Variable Speed Limit Research Articles

An advanced real-time crash prediction framework for combined hard shoulder running and variable speed limits system using transformer

One of the important applications of real-time crash prediction analysis lies in the field of proactive traffic management, where instantaneous crash risk evaluation and dynamic decision-making are prerequisites. This research proposes an integrated and advanced real-time crash risk prediction framework for Variable Speed Limits (VSL) and Hard Shoulder Running (HSR) implemented freeways considering their operational periods. Statistical methods are utilized to identify the significant crash contributing factors (related to traffic, roadway geometry, and weather conditions) and explain their relationships with crashes. Time-Embedded Transformer models are proposed to predict the likelihood of real-time crash events. The sensitivity and false alarm rate of the proposed AM peak model are found to be 0.76 and 0.27, respectively, whereas the values are 0.78 and 0.24, respectively for the PM peak model. Additionally, the results indicate substantial improvements in model prediction performance (i.e., an increment of sensitivity values by 7.04% and 8.33% in the AM and PM models, respectively) after incorporating the general safety condition of a freeway segment as an input feature while estimating real-time crash prediction models. Practitioners and policymakers could apply this method to obtain a more accurate real-time crash likelihood estimation, identify important crash precursors, dynamically update algorithms, and enhance safety aspects while operating traffic management strategies on freeways.

Variable speed limit control strategy considering traffic flow lane assignment in mixed-vehicle driving environment

To accurately predict traffic flow and optimize the operations of freeway bottleneck areas in a mixed-vehicle driving environment, this paper proposes a traffic prediction model and a variable speed limit (VSL) cooperative control strategy. Firstly, a lane-level short-term traffic prediction model, physics informed Transformer and cell transmission model (PIT-CTM), is constructed by combining the Transformer neural network and lane-level cell transmission model (CTM) based on the physics-informed deep learning framework. On this basis, the accuracy and transferability of PIT-CTM are analysed. Secondly, a lane assignment decision model is presented, which enables the dynamic planning of the optimal traffic distribution across lanes. Furthermore, a lane-level VSL control model is constructed based on the model predictive control (MPC) framework. The model induces vehicles to change lanes earlier by setting the speed limit difference between lanes. By regulating the input flow in the bottleneck area of the freeway, it reduces conflicts between mainline vehicles and ramp vehicles. Finally, the feedback regulation between the lane assignment decision model and the lane-level VSL control model promotes the cooperative optimisation of the lateral and longitudinal flows and adapts the control strategy to the dynamic traffic characteristics. A three-lane freeway merging zone is selected, the numerical experiment is conducted and compared with differential lane-level VSL. The results show that the strategy can effectively optimise the mixed-vehicle traffic state and maintain better control performance under any connected and autonomous vehicle (CAV) penetration rates.

Development and application of a risk analysis methodology for road traffic accidents

Collision, rollover, swerving, and vehicle fire are road traffic accidents (RTAs) caused by driver, vehicle, and environment-related factors, resulting in diverse severity levels, such as property damages, injuries, and fatality. In addition, vagueness in detectability, e.g., close circuit television and driver admittance, culminates RTAs' risk analysis into a multifaceted problem. The present study performed root cause analysis to investigate RTAs, where the fishbone diagram categorized RTAs (e.g., collision and swerving) and identified primary (e.g., driver and environment) and secondary (e.g., overspeeding and tire burst) root causes. For 1158 km of intercity highways in Qassim (Saudi Arabia), fuzzy-failure mode and effect analysis evaluated 171 failure modes using 3500 RTAs' recorded over three years and knowledge of practice and experts. The study highlights collision, driver-related issues, and property damage as the leading category, root cause, and consequence. Comparing the baseline with the first realistic mitigation (speed camera installation) acknowledged a 53 % reduction in risk probability number. Automated driver assistance systems, rumble strips, variable speed limits, and increasing penalties for traffic violations hypothetically dropped 76 % of cumulative risk. The methodology handles limited data and subjective risk factors to help decision-makers minimize RTAs in Saudi Arabia and other jurisdictions.

