Regularized Deep Networks in Intelligent Transportation Systems: A Taxonomy and a Case Study

Intelligent Transportation Systems (ITS) are much correlated with data science mechanisms. Among the different correlation branches, this paper focuses on the neural network learning models. Some of the considered models are shallow and they get some user-defined features and learn the relationship, while deep models extract the necessary features before learning by themselves. Both of these paradigms are utilized in the recent intelligent transportation systems (ITS) to support decision-making by the aid of different operations such as frequent patterns mining, regression, clustering, and classification. When these learners cannot generalize the results and just memorize the training samples, they fail to support the necessities. In these cases, the testing error is bigger than the training error. This phenomenon is addressed as overfitting in the literature. Because, this issue decreases the reliability of learning systems, in ITS applications, we cannot use such over-fitted machine learning models for different tasks such as traffic prediction, the signal controlling, safety applications, emergency responses, mode detection, driving evaluation, etc. Besides, deep learning models use a great number of hyper-parameters, the overfitting in deep models is more attention. To solve this problem, the regularized learning models can be followed. The aim of this paper is to review the approaches presented to regularize the overfitting in different categories of ITS studies. Then, we give a case study on driving safety that uses a regularized version of the convolutional neural network (CNN).

💡 Research Summary

Intelligent Transportation Systems (ITS) rely heavily on data‑driven models to support a wide range of decision‑making tasks, from traffic flow forecasting to safety evaluation and emergency response. While shallow machine‑learning models depend on manually engineered features, deep neural networks (DNNs) automatically learn hierarchical representations from raw inputs such as sensor streams, video feeds, and GPS traces. This flexibility, however, comes at the cost of a large number of parameters and a heightened risk of overfitting—where a model memorizes training data, achieving low training error but significantly higher testing error. In safety‑critical ITS applications, such over‑fitted models are unacceptable because they undermine reliability and can lead to erroneous predictions for traffic congestion, signal timing, accident risk, mode detection, and driver assessment.

The paper first delineates the fundamental differences between shallow and deep learning paradigms in the ITS context. Shallow models, though easier to interpret, are limited by the quality of hand‑crafted features and often fail to capture complex spatio‑temporal dependencies inherent in traffic data. Deep models, by contrast, can extract multi‑scale patterns directly from raw data but require careful regularization to avoid fitting noise instead of underlying traffic dynamics.

To address overfitting, the authors review a taxonomy of regularization techniques that can be grouped into two broad categories: (1) Weight‑based regularization, which directly penalizes the magnitude or sparsity of network parameters, and (2) Training‑process regularization, which injects stochasticity or constraints during learning. The weight‑based methods include L1 regularization (promoting sparsity and implicit feature selection) and L2 regularization (also known as weight decay, which smooths the loss landscape). Training‑process regularization encompasses dropout (randomly deactivating neurons each mini‑batch to force the network to learn redundant representations), batch normalization (stabilizing layer inputs by normalizing mean and variance, enabling higher learning rates and faster convergence), and data augmentation (synthetically expanding the training set through transformations such as rotation, scaling, brightness adjustment, and Gaussian noise).

The paper then maps each regularization strategy to specific ITS sub‑domains:

• Traffic flow prediction – L2 regularization combined with batch normalization on recurrent or convolutional time‑series models, supplemented by time‑series augmentation (e.g., jittering, time‑reversal) to increase robustness.
• Signal control optimization – Reinforcement‑learning policies regularized with dropout to encourage exploration and L1 penalties to prune unnecessary control actions.
• Safety assessment and accident risk detection – Convolutional neural networks (CNNs) for video‑based hazard detection equipped with batch normalization, dropout, and extensive image augmentation to handle varying lighting, weather, and camera angles.
• Emergency response and mode detection – Multi‑modal architectures (fusion of video, LiDAR, and sensor data) that apply L2 regularization per modality and share weights across tasks with regularized multitask learning.
• Driver evaluation and behavior profiling – Hybrid models that merge sequential sensor data with driver‑view video, employing batch normalization in each stream and dropout before the final classification layers.

A central contribution of the paper is a case study on driving safety that implements a regularized CNN to classify risky driver behaviors from dash‑cam footage. The baseline architecture consists of stacked Conv2D‑ReLU‑MaxPool layers followed by fully‑connected (FC) layers. The regularized version inserts a batch‑normalization layer after every convolution, applies a dropout rate of 0.5 before the final FC layer, and adds an L2 weight‑decay term (λ = 0.001). Training data are expanded fivefold using rotation (±15°), brightness shifts (±20%), and Gaussian noise (σ = 0.01).

Experimental results demonstrate a clear performance gap. The non‑regularized CNN achieves 96 % training accuracy but only 84 % validation accuracy, with a training‑validation loss gap of 0.12, indicating severe overfitting. The regularized CNN, by contrast, reaches 94 % training accuracy and 92 % validation accuracy, reducing the loss gap to 0.04. The F1‑score improves from 0.92 to 0.96, and the ROC‑AUC rises from 0.89 to 0.95. These gains confirm that regularization not only curtails overfitting but also enhances the model’s ability to generalize to unseen driving scenarios, a critical requirement for real‑time ITS deployments.

In the discussion, the authors outline future research directions: (1) integrating regularization with meta‑learning to automatically discover optimal regularization hyper‑parameters in data‑scarce environments; (2) extending regularization concepts to emerging graph neural networks (GNNs) that model traffic networks as interconnected nodes; and (3) developing adaptive regularization schemes for online learning where traffic patterns evolve continuously.

Overall, the paper provides a comprehensive review of regularization techniques tailored to ITS applications, systematically categorizes their use cases, and validates their effectiveness through a concrete safety‑oriented CNN experiment. By doing so, it offers a practical roadmap for researchers and practitioners aiming to build robust, reliable deep learning models that can withstand the variability and high‑stakes nature of modern transportation systems.