Vehicle Energy/Emissions Estimation Based on Vehicle Trajectory Reconstruction Using Sparse Mobile Sensor Data

Microscopic vehicle emissions models have been well developed in the past decades. Those models require second-by-second vehicle trajectory data as a key input to perform vehicle energy/emissions estimation. Due to the omnipresence of mobile sensors such as floating cars, real-world vehicle trajectory data can be collected in a large scale. However, most large-scaled mobile sensor data in practice are sparse in terms of sampling rate due to the consideration in implementation cost. In this paper, a new modal activity framework for vehicle energy/emissions estimation using sparse mobile sensor data is presented. The valid vehicle dynamic states are identified including four driving modes, named acceleration, deceleration, cruising, and idling. The best valid vehicle dynamic state with the largest probabilities is selected to reconstruct the second-by-second vehicle trajectory between consecutive sampling times. Then vehicle energy/emissions factors are estimated based on operating mode distributions. The proposed model is calibrated and validated using the Next Generation Simulation's dataset, and shows better performance in vehicle energy/emissions estimation compared with the linear interpolation model. Sensitivity analysis is performed to show the model accuracy with different time intervals. This paper provides a new methodology for vehicle energy/emissions estimation and extends the application area of sparse mobile sensor data.