Hybrid Deep-Learning Approach with Geoffrey E. Havers-Based Loss Function and Evaluation Metric for Multilocation Traffic-Flow Forecasting

Traffic forecasting can enhance the efficiency of traffic control strategies such as routing decisions, variable speed limits, and ramp metering, resulting in a decrease in congestion, pollutants, and expenses, and an improvement in journey time predictability. Traffic forecasting, however, remains challenging because of the complex, heterogeneous, and cyclic nature of traffic data. To address this complexity, this research employs a multi-input hybrid deep self-attention network (MIHDSAN) for multilocation forecasting. The model inputs are selected using correlation analysis. New tunable loss and evaluation metrics formulations are proposed based on the traffic-modeling Geoffrey E. Havers (GEH) statistic. The proposed method was validated on two independent real-world traffic datasets from Stockton and Oakland, California. The weekly periodicity was the more relevant periodic input feature compared with daily variations; however, the daily variation was also significant for the Stockton dataset. The inclusion of weekly traffic periodicity (>95% correlated) improved the performance of the model by 3%. Adding daily periodicity was only beneficial for the Stockton dataset (91% correlated). The proposed GEH metric and its standard acceptance criterion offer both quantitative and qualitative means of evaluating the forecasts produced. The GEH loss function was consistent and outperformed current industry-standard methodologies of mean absolute error (MAE) in 80% and mean squared error (MSE) in 94% of cases. Therefore, this research presents evidence to suggest that the proposed GEH loss and evaluation functions validated in this paper become a standard criterion for traffic forecasting.

Enhancing Traffic Efficiency and Sustainability through Strategic Placement of Roadside Units and Variable Speed Limits in a Connected Vehicle Environment

Enhancing Traffic Efficiency and Sustainability through Strategic Placement of Roadside Units and Variable Speed Limits in a Connected Vehicle Environment

Effects of passing rates on driving behaviour in variable speed limit-controlled highways: Evidence of external pressure from a driving simulator study

Variable message signs on motorways can show dynamically changing speed limits for traffic safety or efficiency reasons. While the effects of variable speed limits have been studied in the literature, the effect of (drivers in) vehicles operating under different speed limit information − caused by transitioning from one speed limit to another − has received relatively little attention. During such a transition, drivers can either be overtaken by vehicles operating under a higher speed limit, or conversely, drivers overtake slower vehicles operating under a lower speed limit. In both scenarios, opposite forms of external pressure are expected to be exerted by the surrounding traffic. Sixty-seven participants performed three randomised drives (201 drives in total), each reflecting one of three different passing rates scenarios, i.e., the number of vehicles overtaking the driver, or vice versa, caused by a speed limit change. Passing rates varied from as low as 90 veh/h to as high as 360 veh/h. Increasing passing rates act as a proxy for increasing levels of external pressure applied to drivers. Statistical analyses are conducted using a Linear Mixed Model (LMM) and a Generalised Estimating Equations (GEE) approach, accounting for correlation caused by the panel nature of the data. The LMM indicates that the driving behaviour indicators are indeed affected under different passing rates and that these differences are statistically significant. Results indicate that drivers in higher passing rate scenario(s) tend to accelerate and drive faster compared to the low(er) passing rate scenario(s). Further, the GEE model for speed selection indicates that drivers from different age groups and gender select different speeds in response to the impact of external pressure caused by surrounding traffic. Similarly, the GEE model for speed variation within a driver suggests differential speed variations of age groups and gender under different passing rates. Overall, this study finds pronounced effects directly related to the imposed external pressure via the surrounding traffic, where high passing rates lead to significant speed variations that increase the chances of drivers engaging in safety-critical events.

