This study assesses the impact of the introduction of connected and automated vehicles on Virginia freeway corridors. Three vehicle types: legacy vehicles (LV), automated vehicles (AV), and connected automated vehicles (CAV), were considered in mixed traffic scenarios. Previous relevant studies were reviewed and the proper operating parameters for LV, AV, and CAV identified. AV and CAV driving behavior models were developed in the VISSIM environment. According to the basic freeway test network results, AV and CAV increase road capacity by 29% and 91%. In the merging freeway test network, AV and CAV increase road capacity by 48% and 60% compared with LV, respectively. A model with diverse LV, AV, and CAV market penetration and diverse traffic demand was tested on I-95 in Virginia, where the research team tested the speed and throughput. Under the current traffic demand, the average speed was higher when there were more AV and no CAV in the traffic flow. However, the average speed of CAV in a congested segment is higher than LV. In the case of throughput, CAV shows poor performance under current traffic demand. With increased traffic demand, high penetrations of AV and CAV present better performance because of their short headway and homogeneity. Therefore, the study predicts that in the future, as the traffic demand grows, AV and CAV can reduce traffic congestion.
In this study, a GPS tracking dataset was utilized to predict the probability of a vehicle stopping along a scenic corridor in a national park setting. The Moose-Wilson Corridor (MWC) in Grand Teton National Park was evaluated for vehicle stopping/visiting determined from GPS collected data along the corridor. Four attractions were evaluated, which consisted of Death Canyon, Granite Canyon, the Laurance S. Rockefeller Preserve, and Sawmill Ponds. A decision tree analysis was implemented to determine the probabilities of visitors’ stopping patterns at park attractions. A benefit to the decision tree method is the easy to read structure and simple visual representation. An 814-observation sample set was split into training and testing datasets that resulted in model accuracies as high as 94%. A promising outcome of this methodology is that it provides a visual reference to help identify attraction relationships. Similarities in the tree structure were determined for Death Canyon and Granite Canyon owing to their child nodes being composed of other MWC attractions. Alternatively, the Laurance S. Rockefeller Preserve and Sawmill Ponds attractions determined more variability in their tree structure. The implementation of a data analysis method such as the one presented in this paper could help national park managers prepare for the incoming era of technology and data.
In national parks, there is a tension between providing areas for vehicles and accommodating other visitor activities in the park. This tension often means there is more demand for vehicle parking than the supply can accommodate. This study examined one of Grand Teton National Park’s (GRTE) visitor attractions with visitor parking provided, the Laurance S. Rockefeller Preserve (LSR) in Jackson, Wyoming. With approximately 54 designated parking spots available, a study to determine the queueing theory measures of performance was conducted under observed and higher-value parameters to evaluate the system. It was determined that under the observed average arrival rate of 25 vehicles per hour (vph) and 35 vph with an average service rate (time a vehicle was parked) of 1 h and 20 min, the system resulted in good performance with a facility utilization value of 61% and 84%, respectively. However, under the higher-value parameters, the results were poorer, with facility utilization values greater than 88%. This study provides a reference for the evaluation of performance measures and can be applicable to future changes in the LSR at GRTE, other national parks parking, or in general parking areas where queueing may be anticipated.
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