AI Research
Introducing Mobility AI: Advancing urban transportation
1. Measurement: Understanding mobility patterns
Accurately evaluating the current state of the transportation network and mobility patterns is the first step to improving mobility. This involves gathering and analyzing real-time and historical data from various sources to understand both current and historical conditions and trends. We need to track the effects of changes as we implement them in the network. ML powers estimations and metric computations, while statistical approaches measure impact. Key areas include:
Congestion functions
Similar to well-known fundamental diagrams of traffic flow, congestion functions mathematically describe how rising vehicle volume increases congestion and reduces travel speeds, providing crucial insights into traffic behavior. Unlike fundamental diagrams, congestion functions are built based on a portion of vehicles (e.g., floating car data) rather than all traveling vehicles. We have advanced the understanding of congestion formation and propagation using an ML approach that created city-wide models, which enable robust inference on roads with limited data and, through analytical formulation, reveal how traffic signal adjustments influence flow distribution and congestion patterns in urban areas.
Foundational geospatial understanding
We develop novel frameworks, leveraging techniques like self-supervised learning on geospatial data and movement patterns, to learn embeddings that capture both local characteristics and broader spatial relationships. These representations improve the understanding of mobility patterns and can aid downstream tasks, especially where data might be sparse or when complementing other data modalities. Collaboration with related Google Research efforts in Geospatial Reasoning using generative AI and foundation models is crucial for advancing these capabilities.
Parking insights
Understanding urban intricacies includes parking. Building on our work using ML to predict parking difficulty, Mobility AI aims to provide better insights for managing parking availability, crucial for various people, including commuters, ride-sharing drivers, commercial delivery vehicles, and the emerging needs of self-driving vehicles.
Origin–destination travel demand estimation
Origin–destination (OD) travel demand, which describes where trips — like daily commutes, goods deliveries, or shopping journeys — start and end, is fundamental to understanding and optimizing mobility. Knowing these patterns is crucial because it reveals exactly where the transportation network is stressed and where services or infrastructure improvements are most needed. We calibrate OD matrices — tables quantifying these trips between locations — to accurately replicate observed traffic patterns, providing a spatially complete understanding essential for planning and optimization of transportation networks.
Performance metrics: Safety, emissions and congestion impact
We use aggregated and anonymized Google Maps traffic trends to assess impact of transportation interventions on congestion, and we build models to assess safety and emissions impact. To build safety metrics scalably, we go beyond reactive crash data by utilizing hard braking events (HBEs). HBEs are shown to be strongly correlated with crashes and can be used for road safety services to pinpoint high-risk locations and predict future collision risks.
To measure environmental impact, we’ve developed AI models in partnership with the National Renewable Energy Laboratory (NREL) that predict vehicle energy consumption (whether gas, diesel, hybrid, or electric). This powers fuel-efficient routing in Google Maps, estimated to have helped avoid 2.9M metric tons of GHG emissions in the US alone, which is equivalent to taking ~650,000 cars off the road for a year. This capability is fundamental for monitoring climate and health impacts related to transportation choices.
Impact evaluation
Randomized trials are often infeasible for evaluating transportation policy changes. To assess the impact of a change, we need to estimate outcomes in its absence. This can be done by finding cities or regions with similar mobility patterns to serve as a “control group”. Our analysis of NYC’s congestion pricing demonstrates this method through use of sophisticated statistical techniques like synthetic controls to rigorously estimate the policy’s impact and by providing valuable insights for agencies evaluating interventions.
AI Research
Joint UT, Yale research develops AI tool for heart analysis – The Daily Texan
A study published on June 23 in collaboration with UT and Yale researchers developed an artificial intelligence tool capable of performing and analyzing the heart using echocardiography.
The app, PanEcho, can analyze echocardiograms, or pictures of the heart, using ultrasounds. The tool was developed and trained on nearly one million echocardiographic videos. It can perform 39 echocardiographic tasks and accurately detect conditions such as systolic dysfunction and severe aortic stenosis.
“Our teammates helped identify a total of 39 key measurements and labels that are part of a complete echocardiographic report — basically what a cardiologist would be expected to report on when they’re interpreting an exam,” said Gregory Holste, an author of the study and a doctoral candidate in the Department of Electrical and Computer Engineering. “We train the model to predict those 39 labels. Once that model is trained, you need to evaluate how it performs across those 39 tasks, and we do that through this robust multi site validation.”
Holste said out of the functions PanEcho has, one of the most impressive is its ability to measure left ventricular ejection fraction, or the proportion of blood the left ventricle of the heart pumps out, far more accurately than human experts. Additionally, Holste said PanEcho can analyze the heart as a whole, while humans are limited to looking at the heart from one view at a time.
“What is most unique about PanEcho is that it can do this by synthesizing information across all available views, not just curated single ones,” Holste said. “PanEcho integrates information from the entire exam — from multiple views of the heart to make a more informed, holistic decision about measurements like ejection fraction.”
PanEcho is available for open-source use to allow researchers to use and experiment with the tool for future studies. Holste said the team has already received emails from people trying to “fine-tune” the application for different uses.
“We know that other researchers are working on adapting PanEcho to work on pediatric scans, and this is not something that PanEcho was trained to do out of the box,” Holste said. “But, because it has seen so much data, it can fine-tune and adapt to that domain very quickly. (There are) very exciting possibilities for future research.”
