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A Surrogate-assisted Multi-objective Grey Wolf Optimizer for Empty-heavy Train Allocation Considering Coordinated Line Utilization Balance

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Journal of Bionic engineering 2025年第1期22卷 383-397页

作者： Zhigang Du Shaoquan Ni Jeng-Shyang Pan Shuchuan Chu School of Transportation and Logistics Southwest Jiaotong UniversityChengdu610031China School of Artificial Intelligence Nanjing University of Information Science and TechnologyNanjing210044China National and Local Joint Engineering Lab for Integrated Transportation Intelligence Chengdu610031China National Engineering Lab of Comprehensive Transportation Big Data Application Technology Chengdu610031China

This paper introduces the Surrogate-assisted Multi-objective Grey Wolf Optimizer(SMOGWO)as a novel methodology for addressing the complex problem of empty-heavy train allocation,with a focus on line utilization *** integrating surrogate models to approximate the objective functions,SMOGWO significantly improves the efficiency and accuracy of the optimization *** effectiveness of this approach is evaluated using the CEC2009 multi-objective test function suite,where SMOGWO achieves a superiority rate of 76.67%compared to other leading multi-objective ***,the practical applicability of SMOGWO is demonstrated through a case study on empty and heavy train allocation,which validates its ability to balance line capacity,minimize transportation costs,and optimize the technical combination of heavy *** research highlights SMOGWO's potential as a robust solution for optimization challenges in railway transportation,offering valuable contributions toward enhancing operational efficiency and promoting sustainable development in the sector.

关键词： Surrogate-assisted model Grey wolf optimizer Multi-objective optimization Empty-heavy train allocation

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Calibration of Car-Following Behavior Based on Monocular Camera 11th

Calibration of Car-Following Behavior Based on Monocular Cam...

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11th International Conference on Traffic and transportation Studies, ICTTS 2024

作者： Liu, Yufei Wei, Chong MOT Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport School of Traffic and Transportation Beijing Jiaotong University Beijing100044 China School of Mechanical and Electrical Engineering Hainan University Haikou China

ISBN: (纸本)9789819796434

In the past, most of the research on car-following models in China focused on physical statistics, with relatively little collection and analysis of car-following data. Therefore, it is essential to establish a car-following model suitable for analyzing the car-following characteristics of Chinese drivers based on real driving data. This paper utilizes a vehicle-mounted monocular camera to capture traffic flow images on the highway and uses the YOLOv5 algorithm to detect vehicles. Through the homography transformation between the image plane in the front view and the spatial plane in the top view, the pixel coordinates are converted to 3D spatial coordinates. Then, the ranging model is established, and its effectiveness is verified using the length of the lane dividing lines. The factor analysis is used to determine the main influencing factors of car-following behavior. The appropriate linear car-following models are selected and its parameters are calibrated using multiple linear regression methods. This paper realizes the collection and subsequent processing of car-following behavior data under natural driving conditions, and transforms generalized car-following models into specific research theories for application in practical situations. This method of collecting car-following driving data has strong practicality and can provide effective data support for studying car-following models that accurately reflect the operation conditions of road traffic. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Behavioral research

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Fundamental Diagram Modeling of Mixed Traffic Flow Considering Dedicated Lanes for Human-Driven Vehicles

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IEEE Transactions on Vehicular technology 2025年

作者： Wang, Yi Xu, Zeqi Wu, Yunxia Jiang, Yangsheng Yao, Zhihong Southwest Jiaotong University School of Transportation and Logistics National Engineering Laboratory of Integrated Transportation Big Data Application Technology National United Engineering Laboratory of Integrated and Intelligent Transportation Sichuan610031 China

