The connected vehicle (CV) technology is applied to develop VSL strategies to improve bottleneck discharge rates and reduce system delays. Three VSL control strategies are developed with different levels of complexity and capabilities to enhance traffic stability using: (i) only one CV (per lane) (Strategy 1), (ii) one CV (per lane) coupled with variable message signs (Strategy 2), and (iii) multiple CVs (Strategy 3). We further develop adaptive schemes for the three strategies to remedy potential control failures in real time. These strategies are designed to accommodate different queue detection schemes (by CVs or different sensors) and CV penetration rates. Finally, probability of control failure is formulated for each strategy based on the stochastic features of traffic instability to develop a general framework to (i) estimate expected delay savings, (ii) assess the stability of different VSL control strategies, and (iii) determine optimal control speeds under uncertainty. Compared to VMS-only strategies, the CVbased strategies can effectively impose dynamic control over continuous time and space, enabling (i) faster queue clearance around a bottleneck, (ii) less restrictive control with higher control speed (thus smoother transition), and (iii) simpler control via only one or a small number of CVs.

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Connected automated vehicles (CAVs) hold promise to replace current traffic detection systems in the near future. However, traffic state estimation, particularly flow rate, poses a major challenge at low CAV penetration rates without other supporting infrastructure of sensors. This paper proposes flow rate estimation methods using headway data from CAVs. Specifically, Bayesian inference and deep learning based methods are developed and compared with a naïve method based on a simple arithmetic mean of observed headways. The proposed methods are investigated via numerical experiments to evaluate their performance with respect to the CAV penetration rate, traffic demand, and availability of historical data. The methods are further validated with real data. The results show that the Bayesian inference based method, which estimates the flow rate distribution by integrating current (real-time) data and previous knowledge, can perform well even at low penetration rates with good prior information. However, in high CAV penetration, its relative advantage to the other methods diminishes because the prior information always influences the flow rate estimation. The deep learning based method can be effective with a large amount of data to train the model; however, in low CAV penetration, it tends to converge to the mean of target output values regardless of the observed data. At last, in relatively high CAV penetration, the relative advantage of the advanced methods is negligible and in fact, the naïve method is preferred in terms of accuracy as well as efficiency.

Field test results of a variable speed limit (VSL) control algorithm, a speed-controlling algorithm using shock wave theory (SPECIALIST), were analyzed to elucidate driver response and traffic flow evolution under VSL control. Successful VSL control was characterized by nearly constant, or decreasing, demand over time. In contrast, failed VSL control was attributed to ( a) significant increase in demand (during control) and ( b) significant net inflow from ramps. The demand increase was found to be the leading cause of the failed control, underscoring that the efficacy of the VSL control greatly relies on its ability to incorporate demand patterns during control. On the basis of these findings, some potential improvements are offered, including a parameter design strategy that incorporates demand patterns.