AI Research
New Research Shows Language Choice Alone Can Guide AI Output Toward Eastern or Western Cultural Outlooks
A new study shows that the language used to prompt AI chatbots can steer them toward different cultural mindsets, even when the question stays the same. Researchers at MIT and Tongji University found that large language models like OpenAI’s GPT and China’s ERNIE change their tone and reasoning depending on whether they’re responding in English or Chinese.
The results indicate that these systems translate language while also reflecting cultural patterns. These patterns appear in how the models provide advice, interpret logic, and handle questions related to social behavior.
Same Question, Different Outlook
The team tested both GPT and ERNIE by running identical tasks in English and Chinese. Across dozens of prompts, they found that when GPT answered in Chinese, it leaned more toward community-driven values and context-based reasoning. In English, its responses tilted toward individualism and sharper logic.
Take social orientation, for instance. In Chinese, GPT was more likely to favor group loyalty and shared goals. In English, it shifted toward personal independence and self-expression. These patterns matched well-documented cultural divides between East and West.
When it came to reasoning, the shift continued. The Chinese version of GPT gave answers that accounted for context, uncertainty, and change over time. It also offered more flexible interpretations, often responding with ranges or multiple options instead of just one answer. In contrast, the English version stuck to direct logic and clearly defined outcomes.
No Nudging Needed
What’s striking is that these shifts occurred without any cultural instructions. The researchers didn’t tell the models to act more “Western” or “Eastern.” They simply changed the input language. That alone was enough to flip the models’ behavior, almost like switching glasses and seeing the world in a new shade.
To check how strong this effect was, the researchers repeated each task more than 100 times. They tweaked prompt formats, varied the examples, and even changed gender pronouns. No matter what they adjusted, the cultural patterns held steady.
Real-World Impact
The study didn’t stop at lab tests. In a separate exercise, GPT was asked to choose between two ad slogans, one that stressed personal benefit, another that highlighted family values. When the prompt came in Chinese, GPT picked the group-centered slogan most of the time. In English, it leaned toward the one focused on the individual.
This might sound small, but it shows how language choice can guide the model’s output in ways that ripple into marketing, decision-making, and even education. People using AI tools in one language may get very different advice than someone asking the same question in another.
Can You Steer It?
The researchers also tested a workaround. They added cultural prompts, telling GPT to imagine itself as a person raised in a specific country. That small nudge helped the model shift its tone, even in English, suggesting that cultural context can be dialed up or down depending on how the prompt is framed.
Why It Matters
The findings concern how language affects the way AI models present information. Differences in response patterns suggest that the input language influences how content is structured and interpreted. As AI tools become more integrated into routine tasks and decision-making processes, language-based variations in output may influence user choices over time.
Notes: This post was edited/created using GenAI tools. Image: DIW-Aigen.
Read next: Jack Dorsey Builds Offline Messaging App That Uses Bluetooth Instead of the Internet
AI Research
Indonesian volcano Mount Lewotobi Laki-laki spews massive ash cloud as it erupts again
Indonesia’s Mount Lewotobi Laki-laki has begun erupting again – at one point shooting an ash cloud 18km (11mi) into the sky – as residents flee their homes once more.
There have been no reports of casualties since Monday morning, when the volcano on the island of Flores began spewing ash and lava again. Authorities have placed it on the highest alert level since an earlier round of eruptions three weeks ago.
At least 24 flights to and from the neighbouring resort island of Bali were cancelled on Monday, though some flights had resumed by Tuesday morning.
The initial column of hot clouds that rose at 11:05 (03:05 GMT) Monday was the volcano’s highest since November, said geology agency chief Muhammad Wafid.
“An eruption of that size certainly carries a higher potential for danger, including its impact on aviation,” Wafid told The Associated Press.
Monday’s eruption, which was accompanied by a thunderous roar, led authorities to enlarge the exclusion zone to a 7km radius from the central vent. They also warned of potential lahar floods – a type of mud or debris flow of volcanic materials – if heavy rain occurs.
The twin-peaked volcano erupted again at 19:30 on Monday, sending ash clouds and lava up to 13km into the air. It erupted a third time at 05:53 on Tuesday at a reduced intensity.
Videos shared overnight show glowing red lava spurting from the volcano’s peaks as residents get into cars and buses to flee.
More than 4,000 people have been evacuated from the area so far, according to the local disaster management agency.
Residents who have stayed put are facing a shortage of water, food and masks, local authorities say.
“As the eruption continues, with several secondary explosions and ash clouds drifting westward and northward, the affected communities who have not been relocated… require focused emergency response efforts,” say Paulus Sony Sang Tukan, who leads the Pululera village, about 8km from Lewotobi Laki-laki.
“Water is still available, but there’s concern about its cleanliness and whether it has been contaminated, since our entire area was blanketed in thick volcanic ash during yesterday’s [eruptions],” he said.
Indonesia sits on the Pacific “Ring of Fire” where tectonic plates collide, causing frequent volcanic activity as well as earthquakes.
Lewotobi Laki-laki has erupted multiple times this year – no casualties have been reported so far.
However, an eruption last November killed at least ten people and forced thousands to flee.
Laki-Laki, which means “man” in Indonesian, is twinned with the calmer but taller 1,703m named Perempuan, the Indonesian word for “woman”.
Additional reporting by Eliazar Ballo in Kupang.
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