Search Results (27)

Digital Twins (DTs) are becoming essential tools for real-time decision-making in transportation systems. This paper presents a macroscopic traffic digital twin developed for a 50 km segment of the C-32 interurban highway in Spain. The digital twin replicates highway conditions using real-time data from roadside sensors and connected vehicles via Vehicle-to-Everything (V2X) communications. It supports intelligent decision-making for traffic management, particularly during incident situations, by recommending macroscopic strategies such as variable speed limits and re-routing. Unlike many existing DTs focused on microscopic modeling or urban contexts, our approach emphasizes a macroscopic scale suitable for interurban highways, enabling faster computation and system-wide insights. The decision-making module evaluates candidate strategies using real-time simulations and selects the most effective option based on key performance indicators (KPIs), including congestion, travel time, and emissions. The system has been validated under realistic traffic scenarios using historical data, considering both congestion and pollution use cases. Strategies are communicated back to the physical infrastructure via V2I messages (IVIM) and a mobile application using the cellular communication network, enabling a closed-loop architecture. This paper contributes a scalable, real-time, and field-integrated macroscopic DT framework for highway traffic management. Full article

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. Full article

Congestion on port motorways often leads to reduced capacity and traffic efficiency, while the growing prevalence of connected vehicles (CVs) offers new opportunities for improving traffic control. This paper proposes a hierarchical control method integrating ramp metering (RM) and variable speed limits (VSLs) explicitly designed for port motorway environments dominated by CVs. The method uses real-time CV data to reduce congestion through a hierarchical control framework in which the upper-level optimization determines system-wide parameters, and the lower-level execution translates them into local control commands. A microscopic simulation using SUMO in the Guoju area of the Chuanshan Port Motorway demonstrated that the proposed method increases traffic capacity by approximately 16% compared to the no-control scenario and improves traffic efficiency by 4.8% and 4.5% compared to PI-ALINEA and MTFC-FB, respectively. Further experiments in varying CV penetration rates (MPRs) from 60% to 100% revealed that while lower MPRs result in higher traffic fluctuations, the method remains effective and robust, particularly when MPRs exceed 80%. This highlights its ability to mitigate congestion and enhance the utilization of the existing infrastructure. Full article

Moving bottlenecks, characterized by their high frequency and unpredictability, pose significant challenges to timely response and management, often resulting in road congestion and increased risk of traffic accidents. To address these issues, this paper proposes an adaptive event-triggered variable speed limit (AET-VSL) method based on a state compensation model, which emphasizes the concept of symmetry in the optimization of multi-segment speed limits. This symmetry approach facilitates a balanced and efficient control strategy that adjusts speed limits in a way that harmonizes traffic flow across multiple road segments, reducing congestion and improving overall traffic stability. The state compensation model builds on the classical METANET traffic flow model, incorporating coordination between road segments to reduce congestion while minimizing disruptions to traffic flow stability. By dynamically adjusting speed limits using real-time traffic data, the AET-VSL method addresses fluctuations in traffic conditions and ensures adaptive control to manage bottlenecks efficiently. A simulation framework was employed to evaluate the proposed strategy across varying traffic scenarios. Results demonstrate that AET-VSL outperforms traditional methods, providing consistent improvements in traffic performance. For instance, under low-traffic-flow conditions, AET-VSL reduced waiting time (WT) by 41.36%, potential collisions (PCs) by 51.92%, and fuel consumptionfuel consumption (FC) by 34.07%. This study highlights the novelty and effectiveness of AET-VSL, offering a scalable and reliable solution for dynamic traffic management and showcasing its potential to enhance traffic safety and efficiency. Full article

The merging behavior of vehicles at entry ramps and the speed differences between ramps and mainline traffic cause merging traffic bottlenecks. Current research, primarily focusing on single traffic control strategies, fails to achieve the desired outcomes. To address this issue, this paper explores an integrated control strategy combining Variable Speed Limits (VSL) and Lane Change Control (LCC) to optimize traffic efficiency in ramp merging areas. For scenarios involving multiple ramp merges, a multi-agent reinforcement learning approach is introduced to optimize control strategies in these areas. An integrated control system based on the Factored Multi-Agent Centralized Policy Gradients (FACMAC) algorithm is developed. By transforming the control framework into a Decentralized Partially Observable Markov Decision Process (Dec-POMDP), state and action spaces for heterogeneous agents are designed. These agents dynamically adjust control strategies and control area lengths based on real-time traffic conditions, adapting to the changing traffic environment. The proposed Factored Multi-Agent Centralized Policy Gradients for Integrated Traffic Control in Dynamic Areas (FM-ITC-Darea) control strategy is simulated and tested on a multi-ramp scenario built on a multi-lane Cell Transmission Model (CTM) simulation platform. Comparisons are made with no control and Factored Multi-Agent Centralized Policy Gradients for Integrated Traffic Control (FM-ITC) strategies, demonstrating the effectiveness of the proposed integrated control strategy in alleviating highway ramp merging bottlenecks. Full article

