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Carnegie Mellon study finds that rideshare launches boost regional GDP and flexible jobs

A new study from Carnegie Mellon University and Oxford Saïd Business School found that the entry of ride-hailing platforms drove measurable increases in economic output and intermittent employment. Regional GDP per capita increased, and the number of seasonal, temporary, or intermittent jobs rose after Uber and Lyft entered the region.

Can large language models capture human risk preferences?

This study reveals that off-the-shelf large language models do not exhibit universal risk profiles and can be affected by prompt language, leading to systematic distortions. The findings highlight the need for rigorous empirical calibration before deploying these models in computational social science and choice modeling.

SourceTsinghua University Press·JournalCommunications in Transportation Research·DateJul 23, 2026

One-pedal driving in electric vehicles: A boon for automation, but a challenge for manual control

Research finds that one-pedal driving in electric vehicles yields narrower distributions of speed and acceleration when integrated with Adaptive Cruise Control, improving traffic flow stability. However, manual control produces pronounced speed oscillations and sharper decelerations, highlighting the need for automation.

SourceTsinghua University Press·JournalCommunications in Transportation Research·DateJul 23, 2026

What drives the switch to electric vehicles? Research reveals a "behavioral asymmetry" between fuel and EV owners

A study reveals a contrast in decision-making pathways between fuel vehicle and EV owners, highlighting pragmatic benefits for FV owners and habit-driven loyalty among EV owners. The research suggests segment-specific strategies to convert fuel-to-EV owners and retain existing EV users.

SourceTsinghua University Press·JournalCommunications in Transportation Research·DateJul 21, 2026

New protocol reveals the hidden climate and air pollution costs of shopping

A new study introduces a standardized method for measuring greenhouse gas and air pollutant emissions generated across the entire retail journey. The protocol covers online and traditional in-store shopping and provides nationwide parameters for China from 1990 to 2023, identifying opportunities for meaningful reductions.

New electrochemical device targets climate change by sucking CO2 out of air

A new collaborative study has developed an electrochemical device that can pull carbon dioxide directly from the atmosphere using electricity and water-based chemistry, addressing the planet's excess CO2 problem. The technology is designed to reduce new emissions and remove CO2 that has already accumulated in the atmosphere.

SourceUniversity of Illinois at Urbana-Champaign, News Bureau·JournalEnvironmental Science & Technology·DateJul 13, 2026

Machine learning to predict wind shear

A machine learning model, trained on 19 key parameters, can predict wind shear events with a minimum of 15 seconds warning. The model's outputs showed deviations from real outcomes within 5% across all forecast horizons, suggesting improved aviation safety.

SourcePNAS Nexus·JournalPNAS Nexus·DateJun 9, 2026

Protected bike lanes causally increase NYC bikeshare ridership, but benefits are not distributed equally

A new study found that protected bike lanes in NYC causally increase Citi Bike ridership, with a statistically significant effect of approximately 379 additional rides per station per month. However, painted bike lanes and sharrows showed no detectable causal effect, highlighting the importance of design details in cycling infrastructure.

SourceNYU Tandon School of Engineering·Journalnpj Sustainable Mobility and Transport·TypeData/statistical analysis·DateJun 8, 2026

"Reading the invisible": POSTECH-led team develops AI framework accounting for hidden defects in metal 3D printing

A research team led by POSTECH developed an AI framework that can predict and account for microscopic defects in metal 3D printing, improving the reliability of metal components. The framework achieves a Mean Absolute Error (MAE) of just 9.51 MPa, outperforming conventional approaches.

A perspective paper looks at digital transportation’s basic theories

A perspective paper on digital transportation explores its basic theories and proposed research directions. The author highlights the need for a comprehensive theoretical framework to guide the development of modern vehicles and digital transformation of transportation infrastructure.

SourceSciOpen·JournalJournal of Highway and Transportation Research and Development (English Edition)·DateMay 12, 2026

How to quantify the impact of daily driving behavior on electric vehicle battery health?

A new study developed a multi-scale dynamic driving environment to assess the impact of daily driving behavior on electric vehicle battery health. The framework uses deep reinforcement learning to optimize battery health and energy efficiency, and its results show that stable driving behavior can extend battery lifetime by approximatel...

SourceTsinghua University Press·JournalCommunications in Transportation Research·DateApr 15, 2026

How can autonomous vehicles learn new traffic scenarios without forgetting old ones?

Researchers proposed a dynamically expandable learning framework for interactive trajectory prediction to enable models to adapt to evolving traffic environments. The approach effectively mitigates catastrophic forgetting and maintains stable predictions for critical interaction scenarios across multiple learning stages.

SourceTsinghua University Press·JournalCommunications in Transportation Research·DateApr 15, 2026

Latency-aware trajectory prediction

Researchers propose a novel latency-aware trajectory prediction framework, LatenAux, empowered by a consolidated auxiliary learning paradigm. The framework transforms latency from a hindrance into an opportunity for improved performance. Extensive experiments validate its effectiveness and superiority.

SourceTsinghua University Press·JournalCommunications in Transportation Research·DateApr 15, 2026

TrafficPerceiver enables instruction-driven understanding and segmentation in challenging traffic scenes

Researchers propose TrafficPerceiver, a unified multimodal framework that supports coarse-grained scene understanding tasks and fine-grained target-oriented segmentation tasks. The method achieves superior performance on the CTSU dataset, demonstrating its potential for building robust traffic perception systems.

SourceTsinghua University Press·JournalCommunications in Transportation Research·DateApr 14, 2026

Global EV transition hinges on policy adoption, cost reductions

A new study finds that aggressive policy action and cost reductions can accelerate the global shift to electric vehicles, reducing energy use and carbon emissions. The study suggests a uniform 20% reduction in non-energy costs could dramatically increase EV adoption rates, with market share rising from 25-50% to 70-85% globally by 2035.

SourceCornell University·JournalResources Environment and Sustainability·DateApr 7, 2026

Finding order in disorder: A new mechanism that amplifies transverse electron transport

A study by researchers at Pohang University of Science & Technology discovered that engineered disorder can amplify transverse electron transport in magnetic materials. The findings suggest that deliberately using disorder in materials design could lead to new opportunities in spintronics and thermoelectric energy-conversion technologies.

SourcePohang University of Science & Technology (POSTECH)·JournalPhysical Review Letters·DateMar 24, 2026

Here's why seafarers have little confidence in autonomous ships

A Norwegian University of Science and Technology study highlights seafarers' concerns about autonomous ships' technical safety, trust in technology, and crew competence. The researchers aim to ensure safer use of advanced technology and increase seafarers' trust in autonomy by addressing the challenges highlighted by the seafarers.

SourceNorwegian University of Science and Technology·JournalWMU Journal of Maritime Affairs·TypeSurvey·DateMar 11, 2026

Magnetic microrobot swarms enable contactless manipulation of objects through fluidic torque

Researchers demonstrated a breakthrough in microrobotics: swarms of magnetic microrobots can manipulate objects without physical contact by harnessing fluid-generated torque. The microrobots act as motors to move millimeter-sized passive objects, opening new pathways for precision manufacturing and biomedical applications.

SourceMax Planck Institute for Intelligent Systems·JournalScience Advances·TypeExperimental study·DateFeb 25, 2026