Literature Review of Advancements in Adaptive Ramp Metering

Over a period spanning more than 30 years, several ramp metering algorithms have been developed to improve the operation of freeways. Many of these algorithms were deployed in several regions of the world, and field evaluations have shown their significance to improve traffic conditions on freeways and ramps. Previous reviews of ramp metering algorithms focused more on the research outcomes and evaluations of traditional metering algorithms developed in the early stage of ramp metering research. The purpose of this paper is to cover the more recent developments in ramp metering in relation to the traditional metering strategies. Several local and coordinated ramp metering algorithms were reviewed. In summary, Asservissement Linéaire d’Entrée Autoroutière (ALINEA) was found to be the most widely deployed local ramp metering strategy. The algorithm is simple and implementation costs less than other strategies. It also guarantees the targeted performance goals provided that the on-ramp has sufficient storage. Several extensions were proposed in the literature to fine-tune its performance. Among the coordinated metering strategies, zone based metering is simple to implement and easy to re-configure. System-wide adaptive ramp metering (SWARM) algorithm is more sensitive to calibrate for accurate prediction of traffic states. Heuristic ramp-metering coordination (HERO) algorithm can be useful if both local and coordinated control are desired particularly if the local control is using ALINEA. Fuzzy logic based algorithms are gaining popularity because of the simplicity and the fast re-configuration capability. Advanced real time metering system (ARMS) seems theoretically promising because of its proactive nature to prevent congestion; however, its performance is highly dependent upon accurate predictions. Finally, some guidelines were proposed for future research to develop new proposals and to extend the existing algorithms for guaranteed performance solutions.Scopu

A Dynamic-Zone-Based Coordinated Ramp-Metering Algorithm With Queue Constraints for Minnesota's Freeways

Following about 40 years of successful deployment of coordinated traffic-responsive ramp control, a new generation is being developed for Minnesota's freeways based on density measurements, rather than flow rates. This was motivated from recent research indicating that the critical value of density at which capacity is observed is less sensitive and more stable than capacity, thereby allowing the opportunity for more effective control. The main goals of the new approach are to delay the onset of the breakdown and accelerate system recovery when ramp metering is disabled due to the violation of maximum allowable ramp waiting times. This is obtained by a dynamic zone partitioning of the freeway network to identify critical bottleneck locations and coordinated balancing of ramp delays, which aims to avoid mainline breakdown. The effectiveness of the new control strategy is assessed by comparison with the currently deployed version of the stratified zone metering algorithm through microscopic simulation of a real 12-mi 17-ramp freeway section. Simulations show a decrease in delays of mainline and ramp traffic and an improvement of 8% in overall system delays while avoiding maximum ramp delay violations

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

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

Design and Evaluate Coordinated Ramp Metering Strategies for Utah Freeways

MPC-641During the past few decades, ramp metering control has been widely implemented in many U.S. states, including Utah. Numerous studies and applications have demonstrated that ramp metering control is an effective strategy to reduce overall freeway congestion by managing the amount of traffic entering the freeway. Ramp metering controllers can be implemented as coordinated or uncoordinated systems. Currently, Utah freeway on-ramps are operated in an uncoordinated way. Despite improvements to the operational efficiency of mainline flows, uncoordinated ramp metering will inevitably create additional delays to the ramp flows. Therefore, this project aims to assist the Utah Department of Transportation (UDOT) in deploying coordinated ramp metering systems and evaluating the performance of deployed systems. First, we leverage a method to identify existing freeway bottlenecks using current UDOT datasets, including PeMs and ClearGuide. Based on this, we select the site that may benefit from coordinated ramp metering from those determined locations. A VISSIM model is then developed for this selected corridor and the VISSIM model is calibrated based on collected traffic flow data. We apply the calibrated VISSIM model to conduct simulations to evaluate system performance under different freeway mainline congestion levels. Finally, the calibrated VISSIM model is leveraged to evaluate the coordinated ramp metering strategy of the bottleneck algorithm from both operational and safety aspects

A self-learning motorway traffic control system for ramp metering

Self-learning systems have attracted increasing attention in the ramp metering domain in recent years. These systems are based on reinforcement learning (RL) and can learn to control motorway traffic adaptively. However, RL-based ramp metering systems are still in their early stages and have shown limitations regarding their design and evaluation. This research aims to develop a new RL-based system (known as RAS) for ramp metering to overcome these limitations. A general framework for designing a RL-based system is proposed in this research. It contains the definition of three RL elements in a ramp metering scenario and a system structure which brings together all modules to accomplish the reinforcement learning process. Under this framework, two control algorithms for both single- and multi-objective problems are developed. In addition, to evaluate the proposed system, a software platform combining the new system and a traffic flow model is developed in the research. Based on the platform developed, a systematic evaluation is carried out through a series of simulation-based experiments. By comparing with a widely used control strategy, ALINEA, the proposed system, RAS, has shown its effectiveness in learning the optimal control actions for different control objectives in both hypothetical and real motorway networks. It is found that RAS outperforms ALINEA on improving traffic efficiency in the situation with severe congestion and on maintaining user equity when multiple on-ramps are included in the motorway network. Moreover, this research has been extended to use indirect learning technology to deal with incident-induced congestion. Tests for this extension to the work are carried out based on the platform developed and a commercial software package, AIMSUN, which have shown the potential of the extended system in tackling incident-induced congestion