To mitigate the impact of human-driven vehicles (HDVs) on connected and automated vehicles (CAVs) in mixed traffic environments, the implementation of dedicated lanes has been proposed to achieve partial separation between CAVs and HDVs, thereby improving the operational efficiency of both CAVs and the road segment. The lane management policy, where dedicated lanes for HDV (HDLs) and general lanes (GLs) coexist on a road segment, is referred to as the (G, H) policy. This paper proposes a multi-lane fundamental diagram model for mixed traffic flow and aims to investigate the effects of HDL configuration on the efficiency of road segments under the (G, H) policy. Firstly, different car-following modes in mixed traffic flow are analyzed, and various car-following models are employed to characterize the mixed traffic flow. Secondly, two lane selection principles are introduced to describe the lane choice behavior of HDVs under the (G, H) policy. Based on these principles, five equilibrium states that may exist on the road segment under the (G, H) policy are analyzed. Subsequently, a multi-lane fundamental diagram model incorporating HDL is derived based on the lane selection principles of HDV. Finally, numerical analysis is conducted to investigate the influence of lane configuration schemes under the (G, H) policy on the distribution of equilibrium states, fundamental diagram, and capacity. The results indicate that: (1) Based on the lane choice behavior of vehicles, the equilibrium states of road segment can be classified into five types. The distribution of each equilibrium state under different traffic conditions only depends on the proportion of HDL to the total number of lanes on road segment. A higher number of HDL leads to a reduced applicability of the (G, H) policy under different traffic conditions. (2) Under different penetration rates, as density increases, the overall traffic volume of road segment initially increases and then decreases until reaching the

关键词： Highway administration

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StoryLLaVA: Enhancing Visual Storytelling with Multi-Modal Large Language Models 31

StoryLLaVA: Enhancing Visual Storytelling with Multi-Modal L...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Yang, Li Xiao, Zhiding Huang, Wenxin Zhong, Xian Hubei Key Laboratory of Big Data Intelligent Analysis and Application School of Computer Science and Information Engineering Hubei University China Hubei Key Laboratory of Transportation Internet of Things School of Computer Science and Artificial Intelligence Wuhan University of Technology China

ISBN: (纸本)9798891761964

The rapid development of multi-modal large language models (MLLMs) has positioned visual storytelling as a crucial area in content creation. However, existing models often struggle to maintain temporal, spatial, and narrative coherence across image sequences and frequently lack the depth and engagement of human-authored stories. To address these challenges, we propose Story with Large Language and Vision Assistant (StoryLLaVA), a novel framework for enhancing visual storytelling. Our approach introduces a Topic-Driven Narrative Optimizer (TDNO) that improves both the training data and MLLM models by integrating image descriptions, topic generation, and GPT-4-based refinements. Furthermore, we employ a preference-based ranked story sampling method that aligns model outputs with human storytelling preferences through positive-negative pairing. These two phases of the framework differ in their training methods: the former uses supervised fine-tuning, while the latter incorporates reinforcement learning with positive and negative sample pairs. Experimental results demonstrate that StoryLLaVA outperforms current models in visual relevance, coherence, and fluency, with LLM-based evaluations confirming the generation of richer and more engaging narratives. The enhanced dataset and model are available at https://***/XxxZzD/StoryLLaVA. © 2025 Association for Computational Linguistics.

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LightST: A Simplifying Spatio-Temporal Graph Neural Network for Traffic Flow Forecasting

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IEEE Transactions on big data 2025年

作者： Hu, Jie Zheng, Taichuan Peng, Lilan Teng, Fei Du, Shengdong Li, Tianrui Southwest Jiaotong University School of Computing and Artificial Intelligence Chengdu611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology China

Traffic flow forecasting task plays an essential role in intelligent transportation systems. Accurately capturing the intricate spatio-temporal dependencies in traffic network signals is the core of precise prediction. Recently, a paradigm that models spatio-temporal dependencies through graph neural networks and time series models has become one of the most promising methods to solve this problem. However, existing methods still have limitations due to ineffectively modeling dynamic spatial dependencies and high time and space complexity. To address these issues, we propose a simplifying and powerful general spatio-temporal traffic flow forecasting model called LightST. Specifically, LightST first embeds temporal covariates and spatial position information to enhance the spatio-temporal modeling capabilities. Then, stacked temporal linear layers are introduced to capture temporal dependencies efficiently. Finally,we propose a concise adaptive spatio-temporal embedding graph convolution method to extract implicit spatial dependencies over time via dynamic graph convolution with adaptive spatio-temporal embedding graph generation. Extensive experiment results on four public traffic flow datasets demonstrate the superiority of our LightST concerning computational efficiency and prediction performance. © 2025 IEEE. All rights reserved,

