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.
An analytical modeling framework for assessing the benefits of CAV operations was proposed in @cite_10 . The overall results indicated that CAV improved network mobility performance, when the MPR was low, even in the absence of ML policies. Throughput without managed lane increased by 4 Nearly all of the previous studies evaluated the benefits of CAV at a aggregated level with the emphasis of the overall improvement. In this paper, we further the impact analysis to a traffic flow level, aiming to investigate the traffic flow characteristic with the presence of CAV lanes.
@cite_10
2302987950 2032566227 2904365072 2113040055
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. We present a novel modeling approach to derive closed-form throughput expressions for CSMA networks with hidden terminals. The key modeling principle is to break the interdependence of events in a wireless network using conditional expressions that capture the effect of a specific factor each, yet preserve the required dependences when combined together. Different from existing models that use numerical aggregation techniques, our approach is the first to jointly characterize the three main critical factors affecting flow throughput (referred to as hidden terminals, information asymmetry and flow-in-the-middle) within a single analytical expression. We have developed a symbolic implementation of the model, that we use for validation against realistic simulations and experiments with real wireless hardware, observing high model accuracy in the evaluated scenarios. The derived closed-form expressions enable new analytical studies of capacity and protocol performance that would not be possible with prior models. We illustrate this through an application of network utility maximization in complex networks with collisions, hidden terminals, asymmetric interference and flow-in-the-middle instances. Despite that such problematic scenarios make utility maximization a challenging problem, the model-based optimization yields vast fairness gains and an average per-flow throughput gain higher than 500 with respect to 802.11 in the evaluated networks. In earlier work, we established a decentralized optimal control framework for coordinating online connected and automated vehicles (CAVs) in specific transportation segments, e.g., urban intersections, merging roadways, roundabouts, and speed reduction zones. In this paper, we address coordination of CAVs in a corridor with multiple such scenarios and derive a closed-form analytical solution that includes interior boundary conditions. We evaluate the effectiveness of the solution through simulation in VISSIM. The proposed approach reduces significantly both fuel consumption and travel time for the CAVs compared to the baseline scenario where traditional human-driven vehicles without control are considered. Supporting future large-scale vehicular networks is expected to require a combination of fixed roadside infrastructure and mobile in-vehicle technologies. The need for an infrastructure, however, considerably decreases the deployment area of VANET applications. In this paper, we propose a self-organizing mechanism to emulate a geo-localized virtual infrastructure (GVI). This latter is emulated by a bounded-size subset of vehicles currently populating the geographic region where the virtual infrastructure is to be deployed. An analytical model is proposed to study this mechanism. More precisely, this model is proposed to study the GVI in the frame of its main use: data dissemination in VANETs. Despite being simple, the proposed model can accurately predict the system performance such as the probability that a vehicle is informed, and the average number of duplicate messages received by a vehicle, and allows a careful investigation of the impact of vehicular traffic properties and system parameters on performance criteria. Analytical and simulation results show that the proposed GVI mechanism can periodically disseminate the data within an intersection area, efficiently utilize the limited bandwidth and ensure high delivery ratio.