INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,  
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)  
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue XII, December 2025  
A Machine Learning-Based CNN–Bilstm Framework for Traffic  
Congestion and Travel Time Prediction  
Sanjeev Panwar*, Paramjeet Rawat  
Department of Computer Application, Swami Vivekanand Subharti University, Meerut, Uttar Pradesh,  
India  
DOI : https://doi.org/10.51583/IJLTEMAS.2025.1412000102  
Received: 26 December 2025; Accepted: 31 December 2025; Published: 09 January 2026  
ABSTRACT  
The main issues facing modern metropolitan transportation systems are traffic bottlenecks and variations in  
travel times. They impede mobility, damage the environment, and compromise commute safety. Intelligent trans-  
portation systems and smart city applications need precise estimations of traffic congestion and journey durations  
in the short term to function effectively. This study introduces a hybrid deep learning framework that employs a  
Convolutional Neural Network–Bidirectional Long Short-Term Memory (CNN–BiLSTM) architecture for sim-  
ultaneous forecasting of traffic congestion and travel time. The model employs CNN layers to acquire bidirec-  
tional temporal relationships and BiLSTM units to capture spatial correlations across adjacent road segments.  
The framework just employs fundamental traffic information, such as volume, speed, and flow, enhancing its  
practicality for real-world applications since it does not need additional data sources.  
We used traffic data from the Delhi–Meerut Expressway to evaluate the proposed technique. An extensive ex-  
ploratory data analysis was conducted to understand traffic patterns and confirm the spatial-temporal modeling  
methodology. Experimental findings demonstrate persistent model convergence and enhanced prediction accu-  
racy, achieving a low Mean Squared Error (0.0158), Root Mean Squared Error (0.1257), Mean Absolute Error  
(0.0912), and a Mean Absolute Percentage Error of around 4.6%. Visual comparisons indicate that forecast  
traffic numbers closely align with actual patterns, and an analysis of error distribution demonstrates that the  
model is very effective in the presence of noisy data. The model provides comprehensible outputs, including  
congestion level, anticipated speed, and travel duration, enhancing its practical use. The proposed CNN–  
BiLSTM architecture is an effective, robust, and scalable method for real-time prediction of traffic congestion  
and travel durations in urban areas.  
Keywords: Traffic congestion, travel time prediction, CNN–BiLSTM, spatio-temporal learning.  
INTRODUCTION  
Congestion and variations in travel duration are two of the most significant issues facing modern urban trans-  
portation systems. They adversely affect the economy, compromise commute safety, and damage the environ-  
ment. Accelerated urbanization, demographic growth, and the proliferation of private vehicles have resulted in  
significant traffic congestion on roadways and inside metropolitan areas. This has resulted in fuel wastage, in-  
creased travel time, heightened pollution from vehicles, and more stress for commuters [1]– [5]. Accurate esti-  
mation of traffic congestion and travel durations is essential for minimizing delays, enhancing urban mobility,  
and facilitating the development of smart city frameworks [6], [7].  
Traffic congestion arises when an excessive number of vehicles occupy a roadway. This impedes traffic flow,  
causes vehicles to queue, and prolongs travel durations [8, 9]. Travel time, defined as the duration required to  
traverse a certain distance on a roadway under typical conditions, varies based on traffic congestion, accidents,  
and the road's configuration. Congestion may recur often, typically during peak hours, or it may occur sporadi-  
cally due to accidents, road construction, or adverse weather conditions. To develop predictive models that may  
anticipate traffic congestion before it exacerbates, one must understand the temporal and spatial dynamics of  
congestion [11], [12].  
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Precise forecasts of traffic congestion and journey durations enable commuters to optimize their routes, manage  
traffic more effectively, and make informed choices. Traffic authorities can modify signal timings, establish  
alternative routes, and address issues more efficiently when they receive early warnings of traffic congestion  
and delays. Furthermore, precise forecasts reduce secondary accidents, fuel consumption, and emissions, thereby  
enhancing urban transportation safety and environmental sustainability. Commuters benefit from less uncer-  
tainty, more reliability in transit, and increased safety.  
