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Jiaxing Shen

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AAAI Conference 2025 Conference Paper

FairTP: A Prolonged Fairness Framework for Traffic Prediction

Jiangnan Xia
Yu Yang
Jiaxing Shen
Senzhang Wang
Jiannong Cao

Traffic prediction is pivotal in intelligent transportation systems. Existing works focus mainly on improving overall accuracy, overlooking a crucial problem of whether prediction results will lead to biased decisions by transportation authorities. In practice, the uneven deployment of traffic sensors in different urban areas produces imbalanced data, making the traffic prediction model fail in some urban areas and leading to unfair regional decision-making that eventually severely affects equity and quality of residents’ life. Existing fairness machine learning models struggle to maintain fair traffic prediction over prolonged periods. Although these models might achieve fairness at certain time slots, this static fairness will break down as traffic conditions change. To fill this research gap, we investigate prolonged fair traffic prediction, introducing two novel fairness metrics, i.e., region-based static fairness and sensor-based dynamic fairness, tailored to fairness fluctuations over time and across areas. An innovative prolonged fairness traffic prediction framework, namely FairTP, is then proposed. FairTP achieves prolonged fairness by alternating between “sacrifice” and “benefit” the prediction accuracy of each traffic sensor or area, ensuring that the number of these two actions are balanced over time. Specifically, FairTP incorporates a state identification module to discriminate whether the traffic sensors or areas are in a “sacrifice” or “benefit” state, thereby enabling prolonged fairness-aware traffic predictions. Additionally, we devise a state-guided balanced sampling strategy to select training examples to further enhance prediction fairness by mitigating the performance disparities among areas with uneven sensor distribution over time. Extensive experiments in two real-world datasets show that FairTP significantly improves prediction fairness without causing significant accuracy degradation.

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EAAI Journal 2024 Journal Article

Multi-modal transform-based fusion model for new product sales forecasting

Xiangzhen Li
Jiaxing Shen
Dezhi Wang
Wu Lu
Yuanyi Chen

New product sales prediction is crucial for the digital economy as it enables businesses to make informed decisions about product development, inventory management, marketing strategies, and ultimately driving economic growth and innovation. In the digital economy era, traditional sales forecasting methods often struggle to address the unique challenges of forecasting demand for new products, primarily due to limited historical data and high levels of uncertainty. To address this challenge, we propose a multi-modal transform-based fusion model for new product sales prediction (M2TFM), which integrates multiple data sources (e. g. , product images, attributes, text descriptions and context factors like holidays, weather and trends.) to predict new product sales with remarkable accuracy. The proposed method leverages diffusion embedding to fuse heterogeneous data modalities including images, text, and time series into a unified representation that models their complex interactions. By encoding multi modal data using Transformer self-attention, our approach is able to extract nuanced signals across modalities to make more accurate new product sales forecasts. We perform a comprehensive evaluation on a large e-commerce dataset with more than 10, 000 fashion items, and the results demonstrate that the proposed method is more effective than existing state-of-the-art baselines for new product sales forecasting.

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TIST Journal 2017 Journal Article

DMAD

Jiaxing Shen
Jiannong Cao
Xuefeng Liu
Chisheng Zhang

Wireless networks offer many advantages over wired local area networks such as scalability and mobility. Strategically deployed wireless networks can achieve multiple objectives like traffic offloading, network coverage, and indoor localization. To this end, various mathematical models and optimization algorithms have been proposed to find optimal deployments of access points (APs). However, wireless signals can be blocked by the human body, especially in crowded urban spaces. As a result, the real coverage of an on-site AP deployment may shrink to some degree and lead to unexpected dead spots (areas without wireless coverage). Dead spots are undesirable, since they degrade the user experience in network service continuity, on one hand, and, on the other hand paralyze some applications and services like tracking and monitoring when users are in these areas. Nevertheless, it is nontrivial for existing methods to analyze the impact of human beings on wireless coverage. Site surveys are too time consuming and labor intensive to conduct. It is also infeasible for simulation methods to predict the number of on-site people. In this article, we propose DMAD, a Data-driven Measuring of Wi-Fi Access point Deployment, which not only estimates potential dead spots of an on-site AP deployment but also quantifies their severity, using simple Wi-Fi data collected from the on-site deployment and shop profiles from the Internet. DMAD first classifies static devices and mobile devices with a decision-tree classifier. Then it locates mobile devices to grid-level locations based on shop popularities, wireless signal, and visit duration. Last, DMAD estimates the probability of dead spots for each grid during different time slots and derives their severity considering the probability and the number of potential users. The analysis of Wi-Fi data from static devices indicates that the Pearson Correlation Coefficient of wireless coverage status and the number of on-site people is over 0.7, which confirms that human beings may have a significant impact on wireless coverage. We also conduct extensive experiments in a large shopping mall in Shenzhen. The evaluation results demonstrate that DMAD can find around 70% of dead spots with a precision of over 70%.

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