arXiv subjects

Mohit Apte

Publications and source records attributed to Mohit Apte.

4 recordsLinked to original sources

arXiv2026

Constrained Spatial Pricing of On-Street Parking with Bayesian Demand Calibration

Occupancy-targeted curb pricing, including San Francisco's SFpark pilot, often relies on local threshold rules that do not account for spatial substitution or uncertainty in demand response. We estimate parking-price elasticities from disaggregated SFpark data using a hierarchical Bayesian demand model and use the resulting posterior distribution in a spatially coupled constrained pricing model. Partial pooling across districts gives a posterior mean elasticity of -0.241 with a 95% credible interval of [-0.325, -0.176]. The pricing problem is formulated as a constrained nonlinear program with spatial coupling, temporal price-change limits, neighbor price gaps, and a soft revenue floor, and is solved with Ipopt. Expected-loss and CVaR objectives are evaluated over joint posterior draws rather than a fixed elasticity. On held-out posterior scenarios, the posterior-based CVaR policy has a lower objective than the SFpark threshold rule and historical tariff with posterior probability 1.00, and than a literature-calibrated robust policy with probability 0.85. Epsilon-constraint frontiers show that posterior calibration changes the efficient pricing set. Results from 60 district-windows across Fillmore, Mission, and Marina also report the associated tradeoffs in occupancy-band performance, revenue, and spatial disparity.

math.OC↗

arXiv2026

Stochastic Mixed-Integer Optimization of Dynamic Electricity Tariffs with Consumer Protection

Day-ahead residential tariffs must be posted before demand is observed. Aggressive high prices can cut peaks in simulation, but they can also raise customer bills. This paper measures how much peak reduction is lost when a tariff design problem is required to keep revenue near a flat-tariff baseline and to limit bill increases. Using half-hourly data from 5,566 Low Carbon London households (167.8 million validated readings), we estimate quasi-experimental price response against the standard-tariff comparison group, form bootstrap demand scenarios, and solve stochastic mixed-integer programs with HiGHS. On all 73 eligible held-out test days, a segment-protected stochastic tariff reduces simulated peak demand by 2.29% on average (95% day-bootstrap CI [2.11, 2.48]), with revenue change -2.26% and mean worst-segment bill increase 2.48%. Removing the segment bill cap raises peak reduction only to 2.37%, while the worst segment bill increase rises to 6.81%: in this sample, substantial average protection costs little peak-shaving performance. The same schedules leave a household 95th-percentile bill increase of 8.61% (CVaR95 14.76%), so segment-average caps do not bound household tails. We report the full price-of-protection frontier under consistent household simulation and compare segment protection with a representative-household (tail-aware) variant: the latter cuts household p95 from 8.61% to 7.05% while changing mean peak reduction only from 2.29% to 2.28%. All optimized outcomes are model-based counterfactuals under an opt-in trial; wholesale costs are unavailable, so we do not optimize profit.

math.OC↗

arXiv2024

Dynamic Retail Pricing via Q-Learning -- A Reinforcement Learning Framework for Enhanced Revenue Management

This paper explores the application of a reinforcement learning (RL) framework using the Q-Learning algorithm to enhance dynamic pricing strategies in the retail sector. Unlike traditional pricing methods, which often rely on static demand models, our RL approach continuously adapts to evolving market dynamics, offering a more flexible and responsive pricing strategy. By creating a simulated retail environment, we demonstrate how RL effectively addresses real-time changes in consumer behavior and market conditions, leading to improved revenue outcomes. Our results illustrate that the RL model not only surpasses traditional methods in terms of revenue generation but also provides insights into the complex interplay of price elasticity and consumer demand. This research underlines the significant potential of applying artificial intelligence in economic decision-making, paving the way for more sophisticated, data-driven pricing models in various commercial domains.

cs.LG↗

arXiv2024

Advancing Financial Forecasting: A Comparative Analysis of Neural Forecasting Models N-HiTS and N-BEATS

In the rapidly evolving field of financial forecasting, the application of neural networks presents a compelling advancement over traditional statistical models. This research paper explores the effectiveness of two specific neural forecasting models, N-HiTS and N-BEATS, in predicting financial market trends. Through a systematic comparison with conventional models, this study demonstrates the superior predictive capabilities of neural approaches, particularly in handling the non-linear dynamics and complex patterns inherent in financial time series data. The results indicate that N-HiTS and N-BEATS not only enhance the accuracy of forecasts but also boost the robustness and adaptability of financial predictions, offering substantial advantages in environments that require real-time decision-making. The paper concludes with insights into the practical implications of neural forecasting in financial markets and recommendations for future research directions.

q-fin.CP↗