T3e Webinar Overview

Connected Autonomous Vehicles on a Mixed Traffic Highway—Speed Harmonization, Capacity Analysis, and Lane Management

View Webinar: link to this webinar's archive materials

Date:   Wednesday, February 8, 2017
Time:  1:00 PM – 2:00 PM ET
Cost:  All T3 webinars are free of charge
PDH:  1.0   View PDH Policy

T3 Webinars and T3e Webinars are brought to you by the Intelligent Transportation Systems (ITS) Professional Capacity Building (PCB) Program of the U.S. Department of Transportation's (U.S. DOT) ITS Joint Program Office (JPO). References in this webinar to any specific commercial products, processes, or services, or the use of any trade, firm, or corporation name is for the information and convenience of the public, and does not constitute endorsement, recommendation, or favoring by the U.S. DOT.


Background

Connected autonomous vehicles (CAV) technologies will be in the market in the near future. This requires that transportation systems are ready to operate in a mixed traffic environment where a portion of vehicles are CAVs and the remaining are human-driven vehicles. This presentation includes two topics. First, a CAV-based microscopic trajectory-smoothing concept is proposed to harmonize traffic and improve mobility and environmental impacts. The proposed algorithm can be used to prevent or mitigate traffic speed drops near highway bottlenecks. The second topic investigates how different features of CAVs and corresponding lane management policies can improve throughput capacity of mixed traffic from a macroscopic perspective. An analytical model is proposed to quantify mixed traffic highway capacity. The proposed capacity analysis is then extended to a managed lane model to determine the optimal number of lanes to be allocated to CAVs. Financially, numerical examples are presented to investigate three different CAV technology scenarios: neutral, conservative, and aggressive CAV headway settings.

Target Audiences

The target audiences includes transportation practitioners, transportation researchers, and college and graduate students.

Learning Objectives

By the end of the presentation, the audience shall understand the basic concept of CAV-based traffic control as opposed to traditional infrastructure based control. They shall understand the rationale and principles for trajectory smoothing. They shall also know basic approaches to analyze mixed traffic capacity and to decide corresponding lane management strategies.

Host

Dr. Xiaopeng Li, Assistant Professor, Civil and Environmental Engineering, University of South Florida (SFU)

photo of Xiaopeng Li

Dr. Li is currently an Assistant Professor in the Department of Civil and Environmental Engineering at the University of South Florida (USF). He received a B.S. in Civil Engineering from Tsinghua University, China; a M.S. and a Ph.D. in Civil Engineering along with a M.S. in Applied Mathematics from the University of Illinois at Urban-Champaign, USA. Dr. Li’s major research interests include connected vehicles, autonomous vehicles, sensor networks, and resilient interdependent infrastructure systems, with a focus of understanding and mitigating oscillations and disruptions rising in these systems across various temporal and spatial scales. Prior to joining USF, he worked at Mississippi State University as an Assistant Professor of Transportation Engineering. He is a recipient of a National Science Foundation (NSF) CAREER award and has published 27 peer-reviewed journal papers. He has been the Principal Investigator (PI) or a co-PI for multiple Federal and State research projects, including three sponsored by NSF. Dr. Li has served as a member on the Transportation Network Modeling Committee (ADB30) and the Traffic Flow Theory and Characteristics (AHB45) of the Transportation Research Board (TRB), and as an Associate Department Editor for IIE Transactions Focused Issue on Operations Engineering and Analytics.

Presenter

Amir Ghiasi, PhD Student, Civil and Environmental Engineering, University of South Florida

photo of Amir Ghiasi

Amir Ghiasi is a Ph.D. student in the Civil and Environmental Department at the University of South Florida. He earned his Master’s degree from Sharif University of Technology, Tehran, Iran in January 2013. His primary research focus is on connected autonomous vehicles and microscopic traffic flow theory. His work has been supported by research grants from the National Science Foundation (NSF), the Federal Highway Administration (FHWA), and the National Center for Intermodal Transportation and For Economic Development (NCITEC).