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Jianbo Li

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7 papers

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EAAI Journal 2025 Journal Article

Shared mobility demand prediction via A fast spatiotemporal tensor autoregression

Hongyu Yan
Zhiqiang Lv
Jianbo Li
Benjia Chu
Zhihao Xu

Shared mobility is critical to urban transportation, yet its complex spatiotemporal dynamics challenge traditional prediction methods. We propose the Tucker Decomposition-based Spatiotemporal Tensor Autoregressive Model (T-STAR), which leverages tensor-structured data modeling and Tucker decomposition to efficiently capture multi-dimensional dependencies. Unlike conventional methods, T-STAR preserves high-dimensional structures by decomposing raw spatiotemporal data into a low-rank core tensor and mode-specific factor matrices, reducing complexity and enhancing interpretability by decoupling spatial, temporal, and modal interactions. Experimental results on three benchmark datasets demonstrate T-STAR's strong performance. On the Beijing Taxi Trajectory Dataset (TaxiBJ), T-STAR achieves Mean Absolute Error (MAE) of 23. 53 and Root Mean Square Error (RMSE) of 37. 71, improving performance by 18. 5 % and 21. 2 % over baseline averages. On the New York City Taxi Dataset (NYCtaxi), it records MAE of 18. 18 and RMSE of 46. 87, reducing errors by 22. 7 % and 15. 4 %. In the sparse-demand New York City Bike-Sharing Dataset (NYCbike), it maintains robust accuracy with MAE of 7. 95 and RMSE of 14. 32, outperforming baselines by 14. 1 % and 17. 9 %, respectively. Most notably, T-STAR achieves these results at high speed: on TaxiBJ, it completes a prediction in just 0. 35 seconds–87 % faster than the Adaptive Graph Convolutional Recurrent Network (AGCRN) and 99. 8 % faster than the Diffusion Convolutional Recurrent Neural Network (DCRNN). By retaining over 95 % of key spatiotemporal correlations through Tucker compression, T-STAR reduces prediction error by 20–30 % while delivering real-time performance, offering a scalable framework for urban traffic prediction and shared vehicle scheduling. Code and data are both available at yanhongyu0/TSTAR (github. com)

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IJCAI Conference 2024 Conference Paper

Cross-modal Generation and Alignment via Attribute-guided Prompt for Unsupervised Text-based Person Retrieval

Zongyi Li
Jianbo Li
Yuxuan Shi
Hefei Ling
Jiazhong Chen
Runsheng Wang
Shijuan Huang

Text-based Person Search aims to retrieve a specified person using a given text query. Current methods predominantly rely on paired labeled image-text data to train the cross-modality retrieval model, necessitating laborious and time-consuming labeling. In response to this challenge, we present the Cross-modal Generation and Alignment via Attribute-guided Prompt framework (GAAP) for fully unsupervised text-based person search, utilizing only unlabeled images. Our proposed GAAP framework consists of two key parts: Attribute-guided Prompt Caption Generation and Attribute-guided Cross-modal Alignment module. The Attribute-guided Prompt Caption Generation module generates pseudo text labels by feeding the attribute prompts into a large-scale pre-trained vision-language model. These synthetic texts are then meticulously selected through a sample selection, ensuring the reliability for subsequent fine-tuning. The Attribute-guided Cross-modal Alignment module encompasses three sub-modules for feature alignment across modalities. Firstly, Cross-Modal Center Alignment (CMCA) aligns the samples with different modality centroids. Subsequently, to address ambiguity arising from local attribute similarities, an Attribute-guided Image-Text Contrastive Learning module (AITC) is proposed to facilitate the alignment of relationships among different pairs by considering local attribute similarities. Lastly, the Attribute-guided Image-Text Matching (AITM) module is introduced to mitigate noise in pseudo captions by using the image-attribute matching score to soften the hard matching labels. Empirical results showcase the effectiveness of our method across various text-based person search datasets under the fully unsupervised setting.

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EAAI Journal 2024 Journal Article

Progress and prospects of future urban health status prediction

Zhihao Xu
Zhiqiang Lv
Benjia Chu
Zhaoyu Sheng
Jianbo Li

Predicting future urban health status is significant in terms of identifying urban diseases and urban planning. Current studies have focused on using machine learning and deep learning models to predict future urban populations, urban safety, and traffic conditions. The city is a complex system, and perceiving the future urban health status requires accurate predictions of the multifarious components that make up the urban system. In this work, firstly, the improved meta-analysis approach is applied to the smart city domain. Secondly, considering the city as a complex system, the concept of urban factors is proposed. Eight urban factors are selected and they are graded into three tiers. Finally, methods suitable for predicting urban factors reflecting the health of a city are discussed in depth, and which prediction method is most suitable for a particular urban factor is summarized. Among the final 40 studies included in the meta-analysis, the maximum values for Prediction Improvement Rate (PIR) and Weighted PIR (WPIR) reach 95. 98% and 22. 31%, respectively.