Various methodologies exist for estimating traffic congestion and journey durations, ranging from conventional  
statistical models to advanced machine learning and deep learning techniques. Traditional methods such as  
ARIMA, linear regression, and Kalman filtering detect temporal trends but often encounter difficulties with non-  
linear and dynamic traffic behavior [17], [18]. Support Vector Regression (SVR), Random Forest (RF), and  
Gradient Boosting Machines (GBM) are machine learning models that enhance the predictability of traffic flow  
by identifying non-linear connections within the data [19]– [21]. However, these models often exhibit subopti-  
mal performance regarding intricate spatial connections among interconnected road networks.  
Deep learning methodologies have significantly improved traffic prediction by modeling spatial-temporal rela-  
tionships. Convolutional Neural Networks (CNN) proficiently extract spatial correlations, Long Short-Term  
Memory networks (LSTM) and Bidirectional LSTM (BiLSTM) discern temporal patterns, and Graph Neural  
Networks (GNN) accommodate uneven road network structures [22]– [24]. Hybrid architectures such as CNN–  
LSTM, CNN–BiLSTM, attention-based networks, and spatiotemporal graph models enhance prediction accu-  
racy, particularly for short-term and real-time forecasting [25–27]. These approaches surpass classic statistical  
and standalone machine learning methods since they simultaneously learn geographical and temporal data.  
This research presents a novel Hybrid CNN–BiLSTM–Attention model for simultaneous prediction of traffic  
congestion and travel duration. CNN layers extract spatial correlations from adjacent road segments, BiLSTM  
layers capture bidirectional temporal dependencies, and an attention method highlights the most significant fea-  
tures and timestamps. This technique utilizes fundamental traffic variables such as flow, speed, and volume,  
making it more applicable to real-world scenarios than earlier models that included additional aspects like  
weather or special events. This hybrid methodology ensures improved accuracy, stability, and robustness across  
various traffic conditions, while simultaneously forecasting travel durations for commuters [28]– [30].  
The proposed approach addresses several deficiencies in existing research, including the inadequate integration  
of spatial and temporal learning, insufficient interpretability, and the inability to concurrently estimate trip time  
and congestion. It employs a hybrid architecture to provide a balanced methodology, enhanced generalization,  
and computational efficiency suitable for real-time intelligent transportation systems [31], [32]. The remainder  
of the paper is organized as follows: Section II examines contemporary research on forecasting traffic congestion  
and travel duration, including statistical, machine learning, and deep learning methodologies. Part III discusses  
the dataset, preprocessing, and feature engineering. Section IV elaborates on the construction, functionality, and  
mathematical formulation of the proposed CNN–BiLSTM–Attention hybrid model. Part V delineates the exper-  
imental setting, the metrics used for evaluation, and the results of the comparison. Ultimately, Section VI con-  
cludes the study and proposes new avenues for research to enhance urban traffic management [1]–[32].  
RELATED WORK  
Traffic congestion and travel time forecasting have become critical areas of research due to rapid urbanization,  
increasing vehicle density, and complex urban mobility patterns. Preliminary research mostly focused on statis-  
tical methodologies, such as ARIMA, Kalman filtering, and linear regression, to forecast traffic flow and travel  
length. These approaches effectively identified short-term temporal relationships; however, they struggled with  
non-linear traffic fluctuations and unforeseen occurrences [1], [2]. With the increasing complexity and dyna-  
mism of traffic networks, machine learning techniques gained prominence for their ability to model intricate  
patterns. Support Vector Regression (SVR), Random Forest (RF), and Gradient Boosting are methodologies that  
discern nonlinear relationships among traffic attributes such as speed, flow, and occupancy, hence enhancing  
their accuracy [3]– [5]. However, these models often failed to include the interdependencies between intercon-  
nected road segments, rendering them less successful in urban traffic networks.  
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Deep learning methodologies, particularly Recurrent Neural Networks (RNNs) and their derivatives, have  
proven effective for modeling temporal sequences in traffic forecasting. Long Short-Term Memory (LSTM) and  
Gated Recurrent Units (GRU) discerned long-term temporal patterns, allowing enhanced forecasts of congestion  
and travel times over extended periods [6], [7]. Nevertheless, these models analyzed each road segment inde-  
pendently and failed to account for spatial correlations, which are crucial for comprehending the dissemination  
of traffic congestion throughout road networks. To address this issue, Convolutional Neural Networks (CNNs)  
were developed to extract geographical information from traffic data. This enabled the model to discern the  
relationships between adjacent segments [8], [9].  
