3 citations · 12 across the 28 of their papers we have counts for
30 papers
Computing Headway Bounds under Worst-Case Bunching in Fixed-Line Transit Systems
Michael Yuhas, George Gunter, Jose Paulo Talusan +3
Vehicle bunching is a major problem for transit operators. When vehicles bunch together, the lead vehicle will service the majority of passenger demand, leaving the following vehic…
Dynamic Vehicle Routing Problem with Prompt Confirmation of Advance Requests
Amutheezan Sivagnanam, Ayan Mukhopadhyay, Samitha Samaranayake +2
Transit agencies that operate on-demand transportation services have to respond to trip requests from passengers in real time, which involves solving dynamic vehicle routing proble…
Column Generation for the Micro-Transit Zoning Problem
Hins Hu, Rishav Sen, Jose Paolo Talusan +3
Along with the rapid development of new urban mobility options like ride-sharing over the past decade, on-demand micro-transit services stand out as a middle ground, bridging the g…
CONSENT: A Negotiation Framework for Leveraging User Flexibility in Vehicle-to-Building Charging under Uncertainty
Rishav Sen, Fangqi Liu, Jose Paolo Talusan +5
The growth of Electric Vehicles (EVs) creates a conflict in vehicle-to-building (V2B) settings between building operators, who face high energy costs from uncoordinated charging, a…
Grid-Aware Charging and Operational Optimization for Mixed-Fleet Public Transit
Rishav Sen, Amutheezan Sivagnanam, Aron Laszka +2
The rapid growth of urban populations and the increasing need for sustainable transportation solutions have prompted a shift towards electric buses in public transit systems. Howev…
Online Decision-Making Under Uncertainty for Vehicle-to-Building Systems
Rishav Sen, Yunuo Zhang, Fangqi Liu +5
Vehicle-to-building (V2B) systems integrate physical infrastructures, such as smart buildings and electric vehicles (EVs) connected to chargers at the building, with digital contro…
ESCORT: Efficient Stein-variational and Sliced Consistency-Optimized Temporal Belief Representation for POMDPs
Yunuo Zhang, Baiting Luo, Ayan Mukhopadhyay +2
In Partially Observable Markov Decision Processes (POMDPs), maintaining and updating belief distributions over possible underlying states provides a principled way to summarize act…
MoveOD: Synthesizing Origin-Destination Commute Distribution from U.S. Census Data
Rishav Sen, Jose Paolo Talusan, Abhishek Dubey +3
High-resolution origin-destination (OD) tables are essential for a wide spectrum of transportation applications, from modeling traffic and signal timing optimization to congestion…
Observation Adaptation via Annealed Importance Resampling for Partially Observable Markov Decision Processes
Yunuo Zhang, Baiting Luo, Ayan Mukhopadhyay +1
Partially observable Markov decision processes (POMDPs) are a general mathematical model for sequential decision-making in stochastic environments under state uncertainty. POMDPs a…
Scalable Decision-Making in Stochastic Environments through Learned Temporal Abstraction
Baiting Luo, Ava Pettet, Aron Laszka +2
Sequential decision-making in high-dimensional continuous action spaces, particularly in stochastic environments, faces significant computational challenges. We explore this challe…
Reinforcement Learning-based Approach for Vehicle-to-Building Charging with Heterogeneous Agents and Long Term Rewards
Fangqi Liu, Rishav Sen, Jose Paolo Talusan +5
Strategic aggregation of electric vehicle batteries as energy reservoirs can optimize power grid demand, benefiting smart and connected communities, especially large office buildin…
NS-Gym: Open-Source Simulation Environments and Benchmarks for Non-Stationary Markov Decision Processes
Nathaniel S. Keplinger, Baiting Luo, Iliyas Bektas +5
In many real-world applications, agents must make sequential decisions in environments where conditions are subject to change due to various exogenous factors. These non-stationary…
Shrinking POMCP: A Framework for Real-Time UAV Search and Rescue
Yunuo Zhang, Baiting Luo, Ayan Mukhopadhyay +8
Efficient path optimization for drones in search and rescue operations faces challenges, including limited visibility, time constraints, and complex information gathering in urban…
Enabling MCTS Explainability for Sequential Planning Through Computation Tree Logic
Ziyan An, Hendrik Baier, Abhishek Dubey +2
Monte Carlo tree search (MCTS) is one of the most capable online search algorithms for sequential planning tasks, with significant applications in areas such as resource allocation…
Multi-Agent Reinforcement Learning with Hierarchical Coordination for Emergency Responder Stationing
Amutheezan Sivagnanam, Ava Pettet, Hunter Lee +3
An emergency responder management (ERM) system dispatches responders, such as ambulances, when it receives requests for medical aid. ERM systems can also proactively reposition res…
Forecasting and Mitigating Disruptions in Public Bus Transit Services
Chaeeun Han, Jose Paolo Talusan, Dan Freudberg +3
Public transportation systems often suffer from unexpected fluctuations in demand and disruptions, such as mechanical failures and medical emergencies. These fluctuations and disru…
An Online Approach to Solving Public Transit Stationing and Dispatch Problem
Jose Paolo Talusan, Chaeeun Han, Ayan Mukhopadhyay +3
Public bus transit systems provide critical transportation services for large sections of modern communities. On-time performance and maintaining the reliable quality of service is…
Act as You Learn: Adaptive Decision-Making in Non-Stationary Markov Decision Processes
Baiting Luo, Yunuo Zhang, Abhishek Dubey +1
A fundamental (and largely open) challenge in sequential decision-making is dealing with non-stationary environments, where exogenous environmental conditions change over time. Suc…
Decision Making in Non-Stationary Environments with Policy-Augmented Search
Ava Pettet, Yunuo Zhang, Baiting Luo +5
Sequential decision-making under uncertainty is present in many important problems. Two popular approaches for tackling such problems are reinforcement learning and online search (…
Artificial Intelligence for Smart Transportation
Michael Wilbur, Amutheezan Sivagnanam, Afiya Ayman +3
There are more than 7,000 public transit agencies in the U.S. (and many more private agencies), and together, they are responsible for serving 60 billion passenger miles each year.…
Rolling Horizon based Temporal Decomposition for the Offline Pickup and Delivery Problem with Time Windows
Youngseo Kim, Danushka Edirimanna, Michael Wilbur +4
The offline pickup and delivery problem with time windows (PDPTW) is a classical combinatorial optimization problem in the transportation community, which has proven to be very cha…
Designing Equitable Transit Networks
Sophie Pavia, J. Carlos Martinez Mori, Aryaman Sharma +4
Public transit is an essential infrastructure enabling access to employment, healthcare, education, and recreational facilities. While accessibility to transit is important in gene…
On Designing Day Ahead and Same Day Ridership Level Prediction Models for City-Scale Transit Networks Using Noisy APC Data
Jose Paolo Talusan, Ayan Mukhopadhyay, Dan Freudberg +1
The ability to accurately predict public transit ridership demand benefits passengers and transit agencies. Agencies will be able to reallocate buses to handle under or over-utiliz…
Generative Anomaly Detection for Time Series Datasets
Zhuangwei Kang, Ayan Mukhopadhyay, Aniruddha Gokhale +2
Traffic congestion anomaly detection is of paramount importance in intelligent traffic systems. The goals of transportation agencies are two-fold: to monitor the general traffic co…