Deep Q‐network learning‐based active speed management under autonomous driving environments

AbstractEfficient traffic safety management necessitates real‐time crash risk prediction using expressway characteristics. With the emergence of autonomous vehicles (AVs), the development and evaluation of variable speed limit (VSL) strategies, a key active traffic management technique, become crucial for enhancing safety and mobility in mixed traffic flows. This underscores the need for optimized VSL strategies to accommodate both conventional and AVs. This paper presents a study on the development of VSL control algorithms using deep reinforcement learning in a microscopic traffic simulation. As the rewards function, time‐to‐collision and speed were considered. To enhance traffic safety, VSL strategies were refined across various market penetration of connected AVs. Analysis revealed that safety and traffic density are improved by 53% and 59%, respectively, in market penetration rate (MPR) 50, marking significant safety improvements in congested and low MPR scenarios. These findings present the importance of developing and evaluating VSL strategies for mixed traffic flow, particularly in the context of increasing the prevalence of connected and AVs.

Evaluation of the Operational and Safety Effects of a Variable Speed Limit System in Virginia

Variable speed limit (VSL) systems have been broadly deployed to address safety and mobility concerns both in the U.S. and internationally. In June 2022, the Virginia Department of Transportation activated a VSL system on I-95 northbound about halfway between Washington, D.C. and Richmond, Virginia. While many past VSL deployments have attempted to address urban congestion or mitigate safety issues arising from inclement weather, this deployment was intended to address traffic congestion created by through traffic during peak recreational periods by encouraging more uniform speed selection, dampening the effects of shockwaves, and providing advance warning of congestion. This paper discusses the characteristics of the deployed system, as well as its safety and operational effects, using approximately 1 year of post-deployment data. Results show that drivers did respond to the VSLs, and that mean speeds and speed distributions changed during transitional flow. Early safety results also showed reductions in crash severity, although this finding is based on limited data.

A Lagrangian approach for variable speed limit implementation in C-ITS framework

With the development of Cooperative-Intelligent Transport System (C-ITS) technologies, new strategies based on embedded technologies have emerged to manage road networks. This paper focuses on adapting to this connectivity context a Variable Speed Limit (VSL) system to detect shockwaves and anticipate their propagation based on the kinematic wave theory to dampen them. We provide an alternative framework to adapt the VSL strategy, well-suited for Eulerian approaches, into a Lagrangian context. While the Eulerian approach is based on Loop Detector (LD) and macroscopic traffic indicators (e.g. flow, density), our Lagrangian approach relies on Road Side Units (RSUs) that record the GPS traces shared by Connected Vehicles (CVs). Based on the combination of CV trajectories, Fundamental and Space-Time Diagrams theory, shockwave estimation and prediction processes directly operate on congestion waves, which release the estimation issue for traffic density. The simulation-based analysis reveals that the performance of the Lagrangian approach is comparable to the Eulerian configurations.

Ecologically Oriented Freeway Control Methods Integrated Speed Limits and Ramp Toll Booths Layout

Traffic exhaust pollution, especially in congested areas of freeways, is one of the main causes of air pollution. With the increase in the number of vehicles, traffic and environmental issues have become more prominent. In addition, traffic congestion leads to frequent starting and stopping of vehicles, further exacerbating environmental pollution. This article focuses on the problem of frequent starting and stopping of vehicles, using variable speed limit control to smooth traffic flow, reduce vehicle speed, and alleviate exhaust emissions caused by traffic congestion. At the same time, considering the traffic and environmental benefits of bottleneck areas on freeways, the VT-Micro model is used to calculate exhaust emissions, and a coordinated control method for the mainline and ramp of freeways is proposed. The simulation experiment results show that the total driving time of the mainline and ramp collaborative control method considering environmental benefits has been reduced by 24.69%, CO emissions have been reduced by 4.79%, HC emissions have been reduced by 7.65%, NOx emissions have been reduced by 2.48%, and fuel consumption has been reduced by 4.98%.