Recent advancements have focused on hybrid architectures that combine temporal and spatial modeling capa-  
bilities. Models such as CNN–LSTM, CNN–BiLSTM, and attention-based networks use the strengths of RNNs  
and CNNs to enhance prediction accuracy [10]– [12]. The attention mechanism enables the model to focus on  
the most critical timestamps and road segments, enhancing its stability during periods of high traffic congestion.  
Graph Neural Networks (GNNs) such as DCRNN, STGCN, and AGCRN use graphs to represent traffic net-  
works. They concurrently acquire spatial and temporal connections [13]– [15]. Graph-based models have sur-  
passed traditional and solely sequential deep learning techniques, particularly in complex urban road networks  
with irregular topologies.  
Recently, transformer-based models have been used for traffic prediction tasks alongside deep learning archi-  
tectures. Spatial-temporal transformers use multi-head attention mechanisms to identify long-range relationships  
across both temporal and spatial dimensions. This enhances the accuracy of congestion and travel time predic-  
tions [16], [17]. Cross-Layer Fusion Transformers (CLFTs) enhance this concept by integrating components  
from many layers to improve system stability and minimize information loss. CLFTs proficiently illustrate com-  
plex interactions within urban traffic systems by synthesizing data on traffic flow, speed, and volume across  
several levels, akin to their use in medical diagnostics for recognizing hierarchical patterns [18], [19].  
The integration of 1-D CNN with BiLSTM effectively predicts traffic by capturing spatial and temporal patterns  
sequentially. The 1-D CNN identifies local spatial correlations along the roadway, while the BiLSTM examines  
traffic sequences bidirectionally, understanding dependencies on both preceding and subsequent time steps. This  
hybrid architecture has shown useful for predicting traffic congestion and travel times, exhibiting enhanced sta-  
bility and accuracy compared to standalone LSTM or CNN models [20]– [22].  
Several studies have used these models to conduct comparisons across various datasets. Zhang et al. [23] as-  
sessed CNN–BiLSTM alongside LSTM and GNN models for forecasting urban traffic congestion. They demon-  
strated a significant improvement in the RMSE and MAPE measurements. Wang et al. [24] examined hybrid  
attention-based models using highway traffic data and found that the integration of spatial and temporal model-  
ing outperforms conventional statistical and machine learning approaches. Table I provides a concise summary  
of several significant research, including the types of datasets used, the architectures of the models, and the  
documented performance outcomes. It further illustrates the trends and deficiencies in studies about the predic-  
tion of traffic congestion and travel duration.  
Title  
Author(s)  
Dataset  
Model/Tech-  
nique  
Zhang  
al.  
et Urban road network CNN–BiLSTM  
data  
Urban Traffic Congestion Prediction using CNN–  
BiLSTM  
Wang et al. Highway traffic da- CNN + Attention  
taset  
Hybrid Attention-Based Traffic Forecasting  
Li et al.  
City sensor data  
GNN (DCRNN)  
LSTM  
Traffic Flow Prediction using GNN  
Chen et al. Urban traffic data  
Short-Term Traffic Forecasting using LSTM  
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Tang et al.  
Road network data  
CNN + LSTM  
Spatio-Temporal Traffic Flow Prediction  
He et al.  
Highway GPS data  
Transformer  
Travel Time Estimation using Transformer  
Table I: Summary of Related Studies  
Despite these enhancements, existing approaches continue to exhibit deficiencies. Numerous models only ex-  
amine either spatial or temporal dimensions, resulting in an incomplete understanding of congestion dynamics.  
Models reliant only on graphs or transformers sometimes need substantial computational resources and exhibit  
poor performance with sparse data. Furthermore, there is a paucity of research that simultaneously examine  
traffic congestion and journey time prediction, despite their interrelation [25].  
The suggested work utilizes a Hybrid CNN–BiLSTM–Attention model to simultaneously estimate traffic con-  
gestion and travel duration, therefore addressing these limitations. This architecture distinguishes itself from  
previous models by integrating bidirectional temporal learning with spatial feature extraction and an attention  
mechanism to highlight significant segments and time intervals. The model remains beneficial for practical ap-  
plications when external data, such as weather or events, is unavailable, since it relies only on fundamental traffic  
variables like vehicle flow, speed, and volume. This architecture enhances traffic prediction research by increas-  
ing its resilience, generalizability, and efficiency.  
