no code implementations • 19 Apr 2024 • Rajat Verma, Eunhan Ka, Satish V. Ukkusuri
In this study, we show the application of trip generation and trip distribution modeling using GPS data from smartphones in the state of Indiana.
1 code implementation • 7 Apr 2024 • Rajat Verma, Mithun Debnath, Shagun Mittal, Satish V. Ukkusuri
Using data from multiple publicly available datasets, this metric is computed by trip purpose and travel time threshold for all block groups in the United States, and the data is made publicly accessible.
1 code implementation • 13 Jan 2023 • Washim Uddin Mondal, Vaneet Aggarwal, Satish V. Ukkusuri
We compute the approximation error as $\mathcal{O}(e)$ where $e=\frac{1}{\sqrt{N}}\left[\sqrt{|\mathcal{X}|} +\sqrt{|\mathcal{U}|}\right]$.
no code implementations • 15 Sep 2022 • Washim Uddin Mondal, Vaneet Aggarwal, Satish V. Ukkusuri
In a special case where the reward, cost, and state transition functions are independent of the action distribution of the population, we prove that the error can be improved to $e=\mathcal{O}(\sqrt{|\mathcal{X}|}/\sqrt{N})$.
Multi-agent Reinforcement Learning reinforcement-learning +1
no code implementations • 7 Sep 2022 • Washim Uddin Mondal, Vaneet Aggarwal, Satish V. Ukkusuri
We show that in a cooperative $N$-agent network, one can design locally executable policies for the agents such that the resulting discounted sum of average rewards (value) well approximates the optimal value computed over all (including non-local) policies.
Multi-agent Reinforcement Learning Reinforcement Learning (RL)
1 code implementation • 28 Feb 2022 • Washim Uddin Mondal, Vaneet Aggarwal, Satish V. Ukkusuri
We prove that, if the reward of each agent is an affine function of the mean-field seen by that agent, then one can approximate such a non-uniform MARL problem via its associated MFC problem within an error of $e=\mathcal{O}(\frac{1}{\sqrt{N}}[\sqrt{|\mathcal{X}|} + \sqrt{|\mathcal{U}|}])$ where $N$ is the population size and $|\mathcal{X}|$, $|\mathcal{U}|$ are the sizes of state and action spaces respectively.
2 code implementations • 13 Feb 2022 • Washim Uddin Mondal, Praful D. Mankar, Goutam Das, Vaneet Aggarwal, Satish V. Ukkusuri
This article proposes Convolutional Neural Network-based Auto Encoder (CNN-AE) to predict location-dependent rate and coverage probability of a network from its topology.
no code implementations • 9 Sep 2021 • Washim Uddin Mondal, Mridul Agarwal, Vaneet Aggarwal, Satish V. Ukkusuri
We show that, in these cases, the $K$-class MARL problem can be approximated by MFC with errors given as $e_1=\mathcal{O}(\frac{\sqrt{|\mathcal{X}|}+\sqrt{|\mathcal{U}|}}{N_{\mathrm{pop}}}\sum_{k}\sqrt{N_k})$, $e_2=\mathcal{O}(\left[\sqrt{|\mathcal{X}|}+\sqrt{|\mathcal{U}|}\right]\sum_{k}\frac{1}{\sqrt{N_k}})$ and $e_3=\mathcal{O}\left(\left[\sqrt{|\mathcal{X}|}+\sqrt{|\mathcal{U}|}\right]\left[\frac{A}{N_{\mathrm{pop}}}\sum_{k\in[K]}\sqrt{N_k}+\frac{B}{\sqrt{N_{\mathrm{pop}}}}\right]\right)$, respectively, where $A, B$ are some constants and $|\mathcal{X}|,|\mathcal{U}|$ are the sizes of state and action spaces of each agent.
1 code implementation • 7 Feb 2021 • Rajat Verma, Takahiro Yabe, Satish V. Ukkusuri
The rapid early spread of COVID-19 in the U. S. was experienced very differently by different socioeconomic groups and business industries.
1 code implementation • 1 Jan 2021 • Jiawei Xue, Nan Jiang, Senwei Liang, Qiyuan Pang, Takahiro Yabe, Satish V. Ukkusuri, Jianzhu Ma
We apply the method to 11, 790 urban road networks across 30 cities worldwide to measure the spatial homogeneity of road networks within each city and across different cities.
no code implementations • 26 Nov 2019 • Takahiro Yabe, Kota Tsubouchi, Toru Shimizu, Yoshihide Sekimoto, Satish V. Ukkusuri
Large mobility datasets collected from various sources have allowed us to observe, analyze, predict and solve a wide range of important urban challenges.