Cooperative traffic optimization with multi-agent reinforcement learning and evolutionary strategy: Bridging the gap between micro and macro traffic control

The emergence of connected and autonomous vehicles (CAVs) holds promise for fine-grained traffic control. However, due to the longevity of future mixed traffic scenarios, there is a need for an in-depth exploration of integrating the microscopic speed control of CAVs with the macroscopic variable speed limit (VSL) of human-driven vehicles (HDVs). This paper proposes a Cooperative Traffic Optimization with Multi-agent Reinforcement Learning and Evolutionary VSL (CTO-ME) framework, which combines microscopic CAV control with macroscopic VSL control. The framework incorporates a Graph Attention Mechanism (GATs) into the multi-agent reinforcement learning framework for intelligent decision-making by microscopic-level vehicles. Additionally, an evolutionary strategy is developed to design the VSL network architecture, enabling macroscopic level real-time speed limit adjustments based on infrastructure. A multi-objective reward function is proposed to optimize both micro and macro efficiency and safety, accounting for both vehicle behavior and traffic flow. Experiments on the designed Bottleneck traffic scenarios show that the proposed approach, CTO-ME, is able to achieve superior performance and outperforms other baselines in terms of traffic throughput, average speed, and safety. Specifically, CTO-ME enhances average velocity by 37%, increases overall throughput by 309%, and raises arrival ratio by 70% than traditional Intelligent Driver Model (IDM).

Traffic Flow Optimization at Toll Plaza Using Proactive Deep Learning Strategies

Global urbanization and increasing traffic volume have intensified traffic congestion throughout transportation infrastructure, particularly at toll plazas, highlighting the critical need to implement proactive transportation infrastructure solutions. Traditional toll plaza management approaches, often relying on manual interventions, suffer from inefficiencies that fail to adapt to dynamic traffic flow and are unable to produce preemptive control strategies, resulting in prolonged queues, extended travel times, and adverse environmental effects. This study proposes a proactive traffic control strategy using advanced technologies to combat toll plaza congestion and optimize traffic management. The approach involves deep learning convolutional neural network models (YOLOv7–Deep SORT) for vehicle counting and an extended short-term memory model for short-term arrival rate prediction. When projected arrival rates exceed a threshold, the strategy proactively activates variable speed limits (VSLs) and ramp metering (RM) strategies during peak hours. The novelty of this study lies in its predictive and adaptive capabilities, ensuring efficient traffic flow management. Validated through a case study at Ravi Toll Plaza Lahore using PTV VISSIMv7, the proposed method reduces queue length by 57% and vehicle delays by 47% while cutting fuel consumption and pollutant emissions by 28.4% and 34%, respectively. Additionally, by identifying the limitations of conventional approaches, this study presents a novel framework alongside the proposed strategy to bridge the gap between theory and practice, making it easier for toll plaza operators and transportation authorities to adopt and benefit from advanced traffic management techniques. Ultimately, this study underscores the importance of integrated and proactive traffic control strategies in enhancing traffic management, minimizing congestion, and fostering a more sustainable transportation system.

Statistical and Clustering-Based Assessment of Variable Speed Limits Effects on Motorway Performance from Real-World Observations

Variable Speed Limit (VSL) systems aimed at reducing congestion and improving safety performance have been implemented around the world in previous years. However, field studies have shown controversial results regarding traffic performance improvement. This study integrates statistical testing methods and clustering techniques for assessing the effect of a non-mandatory VSL system on traffic flow performances on a 14-km portion of the Padua–Mestre motorway in Italy. Statistical analysis is conducted on the observed speeds, collected for almost one year, to identify any significant differences provided by VSL activation. The changes in global motorway performances induced by the VSL in typical traffic patterns under recurring congestion are assessed using both statistical tests and two specific clustering algorithms, namely K-means and DBSCAN. The results indicate that the VSL system effectively affects the observed speeds and alleviates congested conditions: the observed reduction in mean travel time ranges is around 4% with the VSL system active across various lanes; the standard deviation of vehicular speeds witnessed a decrease of 12% to 20% in the most congested segments, while no notable distinction is observed in traffic flows.