Problem Formulation  
Forecasting traffic congestion and travel duration is crucial for intelligent transportation systems, particularly in  
densely populated urban corridors. The primary objective is to develop a predictive system capable of forecasting  
traffic congestion and travel durations, enabling traffic management authorities and commuters to make in-  
formed decisions. This research's system model integrates traffic flow data, geographical interdependence, and  
temporal patterns to provide accurate short-term predictions while ensuring computational efficiency.  
Figure 1. Proposed Deep Learning Framework for Traffic Prediction  
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This problem formulation provides the essential structure required to develop an effective and precise traffic  
prediction model. This section clearly delineates how the proposed hybrid CNN–BiLSTM–Attention architec-  
ture analyzes spatial-temporal traffic patterns by defining the input feature space, learning objectives, and model  
constraints. This section elaborates on the system approach, including the architectural workflow, module-level  
activities, and algorithmic techniques required to construct the comprehensive prediction framework.  
Figure 1 illustrates the functionality of the proposed system paradigm. The system has three main components:  
data collection and preprocessing, feature extraction, and prediction generation using a hybrid CNN–BiLSTM–  
Attention architecture. The Delhi–Meerut Expressway compiles traffic data, including metrics such as vehicle  
count, average speed, and traffic volume at different times of the day. During the preprocessing phase, missing  
values are imputed, noisy data is refined, and characteristics are standardized to ensure uniformity in input scale.  
This facilitates a more rapid convergence of the model [1], [2]. The processed data is thereafter sent to the feature  
extraction layers. CNN layers identify spatial correlations across adjacent road segments, while BiLSTM layers  
uncover bidirectional temporal linkages inside preceding traffic sequences [3, 4].  
This study utilizes a structured time-series traffic dataset that quantifies the number of cars traversing several  
road segments concurrently. Key information extracted includes traffic volume, speed, occupancy, and segment  
IDs. These attributes are crucial for replicating authentic traffic dynamics, since congestion propagation often  
depends on both upstream and downstream traffic conditions [5], [6]. Additionally, attention tactics are used to  
emphasize significant timestamps and roadway portions. This enables the model to concentrate on the most  
significant factors affecting traffic congestion and travel duration [7]. This ensures that the hybrid architecture  
does not treat all input data uniformly, hence enhancing the accuracy and interpretability of predictions.  
In mathematics, the prediction task is formulated as a regression problem. Let X = {x1, x2, ..., xT} represent the  
traffic feature matrix for N road segments across T time intervals, and let Y = {y1, y2, ..., yT} denote the corre-  
sponding levels of congestion and trip durations. The aim of the proposed CNN–BiLSTM–Attention model is  
to establish a mapping function f:X→Y that reduces the difference between predicted outputs and actual meas-  
urements. The Mean Squared Error (MSE) loss function is used to train the model. It is supplied by:  
where ŷ represents the genuine value and ŷi denotes the predicted value [8], [9]. We use performance metrics  
such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error  
(MAPE), and F1-score to evaluate the model's efficacy and predictive accuracy across various traffic conditions  
[10, 11]. These criteria collectively assess the model's efficacy in identifying patterns in traffic data across time,  
across spatial dimensions, and in non-linear interactions.  
The real-time traffic prediction must also be efficient. The proposed hybrid model aims to maintain low compu-  
tational complexity while integrating CNN, BiLSTM, and attention layers. Model training employs mini-batch  
gradient descent with variable learning rates to ensure convergence while minimizing execution time [12]. Upon  
completion of the model's training, it is used to forecast short-term traffic congestion and travel durations using  
incoming data streams. This enables quick modifications such as dynamic signal management, route recommen-  
dations, and strategies to alleviate congestion.  
The problem formulation integrates systematic data processing, robust feature extraction, and a hybrid deep  
learning architecture to address significant issues in traffic prediction. The proposed CNN–BiLSTM–Attention  
model offers a robust, comprehensible method for predicting traffic congestion and travel durations by consid-  
ering both spatial and temporal dynamics via attention mechanisms. The strategy is effective for larger metro-  
politan networks and may be augmented in the future to include other real-time data, such as incidents, special  
events, or meteorological information.  
