Nagui Rouphail

As connected vehicle data became available, efforts to employ surrogate safety measures (SSMs) for crash frequency modeling were undertaken. For road safety evaluation, traffic conflicts are quantitatively measured by various SSMs in two dimensions: spatial/temporal proximity (e.g., time-to-collision, TTC) and evasive action (e.g., deceleration rate, DR). However, a single SSM or a single dimension only represents partial images of the true severity of traffic conflicts. Therefore, this study investigates possible enhancements in crash frequency modeling by concurrently using proximity and evasion SSMs. For rear-end crash frequency estimation, five negative binomial regression models and two tree-based models (a regression tree and random forest) were developed. All models were estimated using crashes, traffic volume, and segment length, along with three SSMs (DR, TTC, and modified TTC) extracted from connected vehicle data in Ann Arbor, Michigan. Results show that the multi-SSM model produced a 19.3% reduction in mean absolute error (MAE) compared to the baseline model with no SSM variable, which was significantly higher than those of single-SSM models (4.5−6.9% reductions in MAE). Between all models, the random forest, which is the ensemble machine learning model, produced the highest error reductions (a 44.3% reduction in MAE). These findings show that the concurrent use of proximity and evasion SSMs can yield further enhancements in crash frequency models compared to the singular use of either type of SSM. The proposed modeling method can be used for proactive safety management and assessment using connected vehicle data collected over a short period.

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.

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.