关键词： Graph embeddings

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Cooperative Eco-Driving for Mixed Platoons with Heterogeneous Energy at a Signalized Intersection Based on a Mixed Space-Time-State Network

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IEEE Transactions on Vehicular technology 2025年

作者： Xiao, Guosheng Wu, Yunxia Jiang, Yangsheng Ran, Bin Yao, Zhihong Southwest Jiaotong University School of Transportation and Logistics National Engineering Laboratory of Integrated Transportation Big Data Application Technology National United Engineering Laboratory of Integrated and Intelligent Transportation Sichuan610031 China University of Wisconsin-Madison Department of Civil and Environmental Engineering MadisonWI53706 United States

With the ongoing trend of vehicle automation and electrification, the future will present a mixed traffic environment with different automation and energy consumption types. However, current research focuses more on fully connected or homogeneous energy consumption scenarios in eco-driving, ignoring the impact of human-driven vehicles and electric vehicles (EVs) with high energy efficiency and regenerative braking capabilities. This study proposes a cooperative eco-driving framework for mixed vehicle platoons with heterogeneous energy at a signalized intersection. A Mixed Space-Time-State Network is developed to optimize the trajectory of connected autonomous vehicles (CAVs), while predicting and describing the trajectories of human-driven vehicles. Then, a modified dynamic programming algorithm is formulated, which converts the highly nonlinear optimization problem into a state-space search problem. The solving efficiency is further improved by 85% with a feasible state space reduction algorithm, which can meet the real-time computational requirements. The result indicates that cooperation within the mixed platoon can smooth out the trajectory while considering the composition of energy consumption. In addition, the energy efficiency can be improved by 20.39% with a low CAV penetration rate. © 1967-2012 IEEE.

关键词： Dynamic programming

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The Death and Life of Free-Floating Car Sharing in China: Case Study of Chengdu and Changchun 1st

The Death and Life of Free-Floating Car Sharing in China: Ca...

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1st International Symposium on Intelligent technology for Future transportation, ITFT 2024

作者： Wang, Hongjie Luo, Xia Su, Qiming Bao, Hongqing School of Transportation and Logistics Southwest Jiaotong University Chengdu China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu China National United Engineering Laboratory of Integrated and Intelligent Transportation Southwest Jiaotong University Chengdu China

ISBN: (纸本)9783031841477

In the last decades, free-floating car sharing (FFCS) has become a fast-growing mode of mobility service and promoted in many cities across the world. While lots of researchers have paid increasing attention to FFCS in recent years, little is known regarding the characterization and evolution of FFCS in Chinese cities, where car sharing services are gaining more and more popularity. In this work, we focus on two Chinese cities: Chengdu and Changchun, both of which have witnessed the shutdown of FFCS service and its replacement with station-based car sharing (SBCS). We rebuild 743,796 trips using public data from the smartphone application of a car sharing service, then explore the characteristics and spatiotemporal distribution of FFCS usage, and identify the differences compared to those in Europe and North America. Furthermore, we conduct a comparative analysis of the service coverage, demand spatiotemporal distribution and usage patterns of car sharing services before and after the shutdown of FFCS. Results show that, the shutdown of FFCS did not cause a severe disruption to the car sharing service. Instead, the introduction of SBCS effectively inherited and accommodated the travel demand. This work underscores the importance of considering the unique characteristics of China’s FFCS services when develop operation strategies and demonstrates that a well-designed SBCS could emerge as a viable alternative. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： big data Operation Scheme Transition Shared Mobility

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Research on Temporary Parking Facility Site Selection for Shared Autonomous Vehicles

Research on Temporary Parking Facility Site Selection for Sh...

