Multi-Objective Optimisation of a Variable Speed Limit Control Strategy in a Tunnel Maintenance Work Zone of the Mountain Highway

Traffic congestion and collision problems had been focused widely by transportation researchers. Various traffic management strategies such as Variable Speed Limit (VSL), ramp metering (RM), etc., have been deployed to mitigate traffic congestion and collision. However, most previous studies focused on the impacts of VSL or RM control separately. Therefore, in this study, a model predictive integrated VSL and RM control strategy based on collision probability optimization was proposed. The proposed control strategy predicts future traffic states and collision probability, and considers the optimum VSL and RM input simultaneously by minimizing collision probability. To quantify the safety and mobility impact, the proposed integrated control was implemented in microscopic simulation and compared with VSL control, RM control and without any control scenario respectively. The results indicate that the integrated VSL and RM control strategy can improve safety by approximately 41% and mobility by approximately 13%. In conclusion, the proposed integrated VSL and RM control strategy was better than VSL control strategy and RM control strategy independently.

The primary objective of this paper was to incorporate the reinforcement learning technique in variable speed limit (VSL) control strategies to reduce system travel time at freeway bottlenecks. A Q-learning (QL)-based VSL control strategy was proposed. The controller included two components: a QL-based offline agent and an online VSL controller. The VSL controller was trained to learn the optimal speed limits for various traffic states to achieve a long-term goal of system optimization. The control effects of the VSL were evaluated using a modified cell transmission model for a freeway recurrent bottleneck. A new parameter was introduced in the cell transmission model to account for the overspeed of drivers in unsaturated traffic conditions. Two scenarios that considered both stable and fluctuating traffic demands were evaluated. The effects of the proposed strategy were compared with those of the feedback-based VSL strategy. The results showed that the proposed QL-based VSL strategy outperformed the feedback-based VSL strategy. More specifically, the proposed VSL control strategy reduced the system travel time by 49.34% in the stable demand scenario and 21.84% in the fluctuating demand scenario.

Differential variable speed limit control strategy consider lane assignment at the freeway lane drop bottleneck

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

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.

Most previous prediction based Variable Speed Limit (VSL) control strategies focused on improving traffic mobility based on the macroscopic traffic data. Nowadays, the emerging technologies provide access to the microscopic traffic flow data, which better captures the details of traffic flow dynamics in the VSL controlled environment. Thus, in this paper, the microscopic traffic flow data were utilized as a supplement to predict the evolutions of traffic flow parameters. The proposed VSL control algorithm adopts the Model Predictive Control (MPC) framework, which employs a modified version of the classic traffic flow model METANET to take advantage of the microscopic data in traffic flow predictions. The microscopic traffic simulation software VISSIM was used to establish an experimental simulation platform and perform real time traffic responsive control based on field data. The proposed control strategy was evaluated against the no-VSL control and macroscopic-based VSL controlled scenario. The results show that utilizing the proposed modified METANET model reduced the error in speed prediction accuracy and improved system mobility performance.

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.

Coordinated Variable Speed Limit, Ramp Metering and Lane Change Control of Highway Traffic

A coordinated control framework of freeway continuous merging areas considering traffic risks and energy consumption.

Variable speed limit (VSL) control is a flexible restriction on the rate at which motorists can drive on a given stretch of road. Effective VSL control can increase safety and provide clear guidance for motorists. Previous traffic flow models of VSL control were mostly based on the influence of VSL on average speed (macro) or driver’s expected speed (micro). Few models considered the influence of VSL on driver’s actual driving behavior. In this paper, we first briefly introduce the big traffic data involved in this study and explain the mapping relationship between the data and driving behavior. Then, we analyze the driver’s actual driving behavior under the VSL control. Then, an improved single-lane cellular automaton model is established based on the driving behavior characteristics under VSL control. After that, we calibrate the parameters of the single-lane cellular automaton model with the left lane as the calibration object. Finally, this paper uses the proposed single-lane cellular automaton model to simulate the traffic flow characteristics under VSL control. The numerical simulation results show that the simulation of the variable speed limit in different density intervals presents different results, but these results are consistent with the actual situation of variable speed limit control, which verifies the validity of the proposed model.

