Communications in Transportation Research最新文献

{"title":"Next leap in the sustainable transport revolution: Identifying gaps and proposing solutions for hydrogen mobility","authors":"Fangjie Liu ,&nbsp;Muhammad Shafique ,&nbsp;Xiaowei Luo","doi":"10.1016/j.commtr.2025.100180","DOIUrl":"10.1016/j.commtr.2025.100180","url":null,"abstract":"<div><div>Amid escalating global climate concerns, the reliance of the transportation sector on high-carbon fossil fuels urgently demands sustainable alternatives. Hydrogen has emerged as a potent solution because of its zero-emission usage, but its overall impact hinges on its full life cycle, which this review comprehensively examines. This article delves into the environmental, economic, and safety dimensions of hydrogen as an alternative fuel by systematically reviewing the life cycle assessment (LCA) literature across the production, storage, delivery, and usage phases, with a focus on electrolysis and natural gas reforming methods, among others. A key insight from this study is the critical importance of considering the entire delivery system holistically rather than isolating the delivery phase. Many studies have overlooked two important aspects: first, the distribution of hydrogen as a product itself is often underemphasized; second, the integration of storage and delivery (the “storage-delivery nexus”) is crucial since separating them can lead to misleading conclusions about cost and emissions. For example, while certain delivery methods may appear cost-effective, their associated storage processes (such as hydrogenation and dehydrogenation in liquid organic hydrogen carrier systems) can have significant emission impacts. To address these gaps, this study introduces a novel “surface-level” LCA framework to enhance the assessment of the environmental impacts of hydrogen, promoting a more integrated understanding of the storage-delivery system. This framework aims to provide more accurate insights into hydrogen's life cycle, thereby facilitating better-informed policy-making and technological advancements. This study underscores the imperative for robust policy support, public engagement, and continuous innovation to overcome these barriers, advocating for strategic initiatives that bolster the sustainability and adoption of hydrogen mobility, particularly in hydrogen fuel cell vehicles (HFCVs).</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"5 ","pages":"Article 100180"},"PeriodicalIF":12.5,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144170211","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Traffic simulation optimization considering driving styles","authors":"Yunyang Shi ,&nbsp;Tong Wu ,&nbsp;Tan Guo ,&nbsp;Jinbiao Huo ,&nbsp;Ziyuan Gu ,&nbsp;Yifan Dai ,&nbsp;Zhiyuan Liu","doi":"10.1016/j.commtr.2025.100181","DOIUrl":"10.1016/j.commtr.2025.100181","url":null,"abstract":"<div><div>Parameter calibration is essential for ensuring the accuracy of microscopic traffic simulations. The expected speed is a critical parameter that characterizes behaviors of vehicles in most simulation models, which is influenced by road traffic conditions and the driving characteristics of different drivers. Most existing parameter calibration methods typically concentrate on micro-level parameters such as time headway and lane change motivation, while overlooking the calibration of vehicle expected speeds in consideration of driver behavior habits. This study combines data from highway electronic toll collection (ETC), gantries, and 100-m mileage average speed data, and proposes a method for calibrating vehicle expected speed that considers driving style clustering. The Gaussian mixture model (GMM) algorithm is used to develop driver models with three distinct driving styles: aggressive, moderate, and conservative. To ensure driving diversity and enhance parameter calibration efficiency, we rebuild vehicle driving models and representative parameters based on the classification results. Moreover, the Bayesian optimization algorithm is modified in conjunction with a microscopic traffic simulation model to perform automatic calibration of expected speeds. Experiments conducted on the Shanghai–Hangzhou–Ningbo highway demonstrate that the proposed method significantly reduces the mean absolute percentage error (MAPE) from 20.2% (using default parameters) to 3.1%. Additionally, in the model robustness test, the MAPE reaches 5.01%, indicating a certain level of stability and scalability. This method proposes a tailored calibration method accounting for the heterogeneous driving behaviors of micro-traffic simulation models, achieving satisfactory calibration results for simulation models in highway scenarios.