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06/24/2023
ICN: Interactive Convolutional Network for Forecasting Travel Demand of Shared Micromobility
Accurate shared micromobility demand predictions are essential for trans...
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04/13/2023
Situational-Aware Multi-Graph Convolutional Recurrent Network (SA-MGCRN) for Travel Demand Forecasting During Wildfires
Real-time forecasting of travel demand during wildfire evacuations is cr...
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03/12/2023
Predicting Hurricane Evacuation Decisions with Interpretable Machine Learning Models
The aggravating effects of climate change and the growing population in ...
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03/03/2023
Enhancing Fairness in AI-based Travel Demand Forecasting Models
Artificial Intelligence (AI) and machine learning have been increasingly...
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09/16/2022
Examining spatial heterogeneity of ridesourcing demand determinants with explainable machine learning
The growing significance of ridesourcing services in recent years sugges...
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08/19/2022
Spatial Equity of Micromobility Systems: A Comparison of Shared E-scooters and Station-based Bikeshare in Washington DC
Many cities around the world have introduced dockless micromobility serv...
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11/02/2021
Real-time Forecasting of Dockless Scooter-Sharing Demand: A Context-Aware Spatio-Temporal Multi-Graph Convolutional Network Approach
Real-time demand forecasting for shared micromobility can greatly enhanc...
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09/16/2021
Estimating Wildfire Evacuation Decision and Departure Timing Using Large-Scale GPS Data
With increased frequency and intensity due to climate change, wildfires ...
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09/08/2021
A Clustering-aided Ensemble Method for Predicting Ridesourcing Demand in Chicago
Accurately forecasting ridesourcing demand is important for effective tr...
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03/11/2021
Do e-scooters fill mobility gaps and promote equity before and during COVID-19? A spatiotemporal analysis using open big data
The growing popularity of e-scooters and their rapid expansion across ur...
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10/19/2020
Micromobility Trip Origin and Destination Inference Using General Bikeshare Feed Specification (GBFS) Data
Emerging micromobility services (e.g., e-scooters) have a great potentia...
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10/30/2019
Distilling Black-Box Travel Mode Choice Model for Behavioral Interpretation
Machine learning has proved to be very successful for making predictions...
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02/08/2019
Modeling Heterogeneity in Mode-Switching Behavior Under a Mobility-on-Demand Transit System: An Interpretable Machine Learning Approach
Recent years have witnessed an increased focus on interpretability and t...
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01/22/2019
Mobility-on-demand versus fixed-route transit systems: an evaluation of traveler preferences in low-income communities
Emerging transportation technologies, such as ride-hailing and autonomou...
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11/04/2018
Modeling Stated Preference for Mobility-on-Demand Transit: A Comparison of Machine Learning and Logit Models
Logit models are usually applied when studying individual travel behavio...
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11/04/2018