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Communications in Transportation Research
Scopus
ISSN 2772-4247 · Tsinghua University Press · 205 artikel di indeks
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Artikel Jurnal
Cross-temporal framework for driving behavior impact on electric vehicle battery health
Driving behavior significantly impacts battery health. Existing research mainly improves battery health through powertrain and vehicle control. However, they rarely isolate and quantify the contribution of driving behaviors, which may lead to overestimating theoretical gains and underperformance in…
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VLMPed-CoT: A large vision-language model with a chain-of-thought mechanism for pedestrian crossing intention prediction
Pedestrian crossing intention prediction is crucial for autonomous driving. While existing models have achieved high accuracy, their generalization and robustness remain limited, hindering their performance in real-world scenarios. To overcome these limitations, we introduce the LVLMPed-CoT, a…
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Joint longitudinal-lateral trajectory planning for CAVs in mixed traffic at signalized intersections
Mandatory lane changes (MLCs) pose significant challenges to trajectory planning at intersections, where vehicles are required to change lanes mid-block to reach designated turn lanes before the stop bar. MLCs often generate shockwaves that induce increased vehicle delay and fuel consumption, and…
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KEPT: Knowledge-enhanced prediction of trajectories from consecutive driving frames with vision-language models
Accurate short-horizon trajectory prediction is crucial for safe and reliable autonomous driving. However, existing vision language models (VLMs) often fail to accurately understand driving scenes and generate trustworthy trajectories. To address this challenge, this study introduces KEPT, a…
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Deep learning for vehicle ReID in urban traffic monitoring with visual and temporal information
This study introduces a novel deep learning framework that enhances vehicle re-identification (ReID) accuracy by integrating visual and temporal data. Vehicle ReID, which identifies target vehicles from large volumes of traffic data, is essential for continuous tracking in large-scale monitoring…
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A federated meta-learning approach for interpretable, privacy-preserving, and customizable behavior analysis
Travel behavior analysis provides critical insights to enhance the intelligence of transportation systems, enabling more accurate and efficient management of mobility services. However, it requires centralizing user-sensitive data which may violate regulations and laws about data security. Even…
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Scalable and interoperable C-V2X framework for real-time intelligent decision support in autonomous mobility
While V2X-based vehicle communication and intersection management have been widely studied, most existing approaches remain limited to simulation-based environments or constrained testbeds. Furthermore, standardized message formats such as SAE J2735 lack the extensibility required to support…
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Artikel Jurnal
TrafficPerceiver: A multimodal large language model with reinforcement learning for unified challenging traffic scene perception
Understanding traffic scenes under diverse and challenging conditions is critical for intelligent transportation systems (ITSs). Existing methods primarily focus on ideal scenarios and often lack the ability to perform fine-grained perception or respond to human instructions. To address these…
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Artikel Jurnal
Toward zero-forget continual learning for interactive trajectory prediction: A dynamically expandable approach
Accurate modeling and prediction of driving behavior are crucial for enabling autonomous vehicles to safely navigate complex, interactive traffic environments. While recent continual learning approaches for interactive trajectory prediction aim to learn efficiently from streaming data, they often…
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LLM-PDM: An LLM Persona-Driven Method for replicating personal mobility preferences at scale
Traditional travel surveys are costly, time-consuming and face declining response rates, motivating the exploration of artificial data generation methods. In this research, we propose a novel Persona-Driven Method (PDM) for generating synthetic mobility survey data via large language models (LLMs)…
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Artikel Jurnal
Data-driven decarbonization in the maritime industry: An integrative analytical review
The maritime sector, responsible for around 2.9% of global greenhouse gas (GHG) emissions, is under increasing pressure to decarbonize towards the International Maritime Organization’s (IMO) 2050 targets. A number of exisiting reviews have summarized different aspects such as policy frameworks…
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Review of volume-delay functions integrating traditional models and emerging AI technologies
Link performance functions (LPFs) underpin the estimation of travel times, delays, and congestion in transportation network models. As multimodal systems become increasingly data rich and feature connected and automated vehicles (CAVs), electric vehicles (EVs), and complex queuing phenomena…
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LatenAux: Toward latency-aware trajectory prediction for autonomous driving via consolidated auxiliary learning
Accurate trajectory forecasting is essential for enabling autonomous vehicles to navigate safely in complex traffic environments. Current models typically assume that predictions are latency-free, an idealistic simplification that fails in real-world settings, where processing, computation, and…
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Integrating spatial-temporal risk maps with candidate trajectory trees for explainable autonomous driving planning
With increasing public concern about autonomous vehicles, there is a growing demand for developing explainable autonomous driving planning technology. Traditional risk field methods use handcrafted potential field models to explain driving risks in a scenario. When explaining highly interactive…
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Optimal speed limit under multi-class user equilibrium: A prescriptive approach using mathematical programming
In practice, speed limits on road networks are often determined pragmatically, which can give suboptimal solutions for traffic performance and unfair results for the underlying user classes. This study presents an elegant approach to determine optimal speed limits on a traffic network with…
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PriorFusion: Unified integration of priors for robust road perception in autonomous driving
With the growing interest in autonomous driving, there is an increasing demand for accurate and reliable road perception technologies. In complex environments without high-definition map support, autonomous vehicles must independently interpret their surroundings to ensure safe and robust…
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A parsimonious model for classifying the traffic state of urban road networks: A two-stage regression approach
An effective method of traffic state classification is crucial for managing urban traffic congestion. Existing methods usually assume a given number of state categories, which is not flexible if real applications are required to define different state levels. In this study, a parsimonious…
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Artikel Jurnal
Interpretable machine learning for traffic congestion prediction: Unveiling the impact of different COVID-19 periods
Traffic congestion prediction plays a crucial role in mitigating congestion. However, the COVID-19 pandemic and associated government control measures have significantly altered urban travel behavior, increasing the complexity of traffic congestion prediction. This study aims to predict traffic…
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Artikel Jurnal
Quantitative assessment of mid-air collision probability in urban air mobility: A safety barrier-based framework for integrated operations
Risk assessment is a key issue when handling the increasing use of unmanned aircraft systems (UASs), especially in integrated operational urban airspace. This study proposes a method to systematically quantify the mid-air collision (MAC) risk for different operation types in urban air mobility…