Research

An LLM agent takes forecasted trips and runs them in mainstream traffic simulators (Vissim, SUMO, DTALite, MATSim). The LLM picks the right tool, sets the parameters, and writes the behavior plans, so citywide simulations can be built directly from mobile location data.

Can phone-based origin-destination (OD) data reproduce real road-level traffic? We test this across 35 cities in Mexico, Colombia, Indonesia, and India. The model works well overall but struggles where smartphone use is low.

A full pipeline that turns raw phone location data into trip records for each user. It identifies home and work, detects trips, infers travel mode, and reweights to the population. The trips then aggregate into multi-modal OD matrices.

Multi-ATGCN (Multi-graph Multi-head Adaptive Temporal Graph Convolutional Network) forecasts citywide origin-destination flows in the future. It learns the spatial structure from multiple graphs and adapts to real-time conditions, outperforming strong baselines on several datasets.

Using nationwide phone-derived trip flows as a travel-demand proxy, we benchmark 48 explainable ML models against multiple interpretation techniques. Results reveal strong nonlinear relationships and threshold effects between travel demand and its drivers.

A deep learning framework jointly predicts the activity, time, and location of each user's next trip. We borrow loss functions from image segmentation to handle the highly imbalanced activity distribution across more than 18,000 residents.

We use computer vision to estimate emissions for each vehicle in traffic camera video. A 2.2-million-image dataset classifies 4,923 car models, and an enhanced COPERT formula adds vehicle speed and acceleration recovered by tracking.

A city-scale traffic emission framework that fuses camera videos with phone location data. By inferring the timing of every traffic signal, we show that real stop-and-go cycles raise PM and NOx emissions 40 to 60 percent above standard methods.

A Digital Twin platform that estimates and forecasts road traffic emissions in near real time. By combining open repositories with IoT sensors, it lets users test alternative policy scenarios. Demonstrated for Kista, Stockholm with a 3D interactive interface.

Using 3.4 million driving trips, we measure how Milan's Zone 30 changes emissions. Citywide changes are small (0.6 to 2.7 percent) but uneven across space and time, with the largest jumps on cross-zone trips at rush hour.

A two-level optimization for electric buses with dynamic wireless charging. The upper level decides where to put charging facilities and how big batteries should be; the lower level schedules daily charging under time-of-use electricity prices.

Five million EVCARD trips show that users avoid older, smaller, and pricier shared electric vehicles. State of charge matters most. Users are "greedy": they pick the most-charged car even when their actual trip is short.

A simulation shows how limited battery capacity hurts both user satisfaction and fleet utilization in shared electric vehicles. Faster charging, longer range, and a larger fleet-to-trip ratio all help reduce the problem.

A Hurricane Laura case study: when disasters and pandemics overlap, evacuation flows reshape mobility and spread COVID-19 across counties. Disadvantaged communities face both higher exposure and slower recovery.

We combine SIR-style diffusion models with NLP on social media to study how the US public responds to wildfires. The model captures how concern spreads across regions and which socioeconomic factors shape community reactions.

A comprehensive review of how to model, measure, and optimize the resilience of multi-modal urban transport networks (road, transit, shared mobility), with a look ahead at next-generation infrastructure.

We compare passive phone data with active Waze reports for tracking how extreme weather affects road traffic. Active reports underestimate severity because fewer users go outside as conditions worsen, and they stop reporting.

Twenty years of Chicago transit ridership analyzed with Bayesian time series. After controlling for socioeconomic factors, the pandemic-driven decline was sharpest in commercial areas and in white, educated, high-income neighborhoods.

We track Chicago bikesharing across the pandemic and compare it with transit, driving, and walking. Bikesharing is the most resilient mode, but high-income station areas saw the largest swings, both up and down.

We track pandemic-era US mobility from 150 million phones, summarizing trips per person, person-miles traveled, and share staying home. Daily numbers are public on this platform.

Phone data show that Americans cut travel before any stay-at-home orders, then hit a "floor" once orders began. State-level policies explain only about five percent of the total mobility decline.

We combine social media posts, rainfall, and traffic flow data to automatically detect flooded urban roads in Shenzhen. The system catches 68 to 90 percent of floods with only 1.5 to 2 percent false alarms.

Two years of US county data on vaccines, mobility, and outcomes. During the Omicron wave, vaccines became less effective at preventing cases but kept reducing deaths. An NIH-funded agent-based extension is underway.

Linking COVID-19 outcomes to demographics, occupation, and politics, we find a structural inequality. Social distancing was a "privilege" of advantaged groups; disadvantaged communities bore the heaviest case and death burdens.

Mediation analysis shows that self-reported vaccine hesitancy does not fully explain US vaccination gaps. Structural access barriers (e.g., distance, time, distrust of institutions) play an equally important role.

Structural equation modeling on 4.4 million POI tracking records shows that mobility drives COVID-19 case rates but not death rates. The mediation effect varies sharply across racial and ethnic compositions of US counties.

We measure nationwide mobility inflow at the start of the pandemic and model its changing link with infection rates. The relationship is positive and grows much stronger in regions that have partially reopened.

We analyze 2.7 million food delivery orders in Dubai to explain why customers pick distant restaurants over nearby ones. Explainable ML shows that delivery fees, cuisine, ratings, and neighborhood socioeconomics shape whether orders stay local.

As technical lead of incenTrip, the first nationwide incentive-based travel app, we design personalized rewards that nudge users toward mode switching, carpooling, off-peak travel, and congestion avoidance.

We analyze four million coupons with a Cox survival model. Their effect on user retention peaks early and fades after about 130 days. Front-loading rewards for new users maximizes short-term revenue but raises fairness and privacy concerns.

Five million parking-related Google Maps reviews across 1.1 million US POIs are classified with BERT. Restaurants get the most negative reviews, and denser, lower-income urban areas consistently have worse parking perceptions.

We fine-tune Llama 3 with LoRA to measure public sentiment about accessibility from nationwide Google Maps reviews. Older, more educated areas express more negativity, while local disability rates alone show no clear link to sentiment.

We compare e-scooter sharing with docked bikesharing using generalized additive mixed models. Both thrive in dense, young, higher-income areas, but a one-sided competition has emerged: e-scooters are overtaking bikes.

Shanghai dockless bikes are evaluated for bike-and-ride (BnR) integration with metro stations on four metrics. Land-use mix only matters past a 1.5 km buffer, and larger operators get more BnR trips but no higher per-bike utilization.

Station-level booking and turnover analysis for carsharing optimization shows that operators should target underserved areas. Carsharing is most competitive 1.2 to 2.4 km from bus stops, and geographically differentiated quotas help cities manage fleets.