31 citations · 70 across the 21 of their papers we have counts for
22 papers
Public transit gains and spatially uneven travel demand changes after NYC congestion pricing
Donghang Li, Dingyi Zhuang, Yunlin Li +5
New York City implemented the nation's first cordon-based congestion pricing program in January 2025, providing an opportunity to evaluate how system-wide urban mobility responds t…
Ozone: A Unified Platform for Transportation Research
Ou Zheng, Ruyi Feng, Yufeng Yang +12
Transportation research increasingly depends on heterogeneous data from roadside cameras, UAV imagery, LiDAR, and in-vehicle sensors. Differences in data representations, model int…
Remote work expands pathways to upward career mobility
Yunhan Zheng, Jinhua Zhao
Geographic constraints have long structured access to high-growth career opportunities, concentrating upward mobility within a limited set of cities and organizations. The expansio…
Envisioning global urban development with satellite imagery and generative AI
Kailai Sun, Yuebing Liang, Mingyi He +5
Urban development has been a defining force in human history, shaping cities for centuries. However, past studies mostly analyze such development as predictive tasks, failing to re…
An Integrated Causal Inference Framework for Traffic Safety Modeling with Semantic Street-View Visual Features
Lishan Sun, Yujia Cheng, Pengfei Cui +4
Macroscopic traffic safety modeling aims to identify critical risk factors for regional crashes, thereby informing targeted policy interventions for safety improvement. However, cu…
Data-Driven Discovery of Mobility Periodicity for Understanding Urban Systems
Xinyu Chen, Qi Wang, Yunhan Zheng +3
Human mobility regularity is crucial for understanding urban dynamics and informing decision-making processes. This study first quantifies the periodicity in complex human mobility…
Generative AI for Urban Design: A Stepwise Approach Integrating Human Expertise with Multimodal Diffusion Models
Mingyi He, Yuebing Liang, Shenhao Wang +5
Urban design is a multifaceted process that demands careful consideration of site-specific constraints and collaboration among diverse professionals and stakeholders. The advent of…
Generative AI for Urban Planning: Synthesizing Satellite Imagery via Diffusion Models
Qingyi Wang, Yuebing Liang, Yunhan Zheng +3
Generative AI offers new opportunities for automating urban planning by creating site-specific urban layouts and enabling flexible design exploration. However, existing approaches…
Reimagining Urban Science: Scaling Causal Inference with Large Language Models
Yutong Xia, Ao Qu, Yunhan Zheng +8
Urban causal research is essential for understanding the complex, dynamic processes that shape cities and for informing evidence-based policies. However, current practices are ofte…
Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Study
Dingyi Zhuang, Hanyong Xu, Xiaotong Guo +3
Urban prediction tasks, such as forecasting traffic flow, temperature, and crime rates, are crucial for efficient urban planning and management. However, existing Spatiotemporal Gr…
Sparkle: Mastering Basic Spatial Capabilities in Vision Language Models Elicits Generalization to Spatial Reasoning
Yihong Tang, Ao Qu, Zhaokai Wang +7
Vision language models (VLMs) perform well on many tasks but often fail at spatial reasoning, which is essential for navigation and interaction with physical environments. Many spa…
GETS: Ensemble Temperature Scaling for Calibration in Graph Neural Networks
Dingyi Zhuang, Chonghe Jiang, Yunhan Zheng +2
Graph Neural Networks deliver strong classification results but often suffer from poor calibration performance, leading to overconfidence or underconfidence. This is particularly p…
Advancing Transportation Mode Share Analysis with Built Environment: Deep Hybrid Models with Urban Road Network
Dingyi Zhuang, Qingyi Wang, Yunhan Zheng +4
Transportation mode share analysis is important to various real-world transportation tasks as it helps researchers understand the travel behaviors and choices of passengers. A typi…
Robust Binary and Multinomial Logit Models for Classification with Data Uncertainties
Baichuan Mo, Yunhan Zheng, Xiaotong Guo +2
Binary logit (BNL) and multinomial logit (MNL) models are the two most widely used discrete choice models for travel behavior modeling and prediction. However, in many scenarios, t…
Fairness-Enhancing Vehicle Rebalancing in the Ride-hailing System
Xiaotong Guo, Hanyong Xu, Dingyi Zhuang +2
The rapid growth of the ride-hailing industry has revolutionized urban transportation worldwide. Despite its benefits, equity concerns arise as underserved communities face limited…
The impacts of remote work on travel: insights from nearly three years of monthly surveys
Nicholas S. Caros, Xiaotong Guo, Yunhan Zheng +1
Remote work has expanded dramatically since 2020, upending longstanding travel patterns and behavior. More fundamentally, the flexibility for remote workers to choose when and wher…
Examining the interactions between working from home, travel behavior and change in car ownership due to the impact of COVID-19
Yunhan Zheng, Nicholas Caros, Jim Aloisi +1
COVID-19 has disrupted society and changed how people learn, work and live. The availability of vaccines in the spring of 2021, however, led to a gradual return of many pre-pandemi…
Fairness-enhancing deep learning for ride-hailing demand prediction
Yunhan Zheng, Qingyi Wang, Dingyi Zhuang +2
Short-term demand forecasting for on-demand ride-hailing services is one of the fundamental issues in intelligent transportation systems. However, previous travel demand forecastin…
Deep hybrid model with satellite imagery: how to combine demand modeling and computer vision for behavior analysis?
Qingyi Wang, Shenhao Wang, Yunhan Zheng +4
Classical demand modeling analyzes travel behavior using only low-dimensional numeric data (i.e. sociodemographics and travel attributes) but not high-dimensional urban imagery. Ho…
Simulating the Integration of Urban Air Mobility into Existing Transportation Systems: A Survey
Xuan Jiang, Yuhan Tang, Junzhe Cao +7
Urban air mobility (UAM) has the potential to revolutionize transportation in metropolitan areas, providing a new mode of transportation that could alleviate congestion and improve…
Equality of opportunity in travel behavior prediction with deep neural networks and discrete choice models
Yunhan Zheng, Shenhao Wang, Jinhua Zhao
Although researchers increasingly adopt machine learning to model travel behavior, they predominantly focus on prediction accuracy, ignoring the ethical challenges embedded in mach…
Comparing hundreds of machine learning classifiers and discrete choice models in predicting travel behavior: an empirical benchmark
Shenhao Wang, Baichuan Mo, Yunhan Zheng +2
Numerous studies have compared machine learning (ML) and discrete choice models (DCMs) in predicting travel demand. However, these studies often lack generalizability as they compa…