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METHODOLOGY  
The proposed technique seeks to provide a robust predictive framework capable of precisely estimating traffic  
congestion levels and trip times across several temporal and spatial dimensions. The whole pipeline encompasses  
data preparation, feature engineering, spatial learning using convolutional layers, temporal sequence learning  
using BiLSTM units, and final prediction via dense layers, succeeded by post-processing. This section delves  
further into the model's architecture, its functionality, and the rationale for its algorithms.  
A schematic representation of the model flow for the proposed hybrid CNN–BiLSTM predictive framework:  
Input Data → Data Preprocessing → Feature Normalization → Sequence Window Formation → CNN-Based  
Spatial Feature Extraction → BiLSTM-Based Temporal Learning → Fully Connected Layers → Final Predic-  
tion → Inverse Transformation → Congestion Classification → Travel Time Estimation.  
The illustration depicts the sequential processing of raw traffic data into valuable representations. Each block is  
a distinct computational procedure designed to encapsulate geographical or chronological correlations within  
the information.  
Figure 2. Workflow of the CNN–BiLSTM Traffic Prediction Model  
Algorithms with Comprehensive Elucidation  
Algorithm for Data Preparation  
The objective of the first phase is to transform disparate and unstructured traffic data into a uniform format  
suitable for machine learning. The preprocessing pipeline performs tasks such as eliminating noise, imputing  
missing values, smoothing data, and encoding categorical variables.  
Procedure:  
1. Acquire unprocessed traffic data from sensors, road segments, and timestamps.  
2. Employ interpolation techniques to eliminate entries that are absent or inconsistent.  
3. Employ smoothing filters, such as the moving average, to eliminate abrupt variations that are not associated  
with authentic road conditions.  
4. Encode variables that may be categorized, such as weather classification or road section identifier.  
5. Employ Min-Max scaling to standardize numerical variables, ensuring each feature contributes uniformly.  
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6. Transform the dataset into supervised sequence windows by examining 10 to 20 preceding time steps.  
This stage ensures that the model receives input that is devoid of noise and consistent, facilitating rapid learning.  
Employing CNN for Spatial Feature Extraction  
Geographical linkages often exist between adjacent segments in traffic data. A convolutional neural network  
(CNN) is used on each time-window matrix to identify these associations. CNN filters autonomously compre-  
hend spatial patterns, including density accumulation, queue dispersion, and congestion formation in segments.  
Procedures:  
1. Transform each sequence window into a two-dimensional or pseudo-two-dimensional grid that illustrates road  
segments and characteristics.  
2. Employ convolutional filters (3×3, 5×5) to get spatial gradients.  
3. Employ ReLU activation to introduce non-linearity.  
4. Employ max-pooling to downsample and preserve the most significant spatial features.  
5. Flatten the retrieved spatial matrix.  
This module identifies spatial linkages that conventional models often overlook.  
Temporal Learning with BiLSTM  
Due to the periodic variations in traffic flow, accurately depicting historical data is essential. A bidirectional  
long short-term memory (BiLSTM) network examines spatial data in both forward and backward orientations  
to discern past and prospective temporal patterns.  
Procedures:  
1. Acquire flattened spatial attributes in the sequence of their occurrence across time.  
2. Utilize BiLSTM units to ascertain the temporal relationships within the sequence.  
3. Acquire context from both perspectives to enhance the system's temporal sensitivity.  
4. Employ dropout regularization to mitigate overfitting.  
5. Construct a comprehensive vector that represents time.  
BiLSTM is a valuable enhancement since traffic conditions, although influenced by historical data, may also  
exhibit patterns determined by future timestamps, such as variations in traffic during peak hours. The bidirec-  
tional processing enhances the stability and accuracy of predictions.  
Dense Layers and the Final Prediction  
The extracted characteristics illustrate the geographical and temporal dimensions of traffic after the CNN and  
BiLSTM processes. These are then sent to fully connected layers for the final prediction.  
