Comparison of Feedback Linearization and Model Predictive Techniques for Variable Speed Limit Control

Evaluating Performance of a Proactive Optimal Variable Speed Limit Control Using Different Objective Functions

Variable Speed Limit (VSL) control contributes to potential crash risk reduction by suggesting a suitable dynamic speed limit to achieve more stable and uniform traffic flow. In recent studies, researchers adopted macroscopic traffic flow models and perform prediction-based optimal VSL control. The response of drivers to the advised VSL is one of the most critical parameters in VSL-controlled speed dynamics modeling, which significantly affects the accuracy of traffic state prediction as well as the control reliability and performance. Nevertheless, the variations of driver responses were not explicitly modeled. Thus, in this research, the authors proposed a dynamic driver response model to formulate how the drivers respond to the advised VSL during various traffic conditions. The model was established and calibrated using field data to quantitatively analyze the dynamics of drivers’ desired speed regarding the advised VSL and current traffic state variables. A proactive VSL control algorithm incorporating the established driver response model was designed and implemented in field-data-based simulation study. The design proactive control algorithm modifies VSL in real-time according to the traffic state prediction results, aiming to reduce potential crash risks over the experiment site. By taking into account the real-time driver response variations, the VSL-controlled traffic state dynamics was more accurately predicted. The experimental results illustrated that the proposed control algorithm effectively reduces the crash probabilities in the traffic network.

Author(s): Gao, Hang; Cheng, Shenyang; Zhang, Michael | Abstract: The model based variable speed limit (VSL) control has been proven effective to resolve capacity-drop and time delay at a single recurrent bottleneck in previous studies. This project applies VSL controls to the traffic corridors with multi-segment and multi-bottleneck with the objective of reducing fuel consumption and greenhouse gas emissions. Based on a comprehensive review of existing methods, we develop and compare two fuel consumption centered VSL control (FC-VSL) strategies: flow-based control versus density-based control. These control strategies are implemented in SUMO, a microscopic traffic simulation package, on a 10-mile long freeway section. Results show that the density-based control reduces fuel consumption and gas emissions significantly at the cost of slight increase of travel time. The flow-based control, in contrast, reduces congestion and emissions in the downstream segments but transfers the congestion to the segments upstream of the controlled segments, resulting in an overall performance that is worse than the density-based FC-VSL, and no better than imposing static speed limits.

The prevailing variable speed limit (VSL) systems as an effective strategy for traffic control on motorways have the disadvantage that they only work with static VSL zones. Under changing traffic conditions, VSL systems with static VSL zones may perform suboptimally. Therefore, the adaptive design of VSL zones is required in traffic scenarios where congestion characteristics vary widely over space and time. To address this problem, we propose a novel distributed spatial-temporal multi-agent VSL (DWL-ST-VSL) approach capable of dynamically adjusting the length and position of VSL zones to complement the adjustment of speed limits in current VSL control systems. To model DWL-ST-VSL, distributed W-learning (DWL), a reinforcement learning (RL)-based algorithm for collaborative agent-based self-optimization toward multiple policies, is used. Each agent uses RL to learn local policies, thereby maximizing travel speed and eliminating congestion. In addition to local policies, through the concept of remote policies, agents learn how their actions affect their immediate neighbours and which policy or action is preferred in a given situation. To assess the impact of deploying additional agents in the control loop and the different cooperation levels on the control process, DWL-ST-VSL is evaluated in a four-agent configuration (DWL4-ST-VSL). This evaluation is done via SUMO microscopic simulations using collaborative agents controlling four segments upstream of the congestion in traffic scenarios with medium and high traffic loads. DWL also allows for heterogeneity in agents’ policies; cooperating agents in DWL4-ST-VSL implement two speed limit sets with different granularity. DWL4-ST-VSL outperforms all baselines (W-learning-based VSL and simple proportional speed control), which use static VSL zones. Finally, our experiments yield insights into the new concept of VSL control. This may trigger further research on using advanced learning-based technology to design a new generation of adaptive traffic control systems to meet the requirements of operating in a nonstationary environment and at the leading edge of emerging connected and autonomous vehicles in general.

Dynamic areas strategy design for variable speed limiting at fixed freeway bottlenecks

Congestion has become highly recognized as a worldwide traffic problem, as traffic demand has grown steadily over the past few decades. Variable speed limits (VSLs) are an intelligent transportation system (ITS) measure that limits mainline flow to mitigate bottleneck congestion. Currently, VSLs have become proactive based on short-term prediction. Proactive VSLs succeed in simulation evaluations, but few have been deployed in the field and their real-world effectiveness has not been proven. Various factors may lead to this limitation, such as the absence of reliable field application software, accuracy of prediction models, and high computation time for proactive control. To address this research gap, this study reports a preliminary VSL test and details its implementation results on Whitemud Drive, Edmonton, Canada. First, based on field traffic measurements before VSL control, recurrent bottleneck locations are identified. Second, the proactive control algorithm is briefly introduced. Then, a s...

