La congestión vehicular en Lima es caótica dada la escaza infraestructura vial y el acelerado aumento del parque automotor. En contraste, esta investigación propone implementar el sistema de semaforización inteligente, Ramp Meter, basado en el algoritmo AMS-AG, para reducir los tiempos de viaje vehicular en el óvalo Habich, ubicado en el distrito de San Martín de Porres. La metodología propuesta consta en la recolección de datos estáticos y registro de una lista de verificaciones. Luego se procede con la toma de datos de entrada para construir el modelo, tales como; flujos, velocidades y tiempos de viaje. El primer paquete de datos es utilizado para la construcción y calibración del modelo en el software de microsimulación PTV Vissim 9.0, y el segundo para la validación del modelo. De acuerdo con los resultados de los parámetros de performance vehicular (demora, tiempo de viaje y velocidad) y peatonal (tiempo de viaje, velocidad) de la situación actual y lista de verificaciones, se realiza la propuesta de solución. Del mismo modo, a una proyección a 15 años se estima su funcionalidad en el mediano plazo. Se concluye que la propuesta de mejora a través de la implementación del Ramp Meter, basado en el algoritmo AMS-AG, ofrece mejoras en relación con el performance vehicular, sin embargo, pierde rendimiento a medida que incrementa el flujo vehicular. Cumple con el objetivo principal de la investigación de reducirse los tiempos de viaje vehicular. Por último, se afirma que la inversión económica que demanda es una alternativa viable y rentable. / Vehicle congestion in Lima is chaotic given the lack of road infrastructure and the rapid increase in the number of vehicles. In contrast, this research proposes to implement the intelligent traffic light system, Ramp Meter, based on the AMS-AG algorithm, to reduce vehicle travel times at the Habich roundabout, in San Martin de Porres district. The proposed methodology consists of the collection of static data and registering a checklist. Then, we proceed with taking input data to build the model, such as flows, speed and travels times. The first data packet is used for the construction and calibration of the model in the PTV Vissim 9.0 microsimulation software, and the second one is used for model validation. According to the results of the parameters of vehicular performance (delay, travel time and speed) and pedestrian (travel time and speed) of the current situation and checklist, the proposed solution for roundabout is made. Similarly, at a projection to 15 years its functionality is estimated in the medium term. It is concluded that the improvement proposal through the implementation of the Ramp Meter, based on the AMS-AG algorithm, offers improvements in relation to vehicle performance, however, it loses performance as vehicle flow increases. It fulfills the main objective of the research to reduce vehicle travel times. Finally, it is stated that economic investment that it demands is viable and profitable alternative. / Tesis

The purpose of this research was to develop an algorithm that could predict ramp meter wait time at metered freeway on-ramps throughout the state of Utah using existing loop detector systems on the ramps. The loop detectors provided data in 60-second increments that include volume, occupancy, and the metering rate. Using these data sources, several ramp meter queue length algorithms were applied; these predicted queue lengths were then converted into wait times by using the metering rate provided by the detector data. A conservation model and several variations of a Kalman filter model generated predicted queue lengths and wait times that were compared to the observed queue lengths. The Vigos model—the model that yielded the best results—provided wait time estimates that were generally within approximately 45 seconds of the observed wait time. This model is simple to implement and can be automated for the Utah Department of Transportation (UDOT) to provide wait time estimates at any metered on-ramp throughout the state.

Ramp metering, one of the most effective solutions for improving motorway traffic flows, is playing increasingly important role in traffic management systems. Because of its capability to handle nonlinear and non-stationary problems, fuzzy logic based ramp metering algorithms have been always considered as an extremely suitable control measures to handle a complex nonlinear traffic system. This thesis proposes a genetic fuzzy approach to design a traffic-responsive ramp control algorithm for an isolated onramp. For a local ramp meter algorithm, the problem could be described as the inflow optimization of on-ramp, based on the evaluation of motorway traffic condition. If the inflow of on-ramp is considered as the decision variable, the ramp control problem could be treated as a nonlinear optimization problem of maximizing the evaluation function. The adaptive genetic fuzzy approach is actually a control approach to maximize the inflow of on-ramp under the restriction of evaluation function. In this thesis, a well-known fuzzy logic based ramp metering algorithms developed by Bogenberger is introduced and implemented with an on-ramp congestion model of Constellation Drive Interchange in a stochastic microscopic traffic simulator, Aimsun. To improve the performance of fuzzy control system, genetic algorithm is applied to tune the parameterized membership function of each fuzzy input to maintain the flow density of motorway blow the estimated congestion density. The performances of the genetic fuzzy logic control ramp metering are compared with FLC (fuzzy logic control) ramp metering by means of the percentage change of TTT (Total Travel Time) based on no control condition in Aimsun. The simulation results show the genetic fuzzy ramp metering has a more significant improvement on TTT and more strong stability to maintain system flow density than FLC ramp metering.