A Simulation-based Evaluation of Connected Vehicle Technology for Emissions and Fuel Consumption

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Connected vehicle technology (CVT) is being tested at various research laboratories across the United States for its real-world applications. The aim is to properly know its benefits in order to make travel safe, reliable and environment-friendly. Although, with time, vehicles are becoming eco-friendlier and resourceful in energy consumption and utilization, majority of vehicles today still operate on fossil fuels and the resulting emissions are a by-product. Thus, most affordable cars and even trucks in future will continue to run on fossil fuels and might simultaneously be equipped with CVT features. Fossil-fuel dependent vehicles when fully equipped with CVT would need to accelerate or decelerate appropriately around critical junctions and locations of a network to avoid collisions. CVT in vehicles will be most appreciated for safety reasons at least at the roadway merge locations of the network. However, frequent accelerations and decelerations of the vehicles at merge points such as from ramp to freeway will also lead to increase in emissions and fuel consumption. This is precisely what this paper explores and investigates. A computer-based simulation exercise is carried out using an example of a ramp to Interstate 405 freeway merge location in Long Beach, California. Based on some basic but realistic assumptions for vehicular motion made in his paper, it is found that benefit from CVT in emission reduction is marginal for the three traffic conditions of free-flow, transitional period and rush-hour congestion. These findings will help engineers to prepare vehicles with a more sophisticated CVT that can provide better emission reductions and fuel savings at several ramp-to-freeway merge locations that exist across the city of Long Beach.




Chandra, S., & Camal, F. (2016). A Simulation-based Evaluation of Connected Vehicle Technology for Emissions and Fuel Consumption. In Procedia Engineering (Vol. 145, pp. 296–303). Elsevier Ltd. https://doi.org/10.1016/j.proeng.2016.04.077

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