Estimation of traffic energy consumption based on macro-micro modelling with sparse data from Connected and Automated Vehicles

Traffic energy consumption estimation is significant for the sustainable transportation. However, it is difficult to directly employ macro traffic flow data to accurately estimate the traffic energy consumption due to many traffic energy consumption models need second-by-second vehicle trajectory. To solve this problem, this paper proposes a traffic energy consumption model based on the macro-micro data, which the macro data derived from the fixed-location sensors and sparse micro data derived from the Connected and Automated Vehicles (CAVs). The completed vehicle trajectories are constructed by the nonparametric kernel smoothing algorithm and variational theory. To test the performance of the proposed method, the Next Generation Simulation micro (NGSIM) dataset and Caltrans Performance Measurement System macro dataset obtained from the same road and time are used. The results indicate that the proposed method not only can reflect the characteristics of traffic kinematic waves caused by traffic congestion, but also minimize the errors generated by the macro-micro transformation. In addition, it can significantly improve the accuracy of energy consumption estimation.

Automated summary

This paper proposes a traffic energy consumption model based on the macro-micro data, which combines data from fixed-location sensors and Connected and Automated Vehicles (CAVs) to accurately estimate traffic energy consumption. The model constructs vehicle trajectories using nonparametric kernel smoothing algorithm and variational theory. Experimental results show that the proposed method not only reflects the characteristics of traffic kinematic waves caused by congestion, but also minimizes errors caused by the macro-micro transformation, leading to significantly improved accuracy in energy consumption estimation.