A Simplified Theory of Urban Congestion
National Science Foundation (NSF)
This project seeks to validate and expand the hypothesis that the Macroscopic Fundamental Diagram of an urban network, which gives average travel time as a function of average density, depends mainly on the proposed three parameters. To test this hypothesis, the research will employ empirical data from different cities around the world. The analytical framework from prior NSF-funded research will be expanded to understand the conditions where this theory is applicable. This will shed light on the effects in macroscopic network performance of traffic signal control, route choice models and congestion pricing. Stochastic processes techniques will be employed to generate bounds to approximate network capacity reductions due to short blocks, which has proved difficult in the past. The project will develop the concept of macroscopic dynamic traffic assignment by formulating the assignment problem and its solution through a partition of the network into a collection of subnetworks, each described by a Macroscopic Fundamental Diagram. Closed form solutions will be sought for simple configurations, which will be the building blocks to develop numerical algorithms to tackle general networks. This will allow the computation of congestion control strategies, traffic state estimation and forecast over large urban networks and in real-time, all of which is intractable with current methods.
- Rafegh Aghamohammadi, Jorge A. Laval, Dynamic traffic assignment using the macroscopic fundamental diagram: A Review of vehicular and pedestrian flow models, Transportation Research Part B: Methodological, 2018.
- Rafegh Aghamohammadi, Jorge A. Laval, A continuum model for cities based on the macroscopic fundamental diagram: A semi-Lagrangian solution method, Transportation Research Part B: Methodological, 2019.
Implementation of a Variable Speed Limit/Ramp Metering strategy to increase freeway capacity at metered on-ramps
Georgia Department of Transportation Office of Performance-based Management and Research (OPMR) Research and Development Branch
Tu Xu, Hao Zhou
The objectives of this project is to perform detailed micro-simulation and fine-tuning of the VSL control strategy TORBO at two merge bottleneck in the I-285 corridor, and to produce all the necessary input for its implementation in NaviGAtor. TORBO is a VSL and ramp metering (VSL+RM) strategy designed to increase capacity at metered on-ramp bottlenecks and showed that it can reduce travel times by 8% in the corridor.
Statistical inference of a two-regime car-following model
National Science Foundation (NSF)
This project proposed the formulation of a family of two-regime car-following models where both free-flow and congestion regimes obey statistically independent random processes. This formulation generalizes previous efforts based on Brownian and geometric Brownian acceleration processes, each reproducing a different feature of traffic instabilities. The probability density of vehicle positions turns out to be analytical in our model, and therefore parameters can be estimated using maximum likelihood. This allows us to test a wide variety of hypotheses using statistical inference methods, such as the homogeneity of the driver/vehicle population and the statistical significance of the impacts of roadway geometry. The model is flexible so that newer vehicle technologies can be incorporated to test such hypotheses as differences in the car-following parameters of automated and regular vehicles.