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Connected and automated vehicle technologies are expected to significantly contribute in improving mobility and safety. As connected and autonomous vehicles have not been used in practice at large scale, there are still some uncertainties in relation to their applications. Therefore, researchers utilize traffic simulation tools to model the presence of these vehicles. There are several studies on the impacts of vehicle connectivity and automation at the segment level. However, only a few studies have investigated these impacts on traffic flow at the network level. Most of these studies consider a uniform distribution of connected or autonomous vehicles over the network. They also fail to consider the interactions between heterogeneous drivers, with and without connectivity, and autonomous vehicles at the network level. Therefore, this study aims to realistically observe the impacts of these emerging technologies on traffic flow at the network level by incorporating adaptive fundamental diagrams in a mesoscopic simulation tool. The adaptive fundamental diagram concept considers spatially and temporally varying distributions of different vehicle types with heterogeneous drivers. Furthermore, this study considers the intersection capacity variations and fundamental diagram adjustments for arterial links resulting from the presence of different vehicle types and driver classes. The proposed methodology is applied to a large-scale network of Chicago. The results compare network fundamental diagrams and hysteresis loop areas for different proportions of connected and autonomous vehicles. In addition to quantifying impacts of connected and autonomous vehicles, the results demonstrate the impacts of various factors associated with these vehicles on traffic flow at the network level.

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This study involved the development of safety performance functions for rural, low-volume, minor road stop-controlled intersections in Michigan. Facility types included three-leg stop-controlled (3ST) and four-leg stop-controlled (4ST) intersections under state or county jurisdiction and were sampled from each of Michigan’s 83 counties. To isolate lower-volume rural intersections, major roadway traffic volumes were limited to the range of 400–2,000 vehicles per day (vpd). Data were compiled from several sources for 2,023 intersections statewide. These data included traffic crashes, volumes, roadway classification, geometry, cross-sectional features, and other site characteristics covering the period of 2011–2015. Random effects negative binomial regression models were specified for each stop-controlled intersection type considering factors such as driveway density, lighting presence, turn lane presence, and intersection skew, in addition to volume. To account for the unobserved heterogeneity between counties, mixed effects negative binomial models with a county-specific random effect were utilized. Furthermore, unobserved temporal effects were controlled through the use of a year-specific random effect. Separate models were developed for fatal/injury crashes, property damage crashes, and select target crash types. The analysis found that skew angles of greater than five degrees led to significantly greater crash occurrence for both 3ST and 4ST intersections, while greater than two driveways near the intersection led to significantly greater angle crashes at 4ST intersections. Other factors were found to have little impact on crash occurrence. Comparison with the Highway Safety Manual (HSM) base models showed that the HSM models over-predict crashes on 4ST intersections and 3ST intersections with volumes between 1,200 and 2,000 vpd.

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