Taxi Utilization Rate Maximization by Dynamic Demand Prediction: A Case Study in the City of Chicago

Abstract

The explosive popularity of transportation network companies (TNCs) in the last decade has imposed dramatic disruptions on the taxi industry, but not all the impacts are beneficial. For instance, studies have shown taxi capacity utilization rate is lower than 50% in five major U.S. cities. With the availability of taxi data, this study finds the taxi utilization rate is around 40% in June 2019 (normal scenario) and 35% in June 2020 (COVID 19 scenario) in the city of Chicago, U.S. Powered by recent advances in the deep learning of capturing non-linear relationships and the availability of datasets, a real-time taxi trip optimization strategy with dynamic demand prediction was designed using long short-term memory (LSTM) architecture to maximize the taxi utilization rate. The algorithms are tested in both scenarios—normal time and COVID 19 time—and promising results have been shown by implementing the strategy, with around 19% improvement in mileage utilization rate in June 2019 and 74% in June 2020 compared with the baseline without any optimizations. Additionally, this study investigated the impacts of COVID 19 on the taxi service in Chicago.