Anastasia Spiliopoulou

Queue Management Techniques for Metered Freeway On-Ramps

Ramp metering is beneficial for freeway throughput, but the ramp queues that are created should be prevented from extending to adjacent street junctions. A recently proposed ramp queue controller is investigated in conjunction with the local ramp-metering algorithm ALINEA. Microscopic simulation is used to compare the queue controller with a popular queue override scheme that is based on specifically designed scenarios and evaluation criteria. It is found that the queue controller outperforms the queue override and leads to fewer instances of ramp queue spillover. The concept is also demonstrated to work properly in an operational field environment.

Real-time merging traffic control for throughput maximization at motorway work zones

Work zones on motorways necessitate the drop of one or more lanes which may lead to significant reduction of traffic flow capacity and efficiency, traffic flow disruptions, congestion creation, and increased accident risk. Real-time traffic control by use of green–red traffic signals at the motorway mainstream is proposed in order to achieve safer merging of vehicles entering the work zone and, at the same time, maximize throughput and reduce travel delays. A significant issue that had been neglected in previous research is the investigation of the impact of distance between the merge area and the traffic lights so as to achieve, in combination with the employed real-time traffic control strategy, the most efficient merging of vehicles. The control strategy applied for real-time signal operation is based on an ALINEA-like proportional–integral (PI-type) feedback regulator. In order to achieve maximum performance of the control strategy, some calibration of the regulator’s parameters may be necessary. The calibration is first conducted manually, via a typical trial-and-error procedure. In an additional investigation, the recently proposed learning/adaptive fine-tuning (AFT) algorithm is employed in order to automatically fine-tune the regulator parameters. Experiments conducted with a microscopic simulator for a hypothetical work zone infrastructure, demonstrate the potential high benefits of

Real-time route diversion control at congested motorway off-ramp areas - Part I: User-optimum route guidance

This paper investigates the application of real-time route diversion policies in the case where recurrent motorway traffic congestion is created due to a saturated off-ramp. In particular, the proposed route diversion policies attempt to avoid the off-ramp queue spill-over onto the motorway mainstream and the resulting motorway congestion by re-routing the motorway vehicles through nearby off-ramps towards the same destination. In particular, this paper addresses the case where real-time route guidance strategies, based on user-optimum considerations, are sufficient to resolve the spill-over and motorway congestion problem; while other cases are treated in a companion paper. The proposed route guidance concepts are tested for a hypothetical but quite typical network infrastructure and traffic scenario by use of macroscopic simulation. The obtained simulation results are compared to the case where no route guidance is applied to the network and reveal interesting features and the potential for significant improvements.

Overview of nonlinear programming methods suitable for calibration of traffic flow models

The calibration of a macroscopic traffic flow model aims at enabling the model to reproduce, as accurately as possible, the real traffic conditions on a motorway network. Essentially, this procedure targets the best value for the parameter vector of the model and this can be achieved using appropriate optimization algorithms. The parameter calibration problem is formulated as a nonlinear, non-convex, least-squares optimization problem, which is known to attain multiple local minima; for this reason gradient-based solution algorithms are not considered to be an option. The methodologies that are more appropriate for application to this problem are mainly some meta-heuristic algorithms which use direct search approaches that allow them to avoid bad local minima. This paper presents an overview of the most suitable nonlinear programming methods for the calibration procedure of macroscopic traffic flow models. Furthermore, an application example, where two well-known macroscopic traffic flow models are evaluated through the calibration procedure, is presented.

Macroscopic traffic flow model calibration using different optimization algorithms

This study tests and compares different optimization algorithms employed for the calibration of a macroscopic traffic flow model. In particular, the deterministic Nelder–Mead algorithm, a stochastic genetic algorithm and the stochastic cross-entropy method are utilized to estimate the parameter values of the METANET model for a particular freeway site, using real traffic data. The resulting models are validated using various traffic data sets and the optimization algorithms are evaluated and compared with respect to the accuracy of the produced validated models as well as the convergence speed and the required computation time. The validation results showed that all utilized optimization algorithms were able to converge to robust model parameter sets, albeit achieving different performances considering the convergence speed and the required computation time.

Exploitation of ACC systems towards improved traffic flow efficiency on motorways

This study presents an ACC (Adaptive Cruise Control)-based traffic control strategy which aims to adapt in real time the driving behavior of ACC-equipped vehicles to the prevailing traffic conditions so that the motorway traffic flow efficiency is improved. The potential benefits obtained by applying the proposed control concept are demonstrated for different ACC penetration rates by use of validated microscopic simulation applied to a real motorway stretch where recurrent traffic congestion is created due to an on-ramp bottleneck. The simulation results demonstrate that, even for low penetration rates of ACC vehicles, the proposed control concept improves the average vehicle delay and fuel consumption by reducing the space-time extent of congestion compared to the case of only manually driven or regular ACC vehicles.

Real-Time Merging Traffic Control at Congested Freeway Off-Ramp Areas

This study presents a real-time merging traffic control algorithm to mitigate the problem of freeway congestion resulting from an overspilling off-ramp. The proposed control algorithm aims at maximizing the merge area outflow of the surface street and at the same time preventing the off-ramp queue spillover into the freeway mainstream and the resulting freeway congestion. The potential benefits obtained by applying the proposed control concept are demonstrated by the use of microscopic simulation applied to a real freeway network where recurrent freeway traffic congestion is created as a result of an overspilling off-ramp. The simulation results demonstrated that the proposed control algorithm may improve the prevailing traffic conditions, preventing the formation of freeway congestion and benefiting freeway drivers and surface street users.

Adaptive Cruise Control Operation for Improved Motorway Traffic Flow

This study presents an ACC (adaptive cruise control)–based traffic control strategy which aims to adapt in real time the driving behavior of ACC-equipped vehicles to the prevailing traffic conditions so that motorway traffic flow efficiency is improved. The potential benefits obtained by applying the proposed control concept are demonstrated for different ACC penetration rates by use of validated microscopic simulation applied to a real motorway stretch where recurrent traffic congestion is created under the current manual driving conditions because of an on-ramp bottleneck. The simulation results demonstrate that, even for low penetration rates of ACC vehicles, the proposed control concept improves the average vehicle delay and fuel consumption by reducing the space-time extent of congestion compared with the case of only manually driven or regular ACC vehicles.