Study on particulate matter emissions from traffic by cellular automaton model with slow-to-start effect

TL;DR

This study uses cellular automaton models with slow-to-start rules to analyze particulate matter emissions from traffic, revealing how boundary conditions and traffic states influence emission levels.

Contribution

It introduces a simulation-based analysis of PM emissions using cellular automaton models with slow-to-start effects under different boundary conditions, highlighting factors affecting emissions.

Findings

Maximum emissions occur at metastable states under periodic conditions.

Traffic congestion significantly impacts particulate matter emissions.

Injection and removal probabilities greatly influence emission levels.

Abstract

Based on the empirical particulate emission model, we studied Particulate Matter (PM) emission of some typical cellular automata VDR model and TT model with slow-to-start rules under periodic condition and open boundary condition. By simulations, it is found that the emission of the slow-to-start rule model reaches the maximum emission at metastable state under periodic boundary condition. Under open boundary condition, the phase diagram to reflect traffic congestion is obtained. The injection probability and removal probability have a great impact on PM emissions. Moreover, the effects of motion status on emissions in the VDR model and TT model are studied under two different boundary conditions. Numerical simulation shows that the PM emission of decelerating traffic flow reaches the maximum in the congestion state under periodic boundary condition. Under the open boundary conditions with the same departure probability, the variation trend of particulate emission varies with the state of different vehicle movements. For different removal probabilities, the different maximum concentration of particulate matter is emitted even if the vehicle is in the same motion state.