NextGen Transportation Lab

Dr. Jun Liu

Assistant Professor, Department of Civil, Construction and Environmental Engineering

College of Engineering, The University of Alabama

NextGen Transportation: Connected + Automated + Vehicles + Infrastructures + Travelers + Policies + Data

The NextGen Transportation Lab brings a unique perspective to understand the roles of travelers, vehicles, infrastructures and policies in future transportation systems. The Lab conducts data-driven and simulation-enabled research. The research outcomes lead to actionable solutions to transportation challenges related to planning, operation, safety and environment.

Example Topics:

Data Science for Connected and Automated Vehicles

Shared Mobility with Autonomous Vehicles

Agent-Based Simulation for SAVs in Tuscaloosa, Alabama Area

Agent-Based Simulation for SAVs in Austin, Texas Area

Shared Low-Speed Light-Weight Autonomous Mobility (SLLAM)

This study proposes a Shared Low-speed Light-weight Autonomous Mobility (SLLAM) system that makes use of the connected and underutilized space in pedestrian access routes (PARs) in small cities. The SLLAM system is envisioned to be part of micro-mobility, operating a fleet of self-driving single-occupancy vehicles (SOVs) with a width less than 5 ft.

Urban Air Mobility in Small Cities

The Urban Air Mobility (UAM) relying on small-size vertical take-off and landing (VTOL) air vehicles has been trending as a possible future intra-region mobility service. Studies have been largely focused on large cities and there is limited discussion about implementing such a mobility system in small cities where the travel demand exhibits different spatiotemporal patterns. Using a case study of Tuscaloosa, Alabama area, this study provides some initial thoughts on planning airways for UAM in small cities.

Advanced Traffic Safety Modeling

Traffic crashes are outcomes of human activities interacting with the diverse cultural, socio-economic and geographic contexts, presenting a spatial and temporal nature. However, many existing transportation safety studies, due to data limitations or research purposes, were not intended to reveal the spatial or temporal patterns of transportation safety. The results from existing studies are often used to develop countermeasures for the entire study region, e.g., state, which could be a legitimate study purpose. This project is to advance the traffic crash modeling process by incorporating the spatial and temporal nature of transportation systems. The project will make use of geo- and time-referenced safety data and develop advanced spatio-temporal models. The models can be used to reveal the potentially spatially and temporally varying patterns of transportation safety.

Google Scholar: https://scholar.google.com/citations?user=zaBtp84AAAAJ&hl=en

Scopus: https://www.scopus.com/authid/detail.uri?authorId=57188712384

More info: https://jliu.people.ua.edu/

If you are interested in working with us, please contact Dr. Liu: jliu@eng.ua.edu