Multimodal Intelligent Transportation System: Comprehensive Practice of CNN Traffic Sign Recognition, Random Forest Risk Prediction, and NLP Complaint Analysis

This article introduces an open-source intelligent transportation system project integrating three technical stacks: computer vision, traditional machine learning, and natural language processing. It covers the technical principles and collaborative applications of three sub-modules—CNN image classification (traffic sign recognition), random forest risk assessment (road risk prediction), and TF-IDF sentiment analysis (user complaint handling)—demonstrating the comprehensive practice of multimodal technology in the intelligent transportation field.

Intelligent Transportation Systems (ITS) need to integrate multiple AI technologies: not only to 'see' (recognize traffic signs, etc.), but also to 'predict' (forecast road section risks), and even to 'understand' (comprehend user complaints). The project Image-Based-Traffic-Sign-Classification-Using-Deep-Learning, published by developer kowshika-santhosh-2111data on GitHub, embodies this multimodal fusion idea and includes three functional modules.

Traffic sign recognition is a fundamental capability of ADAS, facing challenges such as lighting changes, occlusion, and diverse categories. CNN extracts hierarchical features (shallow edges/corners, deep semantic features) through convolutional layers, reduces dimensionality and enhances translation invariance via pooling layers, and finally outputs category probabilities through fully connected layers. The project may use the GTSRB dataset (50,000+ images, 43 categories) and improve robustness through data augmentation techniques like random rotation and translation.

This module uses structured tabular data (historical accidents, road features, traffic flow, weather, etc.) to predict road section risk levels. Random forest builds multiple decision trees through Bootstrap sampling and random feature selection, reducing variance and ensuring stability. Its advantages include no need for standardization preprocessing, tolerance for missing values/outliers, and the ability to output feature importance, helping traffic departments identify high-risk driving factors (such as sharp curves, poor road surfaces).

User complaint texts can help prioritize urgent issues. The project uses TF-IDF to extract text features (combining term frequency and inverse document frequency) and is paired with a logistic regression classifier (fast training, strong interpretability). Although Transformer models are more advanced, TF-IDF + logistic regression is still practical in scenarios with limited data/computation or where interpretability is required.

The three modules complement each other: CNN provides real-time perception, random forest offers macro risk assessment, and NLP introduces user feedback. For example, a road section with high risk prediction + a large number of negative complaints + CNN-identified sign damage—these three pieces of evidence can improve decision reliability and priority judgment accuracy.

This project demonstrates the integrated application of deep learning (CNN), traditional machine learning (random forest), and NLP (TF-IDF + logistic regression) in the intelligent transportation field. For learners, it not only provides implementation references for three types of tasks but also reveals that real-world problems need to be solved through the collaboration of multiple technologies rather than the isolated application of a single algorithm.