January 13-17, 2019
Washington, D.C.
TRB 98th Annual Meeting

Data-Driven Simulation to Support Decision Making in the 21st Century: Barriers and Potential Benefits

Sunday, January 07, 2018, 9:00 AM-12:00 PM, Convention Center 2018
James Sturrock, Federal Highway Administration (FHWA), presiding
Sponsored by Standing Committee on Traffic Flow Theory and Characteristics

Simulation modeling is an important component of transportation agencies’ decision-making processes. Recent advancements in data analytics, performance measurement and management, and vehicle connectivity and automation have created opportunities and challenges for transportation system researchers and users that require transformative changes in simulation modeling algorithms, calibration and validation methods, and applications. The simulation algorithms will need to be updated to reflect a traffic stream with mixed levels of emerging technologies—such as connectivity and automation—that affect both microscopic and macroscopic traffic characteristics.

Data Driven Analysis Techniques in Reliability Space
Karl Wunderlich, Noblis, Inc.


Helping to Converge the Practice of Transportation System Simulation
George List, North Carolina State University


A Framework for Validating Traffic Simulation Models at the Vehicle Trajectory Level
Michalis Xyntarakis, Cambridge Systematics, Inc.; Vassili Alexiadis, Cambridge Systematics, Inc.; Vincenzo Punzo, University of Naples Federico II; Erin Flanigan, Cambridge Systematics, Inc.


Data Plan for the NYC Connected Vehicle Pilot Deployment Evaluation
Keir Opie, Cambridge Systematics, Inc.


Guidelines for Designing Active Transportation and Demand Management (ATDM) Strategies Through Understanding Travelers' Motivations in Decision-making: Data Collection, Analysis and Modeling
Samer Hamdar, George Washington University


Challenges to Simulating the Traffic and Energy Impacts of Connected and Automated Vehicle Systems
Steven Shladover, University of California, Berkeley


Data Inputs and Impacts to Connected and Automated Vehicle Modeling: Takeaways from ISTTT 22
Alexander Skabardonis, University of California, Berkeley