A variable speed limit control approach for freeway tunnels based on the model-based reinforcement learning framework with safety perception

To improve the traffic safety and efficiency of freeway tunnels, this study proposes a novel variable speed limit (VSL) control strategy based on the model-based reinforcement learning framework (MBRL) with safety perception. The MBRL framework is designed by developing a multi-lane cell transmission model for freeway tunnels as an environment model, which is built so that agents can interact with the environment model while interacting with the real environment to improve the sampling efficiency of reinforcement learning. Based on a real-time crash risk prediction model for freeway tunnels that uses random deep and cross networks, the safety perception function inside the MBRL framework is developed. The reinforcement learning components fully account for most current tunnels' application conditions, and the VSL control agent is trained using a deep dyna-Q method. The control process uses a safety trigger mechanism to reduce the likelihood of crashes caused by frequent changes in speed. The efficacy of the proposed VSL strategies is validated through simulation experiments. The results show that the proposed VSL strategies significantly increase traffic safety performance by between 16.00% and 20.00% and traffic efficiency by between 3.00% and 6.50% compared to a fixed speed limit approach. Notably, the proposed strategies outperform traditional VSL strategy based on the traffic flow prediction model in terms of traffic safety and efficiency improvement, and they also outperform the VSL strategy based on model-free reinforcement learning framework when sampling efficiency is considered together. In addition, the proposed strategies with safety triggers are safer than those without safety triggers. These findings demonstrate the potential for MBRL-based VSL strategies to improve traffic safety and efficiency within freeway tunnels.

Integrating variable speed limit and ramp metering to enhance vehicle group safety and efficiency in a mixed traffic environment

The integrated variable speed limits and ramp metering (VSL-RM) strategy is a useful method to avoid crashes and alleviate congestion on expressways. Previous studies have mostly focused on specific roadway segments and optimized them with the single goal of efficiency or safety, which does not allow for a prompt response to high-risk moving vehicle groups to improve safety and efficiency. To reduce the crash and congestion risk of vehicle groups in real time, this study developed three VSL-RM strategies with different optimization objectives based on predicted risks in a mixed traffic flow environment including connected vehicles (CVs) and regular vehicles (RVs). Due to the different behaviors of CVs and RVs under the VSL-RM control strategy, a mixed traffic METANET model was introduced to predict traffic flow parameters, e.g., volume and speed. Furthermore, two risk prediction models were utilized to predict crash risk and congestion risk based on the traffic flow parameters predicted by the mixed-traffic METANET model. The objectives of the three VSL-RM strategies were to minimize the crash risk, congestion risk, and both crash and congestion risks of the vehicle groups, respectively. These strategies were evaluated using a well-calibrated micro-simulation network. The mixed flow METANET model and the risk prediction model were validated to be consistent with the simulated traffic flow. The results demonstrated that the three strategies could simultaneously improve safety and efficiency benefits in most scenarios. However, the safety-targeted strategy provided the highest safety benefits, while the efficiency-targeted strategy provided the highest efficiency benefits. The bi-objective strategy outperformed the other two strategies in balancing the benefits of safety and efficiency. Moreover, increasing the CV penetration rate resulted in higher benefits for all three strategies.

Collaborative Misbehaviour Response System for Improving Road Safety

This paper advocates for a proactive approach to traffic safety by introducing a collaborative Misbehaviour Response System (MBR) designed to preemptively address hazardous driving behaviours such as wrong-way driving and distracted driving. The system integrates with electric vehicles (EVs), leveraging advanced technologies like ADAS, edge computing, and cloud services to enhance road safety. Upon detection of misbehaviour, the MBR system utilizes data from interconnected parking facilities to identify the nearest safe location and provides navigation guidance to authorities and nearby vehicles. The paper presents a prototype of the MBR system, demonstrating its efficiency in detecting misbehaviours and coordinating swift responses. It also discusses the system’s limitations and societal implications, outlining future research directions, including integration with autonomous vehicle systems and variable speed limit technologies, to further improve road safety through proactive and context-aware response mechanisms.

