Short-Term Forecasting of Passenger Demand under On-Demand Ride Services: A Spatio-Temporal Deep Learning Approach

Short-term passenger demand forecasting is of great importance to the on-demand ride service platform, which can incentivize vacant cars moving from over-supply regions to over-demand regions. The spatial dependences, temporal dependences, and exogenous dependences need to be considered simultaneously, however, which makes short-term passenger demand forecasting challenging. We propose a novel deep learning (DL) approach, named the fusion convolutional long short-term memory network (FCL-Net), to address these three dependences within one end-to-end learning architecture. The model is stacked and fused by multiple convolutional long short-term memory (LSTM) layers, standard LSTM layers, and convolutional layers. The fusion of convolutional techniques and the LSTM network enables the proposed DL approach to better capture the spatio-temporal characteristics and correlations of explanatory variables. A tailored spatially aggregated random forest is employed to rank the importance of the explanatory variables. The ranking is then used for feature selection. The proposed DL approach is applied to the short-term forecasting of passenger demand under an on-demand ride service platform in Hangzhou, China. Experimental results, validated on real-world data provided by DiDi Chuxing, show that the FCL-Net achieves better predictive performance than traditional approaches including both classical time-series prediction models and neural network based algorithms (e.g., artificial neural network and LSTM). This paper is one of the first DL studies to forecast the short-term passenger demand of an on-demand ride service platform by examining the spatio-temporal correlations.Comment: 39 pages, 10 figure

The role of wind stress in driving the along-shelf flow in the Northwest Atlantic Ocean

Author Posting. © American Geophysical Union, 2021. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research: Oceans 126(4), (2021): e2020JC016757, https://doi.org/10.1029/2020JC016757.The along-shelf circulation in the Northwest Atlantic (NWA) Ocean is characterized by an equatorward flow from Greenland's south coast to Cape Hatters. The mean flow is considered to be primarily forced by freshwater discharges from rivers and glaciers while its variability is driven by both freshwater fluxes and wind stress. In this study, we hypothesize and test that the wind stress is important for the mean along-shelf flow. A two-layer model with realistic topography when forced by wind stress alone simulates a circulation system on the NWA shelves that is broadly consistent with that derived from observations, including an equatorward flow from Greenland coast to the Mid-Atlantic Bight (MAB). The along-shelf sea-level gradient is close to a previous estimate based on observations. The along-shelf flows exhibit strong seasonal variations with along-shelf transports being strong in fall/winter and weak in spring/summer, consistent with available observations. It is found that the NWA shelf circulation is affected by both wind-driven gyres through their western boundary currents and wind-stress forcing on the shelf especially along the coasts of Newfoundland and Labrador. The local wind stress forcing has more direct impacts on flows in shallower waters along the coast while the open-ocean gyres tend to affect the circulations along the outer shelf. Our conclusion is that wind stress is an important forcing of the main along-shelf flows in the NWA. One objective of this study is to motivate further examination of whether wind stress is as important as freshwater forcing for the mean flow.Both Yang and Chen are also supported by NOAA Climate Program Office's Climate Variability and Prediction Program under grant NA20OAR4310398. JY is supported by Woods Hole Oceanographic Institution (WHOI) W. V. A. Clark Chair for Excellence in Oceanography and NSF Ocean Science Division under grant OCE1634886. Chen is supported by WHOI Independent Research and Development award.2021-09-3