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"5 ","pages":"Article 100181"},"PeriodicalIF":12.5,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144170210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MetaSSC: Enhancing 3D semantic scene completion for autonomous driving through meta-learning and long-sequence modeling","authors":"Yansong Qu ,&nbsp;Zixuan Xu ,&nbsp;Zilin Huang ,&nbsp;Zihao Sheng ,&nbsp;Sikai Chen ,&nbsp;Tiantian Chen","doi":"10.1016/j.commtr.2025.100184","DOIUrl":"10.1016/j.commtr.2025.100184","url":null,"abstract":"<div><div>Semantic scene completion (SSC) plays a pivotal role in achieving comprehensive perceptions of autonomous driving systems. However, existing methods often neglect the high deployment costs of SSC in real-world applications, and traditional architectures such as three-dimensional (3D) convolutional neural networks (3D CNNs) and self-attention mechanisms struggle to efficiently capture long-range dependencies within 3D voxel grids, limiting their effectiveness. To address these challenges, we propose MetaSSC, a novel meta-learning-based framework for SSC that leverages deformable convolution, large-kernel attention, and the Mamba (D-LKA-M) model. Our approach begins with a voxel-based semantic segmentation (SS) pretraining task, which is designed to explore the semantics and geometry of incomplete regions while acquiring transferable meta-knowledge. Using simulated cooperative perception datasets, we supervise the training of a single vehicle's perception via the aggregated sensor data from multiple nearby connected autonomous vehicles (CAVs), generating richer and more comprehensive labels. This meta-knowledge is then adapted to the target domain through a dual-phase training strategy—without adding extra model parameters—ensuring efficient deployment. To further enhance the model's ability to capture long-sequence relationships in 3D voxel grids, we integrate Mamba blocks with deformable convolution and large-kernel attention into the backbone network. Extensive experiments show that MetaSSC achieves state-of-the-art performance, surpassing competing models by a significant margin while also reducing deployment costs.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"5 ","pages":"Article 100184"},"PeriodicalIF":12.5,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144154446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Closed-loop coordination of connected and automated vehicles in structured road networks","authors":"Shijie Cong ,&nbsp;Mingyu Zheng ,&nbsp;Wei Zhang ,&nbsp;Yiming Bie ,&nbsp;Emilian Szczepański","doi":"10.1016/j.commtr.2025.100182","DOIUrl":"10.1016/j.commtr.2025.100182","url":null,"abstract":"","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"5 ","pages":"Article 100182"},"PeriodicalIF":12.5,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143895922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Survey of research on autonomous driving testing with large models","authors":"Songyan Liu ,&nbsp;Shijie Cong ,&nbsp;Lan Yang","doi":"10.1016/j.commtr.2025.100179","DOIUrl":"10.1016/j.commtr.2025.100179","url":null,"abstract":"","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"5 ","pages":"Article 100179"},"PeriodicalIF":12.5,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143886676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Physical enhanced residual learning (PERL) framework for vehicle trajectory prediction","authors":"Keke Long,&nbsp;Zihao Sheng,&nbsp;Haotian Shi,&nbsp;Xiaopeng Li,&nbsp;Sikai Chen,&nbsp;Soyoung Ahn","doi":"10.1016/j.commtr.2025.100166","DOIUrl":"10.1016/j.commtr.2025.100166","url":null,"abstract":"<div><div>While physics models for predicting system states can reveal fundamental insights owing to their parsimonious structure, they may not always yield the most accurate predictions, particularly for complex systems. As an alternative, neural network (NN) models usually yield more accurate predictions; however, they lack interpretable physical insights. To articulate the advantages of both physics and NN models while circumventing their limitations, this study proposes a physics-enhanced residual learning (PERL) framework that adjusts a physics model prediction with a corrective residual predicted from a residual learning NN model. The integration of the physics model preserves interpretability and tremendously reduces the amount of training data compared with pure NN models. We apply PERL to a vehicle trajectory prediction problem with real-world trajectory data of both a human-driven vehicle (HV) and an autonomous vehicle (AV), using an adapted Newell car-following model as the physics model and four kinds of neural networks (Gated Recurrent Unit (GRU), Convolution long short-term memory (CLSTM), Variational Autoencoder (VAE), and the Informer model) as the residual learning model. We compare this PERL model with pure physics models, NN models, and other physics-informed neural network (PINN) models. The results reveal that PERL yields the best prediction when the training data are small. The PERL model converges quickly during training. Moreover, compared with the NN and PINN models, the PERL model requires fewer parameters to achieve similar predictive performance. A sensitivity analysis revealed that the PERL model consistently outperforms the physics models, NN models and PINN models with different physics and residual learning models given a small training dataset. Among these, the PERL model based on CLSTM achieved the most accurate predictions.