Variable Speed Limit Control for Mixed Traffic Flow on Highways Based on Deep-Reinforcement Learning

Variable Speed Limit Control for Mixed Traffic Flow on Highways Based on Deep-Reinforcement Learning

Multi-Agent Deep Reinforcement Learning for Multi-Lane Freeways Differential Variable Speed Limit Control in Mixed Traffic Environment

In advanced freeway traffic management systems, variable speed limit control (VSLC) is frequently discussed as one of the control measures. However, in a mixed traffic environment where connected and automated vehicles (CAVs) and human-driven vehicles coexist, the existing VSLC strategies for multi-lane freeways have two major shortcomings: the lack of precise control at the individual vehicle level, and the implementation of uniform VSLC across all lanes. This paper proposes a novel differential variable speed limit control (DVSLC) strategy based on multi-agent reinforcement learning (MARL) in a mixed traffic environment (abbreviated as MARL-DVSLC). The proposed MARL-DVSLC approach utilized a centralized training with decentralized execution paradigm to learn the joint actions of variable speed limit controllers across all lanes, thereby setting different speed limits for each lane. The reward function takes into account the total time spent (TTS) on freeways to improve traffic mobility. Note that MARL-DVSLC disseminates speed limit information to CAVs via infrastructure-to-vehicle (I2V) communication. The effectiveness of MARL-DVSLC is verified under different simulation scenarios. Moreover, its performance is compared with the feedback-based VSLC method, the DVSLC method based on deep deterministic policy gradient (DDPG) (abbreviated as DDPG-DVSLC), and the no-control case in relation to performance. The results indicate that the proposed strategy can effectively improve traffic efficiency and reduce the spatiotemporal range of traffic congestion at a 30% penetration rate of CAVs. Compared with the suboptimal DDPG-DVSLC method, the proposed strategy can improve TTS by 12.88% with stable traffic demand and 10.24% with fluctuating traffic demand.

Data-driven exploration of traffic speed patterns to identify potential road links for variable speed limit sign implementation

ABSTRACT The focus of this study is to identify potential road links suitable for implementing a variable speed limit (VSL) sign by analyzing real-world traffic speed data collected over one year in Charlotte, North Carolina, United States. Exploratory and bivariate analyses were conducted to examine variations in traffic speed patterns during weekdays and weekends across eight specific timespans. The results revealed that road links with lower posted speed limits consistently experienced traffic speeds exceeding the posted speed limits. The mean traffic speeds are generally close to the posted speed limits for road links with higher posted speed limits while the 85th percentile speeds exceeded the posted speed limits, indicating a potential need for VSL sign implementation. The road links with 40 mph or 50 mph posted speed limits have a unique pattern compared to road links of other posted speed limit clusters. The mean traffic speed on these road links decreased as the standard deviation increased. The findings contribute to an improved understanding of traffic speed patterns and provide valuable insights for implementing a VSL sign.

Reinforcement learning approach to develop variable speed limit strategy using vehicle data and simulations

A variety of studies have been conducted to evaluate real-time crash risk using vehicle trajectory data and to establish active traffic safety management measures. Speed management is an effective way to control traffic flow on freeways and to enhance safety. Currently, the variable speed limit (VSL) system is mainly applied in a limited manner during traffic congestion or bad weather. However, it is necessary to manage traffic safety proactively to prevent crashes by providing an appropriate target safety speed to minimize the real-time crash risk. Herein, a methodology for proactive traffic safety management is developed through speed management based on the estimation of real-time crash risk. The developed methodology evaluates performance through simulations and it consists of two components. First, a crash risk analyzer evaluates freeway crash risk by developing a real-time crash risk model based on real-world vehicle trajectory data matched with crash traffic flow. Then a speed manager implements a reinforcement learning-based VSL system in the simulation environment, which includes the crash risk derived from the crash risk analyzer through VISSIM-COM interfaces. The performance of the developed methodology was evaluated through VISSIM simulation analysis, and the results demonstrated its feasibility. The real-time crash risk was reduced by approximately 55% when the target safety speed information derived from the reinforcement learning model was provided in a scenario where one lane was closed due to a crash. The findings were further applied to establish an operations strategy for VSL systems based on both crash risk and actual traffic conditions.