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AAAI Conference 2021 Conference Paper

Outlier Impact Characterization for Time Series Data

Jianbo Li
Lecheng Zheng
Yada Zhu
Jingrui He

For time series data, certain types of outliers are intrinsically more harmful for parameter estimation and future predictions than others, irrespective of their frequency. In this paper, for the first time, we study the characteristics of such outliers through the lens of the influence functional from robust statistics. In particular, we consider the input time series as a contaminated process, with the recurring outliers generated from an unknown contaminating process. Then we leverage the influence functional to understand the impact of the contaminating process on parameter estimation. The influence functional results in a multi-dimensional vector that measures the sensitivity of the predictive model to the contaminating process, which can be challenging to interpret especially for models with a large number of parameters. To this end, we further propose a comprehensive single-valued metric (the SIF) to measure outlier impacts on future predictions. It provides a quantitative measure regarding the outlier impacts, which can be used in a variety of scenarios, such as the evaluation of outlier detection methods, the creation of more harmful outliers, etc. The empirical results on multiple real data sets demonstrate the effectivenss of the proposed SIF metric.

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IJCAI Conference 2018 Conference Paper

A Local Algorithm for Product Return Prediction in E-Commerce

Yada Zhu
Jianbo Li
Jingrui He
Brian L. Quanz
Ajay A. Deshpande

With the rapid growth of e-tail, the cost to handle returned online orders also increases significantly and has become a major challenge in the e-commerce industry. Accurate prediction of product returns allows e-tailers to prevent problematic transactions in advance. However, the limited existing work for modeling customer online shopping behaviors and predicting their return actions fail to integrate the rich information in the product purchase and return history (e. g. , return history, purchase-no-return behavior, and customer/product similarity). Furthermore, the large-scale data sets involved in this problem, typically consisting of millions of customers and tens of thousands of products, also render existing methods inefficient and ineffective at predicting the product returns. To address these problems, in this paper, we propose to use a weighted hybrid graph to represent the rich information in the product purchase and return history, in order to predict product returns. The proposed graph consists of both customer nodes and product nodes, undirected edges reflecting customer return history and customer/product similarity based on their attributes, as well as directed edges discriminating purchase-no-return and no-purchase actions. Based on this representation, we study a random-walk-based local algorithm for predicting product return propensity for each customer, whose computational complexity depends only on the size of the output cluster rather than the entire graph. Such a property makes the proposed local algorithm particularly suitable for processing the large-scale data sets to predict product returns. To test the performance of the proposed techniques, we evaluate the graph model and algorithm on multiple e-commerce data sets, showing improved performance over state-of-the-art methods.

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EAAI Journal 2017 Journal Article

Hesitant distance set on hesitant fuzzy sets and its application in urban road traffic state identification

Fangwei Zhang
Jianbo Li
Jihong Chen
Jing Sun
Augustine Attey

Since fuzziness lacks the distinction between a set and its complement, it is difficult to measure the distance between different hesitant fuzzy sets (HFSs) by a single value. In this study, a new concept “hesitant distance set (HDS)” is proposed, where the distance between different HFSs can be characterized by a series of different values. This study has three primary contributions. Firstly, most of the existing distance measures on HFSs are based on vector operation, while the novel proposed HDSs are based on set operation. Secondly, a statistical method is proposed to compare different HDSs, and some important properties of the comparison method are introduced. Thirdly, the characteristics of the novel HDSs and the classical hesitant distances are studied comparatively. Finally, the practicality and validity of the HDSs on HFSs are illustrated through an urban road traffic state identification example.

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TCS Journal 2007 Journal Article

Some approximation algorithms for the clique partition problem in weighted interval graphs

Mingxia Chen
Jianbo Li
Jianping Li
Weidong Li
Lusheng Wang

Interval graphs play important roles in analysis of DNA chains in Benzer [S. Benzer, On the topology of the genetic fine structure, Proceedings of the National Academy of Sciences of the United States of America 45 (1959) 1607–1620], restriction maps of DNA in Waterman and Griggs [M. S. Waterman, J. R. Griggs, Interval graphs and maps of DNA, Bulletin of Mathematical Biology 48 (2) (1986) 189–195] and other related areas. In this paper, we study a new combinatorial optimization problem, named the minimum clique partition problem with constrained bounds, in weighted interval graphs. For a weighted interval graph G and a bound B, partition the weighted intervals of this graph G into the smallest number of cliques, such that each clique, consisting of some intervals whose intersection on a real line is not empty, has its weight not beyond B. We obtain the following results: (1) this problem is NP -hard in a strong sense, and it cannot be approximated within a factor 3 2 − ε in polynomial time for any ε > 0; (2) we design three approximation algorithms with different constant factors for this problem; (3) for the version where all intervals have the same weights, we design an optimal algorithm to solve the problem in linear time.

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