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"5 ","pages":"Article 100166"},"PeriodicalIF":12.5,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143825944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A causality-based explainable AI method for bus delay propagation analysis","authors":"Qi Zhang ,&nbsp;Zhenliang Ma ,&nbsp;Zhiyong Cui","doi":"10.1016/j.commtr.2025.100178","DOIUrl":"10.1016/j.commtr.2025.100178","url":null,"abstract":"<div><div>Public transportation networks are highly interconnected, where disruptions like traffic congestion propagate bus delays and impact performance. Identifying delay causes is crucial, yet most studies rely on correlation-based methods rather than causal analysis. Attribution methods like the Shapley value quantify factor contributions but often overlook causal dependencies, leading to potential bias. This study uses a causal discovery model to uncover causal relationships between bus delays and various factors (e.g., operational factors, calendar, and weather). Based on this causal graph, an explainable Artificial Intelligence (AI) method quantifies each factor's contribution to delays, focusing on how these contributions vary at different stops along a route. By integrating scheduled route data and real-time vehicle locations, we analyze factor contributions over time and space, exploring various scenarios along the route. Cross-validation is conducted by comparing the importance ranking of factors with the Seemingly Unrelated Regression Equations (SURE). Results show significant variations in factors contributing to delays along the route. Delays at upstream stops propagate downstream, indicating a cascading effect. Operational factors dominate, accounting for 50%–83% of delays. Notably, delays from the preceding two to three stops have a larger impact than just the immediately preceding one stop, and origin delays strongly affect the first half of the route.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"5 ","pages":"Article 100178"},"PeriodicalIF":12.5,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143808766","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CASAformer: Congestion-aware sparse attention transformer for traffic speed prediction","authors":"Yifan Zhang ,&nbsp;Qishen Zhou ,&nbsp;Jianping Wang ,&nbsp;Anastasios Kouvelas ,&nbsp;Michail A. Makridis","doi":"10.1016/j.commtr.2025.100174","DOIUrl":"10.1016/j.commtr.2025.100174","url":null,"abstract":"<div><div>Accurate and efficient traffic speed prediction is crucial for improving roaDongguand safety and efficiency. With the emerging deep learning and extensive traffic data, data-driven methods are widely adopted to achieve this task with increasingly complicated structures and progressively deeper layers of neural networks. Despite the design of the models, they aim to optimize the overall average performance without discriminating against different traffic states. However, the fact is that predicting the traffic speed under congestion is normally more important than under free flow since the downstream tasks, such as traffic control and optimization, are more interested in congestion rather than free flow. Most of the State-Of-The-Art (SOTA) models unfortunately do not differentiate between the traffic states during training and evaluation. To this end, we first comprehensively study the performance of the SOTA models under different speed regimes to illustrate the low accuracy of low-speed prediction. We further propose and design a novel Congestion-Aware Sparse Attention transformer (CASAformer) to enhance the prediction performance under low-speed traffic conditions. Specifically, the CASA layer emphasizes the congestion data and reduces the impact of free-flow data. Moreover, we adopt a new congestion adaptive loss function for training to make the model learn more from the congestion data. Extensive experiments on real-world datasets show that our CASAformer outperforms the SOTA models for predicting speed under 40 mph in all prediction horizons.