TY  - JOUR
PY  - 2022//
TI  - Capitalizing on drone videos to calibrate simulation models for signalized intersections and roundabouts
JO  - Transportation research record
A1  - Shoaib Samandar, M.
A1  - Chun, Gyounghoon
A1  - Yang, Guangchuan
A1  - Chase, Thomas
A1  - Rouphail, Nagui M.
A1  - List, George F.
SP  - 96
EP  - 111
VL  - 2676
IS  - 12
N2  - Simulation is an indispensable tool for the assessment of highway-related capital investments and operational changes. Model calibration, a challenging task in any simulation study, is a crucial step. The model?s robustness, accuracy, and quality are directly dependent on it. Many parameters exist, and field observations are often lacking to aid in their correct specification. Recently, videos from drones have created a uniquely powerful way to aid this process. Observations of the inputs (demand), outputs (vehicles processed), processing rates (e.g., saturation flow rates), and performance results (times in system, queue dynamics, and delays) are all available simultaneously. For signalized intersections, only the signal timing events are missing, and those data can be obtained from signal timing logs. This paper illustrates how modeling teams can use drone data to calibrate model parameters pertaining to intersection operation. It shows how saturation flow rates can be adjusted for signalized intersections so that queue dynamics and delays can be matched. For roundabouts, it illustrates how critical gaps and move-up times can be adjusted to match field observations of performance. Three real-world settings with associated drone data are used as case study examples.<p />  <p>Language: en</p>
LA  - en
SN  - 0361-1981
UR  - http://dx.doi.org/10.1177/03611981221096120
ID  - ref1
ER  -