Cathy Wu

@cathywu.bsky.social

1653 Followers 621 Following

AI & Transportation | MIT Associate Professor Interests: AI for good, sociotechnical systems, machine learning, optimization, reinforcement learning, public policy, gov tech, open science. Science is messy and beautiful. http://www.wucathy.com

We introduce Mitigating Metropolitan Carbon Emissions with Dynamic Eco-driving at Scale. The central question of the research study is: From a climate change mitigation perspective, could intelligent control of vehicles move the needle? This is the first study to systematically quantify the city-scale benefits of eco-driving.
Beautiful pavilion-topped pedestrian walkway and bike path along 40th street across SR-520 in Redmond, WA
Flyer announcing Prof. Cathy Wu from MIT presenting at the TRB Conference on Data and AI for Transportation Advancement. Two sessions are highlighted: one on May 28 about accelerating transportation research transparency practices, and another on May 29 about mitigating metropolitan carbon emissions using dynamic eco-driving.
Flyer announcing a presentation at the Transportation Research Symposium 2025, scheduled for Monday, May 26, 13:15-15:15 in Rotterdam, The Netherlands. Session topic is 'Traffic control in the era of new technologies,' with the presentation titled 'Towards Accelerating Transportation Research: Measuring the State of Transparency Practices.' Multiple authors from various international institutions are listed, including Ruth Lu, an undergraduate researcher at MIT, whose photo is featured prominently. A QR code and the website rerite.org are displayed in the upper right corner. The flyer background is orange with black and white text.
Light rail train platform at Downtown Bellevue towards Downtown Redmond
Aerial panoramic view of a sprawling industrial park: rows of blue-roofed warehouses and factory buildings stretch toward a wide river, where container cranes line the distant shoreline. Farmland and access roads frame the scene beneath a clear, bright blue sky.
A diagram illustrating the "Evolution of V2X use cases towards cooperative driving," categorizing advancements into three main groups: Automated Driving, Advanced Safety, and Traffic Efficiency/Safety. These groups progressively move through stages labeled Connected Driver Warning, Connected Driver Assistance, and Connected Cooperative Driving. Examples of use cases include Automated Valet Parking, Cooperative Adaptive Cruise Control, Dynamic Intersection Management, Tele-Operated Driving for Automated Vehicles, and Hazard Information Collection and Sharing. Icons depict vehicles interacting via wireless communication, sensors, cameras, and various road safety scenarios.
In building IntersectionZoo, three main logical layers are used to abstract functionality as shown in this figure. In the traffic scenario modeling layer, we first build data-driven simulation environments of signalized intersections and then use them to build traffic scenarios at those intersections. Concretely, an intersection is first defined by factors such as lane lengths, lane counts, road grades, turn lane configuration, and speed limit of each approach. Then, vehicle type, age, and fuel type distributions are used with appropriate traffic flow rates and human driven vehicle behaviors to define a realistic traffic flow. Each intersection is then used to define traffic scenarios by further assigning representative atmospheric temperature and humidity values based on the season. Further scenario variations can be achieved by changing the eco-driving adoption level (0%-100%). Once traffic scenarios are modeled, the CMDP modeling layer is used to contain them as Conextual Markov Decision Process (CMDP) and define the state, action, reward to be used with the reinforcement learning algorithms. CMDP is used to model problem variations. In IntersectionZoo, each city is modeled as a CMDP with each traffic scenario stemming from the city as a problem variation. In a CMDP, each problem variation is an MDP defined by a problem variation context and called a context-MDP. In eco-driving, those context-MDPs are defined by the following state, action and reward functions and formulate as a multi-agent control problem. In user configuration layer, we provide users the flexibility to configure their experimental setup.
Flyer containing event details. Program links: - https://annualmeeting.mytrb.org/OnlineProgram/Details/22044 - https://annualmeeting.mytrb.org/OnlineProgram/Details/22715 - https://i24motion.org/news/join-us-trb-2025-motion-workshop-and-reception
Sunday, January 5th Open-Source Data Repositories and Technologies Workshop Session: Workshop Location: Convention Center, 150A Time: 9:00 AM - 12:00 PM ET Monday, January 6th Understanding Driver–Pedestrian Interactions to Predict Driver Yielding: Naturalistic Open-Source Data Set Collected in Minnesota Session: Lectern+Poster Location: Convention Center, Salon B Time: 8:00 AM - 9:45 AM ET Benchmarking the Capabilities of LLMs in Transportation System Engineering Session: Poster Location: Convention Center, Hall A Time: 1:30 PM - 3:15 PM ET V2XPnP: A Real-World Sequential Dataset and Fusion Pipeline for Perception and Prediction with Vehicle-to-Everything Communication Session: Poster Location: Convention Center, Hall A Time: 1:30 PM - 3:15 PM ET Introducing RERITE: REproducible Research In Transportation Engineering (RERITE) Working Group Session: ACP50 Committee Meeting Location: Marriot Marquis, M2 Time: 1:30 PM - 5:30 PM ET Tuesday, January 7th An Open-Source Framework for Evaluating Cooperative Perception in Urban Areas Session: Poster Location: Convention Center, Hall A Time: 6:00 PM - 7:30 PM ET V2X-Real: a Large-scale Dataset for V2X Cooperative Perception Session: Poster Location: Convention Center, Hall A Time: 6:00 PM - 7:30 PM ET Imputation and Correction of Traffic Signal Data for Waymo Open Motion Dataset Session: Poster Location: Convention Center, Hall A Time: 6:00 PM - 7:30 PM ET Annual Zephyr Reception Session: Reception Location: Busboys & Poets. Time: 8:00 PM - 10:00 PM ET Wednesday, January 8th Building Digital Infrastructure: Scalable and Reproducible Data Integration and Traffic Theory for Congestion Bottleneck Identification and Traffic Management Strategy Evaluation Session: Poster Location: Convention Center, Hall A Time: 8:00 AM - 9:45 AM ET I-24 MOTION research workshop Session: Workshop Location: Open Gov Hub meeting space Time: 2:00 PM - 5:00 PM ET
Here we apply RL to solve a space of tasks (left, x-axis). We succeed when we fill up the red area (top left, generalization performance across tasks). Our new method MBTL can do this an order of magnitude more efficiently (17x in this example) than standard training paradigms like independent training and multi-task training.