arXiv subjects

Haris Koutsopoulos

Publications and source records attributed to Haris Koutsopoulos.

6 recordsLinked to original sources

arXiv2025

RAST-MoE-RL: A Regime-Aware Spatio-Temporal MoE Framework for Deep Reinforcement Learning in Ride-Hailing

Ride-hailing platforms face the challenge of balancing passenger waiting times with overall system efficiency under highly uncertain supply-demand conditions. Adaptive delayed matching, which controls the holding intervals for batched sets of requests and vehicles, reveals an inherent trade-off between matching and pickup delays. The resulting environment with temporally varying request arrival patterns and dynamic congestion calls for more expressive networks with sufficient capacity to capture their non-stationarity. To address the limitations of existing methods that rely on shallow encoders that cannot capture dynamic supply-demand patterns and congestion effects, we introduce the Regime-Aware Spatio-Temporal Mixture-of-Experts (RAST-MoE) framework, which formalizes adaptive delayed matching as a regime-aware Markov Decision Process and equips RL agents with a self-attention MoE encoder. Instead of relying on a single monolithic network, our design allows different experts to specialize automatically in varying operational conditions, improving representation capacity while maintaining per-sample computation efficiency. Despite its modest size of only 12M parameters, our framework consistently outperforms strong baselines. On real-world Uber trajectory data from San Francisco, it reduces average matching delay by 10%, and pickup delay by 15%. In addition, it demonstrates robustness to unseen demand regimes, stable training behavior without reward hacking, and expert specialization to different regimes. This study shows the strength of MoE-enhanced RL for large-scale decision-making tasks with complex spatiotemporal dynamics.

cs.LG↗

arXiv2025

AlphaOPT: Formulating Optimization Programs with Self-Improving LLM Experience Library

Optimization modeling underlies critical decision-making across industries, yet remains difficult to automate: natural-language problem descriptions must be translated into precise mathematical formulations and executable solver code. Existing LLM-based approaches typically rely on brittle prompting or costly retraining, both of which offer limited generalization. Recent work suggests that large models can improve via experience reuse, but how to systematically acquire, refine, and reuse such experience in structurally constrained settings remains unclear. We present \textbf{AlphaOPT}, a self-improving experience library that enables LLMs to learn optimization modeling knowledge from limited supervision, including answer-only feedback without gold-standard programs, annotated reasoning traces, or parameter updates. AlphaOPT operates in a continual two-phase cycle: a \emph{Library Learning} phase that extracts solver-verified, structured insights from failed attempts, and a \emph{Library Evolution} phase that refines the applicability of stored insights based on aggregate evidence across tasks. This design allows the model to accumulate reusable modeling principles, improve transfer across problem instances, and maintain bounded library growth over time. Evaluated on multiple optimization benchmarks, AlphaOPT steadily improves as more training data become available (65\% $\rightarrow$ 72\% from 100 to 300 training items) and outperforms the strongest baseline by 9.1\% and 8.2\% on two out-of-distribution datasets. These results demonstrate that structured experience learning, grounded in solver feedback, provides a practical alternative to retraining for complex reasoning tasks requiring precise formulation and execution. All code and data are available at: https://github.com/Minw913/AlphaOPT.

cs.AI↗

arXiv2025

Real Time Headway Predictions in Urban Rail Systems and Implications for Service Control: A Deep Learning Approach

Efficient real-time dispatching in urban metro systems is essential for ensuring service reliability, maximizing resource utilization, and improving passenger satisfaction. This study presents a novel deep learning framework centered on a Convolutional Long Short-Term Memory (ConvLSTM) model designed to predict the complex spatiotemporal propagation of train headways across an entire metro line. By directly incorporating planned terminal headways as a critical input alongside historical headway data, the proposed model accurately forecasts future headway dynamics, effectively capturing both their temporal evolution and spatial dependencies across all stations. This capability empowers dispatchers to evaluate the impact of various terminal headway control decisions without resorting to computationally intensive simulations. We introduce a flexible methodology to simulate diverse dispatcher strategies, ranging from maintaining even headways to implementing custom patterns derived from observed terminal departures. In contrast to existing research primarily focused on passenger load predictioning or atypical disruption scenarios, our approach emphasizes proactive operational control. Evaluated on a large-scale dataset from an urban metro line, the proposed ConvLSTM model demonstrates promising headway predictions, offering actionable insights for real-time decision-making. This framework provides rail operators with a powerful, computationally efficient tool to optimize dispatching strategies, thereby significantly improving service consistency and passenger satisfaction.