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"5 ","pages":"Article 100174"},"PeriodicalIF":12.5,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143808765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Counterfactual explanations for deep learning-based traffic forecasting","authors":"Rushan Wang ,&nbsp;Yanan Xin ,&nbsp;Yatao Zhang ,&nbsp;Fernando Perez-Cruz ,&nbsp;Martin Raubal","doi":"10.1016/j.commtr.2025.100176","DOIUrl":"10.1016/j.commtr.2025.100176","url":null,"abstract":"<div><div>Deep learning models are widely used in traffic forecasting and have achieved state-of-the-art prediction accuracy. However, their black-box nature presents challenges for interpretability and usability, particularly when predictions are significantly influenced by complex urban contextual features. This study aims to leverage an explainable artificial intelligence (AI) approach, counterfactual explanations, to enhance the explainability of deep learning-based traffic forecasting models and elucidate their relationships with various contextual features. We present a comprehensive framework that generates counterfactual explanations for traffic forecasting. The study first implements a graph convolutional network (GCN) to predict traffic speed based on historical traffic data and contextual variables. Counterfactual explanations are generated through a multi-objective optimization process, with four objectives, validity, proximity, sparsity, and plausibility, each emphasizing different aspects of optimization. We investigated the impact of contextual features on traffic speed prediction under varying spatial and temporal conditions. The scenario-driven counterfactual explanations integrate two types of user-defined constraints, directional and weighting constraints, to tailor the search for counterfactual explanations to specific use cases. These tailored explanations benefit machine learning practitioners who aim to understand the model's learning mechanisms and traffic domain experts who seek insights for necessity factors to alter traffic condition. The results showcase the effectiveness of counterfactual explanations in revealing traffic patterns learned by deep learning models and explaining the relationship between traffic prediction and contextual features, demonstrating its potential for interpreting black-box deep learning models.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"5 ","pages":"Article 100176"},"PeriodicalIF":12.5,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143800692","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LC-LLM: Explainable lane-change intention and trajectory predictions with Large Language Models","authors":"Mingxing Peng ,&nbsp;Xusen Guo ,&nbsp;Xianda Chen ,&nbsp;Kehua Chen ,&nbsp;Meixin Zhu ,&nbsp;Long Chen ,&nbsp;Fei-Yue Wang","doi":"10.1016/j.commtr.2025.100170","DOIUrl":"10.1016/j.commtr.2025.100170","url":null,"abstract":"<div><div>To ensure safe driving in dynamic environments, autonomous vehicles should possess the capability to accurately predict lane change intentions of surrounding vehicles in advance and forecast their future trajectories. Existing motion prediction approaches have ample room for improvement, particularly in terms of long-term prediction accuracy and interpretability. In this study, we address these challenges by proposing a Lane Change-Large Language Model (LC-LLM), an explainable lane change prediction model that leverages the strong reasoning capabilities and self explanation abilities of Large Language Models (LLMs). Essentially, we reformulate the lane change prediction task as a language modeling problem, processing heterogeneous driving scenario information as natural language prompts for LLMs and employing supervised fine-tuning to tailor LLMs specifically for lane change prediction task. Additionally, we finetune the Chain-of-Thought (CoT) reasoning to improve prediction transparency and reliability, and include explanatory requirements in the prompts during the inference stage. Therefore, our LC-LLM not only predicts lane change intentions and trajectories but also provides CoT reasoning and explanations for its predictions, enhancing its interpretability. Extensive experiments based on the large-scale highD dataset demonstrate the superior performance and interpretability of our LC-LLM in lane change prediction task. To the best of our knowledge, this is the first attempt to utilize LLMs for predicting lane change behavior. Our study shows that LLMs can effectively encode comprehensive interaction information for understanding driving behavior.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"5 ","pages":"Article 100170"},"PeriodicalIF":12.5,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143738664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}