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MOBILITY RESEARCH PROJECTS AT CENTER FOR ADVANCED MULTIMODAL MOBILITY
SOLUTIONS AND EDUCATION (CAMMSE)
Wei (David) Fan, Ph.D., P.E.Director, USDOT CAMMSE University Transportation Center
Associate Professor, Department of Civil and Environmental EngineeringUniversity of North Carolina at Charlotte
Prepared for the First Annual National Mobility Summit ofUS DOT University Transportation Centers
April 12, 2018
Center for Advanced Multimodal Mobility Solutions and Education (CAMMSE)
University Transportation Center• Wei Fan, Ph.D., P.E., Director of CAMMSE
• Associate Professor, Department of Civil & Environmental Engineering
Associate Editor, IEEE Transactions on Intelligent Transportation Systems
Associate Editor, ASCE Journal of Transportation Engineering, Part A: Systems
Associate Editor, International Journal of Transportation Science and Technology
Member, ASCE Connected & Autonomous Vehicles Impacts, Advanced Technologies, Public Transport, and Rail Transportation Committees
Member, NSF, NCHRP and TCRP Review Panels
NCHRP 08-116 Framework for CV Pilot and Smart Cities Data Analytics for Policy Guidance
NCHRP 03-131 Guidance for Planning and Implementing Multimodal, Integrated Corridor Management, August 2017 - Present
Center for Advanced Multimodal Mobility Solutions and Education (CAMMSE)
University Transportation Center
• CAMMSE, a Tier 1 UTC
• A consortium of five universities
University of North Carolina at Charlotte (Lead)
University of Texas at Austin
University of Connecticut
Washington State University
Texas Southern University
Center for Advanced Multimodal Mobility Solutions and Education (CAMMSE)
University Transportation Center
CAMMSE @ UNC Charlotte Funded Projects, 2016-2017 University Principle
Investigator Category Title of the Funded Project
University of North Carolina at Charlotte
Wei Fan Advanced Research
Estimation of Origin-Destination Matrix and Identification of User Activities Using Public Transit Smart Card Data
Wei Fan Applied Research
Improving the Movements of People and Freight: A Case Study of the Piedmont Atlantic Megaregion
University of Texas at Austin
Randy Machemehl Applied Research
Forecasting Ridership for Commuter Rail in Austin
Randy Machemehl Advanced Research
Corridor Level Adaptive Signal Control
University of Connecticut
Nicholas Lownes Basic Research
Stochastic Multimodal Network Modeling
Nicholas Lownes Basic Research
Robust Routing, Assignment, and Simulation of Transit Systems
Washington State University
Xianming Shi Applied Research
The Use of Connected Vehicle Technology to Facilitate Multimodal Winter Travel
Jia Yan Applied Research
The Effect of Competition of Transport Modes on Mobility
Texas Southern University
Mehdi Azimi Yi Qi
Applied Research
Use of Vessel Automatic Information System Data to Improve Multi-modal Transportation in and around the Ports
Yi Qi Applied Research
Use of Innovative Intersection Designs for Improving Mobility and Reducing Roadway Traffic Congestion
Center for Advanced Multimodal Mobility Solutions and Education (CAMMSE)
University Transportation Center
CAMMSE @ UNC Charlotte Funded Projects, 2017-2018 University Principle
Investigator(s) Category Title of the Funded Project
University of North Carolina at Charlotte
Wei Fan
Applied Research
Use of Multisensor Data in Modeling Freeway Travel Time Reliability
Wei Fan Martin Kane
Applied Research
Using General Transit Feed Specification (GTFS) Data as a Basis for Evaluating and Improving Public Transit Equity
Wei Fan Yu Wang
Applied Research
Evaluating the Potential Use of Crowdsourced Bicycle Data in North Carolina
Wei Fan Applied Research
Impact of Connected and Automated Vehicles (CAVs) on Freeway Capacity
Wei Fan Advanced Research
Optimal Variable Speed Limit Control for the Mixed Traffic Flows in a Connected and Autonomous Vehicle Environment
University of Texas at Austin
Randy Machemehl Applied Research
Characterization of Bicycle Rider Behavior among Various Street Environments
Randy Machemehl Advanced Research
Evolution of Advanced Transit Signal Priority with Gap-Based Signal Recovery Strategy
Stephen Boyles Applied Research
Assessment of Parcel Delivery Systems Using Unmanned Aerial Vehicles
Christian Claudel Advanced Research
Deep-learning Based Trajectory Forecast for Safety of Intersections with Multimodal Traffic
Center for Advanced Multimodal Mobility Solutions and Education (CAMMSE)
University Transportation CenterCAMMSE @ UNC Charlotte Funded Projects, 2017-2018 – Cont. University Principle
Investigator(s) Category Title of the Funded Project
University of Connecticut
Nicholas Lownes Charles Patton Kelly Bertolaccini
Advanced Research
Investigating the Linkage between Transit Access to Services and Affordable Housing Availability
Karthik Charan Konduri
Basic Research