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International Conference on Artificial Intelligence and Autonomous transportation, AIAT 2024

作者： Wei, Yixuan Liu, Zhiyong Zhang, Shuang Li, Qiaoming School of Traffic and Transportation Beijing Jiaotong University Beijing100044 China Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Beijing Jiaotong University Beijing100044 China Beijing Subway Operation Technology Consulting Stock Co.Ltd. Beijing100082 China

ISBN: (纸本)9789819639687

Shared Autonomous Vehicles (SAVs) are gradually developing, and their parking demand exhibits a short-term and frequent nature that traditional parking facilities cannot meet. To address this issue, this paper proposes a two-stage temporary parking facility location model based on mixed-integer programming for SAV parking. The first stage of the model focuses on the location selection of parking facilities, while the second stage addresses the dispatching of SAVs, with the objective of minimizing the combined costs of facility construction and vehicle dispatching. To enhance solution efficiency, this study introduces a Callback mechanism to improve the classical Benders decomposition algorithm, thus avoiding the computational overhead of repeatedly constructing the master problem. The feasibility of the model and the effectiveness of the algorithm were validated through case studies on both the Sioux Falls road network and the large-scale road network of Langfang City. The results demonstrate that the Callback-enhanced Benders decomposition algorithm not only significantly improves solution speed but also performs effectively on large-scale urban road networks, offering valuable insights and methods for large-scale SAV parking location optimization. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Integer programming

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Dual Graph Inference Network for Weakly Supervised Semantic Segmentation

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Zhang, Jia Peng, Bo Wu, Xi Southwest Jiaotong University School of Computing and Artificial Intelligence Sichuan Chengdu611756 China Southwest Jiaotong University National Engineering Laboratory of Integrated Transportation Big Data Application Technology Chengdu611756 China Chengdu University of Information Technology School of Computer Science Sichuan Chengdu610225 China

Establishing global contextual relationships between objects is crucial for weakly supervised semantic segmentation (WSSS) tasks that lack pixel-level labels. Limited by the efficiency of convolutional operations in capturing long-range dependencies with a limited receptive field and to bridge the gap between image-level annotations and pixel-level labels, we propose a Dual Graph Reasoning Mapping (DGRM) module. When integrated into a convolutional network, it conducts contextual graph reasoning on both spatial and interaction spaces of visual features. The first component of this graph reasoning module involves incorporating commonsense knowledge extracted from an external knowledge base into visual features to promote global contextual reasoning for visual graphs. The second component focuses on reasoning in the projected interaction space, utilizing abstracted object class attributes from high-level visual features to establish dependencies among channels in a potential low-dimensional space. Moreover, to capture correspondences at different semantic levels, we model the feature maps in a pyramid-like structure for graph reasoning at various levels. Extensive experiments on popular datasets, such as PASCAL VOC 2012 and MS COCO 2014, demonstrate the superiority of our approach. Our code is provided at https://***/JIAZHANG666/DGRM. © 2025 IEEE.

关键词： Semantic Segmentation

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AdvMap: Crafting Adversarial Maps to Counter AI Aimbot in First-Person Shooter Games

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IEEE Transactions on Games 2025年

作者： Yan, Kai Chen, Xianyi Cui, Qi Yuan, Haoqin Tan, Zhenshan Nanjing University of Information Science and Technology Engineering Research Center of Digital Forensics Ministry of Education the School of Computer Science Nanjing210044 China Beijing Jiaotong University Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Beijing100044 China

AI-based automatic aiming cheats (a.k.a., AI aimbots) have proliferated in first-person shooter (FPS) games, which grant malicious users an unfair gameplay advantage. Since AI aimbots operate independently of game data and are developed using object detection algorithms, they are difficult to detect with traditional anti-cheating methods. To actively counter AI aimbots, we propose AdvMap, which introduces invisible adversarial perturbations into game scene elements. In optimizing these adversarial perturbations, we design a mixture-of-misleading loss function that increases the total target confidence score within each misleading bounding box. It mitigates the risk of segment missing even when AdvMap is obscured, thereby enhancing the robustness. Besides, an L1-norm constraint with a small scale is employed during each update of the adversarial perturbations, which preserves the fidelity of the game scene. Additionally, to enable effective adaptation to interact with various elements within game environments, we introduce an Image-Subspace-Based Multi-Directional (ISMD) optimization strategy. It enables the adversarial perturbations to adaptively fit into each element by leveraging the mapping relationship between the game's 3D scenes and its corresponding 2D images. Furthermore, we construct a comprehensive benchmark, which includes various FPS games with different graphics styles and perspectives. Extensive experimental results demonstrate the efficacy of our method in countering various AI aimbot tools on different state-of-the-art object detection methods. © 2021 IEEE.

关键词： Adversarial machine learning

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