tions and frequent stops at intersections. However, low traffic and continuous progression along streets do not guarantee the lowest fuel consumption and emissions. Excessive speeding, which may occur on roads with low traffic, may cause increased emissions for several pollutants. The best flow of traffic on arterial streets, in terms of fuel consumption and emissions, is the one with the fewest stops, shortest delays, and moderate speeds maintained throughout the commute (1).One of the ways to reduce excessive stop-and-go driving on urban streets is to optimize signal timings. Historically, signal timing optimization tools were developed to reduce delays and stops experienced by urban drivers. The concept of optimizing signal timings to reduce fuel consumption and emissions was first addressed by Robertson et al. (2). However, at that time traffic was simulated by macroscopic and analytical tools, and individual driving behavior was not considered. Similarly, the relationship between traffic activity, fuel consumption, and vehicular emissions, which was applied to all vehicles, was a simplistic and linear relationship (2).In recent years powerful tools for traffic modeling, fuel consumption, and emissions modeling have been developed. Microscopic simulation tools, such as VISSIM, have been used for more than a decade to model individual traffic behavior (3). Similarly, emissions models, such as the comprehensive modal emission model (CMEM), were developed to estimate second-by-second emissions of individual vehicles based on modes of a common driving cycle (4). These two types of microscopic models were coupled to estimate instantaneous emissions based on second-by-second activities of individually behaved vehicles (5-7).However, signal timing optimization models have been developed that now use microscopic traffic models to evaluate and improve the quality of signal timings (8,9). Researchers have reported that these new signal optimization tools generate signal timings that reduce delays and stops when compared with the ones generated by macroscopic optimization tools (10). However, no research has been performed that integrates all these new microscopic tools in order to find the best signal timings that would minimize fuel consumption and emissions. The research reported here aims to fill that gap in existing practice by integrating a microscopic traffic simulator, a comprehensive microscopic emission estimation model, and a stochastic signal optimization tool to provide signal timings that minimize fuel consumption and vehicular emissions. BACKGROUNDIn previous decades, many researchers have evaluated the effects of traffic signal timings on the environment (11-18). Effects are evaluated through an investigation of the amount of fuel consumption

Genetic algorithm optimizations of traffic signal timings have been shown to be effective, continually outperforming traditional optimization tools such as Synchro and TRANSYT-7F. However, their application has been limited to scholarly research and evaluations. Only one tool has matured to a commercial deployment: direct CorSim optimization, a feature of TRANSYT-7F. A genetic algorithm formulation, VisSim-based genetic algorithm optimization of signal timings (VISGAOST), is presented; it builds on the best of the recorded methods by extending their capabilities. It optimizes four basic signal timing parameters with VisSim microsimulation as an evaluation environment. The program brings new optimization features not available in the direct CorSim optimization, such as the optimization of phasing sequences, multiple coordinated systems and uncoordinated intersections, fully actuated isolated intersections, and multiple time periods. The formulation has two features that enhance and reduce computational time: optimization resumption and parallel computing. The program has been tested on two VisSim networks: a hypothetical grid network and a real-world arterial of actuated–coordinated intersections in Park City, Utah. The results show that timing plans optimized by the genetic algorithm outperformed the best Synchro plans in both cases, reducing delay and stops by at least 5%.

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a b s t r a c tTwo-dimensional multi-objective optimizations have been used for decades for the problems in traffic engineering although only few times so far in the optimization of signal timings. While the other engineering and science disciplines have utilized visualization of 3-dimensional Pareto fronts in the optimization studies, we have not seen many of those concepts applied to traffic signal optimization problems. To bridge the gap in the existing knowledge this study presents a methodology where 3-dimensional Pareto Fronts of signal timings, which are expressed through mobility, (surrogate) safety, and environmental factors, are optimized by use of an evolutionary algorithm. The study uses a segment of 5 signalized intersections in West Valley City, Utah, to test signal timings which provide a balance between mobility, safety and environment. In addition, a set of previous developed signal timing scenarios, including some of the Connected Vehicle technologies such as GLOSA, were conducted to evaluate the quality of the 3-dimensional Pareto front solutions. The results show success of 3-dimensinal Pareto fronts moving towards optimality. The resulting signal timing plans do not show large differences between themselves but all improve on the signal timings from the field, significantly. The commonly used optimization of standard single-objective functions shows robust solutions. The new set of Connected Vehicle technologies also shows promising benefits, especially in the area of reducing inter-vehicular friction. The resulting timing plans from two optimization sets (constrained and unconstrained) show that environmental and safe signal timings coincide but somewhat contradict mobility. Further research is needed to apply similar concepts on a variety of networks and traffic conditions before generalizing findings.