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.
Tolled facilities are undoubtedly expected to alter the distribution of traffic across the transportation network. On the other hand, traffic volumes and loading have an impact on deteriorating pavement conditions. These traffic volumes are considered by Departments of Transportation (DOTs) while allocating annual budgets to maintain and rehabilitate roadway segments to sustain pavement performance targets. This research studies the specific site around I-66 inside the Beltway, which newly applied dynamic tolls during a.m. and p.m. peak hours. An integrated traffic-management/pavement-treatment framework was applied to address both the operational and the pavement performance of the network. Aimsun hybrid macro/meso dynamic user equilibrium experiments were used to simulate the network with the modified cost function taking care of the dynamic pricing along the I-66 tolled facility. An optimization was Python-coded into a Pyomo framework to specify the optimal maintenance and rehabilitation treatment plan, taking into account critical condition index (CCI) deterioration based on the traffic load distribution on the network. Finally, the results of the simulation showed the importance of having an optimized treatment schedule to achieve optimal pavement performance outcomes, with a difference in CCI index that could range all the way from 68 to 95.
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