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New variable speed limit (VSL) schemes are developed based on the Kinematic Wave theory to increase freeway bottleneck discharge rates under two scenarios: (i) steady queue and (ii) oscillatory queue. The key principle is to impose VSL control some distance upstream of a bottleneck to starve the inflow to the bottleneck and dissipate the queue. Once the queue near the bottleneck vanishes, another less restrictive VSL is imposed upstream to (i) resolve the heavy queue generated by the first VSL and (ii) regulate the inflow to the bottleneck to sustain the stable maximum bottleneck discharge rate and prevent traffic breakdown. Several strategies are developed for each scenario ranging from the simplest strategy that maximizes the delay saving to more sophisticated strategies for upstream queue management. An analysis of the model parameters suggests that a wide range of the first speed limit (to clear the queue around the bottleneck) can be imposed to realize significant delay savings. [ABSTRACT FROM AUTHOR]

Abstract: This paper studies traffic hysteresis arising in traffic oscillations from a behavioral perspective. It is found that the occurrence and type of traffic hysteresis is closely correlated with driver behavior when experiencing traffic oscillations and with the time driver reaction begins relative to the starting deceleration wave. Statistical results suggest that driver behavior is different depending on its position along the oscillation. This suggests that different car-following models should be used inside the different stages of an oscillation in order to replicate realistic congestion features. [Copyright &y& Elsevier]

Abstract: This paper demonstrates the capabilities of wavelet transform (WT) for analyzing important features related to bottleneck activations and traffic oscillations in congested traffic in a systematic manner. In particular, the analysis of loop detector data from a freeway shows that the use of wavelet-based energy can effectively identify the location of an active bottleneck, the arrival time of the resulting queue at each upstream sensor location, and the start and end of a transition during the onset of a queue. Vehicle trajectories were also analyzed using WT and our analysis shows that the wavelet-based energies of individual vehicles can effectively detect the origins of deceleration waves and shed light on possible triggers (e.g., lane-changing). The spatiotemporal propagations of oscillations identified by tracing wavelet-based energy peaks from vehicle to vehicle enable analysis of oscillation amplitude, duration and intensity. [ABSTRACT FROM AUTHOR]

Currently, model predictive control (MPC) for adaptive cruise control (ACC) systems relies on the prediction of the leader's motion to plan the follower's trajectory. However, such predictions must be accurate to guarantee string stability, which represents an ongoing challenge for machine learning approaches. This issue can be circumvented by simply incorporating the leader's history, which follows from Newell's car-following (CF) model where a trajectory under congestion corresponds to a temporal–spatial shift of the leader's past trajectories. By leveraging this insight, this paper develops a family of MPC models based on Newell's CF model, labeled Newell MPCs, which are safe and can reduce traffic congestion. Specifically, We first present baseline Newell MPCs to replicate the original Newell's CF model, including the X b o u n d -Model, which uses the shifted leader trajectory as an upper envelope; and the X r e f -Model, which adopts the shifted leader trajectory as a reference to avoid the issue of infeasible solution triggered by hard constraints. To further improve the control performance, we propose the X V -Model which uses the leader speed history as an additional reference to enhance the model robustness and regulate speed over/under-shootings. In addition, we extend the single-leader Newell's model through incorporating multiple leaders and propose the X m u l -Model, which can achieve driver anticipation, and correspondingly reduce reaction time and improve string stability. Finally, based on the X V -Model, we present two additional extensions: (i) the X V r e l a x -Model, which incorporates driver relaxation to achieve smooth response to merging traffic; and (ii) the X V s s -Model, which achieves strict string stability to further dampen traffic oscillations. The proposed Newell MPCs are tested using both numerical simulations and field studies on a stock 2019 Honda Civic using Openpilot and Comma.ai; the source code is available at https://github.com/HaoZhouGT/openpilot. • A family of MPC ACCs based on Newell's CF model. • Leader history can outperform prediction for string stability. • Road test of Newell MPC on a production ACC car model. [ABSTRACT FROM AUTHOR]

Commercial adaptive cruise control (ACC) systems are bi-level: an upper-level planner decides the target trajectory and the low-level system executes it. Existing literature on ACCs mostly focus on the planner algorithms or the actuator delay, while the transition process between them, e.g. the low-level control design and its impact are often ignored. This paper tries to fill this gap by digging into the codebase of a recent open-source self-driving system, Openpilot (OP), Comma.ai, from which we extract and formulate the algorithms at both the upper and lower levels. For linear ACCs, the paper extends the transfer function analysis from planners only to full control loops and investigates the impact of slow/fast low-level control on the overall string stability (SS). For MPC ACCs, it studies their planning characteristics based its optimization objectives and approximates the low-level impact using an ODE approach. We find that low-level control has a significant impact on the overall SS of ACCs: (i) slow low-level control undermines SS under small frequencies and improves SS given large frequencies for linear systems, (ii) MPC features a varying gain throughout an oscillation, where the fast low-level control typically results in a 'fast-slow' changing process of the MPC gain, which benefits the SS, whereas the slow low-level control leads to a 'slow-fast' varying gain which undermines the SS, (iii) slow low-level control are common as they arise from comfort-oriented control gains, from a "weak" actuator performance or both, and (iv) the SS is very sensitive to the integral gain under slow low-level control for both PI and PIF controllers. Overall, the study recommends fast low-level control for ensuring vehicular SS to reduce traffic congestion, considering that large congestion waves usually feature both small frequencies and large amplitudes, although slow controllers could perform even better provided a short and small leader perturbation. The findings of this paper are verified both numerically and experimentally. For the first time in the literature we implement custom ACC algorithms on market cars, and achieve SS on open roads with a random leader by only tuning the low-level controllers. The source code is shared at https://github.com/HaoZhouGT/openpilot to support on-road experiments of arbitrary car-following models, which may be of interest to other studies. • Open-source ACC algorithms from Openpilot, Comma.ai with focus on low-level control. • Slow low-level control undermines string stability even if the planner is stable. • Slow low-level control can arise from control gains, gas/brake systems, or both. • A new experimental method to run custom ACCs on commercial vehicles. [ABSTRACT FROM AUTHOR]