01659725

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On-demand, app-based ride services like Uber and Lyft have become an important part of today’s transportation system with its flexibility and quick responsiveness. Compared with traditional taxicabs, Uber-like taxis have loggers to monitor and record trip information such as pickup location and trip distance, which can be a valuable data source for knowledge discovering. Nowadays, a Real-time prediction for ride service demand (always reflected by the number of pickups) is increasingly crucial for the purpose of improving the efficiency and sustainability of the urban transportation system. Newly aroused application topics like ride sharing and autonomous mobility dispatching are based on solid demand predictions. In this paper, the authors propose a deep learning based approach to make dynamic predictions for Uber pickups using historical data. A Long Short-Term Memory (LSTM) Networks model is developed to learn the long-term dependencies of the pickups over time. With the experimental comparison of time-varying Poisson model and regression tree model, the results demonstrate the superior performance of the proposed deep learning model.

This paper was sponsored by TRB committee ABJ70 Standing Committee on Artificial Intelligence and Advanced Computing Applications.

18-06738

English

12p

2018

Transportation Research Board 97th Annual Meeting

Digital/other

Figures; Maps; References; Tables

Data analysis; Demand responsive transportation; Machine learning; Mathematical prediction; Regression analysis; Schedules and scheduling; Travel demand

Uber

Data and Information Technology; Planning and Forecasting; Public Transportation

Transportation Research Board Annual Meeting 2018 Paper #18-06738

TRIS, TRB, ATRI

Jan 8 2018 11:44AM