Predicting Gasoline Transaction Events in Price-Commitment Scenarios: An Automated Framework
Boyang Li, Ziqi Wang, Yunzhe Qiu, Xi Zhang · 2025
Automatic prediction of consumer transactions in the gasoline market is critical for enabling targeted advertising interventions and improving retailer profitability. However, the absence of daily gasoline consumption data at the consumer poses significant challenges in accurately assessing consumer tank levels and predicting transaction events. This difficulty is further exacerbated under price commitment scenarios, where transaction prices are protected by policy, leading to distinct shifts in daily consumption patterns and transaction events in response to market price fluctuations. To address these challenges, we introduce a time-to-event prediction framework designed for gasoline markets under price commitment. Our model dynamically tracks individual gasoline tank levels by integrating transaction prices, price volatility, and account remaining volume, which enhances adaptability to market fluctuations and improves prediction accuracy. The framework employs a lightweight neural network to establish a hazard function, capturing nonlinear dynamics between time-varying gasoline levels, static consumer profiles, and transaction likelihood. Through validation using a North China retail dataset, our method demonstrates significantly improved transaction timing prediction compared to traditional survival analysis and machine learning benchmarks.