361 research outputs found

    MARVEL: Multi-Agent Reinforcement-Learning for Large-Scale Variable Speed Limits

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    Variable speed limit (VSL) control is a promising traffic management strategy for enhancing safety and mobility. This work introduces MARVEL, a multi-agent reinforcement learning (MARL) framework for implementing large-scale VSL control on freeway corridors using only commonly available data. The agents learn through a reward structure that incorporates adaptability to traffic conditions, safety, and mobility; enabling coordination among the agents. The proposed framework scales to cover corridors with many gantries thanks to a parameter sharing among all VSL agents. The agents are trained in a microsimulation environment based on a short freeway stretch with 8 gantries spanning 7 miles and tested with 34 gantries spanning 17 miles of I-24 near Nashville, TN. MARVEL improves traffic safety by 63.4% compared to the no control scenario and enhances traffic mobility by 14.6% compared to a state-of-the-practice algorithm that has been deployed on I-24. An explainability analysis is undertaken to explore the learned policy under different traffic conditions and the results provide insights into the decision-making process of agents. Finally, we test the policy learned from the simulation-based experiments on real input data from I-24 to illustrate the potential deployment capability of the learned policy

    Discharge Flow Rate Change Under Rainy Conditions on Urban Motorways

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    Queue discharge flow is the most frequently observed phenomenon on urban motorways when demand exceeds capacity. Once a queue is formed, congestion arises, and the number of vehicles that can pass from downstream reduces. This reduction phenomenon is defined as the capacity drop and calculated by taking the difference between capacity and discharge flow at a road section. Obviously, this capacity drop exists after an onset of congestion and may increase in relation to weather conditions, such as rain, snow, or fog, which cause longer queues and delays. In this paper, the effect of rain on discharge flows is investigated and compared with sunny days on Istanbul urban motorways. Besides, rain precipitation during congestion is considered and related to discharge flow. Four different motorway sections were analyzed, and up to 37% discharge flow reduction was determined between sunny and rainy conditions. Motorway sections with higher free flow speed (FFS) were found to be more affected by rain, and discharge flow reduction was bigger compared to the section with the lowest FFS. For 1 mm/m2/h of precipitation, the discharge flow is estimated as 1,719 pcu/h/lane when FFS is 84 km/h, and as 1,560 pcu/h/lane if FFS is 104 km/h.</p

    2nd Symposium on Management of Future motorway and urban Traffic Systems (MFTS 2018): Booklet of abstracts: Ispra, 11-12 June 2018

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    The Symposium focuses on future traffic management systems, covering the subjects of traffic control, estimation, and modelling of motorway and urban networks, with particular emphasis on the presence of advanced vehicle communication and automation technologies. As connectivity and automation are being progressively introduced in our transport and mobility systems, there is indeed a growing need to understand the implications and opportunities for an enhanced traffic management as well as to identify innovative ways and tools to optimise traffic efficiency. In particular the debate on centralised versus decentralised traffic management in the presence of connected and automated vehicles has started attracting the attention of the research community. In this context, the Symposium provides a remarkable opportunity to share novel ideas and discuss future research directions.JRC.C.4-Sustainable Transpor

    Integrated Freeway and Arterial Traffic Control to Improve Freeway Mobility without Compromising Arterial Traffic Conditions Using Q-Learning

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    Freeway and arterial transportation networks are operated individually in most cities nowadays. The lack of coordination between the two increases the severity of traffic congestion when they are heavily loaded. To address the issue, we propose an integrated traffic control strategy that coordinates freeway traffic control (variable speed limit control, lane change recommendations, ramp metering) and arterial signal timing using Q-learning. The agent is trained offline in a single-section road network first, and then implemented online in a large simulation network with real-world traffic demands. The online data are collected to further improve the agent's performance via continuous learning. We observe significant reductions in freeway travel time and number of stops and a slight increase in on-ramp queue lengths by implementing the proposed approach in scenarios with traffic congestion. Meanwhile, the queue lengths of adjacent arterial intersections are maintained at the same level. The benefits of the coordination mechanism is verified by comparing the proposed approach with an uncoordinated Q-learning algorithm and a decentralized feedback control strategy.Comment: 12 pages, 10 figures, 5 table

    Coordination and Analysis of Connected and Autonomous Vehicles in Freeway On-Ramp Merging Areas

