Traffic signal optimization programs have been used widely among transportation professionals. However, none of the existing computer programs can optimize all four traffic control parameters (i.e., cycle length, green split, offset, and phase sequence) simultaneously, even for undersaturated conditions. In this paper, a genetic algorithm-based signal optimization program that can handle oversaturated signalized intersections is presented. The program consists of a genetic algorithm (GA) optimizer and a mesoscopic traffic simulator. The GA optimizer is designed to search for a near-optimal traffic signal timing plan on the basis of a fitness value obtained from the mesoscopic simulator. The proposed program is compared with the newly released TRANSYT-7F version 8.1 on the basis of CORSIM simulation program. Three different demand volume levels-low, medium, and high demand-are tested. For the low-demand and high-demand volume cases, the GAbased program produced statistically better signal timing plans than did TRANSYT-7F in terms of queue time. In the case of mediumdemand volume level, the signal timing plan obtained from the GA-based program produced statistically equivalent queue time compared with TRANSYT-7F. Both programs are judged to provide superior capability for oversaturated conditions due to their queue blockage model when compared with previously available signal timing optimization software.Traffic congestion during peak periods is prevalent for most urban areas. A recent study notes urban arterial systems have experienced increasing traffic congestion (1). Thus, there is a need for effectively managing traffic signal control systems during congested or oversaturated periods. Oversaturated conditions are defined as the condition when vehicles are prevented from moving freely, either because of the presence of vehicles in the intersection itself or because of queue backup in any of the exit links of the intersection (2). Even though oversaturated conditions may last only briefly, the aftereffect may take a long time to clear.Traffic signal coordination and optimization are desirable as costeffective means of reducing urban traffic congestion, especially when additional road construction is impossible because of either high construction cost or lack of available land. Therefore, optimal traffic control plans that would maximize the operational efficiency of existing facilities should be developed and implemented. This can be achieved by maximizing the use of green time and preventing formation of queue blocking of output flows. BACKGROUND Signal OptimizationCurrent traffic signal optimization programs fall into two categories: delay-based models and bandwidth-based models. TRANSYT, a representative delay-based model, minimizes a linear combination of network-wide delay and stops by optimizing cycle length, green split, and offset. In contrast, bandwidth-based programs maximize the sum of directional bands for progression by choosing optimal phase sequence, offset, and cycle length.The limitation of exis...
A radial basis function (RBF) neural network has recently been applied to time-series forecasting. The test results of an RBF neural network in forecasting short-term freeway traffic volumes are provided. Real observations of freeway traffic volumes from the San Antonio TransGuide System have been used in these experiments. For comparison of forecasting performances, Taylor series, exponential smoothing method (ESM), double exponential smoothing method, and backpropagation neural network were also designed and tested. The RBF neural network model provided the best performance and required less computational time than BPN. It seems that RBF and ESM can be a viable forecasting routine for advanced traffic management systems. There are some tradeoffs between RBF and ESM. Although the performance of ESM is inferior to RBF, the former does not need a complicated training process or historic database, and vice versa. However, even in the best performance case, 35 percent of the forecast traffic volumes showed 10 percent or more percentage errors. This means that we cannot heavily depend on the forecast traffic volumes as long as we are utilizing the models tested. Further work is needed to provide a more reliable traffic forecasting model.
A thin viscous film flowing over a step down in topography exhibits a capillary ridge preceding the step. In applications, a planar liquid surface is often desired and hence there is a need to level the ridge. This paper investigates optimal leveling of the ridge by means of a Marangoni stress such as might be produced by a localized heater creating temperature variations at the film surface. The differential equation for the free surface based on lubrication theory and incorporating the effects of topography and temperature gradients is solved numerically for steps down in topography with different temperature profiles. Both rectangular “top-hat” and parabolic profiles, chosen to model physically realizable heaters, were found to be effective in reducing the height of the capillary ridge. Leveling the ridge is formulated as an optimization problem to minimize the maximum free-surface height by varying the heater strength, position, and width. With the optimized heaters, the variation in surface height is reduced by more than 50% compared to the original isothermal ridge. For more effective leveling, we consider an asymmetric n-step temperature distribution. The optimal n-step heater in this case results in (n+1) ridges of equal size; 2- and 3-step heaters reduce the variation in surface height by about 70% and 77%, respectively. Finally, we explore the potential of coolers and step temperature profiles for still more effective leveling.
Chapter 16 of the Highway Capacity Manual 2000 ͑HCM 2000͒ includes models and procedures for calculating capacity and delay at signalized intersections. However, the procedures do not provide estimation of the optimal cycle length which would result in the minimal intersection delay. A quick estimation method for determining the cycle length is described in Appendix A, Chap. 10 of the HCM 2000 for planning level applications. In this method, a simple equation is used to estimate the cycle length if it is not available. However, the estimated cycle length may not be the optimal cycle length from the point of view of achieving minimum intersection delay. To develop a new cycle length model, the Webster's minimum delay cycle length model is first considered. However, based on our study, Webster's minimum delay cycle length model overestimates the optimal cycle length compared to the results from the HCM 2000 delay calculation method, especially under high traffic volume conditions. After investigating three new models developed during this study, an exponential-type cycle length model is recommended. Based on a series of CORSIM simulation runs, the cycle length predicted by this model provides better results than the current quick estimation method of the HCM 2000.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.