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Exploring spatial heterogeneity of e-scooter's relationship with ridesourcing using explainable machine learning

J Jiao, Y Xu, Y Li - Transportation Research Part D: Transport and …, 2024 - Elsevier
The expansion of e-scooter sharing system has introduced several novel interactions within
the existing transportation system. However, few studies have explored how spatial contexts …
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[HTML][HTML] Situational-aware multi-graph convolutional recurrent network (SA-MGCRN) for travel demand forecasting during wildfires

X Zhang, X Zhao, Y Xu, D Nilsson… - … Research Part A: Policy …, 2024 - Elsevier
Natural hazards, such as wildfires, pose a significant threat to communities worldwide. Real-
time forecasting of travel demand during wildfire evacuations is crucial for emergency …
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[HTML][HTML] How weather and built environment factors influence e-scooter ridership: Understanding non-linear and time varying effects

Y Lu, L Zhang, J Corcoran - Journal of Cycling and Micromobility Research, 2024 - Elsevier
Our understanding of non-linear and time varying effects on shared e-scooter ridership
dynamics is limited. Consequently, both operators and city councils supporting shared e …
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Analyzing shared e-scooter trip frequency on urban road segments in Austin, TX

J Jiao, Y Xu - Case Studies on Transport Policy, 2024 - Elsevier
The expansion of e-scooter sharing system presents a mix of advantages and challenges to
the urban transportation system. This research delves into the frequency of shared e-scooter …
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[PDF][PDF] Scooter-Share Travel Demand Forecast: A Context-Aware LSTM Recurrent Neural Network Approach

JJ PI, Y Xu - 2024 - sites.utexas.edu
Shared micromobility has been popular in many cities in the US The rise of shared
micromobility brings significant operational challenges such as fleet management and …
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