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Browsing AI, Robotics and Space by Subject "3509 Transportation, Logistics and Supply Chains"

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    Interaction-aware and driving style-aware trajectory prediction for heterogeneous vehicles in mixed traffic environment
    (IEEE, 2025) Zhang, Qixiang; Xing, Yang; Wang, Jinxiang; Fang, Zhenwu; Liu, Yahui; Yin, Guodong
    Trajectory prediction (TP) of surrounding vehicles (SVs) is crucial for autonomous vehicles (AVs) to understand traffic situations and achieve safe-efficient decision-making and motion planning. However, different drivers’ personalized driving preferences will bring uncertainties for long-term TP in the mixed traffic environment. To this end, this paper proposes a TP model with interaction awareness and driving style awareness for long-term TP of heterogeneous SVs. Firstly, the driving conditions in the highD dataset are distinguished, and three different driving styles of the vehicle in the car-following condition are obtained based on an unsupervised clustering algorithm. Then, an encoder-decoder architecture based on novel lane attention and multi-head attention mechanisms is proposed, where the encoder analyzes historical trajectory patterns and the decoder generates future trajectory sequences. The lane attention mechanism enhances the spatial perception capability of vehicles towards the target lane, and the multi-head attention mechanism extracts high-dimensional global interaction information about the heterogeneous vehicle group (HVG) surrounding the target vehicle (TV). Experimental results show that the proposed model outperforms state-of-the-art models in root-mean-square-error (RMSE) for long-term TP and exhibits excellent adaptability to diverse driving tasks. Moreover, this paper verifies that the driving style topology within the HVG has multiple impacts on the TP accuracy of the TV.
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    Machine Learning driven complex network analysis of transport systems
    (Elsevier, 2025-07) Xia, Yuqin; Wang, Kewei; Tanirat, Purin; Lee, Bryan; Moulitsas, Irene; Li, Jun
    A complex network is a system of interconnected nodes linked by edges, exhibiting non-trivial structural features such as community structure or scale-free distributions. This study develops a novel and generic Machine Learning-driven framework that integrates Complex Network Theory and Machine Learning methods for a comprehensive and multifaceted analysis of transport systems. Specifically, four key functional development and analysis are undertaken: 1) Network analysis, using complex network indicators to study the static properties of the transport systems; 2) Network clustering, employing K-means and hierarchical clustering methods to identify underlying community structures; 3) Network resilience, examining the networks' dynamic characteristics and structural evolution under escalating node attacks to evaluate their robustness; 4) Link and feature prediction, developing Graph Convolutional Networks (GCNs) and Multi-Layer Perceptron (MLP) models to predict hidden links and features. The proposed framework is subsequently applied to two distinct transport systems, namely, the China railway network and the Paris multi-modal transport system. The complex network analysis reveals distinct complex network features in network scale, density, and efficiency, yet both demonstrate a power-law distribution. The clustering analysis based on various node and edge properties exhibits a pattern of concentric circles, radiating outward from the urban to peripheral cities in China railway network, while a high density of short-distance connections within central Paris and a prevalence of long-distance connections in the outskirts. The network attack simulations show fine resilience of the Parisian multi-modal system and low resilience of the China railway network. For link prediction, an encoder-decoder model based on GCN and multiple MLPs are developed for various scenarios. The results for the China railway network reveal critical interregional links, emphasizing the need to strengthen regional connectivity, such as expanding the high-speed railway between Hainan Island and the mainland, and establishing a major transportation artery running from south to north. In the Paris transport system, this study predicts an interesting link extending from southern Paris eastward toward northern Seine-et-Marne, indicating a demand for a direct connection. For both networks, the hidden links are largely concentrated in more developed areas, likely driven by strong economic and social interaction demands, highlighting the need for more balanced transport network development. Overall, the results of this study align closely with existing literature and official transport development plans. This research contributes to the theoretical development in Complex Network Analysis using Machine Learning and offers valuable insight to improve the two transport systems.