A Q-LEARNING BASED INTEGRATED VARIABLE SPEED LIMIT AND HARD SHOULDER RUNNING CONTROL TO REDUCE TRAVEL TIME AT FREEWAY BOTTLENECK

To increase traffic mobility and safety, several types of active traffic management (ATM) strategies, such as variable speed limit (VSL) and hard shoulder running (HSR), are implemented in many countries. While all kinds of ATM strategies show promise in releasing traffic congestion, many studies indicate that stand-alone strategies have very limited capability. This paper proposes an integrated VSL and HSR control strategy based on a reinforcement learning (RL) technique, Q-learning (QL). The proposed strategy bridges a direct connection between the traffic flow data and the ATM control strategies via intensive self-learning processes thus reduces the need for human knowledge. A typical congested interstate highway, I-270 in Maryland, U.S. is simulated using a dynamic traffic assignment (DTA) model to evaluate the proposed strategy. Simulation results indicated that the integrated strategy outperforms the stand-alone strategies and traditional feedback-based VSL strategy in mitigating congestions and reducing travel time on the freeway corridor

On Learning based Parameter Calibration and Ramp Metering of freeway Traffic Systems

Ph.DDOCTOR OF PHILOSOPH

Network Maintenance and Capacity Management with Applications in Transportation

abstract: This research develops heuristics to manage both mandatory and optional network capacity reductions to better serve the network flows. The main application discussed relates to transportation networks, and flow cost relates to travel cost of users of the network. Temporary mandatory capacity reductions are required by maintenance activities. The objective of managing maintenance activities and the attendant temporary network capacity reductions is to schedule the required segment closures so that all maintenance work can be completed on time, and the total flow cost over the maintenance period is minimized for different types of flows. The goal of optional network capacity reduction is to selectively reduce the capacity of some links to improve the overall efficiency of user-optimized flows, where each traveler takes the route that minimizes the traveler’s trip cost. In this dissertation, both managing mandatory and optional network capacity reductions are addressed with the consideration of network-wide flow diversions due to changed link capacities. This research first investigates the maintenance scheduling in transportation networks with service vehicles (e.g., truck fleets and passenger transport fleets), where these vehicles are assumed to take the system-optimized routes that minimize the total travel cost of the fleet. This problem is solved with the randomized fixed-and-optimize heuristic developed. This research also investigates the maintenance scheduling in networks with multi-modal traffic that consists of (1) regular human-driven cars with user-optimized routing and (2) self-driving vehicles with system-optimized routing. An iterative mixed flow assignment algorithm is developed to obtain the multi-modal traffic assignment resulting from a maintenance schedule. The genetic algorithm with multi-point crossover is applied to obtain a good schedule. Based on the Braess’ paradox that removing some links may alleviate the congestion of user-optimized flows, this research generalizes the Braess’ paradox to reduce the capacity of selected links to improve the efficiency of the resultant user-optimized flows. A heuristic is developed to identify links to reduce capacity, and the corresponding capacity reduction amounts, to get more efficient total flows. Experiments on real networks demonstrate the generalized Braess’ paradox exists in reality, and the heuristic developed solves real-world test cases even when commercial solvers fail.Dissertation/ThesisDoctoral Dissertation Industrial Engineering 201

Modelling and Optimisation of Dynamic Motorway Traffic

Ramp metering, variable speed limits, and hard shoulder running control strategies have been used for managing motorway traffic congestion. This thesis presents a modelling and optimisation framework for all these control strategies. The optimal control problems that aim to minimise the travel delay on motorways are formulated based upon a macroscopic cell transmission model with piecewise linear fundamental diagram. With the piecewise linear nature of the traffic model, the optimal control problems are formulated as linear programming (LP) and are solved by the IBM CPLEX solver. The performance of different control strategies are tested on real scenarios on the M25 Motorway in England, where improvements were observed with proper implementation. With considering of the uncertainties in traffic demand and characteristics, this thesis also presents a robust modelling and optimisation framework for dynamic motorway traffic. The proposed robust optimisation aims to minimise both mean and variance of travel delays under a range of uncertain scenarios. The robust optimisation is formulated as a minimax problem and solved by a two stage solution procedure. The performances of the robust ramp metering are illustrated through working examples with traffic data collected from the M25 Motorway. Experiments reveal that the deterministic optimal control would outperform slightly the robust control in terms of minimising average delays, while the robust controller gives a more reliable performance when uncertainty is taken into account. This thesis contributes to the development and validation of dynamic simulation, and deterministic and robust optimisation

An Approach to Guide Users Towards Less Revealing Internet Browsers

When browsing the Internet, HTTP headers enable both clients and servers send extra data in their requests or responses such as the User-Agent string. This string contains information related to the sender’s device, browser, and operating system. Previous research has shown that there are numerous privacy and security risks result from exposing sensitive information in the User-Agent string. For example, it enables device and browser fingerprinting and user tracking and identification. Our large analysis of thousands of User-Agent strings shows that browsers differ tremendously in the amount of information they include in their User-Agent strings. As such, our work aims at guiding users towards using less exposing browsers. In doing so, we propose to assign an exposure score to browsers based on the information they expose and vulnerability records. Thus, our contribution in this work is as follows: first, provide a full implementation that is ready to be deployed and used by users. Second, conduct a user study to identify the effectiveness and limitations of our proposed approach. Our implementation is based on using more than 52 thousand unique browsers. Our performance and validation analysis show that our solution is accurate and efficient. The source code and data set are publicly available and the solution has been deployed