Spatial econometrics to estimate traffic reduction by transforming office space into housing and other land uses: The case for Barcelona

Javier Argota Sánchez-Vaquerizo, Dirk Helbing

Cities Volume 162, July 2025, 105901

Origin-destination (OD) matrices are essential for the analysis, planning, and simulation of urban areas, infrastructure, and transportation systems. However, they are often costly and time-consuming to determine, which reduces their potential use for informed decision-making and planning in cities. This research introduces a novel spatial econometric method that considers spatial spillover effects of socio-demographic, land use, and topological variables to directly estimate traffic OD flows between zones of the Metropolitan Area of Barcelona. Employing a two-part Hurdle model with gradient boosting (XGBoost), our approach achieves low error rates (MAE = 6.109, RMSE = 98.774), comparable to other established models also analyzed, but the proposed method’s simplicity facilitates its practical application in urban planning and policy-making. This is illustrated by applying the proposed model to predict changes in vehicle flows resulting from the conversion of offices into other urban uses such as housing, commerce, education, or storage. Despite the related population increase, we expect a reduction in vehicle trips by up to 10 % even with limited spatial interventions. Our findings suggest the model’s power to assess urban trends and policies, particularly in considering teleworking expansion, housing shortages, and contemporary planning practices promoting alternative mobility modes and densification. This research underscores the dual benefits of methodological innovation and practical policy application, marking a significant advancement in urban planning.

Read the full article at: www.sciencedirect.com

Engineering a swarm – with Sabine Hauert

Swarms in nature, including birds, social insects and cells, coordinate in huge numbers to achieve common goals. Their behaviours are self-organised, emerging from the interactions of every agent with their local environment. For the past 20 years, swarm robotics has taken inspiration from nature to make large numbers of robots work together to achieve common goals. With progress in swarm hardware and AI, the field is now ready to translate these swarms from laboratory to application. Join swarm engineering expert Sabine Hauert as she explores the mechanisms to make ‘swarms for people’, in applications ranging from nanomedicine to environmental monitoring and logistics. The next step is to make swarms easy to design, deploy, monitor, control, and validate towards making swarms that are, and should, be trusted. — Sabine Hauert is Professor of Swarm Engineering at University of Bristol. She leads a team of 20 researchers working on making swarms for people, and across scales, from nanorobots for cancer treatment, to larger robots for environmental monitoring, or logistics (https://hauertlab.com/). Before joining the University of Bristol, Sabine engineered swarms of nanoparticles for cancer treatment at MIT, and deployed swarms of flying robots at EPFL. She is on the board of directors of the Open Source Robotics Foundation and is Executive Trustee of non-profits robohub.org and aihub.org, which connect the robotics and AI communities to the public.

Watch at: www.youtube.com

The Hidden Order of Life: How Nature Breaks Symmetry with Nikta Fakhri


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Living systems are in constant motion, from the inner workings of our cells to the synchronized movements of bird flocks. What sets these systems apart is that they are powered by tiny, energy-consuming components that generate their own movement and forces. In this talk, I’ll uncover the hidden rules that govern this dynamic behavior and explore how breaking certain physical symmetries, like the familiar flow of time, allows life to organize itself in unexpected ways. I will show how these discoveries help us understand the intricate patterns inside cells, reveal surprising new properties of living materials, and offer a fresh perspective on the physics that shapes the natural world around us.

Nikta Fakhri is an associate Professor in the Department of Physics at MIT and Physics of Living Systems Group. She completed her undergraduate degree at Sharif University of Technology in Tehran, Iran and her PhD at Rice University, Houston, TX. She was a Human Frontier Science Program postdoctoral fellow at Georg-August-Universität in Göttingen, Germany before joining MIT. Nikta is an Alfred P. Sloan Research Fellow in Physics. She is the recipient of the 2018 IUPAP Young Scientist Prize in Biological Physics, the 2019 NSF CAREER Award, and the 2022 American Physical Society Early Career Award in Soft Matter Research.

Watch at: www.youtube.com

Mapping global value chains at the product level

Lea Karbevska & César A. Hidalgo 
EPJ Data Science volume 14, Article number: 21 (2025)

Value chain data is crucial for navigating economic disruptions. Yet, despite its importance, we lack publicly available product-level value chain datasets, since resources such as the “World Input-Output Database”, “Inter-Country Input-Output Tables”, “EXIOBASE”, and “EORA”, lack information about products (e.g. Radio Receivers, Telephones, Electrical Capacitors, LCDs, etc.) and instead rely on aggregate industrial sectors (e.g. Electrical Equipment, Telecommunications). Here, we introduce a method that leverages ideas from machine learning and trade theory to infer product-level value chain relationships from fine-grained international trade data. We apply our method to data summarizing the exports and imports of 1200+ products and 250+ world regions (e.g. states in the U.S., prefectures in Japan, etc.) to infer value chain information implicit in their trade patterns. In short, we leverage the idea that due to global value chains, regions specialized in the export of a product will tend to specialize in the import of its inputs. We use this idea to develop a novel proportional allocation model to estimate product-level trade flows between regions and countries. This contributes a method to approximate value chain data at the product level that should be of interest to people working in logistics, trade, and sustainable development.

Read the full article at: epjdatascience.springeropen.com