34 citations · 112 across the 20 of their papers we have counts for
20 papers
ICN: Interactive Convolutional Network for Forecasting Travel Demand of Shared Micromobility
Yiming Xu, Qian Ke, Xiaojian Zhang +1
Accurate shared micromobility demand predictions are essential for transportation planning and management. Although deep learning models provide powerful tools to deal with demand…
Situational-Aware Multi-Graph Convolutional Recurrent Network (SA-MGCRN) for Travel Demand Forecasting During Wildfires
Xiaojian Zhang, Xilei Zhao, Yiming Xu +2
Real-time forecasting of travel demand during wildfire evacuations is crucial for emergency managers and transportation planners to make timely and better-informed decisions. Howev…
Predicting Hurricane Evacuation Decisions with Interpretable Machine Learning Models
Yuran Sun, Shih-Kai Huang, Xilei Zhao
The aggravating effects of climate change and the growing population in hurricane-prone areas escalate the challenges in large-scale hurricane evacuations. While hurricane prepared…
Travel Demand Forecasting: A Fair AI Approach
Xiaojian Zhang, Qian Ke, Xilei Zhao
Artificial Intelligence (AI) and machine learning have been increasingly adopted for travel demand forecasting. The AI-based travel demand forecasting models, though generate accur…
Examining spatial heterogeneity of ridesourcing demand determinants with explainable machine learning
Xiaojian Zhang, Xiang Yan, Zhengze Zhou +2
The growing significance of ridesourcing services in recent years suggests a need to examine the key determinants of ridesourcing demand. However, little is known regarding the non…
Spatial Equity of Micromobility Systems: A Comparison of Shared E-scooters and Station-based Bikeshare in Washington DC
Lin Su, Xiang Yan, Xilei Zhao
Many cities around the world have introduced dockless micromobility services in recent years and witnessed their rapid growth. Shared dockless e-scooters have the potential to bene…
Real-Time Forecasting of Dockless Scooter-Sharing Demand: A Spatio-Temporal Multi-Graph Transformer Approach
Yiming Xu, Xilei Zhao, Xiaojian Zhang +1
Accurately forecasting the real-time travel demand for dockless scooter-sharing is crucial for the planning and operations of transportation systems. Deep learning models provide r…
Estimating Wildfire Evacuation Decision and Departure Timing Using Large-Scale GPS Data
Xilei Zhao, Yiming Xu, Ruggiero Lovreglio +5
With increased frequency and intensity due to climate change, wildfires have become a growing global concern. This creates severe challenges for fire and emergency services as well…
A Clustering-aided Ensemble Method for Predicting Ridesourcing Demand in Chicago
Xiaojian Zhang, Xilei Zhao
Accurately forecasting ridesourcing demand is important for effective transportation planning and policy-making. With the rise of Artificial Intelligence (AI), researchers have sta…
Identifying latent shared mobility preference segments in low-income communities: ride-hailing, fixed-route bus, and mobility-on-demand transit
Xinyi Wang, Xiang Yan, Xilei Zhao +1
Concepts of Mobility-on-Demand (MOD) and Mobility as a Service (MaaS), which feature the integration of various shared-use mobility options, have gained widespread popularity in re…
Do e-scooters fill mobility gaps and promote equity before and during COVID-19? A spatiotemporal analysis using open big data
Xiang Yan, Wencui Yang, Xiaojian Zhang +3
The growing popularity of e-scooters and their rapid expansion across urban streets has attracted widespread attention. A major policy question is whether e-scooters substitute exi…
Optimizing Demand-Responsive Paratransit Operations: A Mixed Integer Programming Approach
Xiaojian Zhang, Yu Yang, Abigail L. Cochran +2
The traditional demand-responsive paratransit system plays an important role in connecting people to health care, particularly those who are carless, low-income, senior, underinsur…
Micromobility Trip Origin and Destination Inference Using General Bikeshare Feed Specification (GBFS) Data
Yiming Xu, Xiang Yan, Virginia P. Sisiopiku +3
Emerging micromobility services (e.g., e-scooters) have a great potential to enhance urban mobility but more knowledge on their usage patterns is needed. The General Bikeshare Feed…
Longitudinal Control of Vehicles in Traffic Microsimulation
Shirin Noei, Xilei Zhao, Carl D. Crane
Current state-of-art traffic microsimulation tools cannot accurately estimate safety, efficiency, and mobility benefits of automated driving systems and vehicle connectivity becaus…
Optimization Models for Estimating Transit Network Origin-Destination Flows with AVL/APC Data
Xinyu Liu, Pascal Van Hentenryck, Xilei Zhao
With the increasing adoption of Automatic Vehicle Location (AVL) and Automatic Passenger Count (APC) technologies by transit agencies, a massive amount of time-stamped and location…
Distilling Black-Box Travel Mode Choice Model for Behavioral Interpretation
Xilei Zhao, Zhengze Zhou, Xiang Yan +1
Machine learning has proved to be very successful for making predictions in travel behavior modeling. However, most machine-learning models have complex model structures and offer…
Modeling Heterogeneity in Mode-Switching Behavior Under a Mobility-on-Demand Transit System: An Interpretable Machine Learning Approach
Xilei Zhao, Xiang Yan, Pascal Van Hentenryck
Recent years have witnessed an increased focus on interpretability and the use of machine learning to inform policy analysis and decision making. This paper applies machine learnin…
Mobility-on-demand versus fixed-route transit systems: an evaluation of traveler preferences in low-income communities
Xiang Yan, Xilei Zhao, Yuan Han +2
Emerging transportation technologies, such as ride-hailing and autonomous vehicles, are disrupting the transportation sector and transforming public transit. Some transit observers…
Modeling Traffic Networks Using Integrated Route and Link Data
Xilei Zhao, James C. Spall
Real-time navigation services, such as Google Maps and Waze, are widely used in daily life. These services provide rich data resources in real-time traffic conditions and travel ti…
Modeling Stated Preference for Mobility-on-Demand Transit: A Comparison of Machine Learning and Logit Models
Xilei Zhao, Xiang Yan, Alan Yu +1
Logit models are usually applied when studying individual travel behavior, i.e., to predict travel mode choice and to gain behavioral insights on traveler preferences. Recently, so…