With the deployment of cooperative intelligent transportation systems (C-ITSs), the telecommunication systems and their performance occupy a key position in ensuring safe, robust, and resilient services to the end-users. Regardless of the adopted protocol, adequate road network coverage might affect the service performance, in terms of traffic and environmental efficiency. In this study, we analyze the traffic efficiency and emission pollutant sensitivity to the location of ad hoc network antennas when the C-ITS services disseminate dynamic messages to control the speed limit and ensure sustainable mobility. We design the experimentation with short-range communication resulting from an ad hoc network and requiring Roadside Units (RSUs) along the road to broadcast messages within their communication range to the end-user. The performance variability according to the RSUs’ location and effective road network coverage are highlighted through our microscopic simulation-based experimentations. This paper develops a sensitivity analysis to evaluate the impact of the network mesh according to the C-ITS service under consideration. Focus is placed on the variable speed limit (VSL) service, controlling upstream speed to restrict congestion and ensure more sustainable mobility. The results show that, while the traffic efficiency improves even at a low market penetration rate (MPR) of the connected vehicles, the environmental efficiency improves only at a high MPR. From the telecommunication perspective, an expansive broadcast strategy appears to be more effective than the conservative approach. Full article

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. Full article

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. Full article

Uncertain emergency events are inevitable and occur unpredictably on the highway. Emergencies with lane capacity drops cause local congestion and can even cause a second accident if the response is not timely. To address this problem, a self-triggered variable speed limit (VSL) intelligent decision-making control strategy based on the improved deep deterministic policy gradient (DDPG) algorithm is proposed, which can eliminate or alleviate congestion in a timely manner. The action noise parameter is introduced to improve exploration efficiency and stability in the early stage of the algorithm training and then maximizes differential traffic flow as the control objective, taking the real-time traffic state as the input. The reward function is constructed to explore the values of the speed limit. The results show that in terms of safety, under different traffic flow levels, the proposed strategy has improved by over 28.30% compared to other methods. In terms of efficiency, except for being inferior to the no-control condition during low-traffic-flow conditions, our strategy has improved over 7.21% compared to the others. The proposed strategy greatly benefits traffic sustainability in Intelligent Transport Systems (ITSs). Full article

Existing transportation infrastructure and traffic control systems face increasing strain as a result of rising demand, resulting in frequent congestion. Expanding infrastructure is not a feasible solution for enhancing the capacity of the road. Hence, Intelligent Transportation Systems are often employed to enhance the Level of Service (LoS). One such approach is Variable Speed Limit (VSL) control. VSL increases the LoS and safety on motorways by optimizing the speed limit according to the traffic conditions. The proliferation of Connected and Autonomous Vehicles (CAVs) presents fresh prospects for improving the operation and measurement of traffic states for the operation of the VSL control system. This paper introduces a method for the detection of multiple congested areas that is used for state estimation for a dynamically positioned VSL control system for urban motorways. The method utilizes Q-Learning (QL) and CAVs as mobile sensors and actuators. The proposed control approach, named Congestion Detection QL Dynamic Position VSL (CD-QL-DPVSL), dynamically detects all of the congested areas and applies two sets of actions, involving the dynamic positioning of speed limit zones and imposed speed limits for all detected congested areas simultaneously. The proposed CD-QL-DPVSL control approach underwent an evaluation across six distinct traffic scenarios, encompassing CAV penetration rates spanning from $10\%$ to $100\%$ and demonstrated a significantly better performance compared to other control approaches, including no control, rule-based VSL, two Speed-Transition-Matrices-based QL-VSL configurations with fixed speed limit zone positions, and a Speed-Transition-Matrices-based QL-DVSL with a dynamic speed limit zone position. It achieved enhancements in macroscopic traffic parameters such as the Mean Travel Time and Total Time Spent by adapting its control policy to every simulated scenario. Full article