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    Freeway on-ramps are typical bottlenecks in the freeway network, where the merging maneuvers of ramp vehicles impose frequent disturbances on the traffic flow and cause negative impacts on traffic safety and efficiency. The emerging Connected and Autonomous Vehicles (CAVs) hold the potential for regulating the behaviors of each individual vehicle and are expected to substantially improve the traffic operation at freeway on-ramps. The aim of this research is to explore the possibilities of optimally facilitating freeway on-ramp merging operation through the coordination of CAVs, and to discuss the impacts of CAVs on the traffic performance at on-ramp merging.In view of the existing research efforts and gaps in the field of CAV on-ramp merging operation, a novel CAV merging coordination strategy is proposed by creating large gaps on the main road and directing the ramp vehicles into the created gaps in the form of platoon. The combination of gap creation and platoon merging jointly facilitates the mainline and ramp traffic and targets at the optimal performance at the traffic flow level. The coordination consists of three components: (1) mainline vehicles proactively decelerate to create large merging gaps; (2) ramp vehicles form platoons before entering the main road; (3) the gaps created on the main road and the platoons formed on the ramp are coordinated with each other in terms of size, speed, and arrival time. The coordination is analytically formulated as an optimization problem, incorporating the macroscopic and microscopic traffic flow models. The model uses traffic state parameters as inputs and determines the optimal coordination plan adaptive to real-time traffic conditions.The impacts of CAV coordination strategies on traffic efficiency are investigated through illustrative case studies conducted on microscopic traffic simulation platforms. The results show substantial improvements in merging efficiency, throughput, and traffic flow stability. In addition, the safety benefits of CAVs in the absence of specially designed cooperation strategies are investigated to reveal the CAV’s ability to eliminate critical human factors in the ramp merging process

    Influence of Spatial Placement of Variable Speed Limit Zones on Urban Motorway Traffic Control

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    Traffic control approaches, in particular Variable Speed Limit (VSL), are often studied as solutions to improve the level of service on urban motorways. However, the efficiency of VSL strongly depends on the spatiotemporal arrangement of VSL zones. It is crucial to determine the lengths and locations of VSL zones for best VSL efficiency before deployment in a real system, as the optimal length of the VSL zone and its distance from the bottleneck directly affects traffic dynamics and, thus, bottleneck control. Therefore, in this study, we perform the analysis of different VSL zones lengths and their positions by using a closed-loop Simple Proportional Speed Controller for VSL (SPSC-VSL). We evaluate the different VSL zone configurations and their impact on traffic flow control and vehicle emissions in a SUMO microscopic simulation on a high traffic demand scenario. The results support the observations of previous researchers on the significant dependence of VSL zone placement on VSL efficiency. Additionally, new data-based (traffic parameters and vehicle emissions) evidence of the performance of the SPSC-VSL design are provided regarding the best placement of consecutive VSL zones for motorway bottleneck control not analysed in previous research

    A Review of Traffic Signal Control.

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    The aim of this paper is to provide a starting point for the future research within the SERC sponsored project "Gating and Traffic Control: The Application of State Space Control Theory". It will provide an introduction to State Space Control Theory, State Space applications in transportation in general, an in-depth review of congestion control (specifically traffic signal control in congested situations), a review of theoretical works, a review of existing systems and will conclude with recommendations for the research to be undertaken within this project

    Deep Reinforcement Learning Approach for Lagrangian Control: Improving Freeway Bottleneck Throughput Via Variable Speed Limit

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    Connected vehicles (CVs) will enable new applications to improve traffic flow. The focus of this dissertation is to investigate how reinforcement learning (RL) control for the variable speed limit (VSL) through CVs can be generalized to improve traffic flow at different freeway bottlenecks. Three different bottlenecks are investigated: A sag curve, where the gradient changes from negative to positive values causes a reduction in the roadway capacity and congestion; a lane reduction, where three lanes merge to two lanes and cause congestion, and finally, an on-ramp, where increase in demand on a multilane freeway causes capacity drop. An RL algorithm is developed and implemented in a simulation environment for controlling a VSL in the upstream to manipulate the inflow of vehicles to the bottleneck on a freeway to minimize delays and increase the throughput. CVs are assumed to receive VSL messages through Infrastructure-to-Vehicle (I2V) communications technologies. Asynchronous Advantage Actor-Critic (A3C) algorithms are developed for each bottleneck to determine optimal VSL policies. Through these RL control algorithms, the speed of CVs are manipulated in the upstream of the bottleneck to avoid or minimize congestion. Various market penetration rates for CVs are considered in the simulations. It is demonstrated that the RL algorithm is able to adapt to stochastic arrivals of CVs and achieve significant improvements even at low market penetration rates of CVs, and the RL algorithm is able to find solution for all three bottlenecks. The results also show that the RL-based solutions outperform feedback-control-based solutions
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