@article{chun_rouphail_samandar_list_yang_akcelik_2022, title={Analytical and Microsimulation Model Calibration and Validation: Application to Roundabouts under Sight-Restricted Conditions}, volume={8}, ISSN={["2169-4052"]}, DOI={10.1177/03611981221115071}, abstractNote={ Analytical models and traffic microsimulation are two widely used platforms for evaluating roundabout operations. The application of the correct inputs and proper specification of calibration parameters should precede the actual simulation, to replicate field traffic conditions. In this sense, simultaneous data collection and estimation of the input, calibration, and validation variables, along with knowledge of their definitions, are crucial. Although simultaneity of data gathering is virtually guaranteed with the use of wide-frame videos captured with an unmanned aerial system (UAS), there are cases where sight distance restrictions may obscure observations of the back of queue and arrival patterns. This paper explores the calibration and validation efforts associated with an analytical platform, SIDRA 9, and a microsimulation model, TransModeler 5, conducted under sight-restricted conditions. Video captured from a drone, followed by trajectory extraction using video processing software, was used to analyze operations on two approaches at a single-lane roundabout. In the process, the team employed a specialized demand estimation method, and developed a novel data collection scheme for estimating the critical headway distribution in TransModeler 5. Because of sight distance constraints, the model validation was limited to the use of the observable system travel time and associated travel speed within the field of view. The comparison results, for both platforms, have confirmed the value of model calibration in more accurately describing field performance. The calibrated models performed differently between the two approaches, with the approach having a larger presence of buses and heavy vehicles yielding slightly poorer results. }, journal={TRANSPORTATION RESEARCH RECORD}, author={Chun, Gyounghoon and Rouphail, Nagui and Samandar, M. Shoaib and List, George and Yang, Guangchuan and Akcelik, Rahmi}, year={2022}, month={Aug} }
 @article{samandar_chun_yang_chase_rouphail_list_2022, title={Capitalizing on Drone Videos to Calibrate Simulation Models for Signalized Intersections and Roundabouts}, volume={6}, ISSN={["2169-4052"]}, DOI={10.1177/03611981221096120}, abstractNote={ 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. }, journal={TRANSPORTATION RESEARCH RECORD}, author={Samandar, M. Shoaib and Chun, Gyounghoon and Yang, Guangchuan and Chase, Thomas and Rouphail, Nagui M. and List, George F.}, year={2022}, month={Jun} }
 @article{ahmed_williams_samandar_chun_2022, title={Investigating the relationship between freeway rear-end crash rates and macroscopically modeled reaction time}, volume={18}, ISSN={["2324-9943"]}, url={https://doi.org/10.1080/23249935.2021.1914769}, DOI={10.1080/23249935.2021.1914769}, abstractNote={This study tests the hypothesis that an analytically estimated driver reaction time required for asymptotic stability, based on the macroscopic Gazis-Herman-Rothery (GHR) model, serves as an indicator of the impact of traffic oscillations on rear-end crashes. If separate GHR models are fit discontinuously for different traffic regimes, the local drop in required reaction time between these regimes can also be estimated. This study evaluates the relationship between rear-end crash rates and that drop in required reaction time. Traffic data from 28 sensors were used to fit the GHR model. Rear-end crash rates, estimated from four years of crash data, exhibited a positive correlation with the drop in required reaction time at the congested regime’s density-breakpoint. A linear relationship provided the best fit. These results motivate follow-on research to incorporate macroscopically derived reaction time in road-safety planning. More generally, the study demonstrates a useful application of a discontinuous macroscopic traffic model.}, number={3}, journal={TRANSPORTMETRICA A-TRANSPORT SCIENCE}, publisher={Informa UK Limited}, author={Ahmed, Ishtiak and Williams, Billy M. and Samandar, M. Shoaib and Chun, Gyounghoon}, year={2022}, month={Dec}, pages={1001–1024} }
 @article{ahmed_karr_rouphail_chun_tanvir_2019, title={Characterizing Lane Changes via Digitized Infrastructure and Low-Cost GPS}, volume={2673}, ISSN={["2169-4052"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85064917506&partnerID=MN8TOARS}, DOI={10.1177/0361198119841277}, abstractNote={ With the expected increase in the availability of trajectory-level information from connected and autonomous vehicles, issues of lane changing behavior that were difficult to assess with traditional freeway detection systems can now begin to be addressed. This study presents the development and application of a lane change detection algorithm that uses trajectory data from a low-cost GPS-equipped fleet, supplemented with digitized lane markings. The proposed algorithm minimizes the effect of GPS errors by constraining the temporal duration and lateral displacement of a lane change detected using preliminary lane positioning. The algorithm was applied to 637 naturalistic trajectories traversing a long weaving segment and validated through a series of controlled lane change experiments. Analysis of naturalistic trajectory data revealed that ramp-to-freeway trips had the highest number of discretionary lane changes in excess of 1 lane change/vehicle. Overall, excessive lane change rates were highest between the two middle freeway lanes at 0.86 lane changes/vehicle. These results indicate that extreme lane changing behavior may significantly contribute to the peak-hour congestion at the site. The average lateral speed during lane change was 2.7 fps, consistent with the literature, with several freeway–freeway and ramp–ramp trajectories showing speeds up to 7.7 fps. All ramp-to-freeway vehicles executed their first mandatory lane change within 62.5% of the total weaving length, although other weaving lane changes were spread over the entire segment. These findings can be useful for implementing strategies to lessen abrupt and excessive lane changes through better lane pre-positioning. }, number={8}, journal={TRANSPORTATION RESEARCH RECORD}, author={Ahmed, Ishtiak and Karr, Alan and Rouphail, Nagui M. and Chun, Gyounghoon and Tanvir, Shams}, year={2019}, month={Aug}, pages={298–309} }