Free-flow road networks, such as suburban highways, are increasingly experiencing traffic congestion due to growing commuter inflow and limited infrastructure. Traditional control mechanisms—traffic signals or local heuristics—are ineffective or infeasible in these high-speed, signal-free environments. We introduce self-regulating cars, a reinforcement learning-based traffic control protocol that dynamically modulates vehicle speeds to optimize throughput and prevent congestion, without requiring new physical infrastructure. Our approach integrates classical traffic flow theory, gap acceptance models, and microscopic simulation into a physics-informed RL framework. By abstracting roads into super-segments, the agent captures emergent flow dynamics and learns robust speed modulation policies from instantaneous traffic observations. Evaluated in the high-fidelity PTV Vissim simulator on a real-world highway network, our method improves total throughput by 5%, reduces average delay by 13%, and decreases total stops by 3% compared to the no-control setting. It also achieves smoother, congestion-resistant flow while generalizing across varied traffic patterns—demonstrating its potential for scalable, ML-driven traffic management.

Effective traffic intersection control is crucial for urban sustainability. State of the art research seeking Artificial Intelligence (AI), for example Deep Reinforcement Learning (DRL) based traffic control requires environment states through various Computer Vision methods, where the collective state of multiple cameras across an intersection constitute the single state for AI. This brings in serious robustness or fault-tolerance concerns on the deployed system. Camera systems are highly susceptible to faults due to multiple possible points of failure. A single fault collapses the AI state and hence the capacity of AI controller to manage the traffic is gone. Also, infrastructure deployment and maintenance is a slow bureaucratic process in the developing countries, which makes camera faults a regular event. In the given paper, we build WebLight (https://github.com/sachin-iitd/WebLight), a web based, independent and alternative, traffic state processing method which can replace the camera dependency completely, or support as a backup mechanism until the camera system is back online, making the AI intersection control robust to camera failures.

Air pollution poses serious health concerns in developing countries, such as India, necessitating large-scale measurement for correlation analysis, policy recommendations, and informed decision-making. However, fine-grained data collection is costly. Specifically, static sensors for pollution measurement cost several thousand dollars per unit, leading to inadequate deployment and coverage. To complement the existing sparse static sensor network, we propose a mobile sensor network utilizing lower-cost PM2.5 sensors mounted on public buses in the Delhi-NCR region of India. Through this exercise, we introduce a novel dataset AirDelhi comprising PM2.5 and PM10 measurements. This dataset is made publicly available, at https://www.cse.iitd.ac.in/pollutiondata, serving as a valuable resource for machine learning (ML) researchers and environmentalists. We present three key contributions with the release of this dataset. Firstly, through in-depth statistical analysis, we demonstrate that the released dataset significantly differs from existing pollution datasets, highlighting its uniqueness and potential for new insights. Secondly, the dataset quality been validated against existing expensive sensors. Thirdly, we conduct a benchmarking exercise (https://github.com/sachin-iitd/DelhiPMDatasetBenchmark), evaluating state-of-the-art methods for interpolation, feature imputation, and forecasting on this dataset, which is the largest publicly available PM dataset to date. The results of the benchmarking exercise underscore the substantial disparities in accuracy between the proposed dataset and other publicly available datasets. This finding highlights the complexity and richness of our dataset, emphasizing its value for advancing research in the field of air pollution.

Road traffic congestion increases vehicular emissions and air pollution. Traffic rule violation causes road accidents. Both pollution and accidents take tremendous social and economic toll worldwide, and more so in developing countries where the skewed vehicle to road infrastructure ratio amplifies the problems. Automating traffic intersection management to detect and penalize traffic rule violations and reduce traffic congestion, is the focus of this paper, using state-of-the-art Convolutional Neural Network (CNN) on traffic camera feeds. There are however non-trivial challenges in handling the chaotic, non-laned traffic scenes in developing countries. Maintaining high throughput is one of the challenges, as broadband connectivity to remote GPU servers is absent in developing countries, and embedded GPU platforms on roads need to be low cost due to budget constraints. Additionally, ambient temperatures in developing country cities can go to 45-50 degree Celsius in summer, where continuous embedded processing can lead to lower lifetimes of the embedded platforms. In this paper, we present DynCNN, an application dynamism and ambient temperature aware controller for Neural Network concurrency. DynCNN effectively uses processor heterogeneity to control the number of threads and frequencies on the accelerator to manage application utility under strict thermal and power thresholds. We evaluate the efficiency of DynCNN on three different commercially available embedded GPUs (Jetson TX2TM, Xavier NXTM and Xavier AGXTM) using a real traffic intersection’s 40 days’ dataset. Experimental results show that in comparison to all existing state-of-the art- GPU governors for two different CPU settings, DynCNN reduces the average temperature and power by ~12°C and 68.82% respectively for one CPU setting (Baseline1) and similarly, it improves the performance by around 31.2% compared to the other CPU setting (Baseline2).

Effective intersection control can play an important role in reducing traffic congestion and associated vehicular emissions. This is vitally needed in developing countries, where air pollution is reaching life threatening levels. This paper presents EcoLight intersection control for developing regions, where budget is constrained and network connectivity is very poor. EcoLight learns effective control offline using state-of-the-art Deep Reinforcement Learning methods, but deploys highly efficient runtime control algorithms on low cost embedded devices that work stand-alone on road without server connectivity. EcoLight optimizes both average case and worst case values of throughput, travel time and other metrics, as evaluated on open-source datasets from New York and on a custom developing region dataset.