Nonlinear optimal control applied to coordinated ramp metering

The goal of this paper is to describe a generic approach to the problem of optimal coordinated ramp metering control in large-scale motorway networks. In this approach, the traffic flow process is macroscopically modeled by use of a second-order macroscopic traffic flow model. The overall problem of coordinated ramp metering is formulated as a constrained discrete-time nonlinear optimal control problem, and a feasible-direction nonlinear optimization algorithm is employed for its numerical solution. The control strategy's efficiency is demonstrated through its application to the 32-km Amsterdam ring road. A number of adequately chosen scenarios along with a thorough analysis, interpretation, and suitable visualization of the obtained results provides a basis for the better understanding of some complex interrelationships of partially conflicting performance criteria. More precisely, the strategy's efficiency and equity properties as well as their tradeoff are studied and their partially competitive behavior is discussed. The results of the presented approach are very promising and demonstrate the efficiency of the optimal control methodology for motorway traffic control problems

Penetration effect of connected and automated vehicles on cooperative on‐ramp merging

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/166263/1/itr2bf00795.pd

Convergence of RProp and variants

This paper examines conditions under which the Resilient Propagation-Rprop algorithm fails to converge, identifies limitations of the so-called Globally Convergent Rprop-GRprop algorithm which was previously thought to guarantee convergence, and considers pathological behaviour of the implementation of GRprop in the neuralnet software package. A new robust convergent backpropagation-ARCprop algorithm is presented. The new algorithm builds on Rprop, but guarantees convergence by shortening steps as necessary to achieve a sufficient reduction in global error. Simulation results on four benchmark problems from the PROBEN1 collection show that the new algorithm achieves similar levels of performance to Rprop in terms of training speed, training accuracy, and generalization

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

Managing lane-changing of algorithm-assisted drivers

Theoretical models of vehicular traffic ascribe the fundamental cause of velocity oscillations and stop-and-go waves to suboptimal or unpredictable human driving behavior. In this paper we ask: if vehicles were controlled or assisted by algorithms, and hence driven “optimally,” would these phenomena simply go away? If they do not, how should a regulator manage algorithm-assisted vehicular traffic for a smooth flow? We study these questions in the context of a mandatory lane-changing scenario from the perspective of an algorithm-assisted driver on a curtailed lane that has to merge to an adjacent free lane with a relatively dense platoon. In a stylized model of algorithm-assisted driving, we liken the blocked-lane driver to a rational self-interested agent, whose objective is to minimize her expected travel time through the blockage, deciding (a) at what velocity to move, and (b) whether to merge to the free lane if an adequate gap arises. Moving at higher velocities reduces travel time, but also reduces the probability of finding a large enough gap to merge. We analyze the problem via dynamic programming, and we show that the optimal policy has a surprising structure: in the presence of uncertainty on adequate-sized gaps in the target lane, it may be optimal for the blocked-lane driver, in certain parameter regimes, to oscillate between high and low velocities while attempting to merge. Hence, traffic oscillations can arise not just due to suboptimal or unpredictable human driving behavior, but also endogenously, as the outcome of a driver’s rational, optimizing behavior. We provide theoretical support for this finding by drawing similarities to bang–bang control. As velocity oscillations are known to be detrimental to a smooth traffic flow, we provide sufficient conditions such that traffic oscillations, due to such optimizing behavior, do not arise. Finally, we investigate the fundamental flow-density and travel time-density diagrams through traffic micro-simulations performed in SUMO. We establish that the proposed approach exhibits consistently near-optimal performance, in a broad variety of traffic conditions

New Framework and Decision Support Tool to Warrant Detour Operations During Freeway Corridor Incident Management

As reported in the literature, the mobility and reliability of the highway systems in the United States have been significantly undermined by traffic delays on freeway corridors due to non-recurrent traffic congestion. Many of those delays are caused by the reduced capacity and overwhelming demand on critical metropolitan corridors coupled with long incident durations. In most scenarios, if proper detour strategies could be implemented in time, motorists could circumvent the congested segments by detouring through parallel arterials, which will significantly improve the mobility of all vehicles in the corridor system. Nevertheless, prior to implementation of any detour strategy, traffic managers need a set of well-justified warrants, as implementing detour operations usually demand substantial amount of resources and manpower. To contend with the aforementioned issues, this study is focused on developing a new multi-criteria framework along with an advanced and computation-friendly tool for traffic managers to decide whether or not and when to implement corridor detour operations. The expected contributions of this study are: * Proposing a well-calibrated corridor simulation network and a comprehensive set of experimental scenarios to take into account many potential affecting factors on traffic manager\u27s decision making process and ensure the effectiveness of the proposed detour warrant tool; * Developing detour decision models, including a two-choice model and a multi-choice model, based on generated optima detour traffic flow rates for each scenario from a diversion control model to allow responsible traffic managers to make best detour decisions during real-time incident management; and * Estimating the resulting benefits for comparison with the operational costs using the output from the diversion control model to further validate the developed detour decision model from the overall societal perspective

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

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

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

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