Arterial roadway energy/emissions estimation using modal-based trajectory reconstruction

With increased traffic congestion and concern with its impacts on fuel consumption and emissions, it is important to have reliable methods of estimating these impacts for a wide variety of traffic scenarios. Estimating traffic energy/emissions for uninterrupted flow (e.g., highways) is fairly well established; however, for roadways with interrupted flow (e.g., arterial corridors with traffic signals), the estimation problem is much more difficult. In this paper, we propose a modal-based trajectory reconstruction method that takes advantage of new wireless traffic sensors. This method first generates approximate vehicle trajectories on arterial links with traffic lights by explicitly modeling driving modes such as steady-state cruise, acceleration, deceleration, and idle. Then, these approximated trajectories are run through a microscopic energy/emissions model to estimate overall energy/emission impacts. This method has been tested on a real-world testbed arterial corridor. It is shown to estimate energy/emissions well within 10% of actual values, compared to standard speed-emission curve techniques that are only within 40% of the actual values.