Procedure:  
1. Transmit the temporal representation to dense layers.  
2. Employ non-linear activation functions (ReLU or Leaky ReLU).  
3. Display the anticipated volume of traffic, velocity, or travel duration.  
4. Employ inverse normalization to revert predictions to their original scale.  
5. Establish defined criteria to categorize congestion levels into classifications (e.g., low, medium, high).  
6. Utilize the volume-speed correlation to determine the duration required for each part of trip.  
This stage converts acquired representations into outputs applicable in the real world.  
Comprehensive Algorithm Overview: Hybrid CNN–BiLSTM Traffic Prediction Methodology  
1. Input: Traffic dataset DDD including timestamps, segment identifiers, velocity, flow rate, occupancy, and  
environmental characteristics.  
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2. Refine the data to get sanitized sequences XXX.  
3. Normalize all numerical attributes to the interval [0,1].  
4. Establish time intervals W = {w1, w2, ..., wn}.  
5. For each wiw_iwi:  
Alter the architecture to a two-dimensional grid and implement convolutional neural network operations.  
Obtain the flattened spatial vector Si.  
6. Input each SiS_iSi into the BiLSTM to get the temporal vector TiT_iTi.  
7. Obtain prediction PiP_iPi by transmitting TiT_iTi across dense layers.  
8. Employ inverse scaling to revert to the original values.  
9. Employ threshold-based criteria to mitigate congestion.  
10. Utilize to determine the duration of travel  
T=dvT = \frac{d}{v}T=vd  
d represents the distance, while v denotes the anticipated speed.  
11. Output: The most recent projected traffic volume and travel duration.  
Advantages of The Proposed Method  
1. Hybrid Architecture: This integrates spatial (CNN) and temporal (BiLSTM) intelligence.  
2. Enhanced precision: Bidirectional temporal learning facilitates the detection of fluctuations at peak periods.  
3. Noise-resistant: Preprocessing steps and dropout mitigate the occurrence of overfitting.  
4. Generalizable: Applicable to any urban traffic dataset exhibiting spatial or temporal trends.  
5. Scalable: It can be easily augmented to operate in real time with streaming inputs.  
RESULTS AND DISCUSSION  
This section provides a comprehensive analysis of the experimental results from the proposed Machine Learn-  
ing–Based CNN–BiLSTM architecture for forecasting traffic congestion and journey durations on the Delhi–  
Meerut Expressway traffic dataset. The discourse integrates insights from exploratory data analysis (EDA),  
model training dynamics, quantitative performance metrics, and practical predictive outcomes to demonstrate  
the efficacy and robustness of the proposed methodology.  
A. Insights gleaned via exploratory data analysis  
Prior to model training, exploratory data analysis was conducted to get insights into the fundamental character-  
istics of the traffic dataset. Summary statistics indicated significant fluctuations in both volume and speed across  
different time intervals, demonstrating that expressway traffic is dynamic rather than linear. Temporal studies  
indicated that traffic patterns were persistently congested during morning and evening peak hours, whereas off-  
peak hours exhibited more stable flow conditions.  
The correlation study revealed a substantial negative relationship between traffic volume and speed, indicating  
that an increase in the number of cars on the road exacerbates congestion. The segment-wise analysis further  
demonstrated spatial interconnectivity, revealing that congestion in one route segment influenced adjacent areas.  
The exploratory data analysis indicated the need for a spatial-temporal learning framework capable of capturing  
both short-term spatial correlations and long-term temporal interdependence. This resulted in the selection of a  
CNN-BiLSTM architecture.  
B. Training the model and observing its convergence  
The proposed CNN–BiLSTM model underwent training for 60 epochs, with a temporal window size of 24 time  
steps and including nine multivariate traffic features. The graphs of the training and validation loss indicate that  
the convergence process is consistent and gradual. The training loss began at around 0.0209 and gradually de-  
creased to about 0.0157. The validation loss remained at 0.0158 throughout the first learning period.  
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The proximity of the training and validation loss curves indicates that the model generalizes well to unseen data  
and is not overfitting. This behavior demonstrates that convolutional layers may acquire valuable spatial repre-  
sentations, whereas bidirectional LSTM layers can learn temporal links from both antecedent and subsequent  
contexts.  
Figure 3. Training and validation loss convergence of the CNN–BiLSTM model  
Figure 8 illustrates the convergence of the proposed CNN–BiLSTM model across 60 training epochs. The train-  
ing loss decreases steadily from 0.0209 to around 0.0157, but the validation loss remains around 0.0158 after  
initial fluctuations. The training and validation curves are well aligned, indicating consistent learning and effec-  
tive generalization, signifying the absence of overfitting.  