The variable speed limit (VSL) is a control instrument of emerging intelligent transportation systems used to help manage motorway traffic. The VSL has the potential to improve traffic safety and reduce both traffic congestion and emissions through better harmonisation of traffic flow. The VSL results in a reduction of speed variations among vehicles travelling along controlled sections. In this paper, a modified logic-tree-based algorithm is proposed to improve the mobility benefits of VSL controllers. The modified algorithm is assessed using a case study in Auckland, New Zealand. Analysis of the case study is based on a critical bottleneck section of the Auckland motorway using the Aimsun micro-simulator. The proposed algorithm is compared with a known algorithm proposed in the literature and used in practice. The simulation results of the case study show that the proposed logic-tree-based VSL control algorithm outperforms the existing method. Compared with a no-control scenario, it results in respective improvements of 6%, 11%, 3%, 9% and 3% in the capacity of the bottleneck section, the total travel time and emissions of carbon dioxide, carbon monoxide and nitrogen oxides.

Exploring the impact of a coordinated variable speed limit control on congestion distribution in freeway

A deep reinforcement learning-based intelligent intervention framework for real-time proactive road safety management

Recent research has investigated the effects of var iable speed limits (VSL) on traffic flow processes. Findings show that VSL profoundly impact lane flow distribution (LFD) and increase the use of the slow lane by reducing the speed difference between the slow lane and the overtaking lanes. Finally, VSL reduce the capacity drop phenomenon on multi-lane sections and significantly increase the proportion of flow on the slow lane. These results highlight the major impact of LFD on the ca pacity of multi-lane sections. The paper presents a new analytical model that esti mates the capacity of multi-lane sections and repro duces the capacity-drop caused by LFD characteristics. It can be applied to estimate the capacity of motorway se ctions with two or three lanes under common traffic condit ions; it can also estimate the impact of VSL on the capacity of multi-lane sections. The multi-lane capacity model is coupled with a dyn amic mesoscopic traffic flow model initially design ed for network applications: the meso-LWR model. The resulting dynamic traffic flow model is relevant for mot orway network applications and VSL evaluation. The dynamic model is implemented on a French motorway site. The results show the ability of the model to reproduce actual traffic situations (capacity, congestion per iod, etc.) without VSL, including the amplitude of capacity dr op phenomenon caused by the under-use of the slow lane. It also shows the ability of the model to estimate the effect of VSL on future traffic flow conditions.

Computationally efficient model predictive control of freeway networks

Robust variable speed limit control with respect to uncertainties

Mitigating Recurrent Congestion via Particle Swarm Optimization Variable Speed Limit Controllers

Variable Speed Limit (VSL) control systems are widely studied as solutions for improving safety and throughput on urban motorways. Machine learning techniques, specifically Reinforcement Learning (RL) methods, are a promising alternative for setting up VSL since they can learn and react to different traffic situations without knowing the explicit model of the motorway dynamics. However, the efficiency of combined RL-VSL is highly related to the class of the used RL algorithm, and description of the managed motorway section in which the RL-VSL agent sets the appropriate speed limits. Currently, there is no existing overview of RL algorithm applications in the domain of VSL. Therefore, a comprehensive survey on the state of the art of RL-VSL is presented. Best practices are summarized, and new viewpoints and future research directions, including an overview of current open research questions are presented.

In recent years, congestion on port motorways has become increasingly frequent, significantly constraining transportation efficiency and contributing to higher pollution emissions. This paper proposes a novel max-pressure-driven integrated control (IFC-MP) for port motorways, inspired by the max pressure (MP) concept, which continuously adjusts the weights of ramp metering (RM) and the variable speed limit (VSL) based on pressure feedback from the on-ramp and upstream, assigning greater control weight to the side with higher pressure. A queue management mechanism is incorporated to prevent on-ramp overflow. The effectiveness of IFC-MP is verified in SUMO, filling the gap where the previous integrated control methods for port motorways lacked micro-simulation validation. The results show that IFC-MP enhances bottleneck throughput by approximately 7% compared to the no-control case, optimizes the total time spent (TTS) by 26–27%, and improves total pollutant emissions (TPEs) by about 11%. Compared to strategies that use only RM and VSL control, or activate VSL control only after RM reaches its lower bound, the time–space distribution of speed under IFC-MP is more uniform, with smaller fluctuations in bottleneck occupancy. Additionally, IFC-MP maintains relatively stable performance under varying compliance levels. Overall, the IFC-MP is an effective method for alleviating congestion on port motorways, excelling in optimizing both traffic efficiency and pollutant emissions.