Traffic congestion is a typical phenomenon when motorways meet urban road networks. At this special location, the weaving area is a recurrent traffic bottleneck. Numerous research activities have been conducted to improve traffic efficiency and sustainability at bottleneck areas. Variable speed limit control (VSL) is one of the effective control strategies. The primary objective of this paper is twofold. On the one hand, turbulent traffic flow is to be smoothed on the special weaving area of motorways and urban roads using VSL control. On the other hand, another control method is provided to tackle the carbon dioxide emission problem over the network. For both control methods, a multi-agent reinforcement learning algorithm is used (MAPPO: multi-agent proximal policy optimization). The VSL control framework utilizes the real-time traffic state and the speed limit value in the last control step as the input of the optimization algorithm. Two reward functions are constructed to guide the algorithm to output the value of the dynamic speed limit enforced within the VSL control area. The effectiveness of the proposed control framework is verified via microscopic traffic simulation using simulation of urban mobility (SUMO). The results show that the proposed control method could shape a more homogeneous traffic flow, and reduces the total waiting time over the network by 15.8%. In the case of the carbon dioxide minimization strategy, the carbon dioxide emission can be reduced by 10.79% in the recurrent bottleneck area caused by the transition from motorways to urban roads. Full article

This paper deals with the study of variable speed limits (VSLs) for traffic control and their integration with user information strategies. As few studies have addressed the integrated VSL and user information strategy, we focus on comparing the adoption of the latter with the VSL alone strategy application and the no-control case, highlighting the benefits the integration brings. The integrated strategy is able to smooth the severity of congestion, shifting its occurrence in a section of the mainstream mostly suited to vehicle accumulation. An application on a real network is carried out. The traffic congestion conditions along the real highway are simulated by means of Dynameq simulation software and the METANET macroscopic model. The VSLs are applied in a control area aiming to evaluate the potential and the limitations of the strategy on a real network as well as the integration of variable speed limits and user information strategies. Two different cases of road congestion caused by the presence of on-ramps are studied. Results show that the integration of the two strategies leads to a redistribution of flows, achieving a reduction in the total travel time spent in the network and an increase in the traveled distances, i.e., reducing the overall network time despite the increase in assigned flows. However, an integrated strategy requires adequate transportation supply and mainly crossing demand. Full article

Current transport infrastructure and traffic management systems are overburdened due to the increasing demand for road capacity, which often leads to congestion. Building more infrastructure is not always a practical strategy to increase road capacity. Therefore, services from Intelligent Transportation Systems (ITSs) are commonly applied to increase the level of service. The growth of connected and autonomous vehicles (CAVs) brings new opportunities to the traffic management system. One of those approaches is Variable Speed Limit (VSL) control, and in this paper a VSL based on Q-Learning (QL) using CAVs as mobile sensors and actuators in combination with Speed Transition Matrices (STMs) for state estimation is developed and examined. The proposed Dynamic STM-QL-VSL (STM-QL-DVSL) algorithm was evaluated in seven traffic scenarios with CAV penetration rates ranging from $10\%$ to $100\%$. The proposed STM-QL-DVSL algorithm utilizes two sets of actions that include dynamic speed limit zone positions and computed speed limits. The proposed algorithm was compared to no control, rule-based VSL, and two STM-QL-VSL configurations with fixed VSL zones. The developed STM-QL-DVSL outperformed all other control strategies and improved measured macroscopic traffic parameters like Total Time Spent ($TTS$) and Mean Travel Time ($MTT$) by learning the control policy for each simulated scenario. Full article

This paper introduces a numerical variable speed limit (VSL) control method on a motorway, modeled by the system of partial differential equations (PDEs) of a non- equilibrium continuum traffic model. The method consists of a macroscopic simulation (i.e., numerical solution of the system of PDEs of the continuum model), introduction of the solution-based cost function and numerical optimization with a differential evolution algorithm (DE). Due to the numerical solution scheme, the method enables application of a wide range of continuum traffic models without prior discretization of PDEs. In this way, the method overcomes the limitations of the basic continuum models and represents a step towards more accurate traffic modelling in control strategies. In this paper, we determine optimal variable speed limits with the DE algorithm on a motorway section modeled by the modified switching curve model, which is a non-equilibrium continuum model consistent with the three-phase traffic flow theory. The effectiveness of the determined variable speed limits is validated using microsimulations of the test section, which show promising reductions of queue lengths and number of stops. Full article

Variable speed limits (VSL) aim at improving safety and traffic fluidity by increasing drivers’ awareness. In the present simulator study, VSL displayed on overhead gantries on a motorway were also displayed on a mobile phone, fixed on the vehicle’s centre console, with distance-based triggers (250 m vs. 500 m from the overhead gantry). Results showed drivers (N = 20) complied with the in-vehicle information, which was congruent with the upcoming gantry. The sooner the in-vehicle VSL, the faster the speed when speed limits increased. Similarly, the sooner the in-vehicle VSL, the slower the speed when speed limits decreased. Later in-vehicle VSL resulted in lower speed homogeneity, which is a safety concern. Speed homogeneity was greater when no in-vehicle VSL were displayed. Finally, the 70 mph VSL were affecting driving behaviour differently. These results suggested that there might be traffic disruption and more erratic longitudinal vehicle control on real roads. Full article