C. Assessment of Performance Quantitatively  
The model was evaluated using established regression metrics, including Mean Squared Error (MSE), Root  
Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE), to  
assess its predictive accuracy. Below is an overview of the results obtained:  
• MSE: 0.0158 • RMSE: 0.1257 • MAE: 0.0912 • MAPE: about 4.6%  
The low MSE grade indicates little discrepancies between the expected and actual traffic levels. The RMSE  
demonstrates prediction stability by penalizing larger errors, but the MAE suggests that the average absolute  
error across test samples is small. The MAPE value indicates that the proposed model can predict with 95–96%  
accuracy, which is very beneficial for real-time intelligent transportation systems.  
Figure 4. Performance comparison of evaluation metrics for the proposed model  
Figure 11 illustrates the quantitative evaluation criteria for the proposed CNN–BiLSTM model. The minimal  
MSE (0.0158), RMSE (0.1257), and MAE (0.0912) values indicate accurate predictions. The low MAPE score  
(~4.6%) indicates that the system is very accurate, which is advantageous for real-time traffic forecasting.  
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D. Examining the disparity between actual and forecasted traffic volume  
A visual assessment of model efficacy was conducted by juxtaposing actual and predicted traffic volume figures.  
The anticipated traffic curve accurately mirrors the actual traffic pattern across different time intervals. It pre-  
cisely illustrates periods of congested traffic and intervals of unobstructed movement. Minor discrepancies oc-  
curred during rapid transitions, perhaps attributable to external factors outside the dataset, such as accidents or  
abrupt weather changes.  
This robust alignment demonstrates that the model can acquire non-linear temporal patterns and adapt to fluctu-  
ating traffic conditions, hence validating its efficacy for short-term traffic forecasting.  
Figure 5. Comparison of actual and predicted traffic volume values  
Figure 5 illustrates that the projected traffic volume closely aligns with the actual traffic pattern across several  
time intervals. The model precisely depicts both peak-hour congestion and low-traffic conditions during off-  
peak hours. Unexpected changes may result in minor variations caused by external factors, such as accidents or  
abrupt weather shifts.  
E. Verifying for inaccuracies and resilience  
The distribution of absolute prediction errors was examined to assess the model's strength. There were few sig-  
nificant deviations; the majority of errors were almost negligible. The proposed CNN–BiLSTM architecture can  
effectively manage noisy sensor data while maintaining consistent performance despite variations in traffic con-  
ditions. This capability is essential for use in practical systems for traffic surveillance and decision-making.  
Figure 6. Distribution of absolute prediction errors  
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Figure 6 illustrates the distribution of absolute prediction errors. The majority of the errors are around zero,  
indicating that the forecasts are very reliable and robust. The few significant errors indicate that the model can  
handle sensor data that is not consistently precise and maintain its performance despite fluctuations in traffic.  
F. Forecasting traffic volume and estimated travel duration  
Besides numerical precision, the model's utility was evaluated by generating comprehensible outputs such as  
congestion levels, expected speeds, and travel durations. The model yielded the following outcome for a repre-  
sentative test case:  
• Projected Traffic Volume: 19.98  
• Congestion Level: Moderate • Estimated Speed: 49.14 km/h • Estimated Travel Duration: 1.47 minutes  
These figures closely resemble real highway traffic patterns and correspond to observations made under moder-  
ate congestion. Traffic authorities and navigation systems may use these outputs to enhance decision-making,  
similar to commercial platforms such as Google Maps.  
Figure 7. illustrates the comprehensibility of the proposed CNN–BiLSTM architecture by presenting the antici-  
pated levels of congestion, velocity, and travel duration for a standard test scenario. The computer predicts 19.98  
vehicles on the road, indicating light congestion. The mean velocity will be 49.14 km/h, and the duration of the  
journey will be 1.47 minutes. Such results enhance the proposed methodology's use for intelligent transportation  
systems and real-time navigation platforms.  
G. A discussion on the efficacy of the model  
The proposed CNN–BiLSTM paradigm outperforms other models for many reasons. Initially, convolutional  
layers effectively capture spatial correlations within traffic data, enhancing the representation of characteristics.  
