Thursday, March 26, 2026

 

Study examines how autonomous vehicles may change morning commutes



Findings on traffic congestion, downtown parking can inform urban planning efforts



Carnegie Mellon University




Autonomous vehicles (AVs), which already operate on the roads of several major U.S. cities and in countries worldwide, are expected to play a large role in shaping the future of cities. In a new study, researchers investigated how AVs may change travel patterns during morning commutes and affect parking in business districts. By providing insights into the changes associated with parking and traffic congestion as the use of AVs rises, the study can inform urban planning efforts.

Conducted by researchers at Carnegie Mellon University and the University of Texas (UT) at Dallas, "Can Autonomous Vehicles Solve the Commuter Parking Problem?" is published in Management Science. “Urban planners have a rare window of opportunity to establish policies that pave the way for the inevitable mass arrival of AVs,” suggests Soo-Haeng Cho, IBM Professor of Operations Management and Strategy at Carnegie Mellon’s Tepper School of Business, who coauthored the study.

To accommodate high demand for parking, cities surrender large spaces to build parking structures, but many morning commuters still struggle to find affordable, convenient places to leave their cars; traffic congestion further complicates many morning commutes. AVs have the potential to address these issues by dropping commuters at their workplaces in a central business district and parking in suburban areas at lower rates, all by themselves. In this way, commuters could avoid high parking fees, and cities could reduce the need to build or maintain large, costly, and largely underutilized parking structures in business areas.

In this study, researchers examined the effect of AVs on the morning commute to a central business district in general, using Pittsburgh, PA, as a case study. They developed a continuous-time game-theoretic traffic model that considered key economic deterrents to driving (e.g., parking fees, traffic congestion, curbside pickup and drop-off) and characterized commuters’ departure-time and parking-location (inside or outside the central business district parking area) patterns.

Based on the study’s model, commuters who all use AVs may choose to park outside the central business district, increasing both vehicle hours and vehicle miles traveled compared with human-driven vehicles, the study found. This change would increase total system cost and suggest potential changes in how land is used in business districts (e.g., repurposing parking spots for commercial and residential areas) after AVs are used more widely.

To reduce total system cost, urban planners may opt to regulate commuters’ decisions by adjusting parking fees or imposing congestion tolls as a short-term measure, or by adjusting infrastructure, for example, converting parking spaces to drop-off spots for AVs. In Pittsburgh, these measures are estimated to reduce total system cost by up to 28.5 percent.

“In our study, we sought not to propose city-specific solutions, but to highlight general tradeoffs and dynamics in human behavior that emerge when AVs, commuters, and infrastructure interact,” explains Neda Mirzaeian, Assistant Professor of Operations Management at UT Dallas’s Jindal School of Management, who led the study. “Our model can serve as a guide, or even an early warning system, to recognize how seemingly small shifts in technology, costs, or incentives can lead to large changes in commuter behavior and system-wide efficiency.”

Sean Qian, H. J. Heinz III Professor of Civil and Environmental Engineering at Carnegie Mellon’s College of Engineering and Heinz College, who coauthored the study adds, “In providing guidance to urban planners—including mobility and infrastructure departments of mayoralties, city councils, town councils, and town boards—our results can identify when and where current policies need to adapt in light of the special needs and characteristics of AVs when AVs become widely deployed.”

 

AI agent accelerates catalyst discovery for sustainable fuel development




Advanced Institute for Materials Research (AIMR), Tohoku University
Figure 1 

image: 

Intelligent design workflow for discovering Cu-based SAA electrocatalysts for CO2RR toward multi-carbon products. (a) Experimental-data-driven theoretical simulations. (b) Catalysis AI Agent-aided descriptor construction. (c) Application of universal design principle. 

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Credit: Xuning Wang et al.





Scientific discovery is often tedious, expensive, incremental trial and error, but the advent of artificial intelligence (AI) is accelerating the process. A multi-institutional team recently used AI to identify a key characteristic of compounds called catalysts that are used to initiate and speed up the chemical reactions that convert carbon dioxide into molecules that can be used to develop sustainable fuels. The team then used the AI - dubbed Catalysis AI Agent by the researchers - to guide their catalysts designs, ultimately discovering the universal design principle for copper-based single-atom alloy (SAAs) catalysts.

They published their results on Feb. 24 in Angewandte Chemie International Edition.

"Copper-based SAAs represent a promising strategy for optimizing the electroreduction - the process of breaking down or converting a compound into something else via electrochemical reactions - of carbon dioxide to multi-carbon products," said co-corresponding author Hao Li, distinguished professor at Advanced Institute for Materials Research, Tohoku University.

The challenge, Li said, is that electroreduction catalysis can be induced with a broad variety of chemical additions to produce specific carbon products. The diversity has not yet been rationalized, meaning no one had developed guidelines to designing copper-based SAAs that could produce the desired carbon products.

In an effort to provide such guidelines, the researchers turned to Catalysis AI Agent. A type of AI called a large language model (LLM), the Catalysis AI Agent learned by training with a massive database built by Li and his team. The database, the Digital Catalysis Platform or DigCat, is currently the largest experimental database and AI platform available for catalysis research.

"Stage one of our systematic investigation was to develop the powerful LLM-based Catalysis AI Agent and use it to mine the DigCat database," Li said, explaining that it examined the catalysis research data available to identify trends or similarities.

