Research Article Transportation Research Record 2021, Vol. 2675(12) 1043–1055 Ó National Academy of Sciences: Transportation Research Board 2021 Article reuse guidelines: sagepub.com/journals-permissions DOI: 10.1177/03611981211031882 journals.sagepub.com/home/trr Using High-Resolution Signal Controller Data in the Calibration of Signalized Arterial Simulation Models Mosammat Tahnin Tariq 1 , Mohammed Hadi 2 , and Rajib Saha 3 Abstract Calibration of traffic simulation models is a critical component of simulation modeling. The increased complexity of the trans- portation network and the adoption of emerging vehicle- and infrastructure-based technologies and strategies have motivated the development of new methods and data collection to calibrate the simulation models. This study proposes the use of high-resolution signal controller data, combined with a two-level clustering technique for scenario identifications and a multi- objective optimization technique for simulation model parameter calibration. The evaluation of the calibration parameters resulting from the multi-objective optimization based on travel time and high-resolution signal controller data measures indi- cate that the simulation model that uses these optimized parameters produces significantly lower errors in the split utilization ratio, green utilization ratio, arrival on green, and travel time compared with a simulation model that uses the software’s default parameters. When compared with a simulation model that uses calibration parameters obtained based on the optimi- zation of the single objective of minimizing the travel time, the multi-objective optimization solution produces comparably low travel time errors but with significantly lower errors for the high-resolution signal controller data measures. Microscopic traffic simulation tools are now commonly used by transportation agencies to support various busi- ness processes. The use and complexity of these simula- tion models are expected to increase with the growing need to assess the emerging vehicle and infrastructure- based technologies and strategies such as active traffic and demand management, connected and automated vehicles, and cooperative driving automation. Traffic simulation tools are usually set with default values of user-adjustable parameters. However, the mod- els with the default values rarely replicate local traffic conditions. Thus, a calibration and validation process is necessary to minimize the deviation between the simula- tion results and field observations before using the models for alternative analysis. When a microscopic simulation model is used without proper calibration and validation, the simulation results are inaccurate and unreliable and thus cannot be used to support the agency’s decisions. Traditionally, the calibration of traffic simulation models has been based on macroscopic traffic flow para- meters and performance measures such as traffic volumes and demand, spot speeds, travel times, and, where avail- able, queue lengths. The models are usually calibrated for an average peak and/or an average off-peak hour or period that are supposed to represent typical traffic conditions on the network being modeled. However, the recent guidance provided by the updated Traffic Analysis Toolbox Volume III, produced by the Federal Highway Administration (FHWA) recommends the use of cluster- ing to identify operational scenarios for use in calibration such as different congestion levels, incident conditions, and weather conditions (1). Conventional traffic data collection and usage meth- ods aggregate traffic measurements such as vehicle flow, speed, and occupancy in 15-min to 1-h intervals. On arterial networks, day-to-day as well as cycle-to-cycle variations in the measurements are important, including the measurements of volumes, vehicle platoon arrivals, discharge rates, and green time utilization. These mea- surements at the signalized intersections significantly affect the estimation of network performance. In recent 1 Department of Civil & Environmental Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA 2 Department of Civil and Environment Engineering, Florida International University, Miami, FL 3 Iteris, Inc., Tampa, FL Corresponding Author: Mosammat Tahnin Tariq, mtari006@fiu.edu