Dedicated lanes for connected and automated vehicles (CAVs) can not only provide the technological accommodation, but also the desired market incentive for road user to adapt CAVs. Thus far, the majority of the impact assessment of CAV focused on the network-wide benefits. In this paper, we investigate the change of the traffic flow characteristic with two configurations of dedicated CAV lane across levels of market penetration. The traffic flow characteristics are quantified from the perspectives of headway distribution, communication density, and speed-flow diagram. The results highlight the contributions of the CAV lane. First, CAV lanes significantly improves the speed-flow characteristics by extending the stable region of the speed-flow curve and yielding a greater optimum flow. The highest value of optimum flow is 3400 vehicle per lane per hour at 90 MPR with one CAV lane. Furthermore, the concentration of CAVs at a lane results a narrower headway distribution (with smaller standard deviation), even with partial market penetration. Moreover, the CAV lane creates a more consistent CAV density which maintains the communication density level at a predictable level, hence decreasing the probability of packet drop.
A multi-resolution modeling was proposed in @cite_12 to study the mobility impact of CAV lanes. Traffic flow-based static traffic assignment and the mesoscopic simulation-based dynamic traffic assignment were adapted in the bi-level framework. The former yielded the MPR-based trends, whereas the latter refined the trend based on traffic congestion. The results indicated that it was not beneficial to provide toll incentive for CAVs at lower MPR due to the marginal increase in highway capacity.
@cite_12
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This research investigates how different levels of modeling can be applied to assess the mobility impacts of Cooperative Adaptive Cruise Control (CACC), as an example of connected vehicle technologies on Managed Lanes (ML) with various incentives (preferential treatments), pricing strategies, and access restrictions. The transportation modeling in this study involves the use of the Static Traffic Assignment (STA) of a demand forecasting model based on a macroscopic traffic model, mesoscopic simulation-based Dynamic Traffic Assignment (DTA), and results from microscopic simulation modeling. The results of this study demonstrate the benefit of using results from tools with different resolution of modeling to support each other’s analyses. In general, the trends obtained based on results from the STA modeling of advanced vehicle technologies in terms of the market share of traffic in ML and the reduction in congestion on General Purpose Lanes (GPL) are consistent with those obtained from DTA. However, DTA results show more significant shifts due to its better modeling of traffic congestion. The results also show that providing toll incentives for CACC-equipped vehicles to use express lanes is not beneficial at lower market penetration due to the small increase in capacity with these market penetrations. Such incentives are beneficial at higher market penetrations, particularly with higher demand levels.