Traffic demand of trucks is rapidly increasing on highway networks, which harms highway traffic mobility, safety and the environment. Variable speed limit (VSL) control is considered to be able to improve the traffic condition in truck-dominant highway segments. However previous research reported rather mixed effects of VSL controller on traffic mobility. While some studies reported improvements in travel time due to the use of VSL control, others reported either no improvement or small deterioration in travel time. In this paper, we demonstrated that the lack of improvement on travel time is due to lane changes that are taking place close to the bottleneck leading to severe capacity drop. We developed a combined lane change and VSL control strategy that recommends lane changes in advance to relief capacity drop in addition to VSL. Microscopic Monte-Carlo simulations on I-710 freeway with high truck demand were used to demonstrate that this combined control strategy is able to generate consistent improvements with respect to travel time, safety and environmental impact under different traffic conditions and incident scenarios.

Optimal location problem for variable speed limit application areas

The effectiveness of the variable speed limit (VSL) control is affected by the deployment locations of VSL signs. In this paper, a procedure was proposed to help determine the deployment location of VSL signs to reduce collision risks at freeway recurrent bottlenecks. The procedure is started from determining the hazardous recurrent bottleneck section by constructing the profile of the collision risks. The length of the hazardous section was determined according to the pre-selected threshold of crash risks. Various scenarios were considered for different lengths of VSL controlled sections with various densities of VSL signs. A modified cell transmission model (CTM) was used for modeling the traffic flow at the freeway bottlenecks under the VSL control. The VSL control factors were optimized by using the genetic algorithm. The safety effects of VSL were greatly affected by the placement of VSL signs. In general, the scenario with a longer VSL controlled section and more speed limit signs was more effective in reducing rear-end collision risks. The cost-benefit analyses showed that the placement of VSL signs in scenario C2 with the controlled section length of 11.7 mi and average VSL density of 1.0 mi had the best benefit/cost effect. Using 12 VSL signs, the collision risks were reduced by 69.16% and the total travel time was increased slightly by 7.73%. The procedure can help determine the optimal deployment of VSL signs on freeways, considering both the safety benefits and the cost.

Variable speed limit (VSL) can be used on freeways to manage traffic flow with the goal of improving capacity. To achieve this objective, it is necessary that both speed and density dynamics be represented accurately. In this study, to deeply understand the effectiveness of VSL control, an analytical model was developed to represent drivers’ response to updated speed limits and macroscopic speed dynamical change with respect to changeable speed limits. Specifically, to model the freeway links having VSL control, the fundamental diagram (FD) was replaced with the VSL control variable in the relaxation term of the METANET. This modification led to the speed control variable appearing linearly, which is preferable for online computation. The density dynamics are based on the cell transmission model (CTM), which is introduced to estimate the transition flow among successive links with some practical constraints. It also offers flexibility in designing active bottleneck in which there is a capacity drop once feeding flow exceeds its capacity. To exploit this benefit, a modification was introduced in the FD of the density dynamics. A VSL control strategy was proposed that explicitly considers traffic characteristics at active bottleneck and its upstream-downstream segments. It can control traffic flow into any type of active bottleneck. Then, the proposed traffic dynamics with the control strategy are implemented in a freeway corridor using the model predictive control (MPC) approach. The analysis was carried out in the calibrated microsimulation model, VISSIM, within a scenario in which shock waves were present. The microsimulation model functions as a proxy for the real-world traffic system. This study reveals that, in terms of mobility, VSL is mostly effective during congestion periods.

Variable speed limit (VSL) control is an effective technology to improve safety near freeway bottlenecks. This study aims to develop a control strategy for mixed traffic flow consisting of both human-driven vehicles (HDVs) and connected and automated vehicles (CAVs) based on collision avoidance theory. A microscopic simulation platform is first established, and four vehicle longitudinal dynamic models including Cruising model, Intelligent Driver Model (IDM), Adaptive Cruise Control model (ACC), Cooperative Cruise Control model (CACC) and one vehicle lateral dynamic model Minimizing Overall Braking Induced by Lane Changes model (MOBIL) are incorporated into the simulation platform. Then, a new VSL control strategy derived from collision avoidance theory is proposed for mixed traffic flow at the initial stage of CAVs’ popularization. Extensive simulation experiments are conducted, and surrogate safety measures and total travel time indicators are utilized to evaluate the safety and efficiency performances of the proposed VSL control. Results indicate that the proposed VSL control strategy can effectively improve the safety performance near freeway bottlenecks with an acceptable efficiency level.