A5062312211- Transportation and Mobility Innovations
- Transportation Planning and Optimization
- Urban Transport and Accessibility
- Electric Vehicles and Infrastructure
- Human Mobility and Location-Based Analysis
- Traffic control and management
- COVID-19 epidemiological studies
- Smart Parking Systems Research
- Traffic Prediction and Management Techniques
- Sharing Economy and Platforms
- Urban and Freight Transport Logistics
- Vehicle Routing Optimization Methods
- Complex Network Analysis Techniques
- Traffic and Road Safety
- Data-Driven Disease Surveillance
- COVID-19 Pandemic Impacts
- Housing Market and Economics
- Autonomous Vehicle Technology and Safety
- Mathematical and Theoretical Epidemiology and Ecology Models
- Human-Automation Interaction and Safety
- Smart Grid Security and Resilience
- Smart Grid Energy Management
- Occupational Health and Safety Research
- Mobile Crowdsensing and Crowdsourcing
- Municipal Solid Waste Management
Rice University
2024-2025
University of Alabama
2020-2024
Tongji University
2013-2024
Purdue University West Lafayette
2015-2022
University Transportation Center for Alabama
2021-2022
Indiana Department of Transportation
2017
Taxi service systems in big cities are immensely complex due to the interaction and self-organization between taxi drivers passengers. An inefficient system leads more empty trips for longer waiting time passengers introduces unnecessary congestion on road network. In this paper, we investigate efficiency level of using real-world large-scale trip data. By assuming a hypothetical system-wide recommendation system, two approaches proposed find theoretical optimal strategies that minimize cost...
Access to healthcare services using public transportation (PT-based accessibility) is a crucial aspect in achieving equity as it affects individuals' ability receive healthcare. Previous research has focused on the spatial features of accessibility. However, less attention been given its temporal characteristics, which can be influenced by transit schedules, multimodal connectivity, congestion, and other factors. This study proposes framework better understand impacts temporally varying...
Abstract Improved mobility not only contributes to more intensive human activities but also facilitates the spread of communicable disease, thus constituting a major threat billions urban commuters. In this study, we present multi-city investigation diseases percolating among metro travelers. We use smart card data from three megacities in China construct individual-level contact networks, based on which disease is modeled and studied. observe that, though differing forms, network layouts,...
Understanding non-epidemiological factors is essential for the surveillance and prevention of infectious diseases, are likely to vary spatially temporally as disease progresses. However, impacts these influencing were primarily assumed be stationary over time space in existing literature. The spatiotemporal mobility-related social-demographic on dynamics remain explored.
For a regular weekday in New York City, the number of taxi trips at 8 P.M. may be 10 times greater than that 5 A.M., while passengers are charged under same pricing scheme. Motivated by temporally non-stationary demand and supply market, time-of-day (TOD) scheme for industry is framed to vary trip cost dynamically over time, so total market revenue maximized. Temporal dynamics modeled as semi-Markov process, which captures leftover drivers, spillover passengers, restoration drivers service...