@article{ahmed_karr_rouphail_chase_2023, title={Modeling Framework for Predicting Lane Change Intensity at Freeway Weaving Segments}, volume={4}, ISSN={["2169-4052"]}, url={https://doi.org/10.1177/03611981231165206}, DOI={10.1177/03611981231165206}, abstractNote={ This study proposes a modeling framework for predicting discretionary lane change (DLC) intensity at weaving segments using traffic flow and site data. The database used to develop the models comprises 294 field observations from 19 sites. Two modeling techniques, using regression trees and linear regression, were employed to predict DLCs per hour and DLCs per vehicle. The proposed models were compared with the lane change model for weaving segments in the Highway Capacity Manual (HCM7). The lane change data were clustered by site, which cautioned the applicability of linear regression for this dataset. Nonetheless, both the regression tree and linear regression models yielded high R-squared values, varying from 0.93 to 0.96. The relative root mean squared error (RMSE)—the ratio of the error to the mean values—varied between 0.18 and 0.30. However, a site-specific validation showed that the linear regression models performed poorly for most sites, although measures were taken to cope with outliers, nonlinearity, and interactions. The tree model improved the prediction of DLCs per hour for more than two-thirds of the sites when compared with the mean value at each site. It also performed well in most cases when applied to a site that was omitted from the model development. The HCM7 model performed well when applied to an omitted site. However, it exhibited the highest overall relative RMSE (0.57), underscoring the necessity of advanced modeling tools with additional predictors. We recommend incorporating observations from more extended periods and varying traffic conditions for each site for future research. }, journal={TRANSPORTATION RESEARCH RECORD}, author={Ahmed, Ishtiak and Karr, Alan and Rouphail, Nagui M. and Chase, R. Thomas}, year={2023}, month={Apr} }
 @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_karr_rouphail_chase_tanvir_2022, title={Characterizing lane changing behavior and identifying extreme lane changing traits}, volume={4}, ISSN={["1942-7875"]}, url={https://doi.org/10.1080/19427867.2022.2066856}, DOI={10.1080/19427867.2022.2066856}, abstractNote={ABSTRACT This study characterizes lane changing behavior of drivers under differing congestion levels and identifies extreme lane changing traits using high-resolution trajectory data. Total lane change frequency exhibited a reciprocal relationship with congestion level, but the distribution of lane change per vehicle remained unchanged as congestion increased. On average, the speed of trajectories increased by 5.4 ft/s after changing a lane. However, this gain significantly diminished as congestion worsened. Further, the average speed of lane changing vehicles was 3.9 ft/s higher than those that executed no lane changes. Two metrics were employed to identify extreme lane changing behavior: critical time-to-line-crossing (TLCc) and lane changes per unit distance. The lowest 1% TLCc varied between 0.71–1.57 seconds. The highest 1% of lane change rates for all lane changing vehicles was 2.5 lane changes per 1,000 ft traveled. Interestingly, no drivers in thisdataset had both excessive lane changes and lane changes with low TLCc.}, journal={TRANSPORTATION LETTERS-THE INTERNATIONAL JOURNAL OF TRANSPORTATION RESEARCH}, publisher={Informa UK Limited}, author={Ahmed, Ishtiak and Karr, Alan F. and Rouphail, Nagui M. and Chase, R. Thomas and Tanvir, Shams}, year={2022}, month={Apr} }
 @article{lin_lin_wang_chase_2021, title={A C-V2X Platform Using Transportation Data and Spectrum-Aware Sidelink Access}, ISSN={["1062-922X"]}, DOI={10.1109/SMC52423.2021.9659109}, abstractNote={Intelligent transportation systems and autonomous vehicles are expected to bring new experiences with enhanced efficiency and safety to road users in the near future. However, an efficient and robust vehicular communication system should act as a strong backbone to offer the needed infrastructure connectivity. Deep learning (DL)-based algorithms are widely adopted recently in various vehicular communication applications due to their achieved low latency and fast reconfiguration properties. Yet, collecting actual and sufficient transportation data to train DL-based vehicular communication models is costly and complex. This paper introduces a cellular vehicle-to-everything (C-V2X) verification platform based on an actual traffic simulator and spectrum-aware access. This integrated platform can generate realistic transportation and communication data, benefiting the development and adaptivity of DL-based solutions. Accordingly, vehicular spectrum recognition and management are further investigated to demonstrate the potentials of dynamic slidelink access. Numerical results show that our platform can effectively train and realize DL-based C-V2X algorithms. The developed slidelink communication scheme can adopt different operating bands with remarkable spectrum detection performance, validating its practicality in real-world vehicular environments.}, journal={2021 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC)}, author={Lin, Chia-Hung and Lin, Shih-Chun and Wang, Chien-Yuan and Chase, Thomas}, year={2021}, pages={1293–1298} }
 @article{aghdashi_davis_chase_cunningham_2020, title={Modeling and Validating Traffic Responsive Ramp Metering in the Highway Capacity Manual Context}, volume={2674}, ISSN={["2169-4052"]}, DOI={10.1177/0361198120949533}, abstractNote={ This paper presents a methodology for modeling traffic responsive (or adaptive) ramp metering in the freeway facilities method based on the sixth edition of the Highway Capacity Manual (HCM6). Currently, the HCM only provides an option to meter on-ramps as user input using 15-min average flow rates with a focus on planning-level analyses. As a result, the possibilities for simulating and modeling ramp meters with any traffic responsive ramp metering algorithm in the HCM context are limited. Moreover, the freeway facilities methodology in the HCM plays a vital role in the analysis of travel time reliability, which is built on a set of operational scenarios. However, with the lack of traffic responsive ramp metering, analysts are burdened with the task of manually entering average effective ramp metering rates for each on-ramp within the set of reliability scenarios. This process can require a substantial amount of time, in addition to increasing the potential for inaccuracy and bias in freeway and performance measure estimations. As a result, this paper is designed to fill a significant research gap by providing a method for analyzing traffic responsive (or adaptive) ramp metering, an active traffic and demand management strategy, using the core freeway facilities methodology in the HCM. The direct application of the method focuses on the MaxView metering algorithm. However, the proposed framework can be used to model any traffic responsive ramp metering algorithm. The results are validated using real-world sites located on the I-540 westbound freeway corridor in North Carolina. }, number={12}, journal={TRANSPORTATION RESEARCH RECORD}, author={Aghdashi, Seyedbehzad and Davis, Joy and Chase, Thomas and Cunningham, Chris}, year={2020}, month={Dec}, pages={91–102} }