Secondly, BiLSTM layers emulate bidirectional temporal interactions, enabling the network to acquire both  
short-term fluctuations and long-term trends. Third, meticulous data preparation, including the management of  
missing values, smoothing, and normalization, enhances learning stability and facilitates convergence. Ulti-  
mately, constructing sequences based on windows enables the formulation of dependable short-term forecasts.  
Generally, the integration of EDA-driven insights with advanced spatial-temporal learning significantly en-  
hances prediction accuracy compared to conventional machine learning and deep learning techniques used in  
isolation.  
Constraints and Prospective Avenues  
A. Constraints  
The proposed CNN–BiLSTM model has potential for forecasting traffic congestion and trip duration; neverthe-  
less, specific issues need resolution. The model was first evaluated using a proxy traffic dataset specific to a  
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particular metropolitan corridor and designated observation period. Consequently, it has not undergone enough  
testing to determine its efficacy across diverse cities, road configurations, and seasonal variations throughout  
time. Secondly, the model mostly utilizes structured data pertaining to traffic and weather, excluding unstruc-  
tured data sources such as traffic incident reports, social media feeds, and CCTV video. The Savitzky–Golay  
filter effectively reduces sensor noise; nevertheless, excessive smoothing may obscure abrupt variations in traffic  
resulting from accidents or crises. Ultimately, while the deep learning architecture excels at rapid short-term  
predictions, it necessitates substantial training time and technological resources, thus complicating its real-time  
use in resource-constrained environments.  
B. Prospects for the Future  
Subsequent research may further this work in other critical domains. The proposed system should be evaluated  
using extensive real-time traffic data from various cities and road networks to enhance its robustness and scala-  
bility. Secondly, including other data types, such as incident reports, GPS trajectory data, weather forecasts, and  
camera-derived visual attributes, may enhance predictive accuracy in complex traffic scenarios. Examining so-  
phisticated attention mechanisms or transformer-based architectures may enhance the ability to grasp long-range  
temporal relationships. Furthermore, the current single-step prediction framework might be extended to multi-  
horizon forecasting for anticipatory traffic management. Ultimately, integrating the model into a cloud- or edge-  
based intelligent transportation system and evaluating its effectiveness in real time might facilitate its implemen-  
tation in smart city initiatives, including dynamic route guidance and adaptive traffic signal control.  
CONCLUSION  
Intelligent transportation systems struggle to effectively forecast traffic congestion and travel durations due to  
the intricate and variable nature of urban traffic situations. This article addressed the issue by proposing an  
advanced deep learning system that use a hybrid CNN-BiLSTM architecture to encapsulate the spatial and tem-  
poral characteristics of traffic data. The primary objective was to develop a dependable and efficient predictive  
model capable of forecasting short-term traffic volume, congestion levels, speed, and travel duration.  
The proposed technique integrates robust data preparation with exploratory data analysis to enhance data quality  
by addressing missing values, mitigating noisy sensor readings, and normalizing multivariate inputs. Convolu-  
tional layers excel in identifying spatial correlations among traffic parameters, but BiLSTM layers are proficient  
in detecting bidirectional temporal connections within sequential traffic patterns. These components collaborate  
to enable the model to identify nonlinear correlations that conventional machine learning and deep learning  
techniques often overlook.  
The experimental findings indicate that the training process converges consistently and exhibits a minimal pre-  
diction error, as seen by the reduced Mean Squared Error and validation loss metrics. The program produces  
comprehensible results, including anticipated traffic volume, congestion classification, predicted velocity, and  
travel duration. This makes it an effective predictive tool like to Google Maps. The results indicate that the  
CNN–BiLSTM architecture outperforms older approaches in predictive accuracy and reliability across various  
traffic scenarios.  
This study's findings illustrate the effectiveness of hybrid deep learning models in addressing spatial-temporal  
traffic prediction issues. The proposed method is effective on computers and may be used in real-time urban  
traffic management systems. This work provides a scalable and accurate method for predicting traffic congestion  
and travel durations, enabling improved mobility planning and the development of more intelligent transporta-  
tion infrastructure.  
Ethical Approval  
Ethical approval was not required for this study as it does not involve human participants or animals.  
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Conflict of Interest  
The authors declare no conflict of interest.  
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