We propose hybrid F-GGRU framework shown in Figure 1, which builds on the fundamental GRU design and improves the model’s learning potential in two significant ways. In order to ensure that the framework learns representative patterns from both congested and smooth traffic situations, we first implement a GAN-based class balancing strategy to address label imbalance in the binary congestion classification problem mathematically algorithmic representation of F-GGRU framework presented in algorithm 1 to algorithm 6. We develop a multisource data fusion approach that combines exogenous (like weather, peak hours, and holidays) and heterogeneous (like Smart City Automatic Number Plate Recognition Cameras, patrol, and drone sensor) inputs into a single time-series sequence. Through encoding and passing these enhanced inputs via the GRU layers, the F-GGRU framework is able to learn contextual impacts and temporal dependencies simultaneously. The fusion approach make the model adaptable to abrupt changes and noise in the data, enhancing its capacity to represent the complexity of real world urban transportation network.

This study evaluates the effectiveness of Gated Recurrent Unit (GRU) models across four experimental configurations designed to enhance traffic congestion prediction accuracy. The experiments were systematically structured as follows: (i) using raw FCD alone, (ii) GAN balanced classification FCD (iii) Combine FCD with weather conditions (FCD–weather) features from weather APIs, and (iv) Applying GANs for class balancing over the merged FCD–weather dataset. These configurations reflect a progressive enhancement in data richness and preprocessing sophistication, enabling the assessment of each augmentation’s contribution to the model’s predictive performance.

The dataset used in this analysis comprises minute-level traffic records for the month of December 2023, collected from the Smart City real time surveillance system, covering seven major urban sectors. Traffic behavior was analyzed at fine temporal granularity, where each segment was annotated using fuzzy logic based labeling strategies derived from vehicle speed and time-based thresholds. Our analysis described in below four scenarios.

This research study developed the F-GGRU framework, a comprehensive methodology for fusing heterogeneous traffic data FCD with exogenous factors like weather conditions and temporal patterns into a unified hybrid feature space. A key innovation lies in its integrated pipeline, which combines fuzzy logic-based automatic labeling, GAN-driven class balancing, and advanced temporal modeling through a gated architecture. Validated against a suite of classical machine learning (Random Forest, XGBoost, etc.) and deep learning (GRU, ANN) benchmarks which achieved accuracies of only 77%–83% with poor performance on the critical “congested” class the proposed F-GGRU framework demonstrated superior performance, achieving over 98% accuracy, near-perfect recall (1.00), and a high ROC-AUC value (0.99). Significantly, rigorous hyperparameter tuning and validation on a second, independent real-world dataset (CityPulse) confirmed the framework’s robustness and generalizability, where it achieved even higher performance (99.42% accuracy, 0.9976 AUC). This proves the model is not a singular solution but an adaptable and robust tool for diverse urban environments. These findings underscore that the framework achievement is rooted in its synergistic combination of intelligent preprocessing (addressing class imbalance with GANs), feature fusion (incorporating impactful weather data), and sequential deep learning. The F-GGRU is therefore not only a highly accurate predictive tool but also a scalable and practical solution for real-time traffic monitoring systems. Its ability to reliably differentiate between traffic conditions enables proactive measures such as dynamic route guidance and adaptive traffic signal control. By providing traffic management authorities with a state-of-the-art, generalizable solution, this work contributes directly to reducing travel delays, optimizing road infrastructure, and enhancing decision-making for smarter, more responsive urban mobility.

Currently we integrated data sources of SCIP FCD from heterogeneous data sources and Weather, peak hours, week days, weekend from exogenous data sources in future we will integrates multiple data sources for further work. We have novel data set of SCIP, having various radar based cameras sensors that generate digital data. Incorporate events data such as accidents, road closures, public protests, planed events or major events from APIs like Google Traffic or Twitter, etc., to improve model con-text-awareness. Integrate the trained model into an edge computing environment or smart traffic control platform for real-time congestion prediction and response.

Another promising direction is the application of transfer learning to the smart city traffic domain. Pre-trained models trained on large-scale traffic or mobility datasets can be fine-tuned with city-specific data, enabling the framework to quickly adapt to new environments with limited labeled data. This strategy could enhance generalization, reduce training costs, and accelerate deployment in different urban contexts.