cs.LG↗

arXiv2023

Reconstructing Transit Vehicle Trajectory Using High-Resolution GPS Data

High-resolution location ("heartbeat") data of transit fleet vehicles is a relatively new data source for many transit agencies. On its surface, the heartbeat data can provide a wealth of information about all operational details of a recorded transit vehicle trip, from its location trajectory to its speed and acceleration profiles. Previous studies have mainly focused on decomposing the total trip travel time into different components by vehicle state and then extracting measures of delays to draw conclusions on the performance of a transit route. This study delves into the task of reconstructing a complete, continuous and smooth transit vehicle trajectory from the heartbeat data that allows for the extraction of operational information of a bus at any point in time into its trip. Using only the latitude, longitude, and timestamp fields of the heartbeat data, the authors demonstrate that a continuous, smooth, and monotonic vehicle trajectory can be reconstructed using local regression in combination with monotonic cubic spline interpolation. The resultant trajectory can be used to evaluate transit performance and identify locations of bus delay near infrastructure such as traffic signals, pedestrian crossings, and bus stops.

stat.AP↗

arXiv2023

Uncertainty Quantification of Spatiotemporal Travel Demand with Probabilistic Graph Neural Networks

Recent studies have significantly improved the prediction accuracy of travel demand using graph neural networks. However, these studies largely ignored uncertainty that inevitably exists in travel demand prediction. To fill this gap, this study proposes a framework of probabilistic graph neural networks (Prob-GNN) to quantify the spatiotemporal uncertainty of travel demand. This Prob-GNN framework is substantiated by deterministic and probabilistic assumptions, and empirically applied to the task of predicting the transit and ridesharing demand in Chicago. We found that the probabilistic assumptions (e.g. distribution tail, support) have a greater impact on uncertainty prediction than the deterministic ones (e.g. deep modules, depth). Among the family of Prob-GNNs, the GNNs with truncated Gaussian and Laplace distributions achieve the highest performance in transit and ridesharing data. Even under significant domain shifts, Prob-GNNs can predict the ridership uncertainty in a stable manner, when the models are trained on pre-COVID data and tested across multiple periods during and after the COVID-19 pandemic. Prob-GNNs also reveal the spatiotemporal pattern of uncertainty, which is concentrated on the afternoon peak hours and the areas with large travel volumes. Overall, our findings highlight the importance of incorporating randomness into deep learning for spatiotemporal ridership prediction. Future research should continue to investigate versatile probabilistic assumptions to capture behavioral randomness, and further develop methods to quantify uncertainty to build resilient cities.

cs.LG↗

arXiv2020

Profiling presence patterns and segmenting user locations from cell phone data

The dynamic monitoring of commuting flows is crucial for improving transit systems in fast-developing cities around the world. However, existing methodology to infer commuting originations and destinations have to either rely on large-scale survey data, which is inherently expensive to implement, or on Call Detail Records but based on ad-hoc heuristic assignment rules based on the frequency of appearance at given locations. In this paper, we proposed a novel method to accurately infer the point of origin and destinations of commuting flows based on individual's spatial-temporal patterns inferred from Call Detail Records. Our project significantly improves the accuracy upon the heuristic assignment rules popularly adopted in the literature. Starting with the historical data of geo-temporal travel patterns for a panel of individuals, we create, for each person-location, a vector of probability distribution capturing the likelihood that the person will appear in that location for a given the time of day. Stacked in this way, the matrix of historical geo-temporal data enables us to apply Eigen-decomposition and use unsupervised machine learning techniques to extract commonalities across locations for the different groups of travelers, which ultimately allows us to make inferences and create labels, such as home and work, on specific locations. Testing the methodology on real-world data with known location labels shows that our method identifies home and workplaces with significant accuracy, improving upon the most commonly used methods in the literature by 79% and 34%, respectively. Most importantly, our methodology does not bear any significant computation burden and is easily scalable and easily expanded to other real-world data with historical tracking.

cs.SI↗