11 citations · 22 across the 6 of their papers we have counts for
16 papers
Economies and Diseconomies of Scale in Segmented Mobility Sharing Markets
Hongmou Zhang, Xiaotong Guo, Jinhua Zhao
On-demand mobility sharing, provided by one or several transportation network companies (TNCs), is realized by real-time optimization algorithms to connect trips among tens of thou…
Preparing urban mobility for the future of work
Nicholas S. Caros, Jinhua Zhao
A gradual growth in flexible work over many decades has been suddenly and dramatically accelerated by the COVID-19 pandemic. The share of flexible work days in the United States is…
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…
Individual Mobility Prediction: An Interpretable Activity-based Hidden Markov Approach
Baichuan Mo, Zhan Zhao, Haris N. Koutsopoulos +1
Individual mobility is driven by demand for activities with diverse spatiotemporal patterns, but existing methods for mobility prediction often overlook the underlying activity pat…
Theory-based residual neural networks: A synergy of discrete choice models and deep neural networks
Shenhao Wang, Baichuan Mo, Jinhua Zhao
Researchers often treat data-driven and theory-driven models as two disparate or even conflicting methods in travel behavior analysis. However, the two methods are highly complemen…
Modeling Epidemic Spreading through Public Transit using Time-Varying Encounter Network
Baichuan Mo, Kairui Feng, Yu Shen +4
Passenger contact in public transit (PT) networks can be a key mediate in the spreading of infectious diseases. This paper proposes a time-varying weighted PT encounter network to…