Optimizing Traffic Control to Reduce Fuel Consumption and Vehicular Emissions Integrated Approach with VISSIM, CMEM, and VISGAOST

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.