The Catalysis AI Agent found that copper-based SAAs appeared to produce the desired carbon products by promoting the formation of certain compounds rather than suppressing the development of other byproducts. This insight prompted the researchers to use the Catalysis AI Agent to analyze correlations between experimental and theoretical data, which led to the revelation that the additives - called dopants - that could be used to induce specific carbon products need to be classified before researchers can elucidate how they interact with a compound and produce a predictable reaction.

With this understanding, the researchers established an energy descriptor - a way to describe the amount of energy needed for specific reactions - to classify SAAs and accurately capture the trends toward certain products in copper-based SAAs.

The researchers were also able to develop what Li called a "remarkably simple structural descriptor" to directly predict the energy activation of carbon products. They tested the approach experimentally and found it could not only describe copper-based dopants, but also other types of metal dopants.

"This universal design principle unravels the promotional mechanism and structure-selectivity relationships governing copper-based SAAs for carbon dioxide electrochemical reduction for carbon products," Li said. "This paradigm shift, moving from empirical trial-and-error towards AI-accelerated and theory-guided catalyst design, holds substantial promise for expediting the discover of next-generation materials. Most strikingly, our study highlights a transformative paradigm in materials science, where a well-trained scientific AI agent and large-scale experimental database not only predict and rationalize catalyst performance, but also inspire generalizable design principles for future discovery."

About the World Premier International Research Center Initiative (WPI)

The WPI program was launched in 2007 by Japan's Ministry of Education, Culture, Sports, Science and Technology (MEXT) to foster globally visible research centers boasting the highest standards and outstanding research environments. Numbering more than a dozen and operating at institutions throughout the country, these centers are given a high degree of autonomy, allowing them to engage in innovative modes of management and research. The program is administered by the Japan Society for the Promotion of Science (JSPS).

See the latest research news from the centers at the WPI News Portal: www.eurekalert.org/newsportal/WPI

Main WPI program site:  www.jsps.go.jp/english/e-toplevel

Advanced Institute for Materials Research (AIMR)

Tohoku University

Establishing a World-Leading Research Center for Materials Science

AIMR aims to contribute to society through its actions as a world-leading research center for materials science and push the boundaries of research frontiers. To this end, the institute gathers excellent researchers in the fields of physics, chemistry, materials science, engineering, and mathematics and provides a world-class research environment.

Reporters may use these materials freely in news coverage with appropriate credit information.

Credit

Xuning Wang et al.

 

AI-powered drug discovery meets field-ready diagnostics in SLAS Technology vol. 37

The latest volume demonstrates how artificial intelligence, automation, and portable technologies are reshaping drug discovery, diagnostics and therapeutic development.

Peer-Reviewed Publication

SLAS (Society for Laboratory Automation and Screening)

SLAS Technology, Vol. 37 

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AI-Powered Drug Discovery Meets Field-Ready Diagnostics in SLAS Technology Vol. 37

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Credit: SLAS Publishing

Oak Brook, IL – Volume 37 of SLAS Technology includes one technical brief, four original research articles, two literature highlights, and four entries from the Special Issue on Revolutionizing Transcriptomics from Single-Cell Insights to RNA-Based Interventions.

Technical Brief

Original Research

Literature Highlight

  • Life Sciences Discovery and Technology Highlights
    The authors highlight several recent advances in laboratory automation, microfluidics and AI-enhanced biosensing, that are transforming biological research through high-throughput genome editing platforms, automated nucleic acid extraction protocols and intelligent strain engineering systems.
  • Life Sciences and Anxiety – Between Enlightenment and Uncertainty
    This entry in the Life Sciences and Society series by SLAS Technology Associate Editor Kerstin Thurow, PhD, explores the paradoxical relationship between scientific advancement and cultural anxiety in the life sciences. Thurow examines how greater biological knowledge can simultaneously empower and burden individuals while raising ethical questions about genetic modification and human enhancement.

Special Issue

  • Revolutionizing Transcriptomics from Single-Cell Insights to RNA-Based Interventions
    This Special Issue on systems genetics examines gene and molecular interaction networks, utilizing high-throughput sequencing and multi-omics technologies to understand how genetic networks influence phenotypes. It emphasizes the significance of personalized medicine, therapeutic target discovery and biomarker identification through integrated genomic and epigenomic approaches.

All active SLAS Discovery and SLAS Technology call for papers are available at: https://www.slas.org/publications/call-for-papers/

This volume of SLAS Technology is available at https://www.slas-technology.org/issue/S2472-6303(25)X0008-X

*****

SLAS Technology reveals how scientists adapt technological advancements for life sciences exploration and experimentation in biomedical research and development. The journal emphasizes scientific and technical advances that enable and improve:

  • Life sciences research and development
  • Drug delivery
  • Diagnostics
  • Biomedical and molecular imaging
  • Personalized and precision medicine

SLAS (Society for Laboratory Automation and Screening) is an international professional society of academic, industry and government life sciences researchers and the developers and providers of laboratory automation technology. The SLAS mission is to bring together researchers in academia, industry and government to advance life sciences discovery and technology via education, knowledge exchange and global community building.

SLAS Technology: Translating Life Sciences Innovation, 2024 Impact Factor 3.7. Editor-in-Chief Edward Kai-Hua Chow, PhD, KYAN Technologies, Los Angeles, CA (USA).

 

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