Publications de Zhixiong Jin | EPFL Graph Search

SaliencyMix plus : Noise-Minimized Image Mixing Method With Saliency Map in Data Augmentation

Data augmentation is vital in deep learning for enhancing model robustness by artificially expanding training datasets. However, advanced methods like CutMix blend images and assign labels based on pixel ratios, often introducing label noise by neglecting ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2025

A gentle introduction and tutorial on Deep Generative Models in transportation research

Zhixiong Jin

Deep Generative Models (DGMs) have rapidly advanced in recent years, becoming essential tools in various fields due to their ability to learn data distributions and generate synthetic data. Their importance in transportation research is increasingly recogn ...

2025

Do enhanced school zone policies improve pedestrians' safety? A deep learning-based case study of Osan City, South Korea

Zhixiong Jin

This study investigates the effectiveness of strengthened penalty policies in South Korean school zones by analyzing the changes in road users' behaviors, focusing on pedestrian-vehicle interactions. This study employed three surrogate safety measurements: ...

ELSEVIER SCI LTD2024

Prioritized Phase Split Optimization for Coordinated Traffic Signal Control in Urban Network Using Deep Reinforcement Learning

Zhixiong Jin

In traffic signal control (TSC), Deep Reinforcement Learning (DRL) has demonstrated its superiority to traditional approaches. However, there remain two challenges in DRL-based traffic signal control. How to cooperatively control decentralized intersection ...

IEEE2023

From Prediction to Prevention: Leveraging Deep Learning in Traffic Accident Prediction Systems

Zhixiong Jin

We propose a novel system leveraging deep learning-based methods to predict urban traffic accidents and estimate their severity. The major challenge is the data imbalance problem in traffic accident prediction. The problem is caused by numerous zero values ...

MDPI2023