Research Project

The research group actively contributes to research covering a number of topics at the intersection of transportation engineering, big data, artificial intelligence, urban science, and information technology. The team works closely with many collaborators to address challenges in transportation operations, safety, planning, etc. It has received much support from agencies such as VDOT and USDOT, which are greatly appreciated. Below is a list of some recent completed/ongoing projects.

Data-Driven Risk Evaluation of Emerging Shared E-Scooters for Micromobility

Research Objectives: The overarching goals of this research project are to understand the risky riding behavior of e-scooter riders and their interactions with other road users and environment. To accomplish the goals, the following specific objectives will be pursued: (1) Development of a sensing device for e-scooter riding data collection; (2) Deployment of the sensing device to collect data in different riding scenarios; and (3) Analyzing, quantifying and mapping conflicts between e-scooters with other entities (e.g., pedestrians and fixed objects).

Sponsor: Perry Honors College/Office of Research, ODU

PI: H. Yang

Development of Horizontal Curve Dataset for Virginia Road Network

Research Objectives: This proposed research aims to develop a GIS-based tool for facilitating VDOT to identify and extract H-curve data with key geographical properties (e.g., curvature, curve length). The processing results will be consolidated with existing VDOT LRS and available traffic data (i.e., AADT).

Sponsor: Virginia Transportation Research Council (VTRC/VDOT)

PI: H. Yang

What is an Effective Way to Measure Arterial Demand When it Exceeds Capacity? An Approach for Smart Scale

Research Objectives: The overall goal of this proposed project is to identify an effective way to estimate demand when it exceeds capacity. The specific objectives include: (1) Survey and document applicable methods for measuring and estimating demand; (2) Identify the strengths and weaknesses of these methods and discuss their suitability for the SMART SCALE process; (3) Evaluate and compare promising methods in case studies; and (4) Recommend how the most promising methods may be used in the SMART SCALE process.

Sponsor: Virginia Transportation Research Council (VTRC/VDOT)

PI: M. Cetin; Co-PIs: H. Yang, K. Xie

Incorporating the 10th Edition ITE rates into VDOT Regulations

Research Objectives: The purpose of this research is to determine which of three options are most suitable for Virginia land development analysis: (1) using the rates in the ITE 9th edition with trip reduction factors; (2) using the rates in the ITE 10th edition but without trip reduction factors; and (3) using the rates in the 10th edition but with trip reduction factors.

Sponsor: Virginia Transportation Research Council (VTRC/VDOT)

PI: K. Xie; Co-PIs: H. Yang, M. Cetin

Effectiveness of TMC Artificial Intelligence (AI) Applications in Case Studies

Research Objectives: This project aims to examine the versatility of AI for addressing traffic operational problems through case studies. Specifically, it intends to quantify the effectiveness of using AI algorithms for innovating a classical operational problem, namely, highway traffic incident detection. This project will leverage the power of microscopic traffic simulation models to design a series of simulation scenarios for training and testing the performance of AI algorithms in detecting incidents on freeways.

Sponsor: Federal Highway Administration (FHWA)

PI: H. Yang; Co-PIs: M. Cetin

Exploration of Corridor-Based Tolling Strategies for Virginia’s Express Toll Lanes

Research Objectives: This proposed research aims to have a thorough evaluation of the Streetlight data to support VDOT in making the best use of the data. The research team will dedicate its efforts to help: (i) identify applicable VDOT work tasks that can be enhanced by the Streetlight data; (ii) determine its accuracy in each application; and (iii) develop basic guidelines to facilitate the use of the Streetlight data.

Sponsor: Virginia Transportation Research Council (VTRC/VDOT)

PI: S. Zhu (GMU); Co-PIs: M. Cetin, H. Yang

Guideline for Using Streetlight Data for Planning Tasks

Research Objectives: This research aims to develop a tool that leverage the simulation outputs from the developed TransModeler simulation model to investigate corridor-based tolling strategies for Express Toll Lanes (ETLs) in Northern Virginia (NOVA).

Sponsor: Virginia Transportation Research Council (VTRC/VDOT)

PI: H. Yang; Co-PIs: M. Cetin

Evaluation of Strategies to Reduce Truck Turnarounds at the Hampton Roads Bridge-tunnel (HRBT)

Research Objectives: This research study aims to (i) identify important characteristics of the over-height (OH) trucks (e.g., by trucking companies) and trends in violation rates based on the archived data; (ii) assess the benefits and impacts of the new OH vehicle detection system to be installed at the HRBT corridor; and determine the major reasons behind OH violations and characteristics of frequent violators before and after installation of the OH system.

Sponsor: Virginia Transportation Research Council (VTRC/VDOT)

PI: M. Cetin; Co-PIs: H. Yang

Portable and Integrated Multi-Sensor System for Data-Driven Performance Evaluation of Urban Transportation Networks

Research Objectives: The primary objectives of this research study are: (i) to deploy some promising low-cost portable and integrated multi sensor systems for data-driven performance evaluation of urban transportation networks and (ii) to develop a system capable of real-time remote monitoring of external changes, such as air quality, noise, humidity, temperature, and crowd and vehicular densities in an urban setting.

Sponsor: Region 2 University Transportation Research Center (UTRC), USDOT

PI: K. Ozbay (NYU); Co-PIs: H. Yang

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