Optimization of Urban Traffic Microsimulation Model for Carbon Emission Reduction

Authors

Bo Liu, Beijing City Interface Technology Co., Ltd., Beijing 100045, China; School of Civil and Transportation Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, ChinaFollow
Jianxin Lin, School of Civil and Transportation Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, ChinaFollow
Yini Liu, School of Civil and Transportation Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China
Dong Zhang, CCCC Water Transportation Consultants Co., Ltd., Beijing 100007, China

Abstract

Abstract: To assess the environmental benefits of transportation management or control strategies, a method to effectively integrate the micro-traffic simulation model and the micro-vehicle emission model is proposed. VISSIM platform is used to build a case micro traffic simulation model. K-means clustering method is used to divide the driving behavior into 4 types based on the acceleration and deceleration characteristics of different speed intervals of the trajectory data, and the global parameters of the simulation model are calibrated based on the driving characteristics, which quantitatively describe the total sensitivity of the parameters and the sensitivity of the interactions between the parameters. The local parameter values are analyzed and calibrated by density-based spatial clustering of applications with noise(DBSCAN) clustering, which optimize the parameter calibration process. The simulated trajectory conditions are calculated, and motor vehicle emission simulator(MOVES) micro-emission model is localized. The emissions of HC, CO, NOx, and CO2 pollutants under different flow directions and different driving behaviors at the intersection are obtained. Research results show that the optimization effect of the simulation model is remarkable. The proposed method can accurately identify the highemission spatial locations and resolve the link between emissions and driving behavior. The application of DBSCAN cluster analysis parameter optimization value is conducive to the realization of the automatic calibration process. The calibration of global parameters reduces the χ2 of velocity distribution from 0.632 7 to 0.130 6, and the χ2 of acceleration distribution from 0.144 1 to 0.052 8, which is very important for the construction of simulation model from the environmental perspective.

Recommended Citation

Liu, Bo; Lin, Jianxin; Liu, Yini; and Zhang, Dong (2024) "Optimization of Urban Traffic Microsimulation Model for Carbon Emission Reduction," Journal of System Simulation: Vol. 36: Iss. 4, Article 7.
DOI: 10.16182/j.issn1004731x.joss.22-1531
Available at: https://dc-china-simulation.researchcommons.org/journal/vol36/iss4/7

First Page

859

Last Page

872

CLC

U491.92; TP391

Recommended Citation

Liu Bo, Lin Jianxin, Liu Yini, et al. Optimization of Urban Traffic Microsimulation Model for Carbon Emission Reduction[J]. Journal of System Simulation, 2024, 36(4): 859-872.

Corresponding Author

Lin Jianxin

DOI

10.16182/j.issn1004731x.joss.22-1531