Problem Motivation
Cities—especially across the Global South—face two intertwined challenges with outsized impact on public health and productivity: degraded air quality and chronic road congestion. High-fidelity sensors and infrastructure-heavy control systems are costly and sparse, leaving large blind spots. Our work closes these gaps using infrastructure-light methods that turn commodity data (phone/CCTV images, sparse air sensors, and coarse mobility datasets) into fine-grained maps and proactive controls that cities can deploy today.
- Public health: Hidden pollution hotspots disproportionately affect vulnerable communities and remain invisible to coarse monitoring.
- Mobility & productivity: Sudden jams can emerge without obvious bottlenecks; reactive fixes arrive too late and often require expensive upgrades.
- Practicality: Methods must operate on what cities already have—phones, CCTVs, a handful of sensors, and open data.
Results and Contributions
A. Scalable Urban Air Pollution Monitoring
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Vision-based sensing from mobile & CCTV images
- Trained interpretable ML models that estimate PM2.5 from everyday images (haze as a physically meaningful proxy), enabling real-time, location-aware phone/CCTV sensing.
- Accuracy: image model MAE 44 µg/m³, improving to 35 µg/m³ via distributed averaging; in-interval accuracy reaches 74% despite noisy labels and limited data.
- Coverage impact: combined with sensor data, the augmented network revealed 189 previously undetected hotspots, affecting >150,000 residents beyond public monitor coverage.
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Interpolation over sparse sensor networks
- Built space-time interpolation (e.g., Space-Time Kriging) to infer pollution at unmonitored locations/times.
- Robustness: monthly hotspot prediction achieves 95% precision and 88% recall with 50% simulated sensor failure; tolerant to label noise and device drift.
- Mechanistic insight: a companion model explains 65% of transient hotspots, highlighting policy-relevant drivers.
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Unified city-scale pipeline
Vision estimates densify spatial coverage; interpolation refines them into calibrated city-wide maps—yielding decision-grade hotspot surfaces from commodity imagery plus a sparse backbone of monitors.
| Metric | Value |
|---|---|
| Image MAE → with distributed averaging | 44 µg/m³ → 35 µg/m³ |
| In-interval accuracy | 74% |
| Hotspot detection (50% sensor failure) | 95% precision / 88% recall |
| Newly revealed hotspots (residents impacted) | 189 (>150k) |
B. Understanding & Mitigating Traffic Jams
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Characterizing sudden traffic jams
- Introduced and formally defined “sudden jams” arising from short, intense bursts; constructed “traffic curves” from coarse sources (loop detectors, Uber Movement) across New York City, Nairobi, and São Paulo.
- Cross-city finding: Nairobi exhibits significantly longer jam durations per road segment than NYC and São Paulo, indicating management gaps and under-utilized capacity.
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Reinforcement learning for congestion control (infrastructure-free)
- Framed freeway management as a centralized, physics-informed RL problem that modulates target speeds to smooth flows without adding infrastructure.
- Simulation on real road networks shows: +5% throughput, −13% average delay, and up to −5% total stops versus baselines.
- Integration: jam characterization informs RL policies about where, when, and how aggressively to intervene.
| Throughput | Average Delay | Total stops |
|---|---|---|
| +5% | -13%% | -5% |
How do they connect?
Both projects convert low-cost signals into high-value city intelligence. Pollution mapping fuses commodity imagery and sparse sensors into dense, actionable risk surfaces; traffic modeling turns coarse mobility data into proactive control. The common thread is scalable sensing + robust modeling that cities can deploy now.
Members
- Ankit Bhardwaj , NYU.
- Shiva Iyer, NYU.
- Anant Balashankar, NYU.
Publications
Air Pollution
- A. Bhardwaj, S. Iyer, Y. Jalan, L. Subramanian. "Learning Pollution Maps from Mobile Phone Images." IJCAI 2022, AI for Good Track. (PDF) (Code)
- A. Bhardwaj, A. Balashankar, S. Iyer, N. Soans, A. Sudarshan, R. Pande, L. Subramanian. "Comprehensive Monitoring of Air Pollution Hotspots Using Sparse Sensor Networks." ACM Journal on Computing and Sustainable Societies, 2025. (PDF) (Code)
Traffic
- A. Bhardwaj*, S. Iyer*, S. Ramesh, J. White, L. Subramanian. "Understanding Sudden Traffic Jams: From Emergence to Impact. " Development Engineering: Journal of Engineering in Economic Development, 2023. (PDF)
- A. Bhardwaj, R. Asim, S. Chauhan, Y. Zaki, L. Subramanian. "Self-Regulating Cars: Automating Traffic Control in Free Flow Road Networks." ACM Journal on Computing and Sustainable Societies, 2025. (PDF) (Code)