Development of Continuous Time, Temporally Constrained and Behaviorally Consistent Tour Pattern Generation System for Modeling the Impacts of Autonomous Vehicle Future
Norman Garrick
Carol Atkinson‐Palombo
Applied Research
What Do We Want from Autonomous Vehicles (AVs)? Using Participatory Planning and Scenario Analysis of Alternative Futures to Identify Stakeholders’ Desired Outcomes from the Strategic Deployment of Emerging Transportation Technology
Washington State University
Xianming Shi Applied Research
Developing Friction Data to Support the Optimal Use of Pre-wet Deicing Salt for Enhanced Winter Mobility
Xianming Shi Applied Research
Modeling the Macroscopic Effects of Winter Maintenance Operations on Traffic Mobility on Washington Highways
Texas Southern University
Yi Qi Mehdi Azimi Qun Zhao
Applied Research
Determination of Freeway Acceleration Lane Length for Smooth ad Safe Truck Merging
Yi Qi Mehdi Azimi Qun Zhao
Applied Research
Innovative Countermeasures for Reducing the Truck Waiting Time at Marine Terminals
Mehdi Azimi Yi Qi Qun Zhao
Applied Research
Investigating the Impact of Different Attributes on Bicycling Mode Share as A Multimodal Connectivity Strategy in Large Cities: A Case Study in Houston
Center for Advanced Multimodal Mobility Solutions and Education (CAMMSE)
University Transportation Center• Current (Year 1) Research Projects @ UNCC (02/03/2017 – 09/30/2018)
Project 01. Estimation of Origin-Destination Matrix and Identification of User Activities Using Public Transit Smart Card Data
Project 02. Improving the Movements of People and Freight: A Case Study of the Piedmont Atlantic Megaregion
• Current (Year 2) Research Projects @ UNCC (10/01/2017 – 09/30/2018) Project 01. Optimal Variable Speed Limit Control for the Mixed Traffic Flows in a Connected
and Autonomous Vehicle Environment
Project 02. Developing a Systematic Approach to Modeling Travel Time Reliability at the Arterial Level
Project 03. Evaluating the Potential Use of Crowdsourced Bicycle Data in North Carolina
Project 04. Using General Transit Feed Specification (GTFS) Data as a Basis for Evaluating and Improving Public Transit Equity
Project 05. Impact of Connected and Automated Vehicles (CAV) on Freeway Capacity
Current CAVs Research Projects at UNC Charlotte
• Project 01. Optimal Variable Speed Limit Control for the Mixed Traffic Flows in a Connected and Autonomous Vehicle Environment
• P.I.: Wei (David) Fan
• Abstract: This project aims to systematically study the optimal variable speed limit control for the mixed traffic flows at the bottlenecks in a connected and autonomous vehicle environment. Several tasks will be conducted, including an extensive literature review on the development and implementation of the VSL control. Several multi-objective nonlinear integer models will be formulated for the VSL control for mixed traffic flows both under normal conditions and in a connected and autonomous vehicle environment. Discrete optimization based solution algorithms will be developed to solve these VSL models. Finally, the developed VSL algorithms will be implemented and evaluated by using a real world freeway corridor as a case study. Sensitivity analyses of the proposed VSL systems will be conducted, and comprehensive numerical results will also be presented.
Current CAVs Research Projects at UNC Charlotte
• Project 05. Impact of Connected and Automated Vehicles (CAV) on Freeway Capacity
• P.I.: Wei (David) Fan• Abstract: As the CAVs start to penetrate into the market, the current HCM methods
cannot be used to evaluate freeway capacity due to the fact that they did not account for the impacts of CAV strategies in the HCM. Several limitations of the current HCM include: 1) There is no guideline related to how current HCM methods should be adjusted in order to be suitable for use in conducting various types of analyses involving CAV strategies; 2) There is no consideration of the general impact of CAV technologies on traffic congestion and delay as well as safety in the HCM analysis; and 3) There is no information about the impact of different CAV penetration rates in the highway system on various facilities under different scenarios. In order to be better prepared for both CAV planning and operations under varying levels of market penetration and traffic demand, there is a critical need to develop and establish the HCM capacity adjustments. This research will develop guidelines and recommendations for estimating and predicting freeway capacity in the presence of CAVs, and therefore will lead to a better understanding of how CAV improves mobility on the freeway system.
Future CAVs Research Projects
• Institutional and Policy– Public policy actions to facilitate implementation
• Infrastructure Design/Operations– Quantitative tools for CV/AV impact assessment
– Active traffic management
– Big data management
• Planning– Modeling effects of AV/CV on land use, vehicle ownership and travel
demand