Effectiveness assessment of the METANET demonstration

"September 1984."Bibliography: leaf 9."...support by the Naval Electronics Systems command under Contract Number N00039-83-C-0466."Joseph G. Karam, Alexander H. Levis

Hybrid model predictive control for freeway traffic using discrete speed limit signals

HYCON2 Show day - Traffic modeling, Estimation and Control 13/05/2014 GrenobleIn this paper, two hybrid Model Predictive Control (MPC) approaches for freeway traffic control are proposed considering variable speed limits (VSL) as discrete variables as in current real world implementations. These discrete characteristics of the speed limits values and some necessary constraints for the actual operation of VSL are usually underestimated in the literature, so we propose a way to include them using a macroscopic traffic model within an MPC framework. For obtaining discrete signals, the MPC controller has to solve a highly non-linear optimization problem, including mixed-integer variables. Since solving such a problem is complex and difficult to execute in real-time, we propose some methods to obtain reasonable control actions in a limited computation time. The first two methods (-exhaustive and -genetic discretization) consist of first relaxing the discrete constraints for the VSL inputs; and then, based on this continuous solution and using a genetic or an exhaustive algorithm, to find discrete solutions within a distance of the continuous solution that provide a good performance. The second class of methods split the problem in a continuous optimization for the ramp metering signals and in a discrete optimization for speed limits. The speed limits optimization, which is much more time-consuming than the ramp metering one, is solved by a genetic or an exhaustive algorithm in communication with a non-linear solver for the ramp metering. The proposed methods are tested by simulation, showing not only a good performance, but also keeping the computation time reduced.Unión Europea FP7/2007–201

Final report covering the period August 1983 - July 1985 for development and application of a methodology for system effectiveness analysis

"August 7, 1985." "OSP Number 94078."Bibliography: p. 88-89.Final reportContract Number N00039-83-C-0466Prepared by: Alexander H. Levis

Macroscopic Traffic Model Validation of Large Networks and the Introduction of a Gradient Based Solver

Traffic models are important for the evaluation of various Intelligent Transport Systems and the development of new traffic infrastructure. In order for this to be done accurately and with confidence the correct parameter values of the model must be identified. The focus of this thesis is the identification and confirmation of these parameters, which is model validation. Validation is performed on two different models; the first-order CTM and the second-order METANET model. The CTM is validated for two UK sites of 7.8 and 21.9 km and METANET for the same two sites using a variety of meta-heuristic algorithms. This is done using a newly developed method to allow for the optimisation method to determine the number of parameters to be used and the spatial extent of their application. This allows for the removal of expert engineering knowledge and ad-hoc decomposition of networks. This thesis also develops a methodology by use of Automatic Differentiation to allow gradient based optimisation to be used. This approach successfully validated the METANET model for the 21.9 km site and also a large network surrounding the city of Manchester of 186.9 km. This proves that gradient based optimisation can be used for the macroscopic traffic model validation problem. In fact the performance of the developed gradient method is superior to the meta-heuristics tested for the same sites. The methodology defined also allows for more data to be obtained from the model such as its Jacobian and the sensitivity of the objective function being used relative to the individual parameters. Space-Time contour plots of this newly acquired data show structures and shock waves that are not visible in the mean speed contour diagrams

Uncertainties and Errors in Predicting Vehicle Exhaust Emissions using Traffic Flow Models

Vehicle exhaust emissions predicted based on the outputs of traffic flow models are used directly to calculate traffic-related emissions, but also indirectly as input to 'air quality - human exposure' models. Both of which inform transport and environmental policies aimed at achieving sustainable mobility. To be effective, these must be based on robust modelling approaches that not only provide point-based emission predictions, but also inform these with an interval of confidence that properly accounts for the propagation of uncertainties and errors through the complex chain of models involved. This research develops a data-driven methodological framework to probabilistic average speed-based emission predictions using two widely deployed macroscopic traffic flow models. These are the Cell Transmission Model (CTM), a discretised first-order LWR-type model, and METANET, a discretised second-order Payne-type model. Studying both allows quantitative comparison in their application to predicting emissions. While this research discusses all potential sources of uncertainty in this modelling chain, it focusses on those arising from the traffic flow modelling output. The methodology starts with an ensemble-based optimisation approach to estimate both calibration and validation prediction errors in the traffic flow model, and then proposes a Monte Carlo sampling approach to propagate these to emission predictions. This allows predicting emissions alongside their upper and lower bounds for any time period and road network, at different levels of detail. To ensure transferability of findings, this methodology has been tested on three motorway road networks, one of which operates under Variable Speed Limits (VSL). This permits the quantitative assessment of VSL-modified traffic flow models. In the results of this research, emissions of Oxides of Nitrogen (NOx) and uncertainty associated with their prediction are specifically reported for each road network under study. Finally, this research argues that the methodological framework developed can (and should) be applied to any other (relatively) simple or complex integrated 'traffic flow - emission' modelling chain used as part of policy and decision making process

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

Ph.DDOCTOR OF PHILOSOPH

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

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