Cellular Automata Model for Analysis and Optimization of Traffic Emission at Signalized Intersection

Traffic emission is one of the most severe issues in our modern societies. A large part of emissions occurs in cities and especially at intersections due to the high interactions between vehicles. In this paper, we proposed a cellular automata model to investigate the different traffic emissions (CO2, PM, VOC, and NOx) and speeds at a two-lane signalized intersection. The model is designed to analyze the effects of signalization by isolating the parameters involved in vehicle-vehicle interactions (lane changing, speed, density, and traffic heterogeneity). It was found that the traffic emission increases (decreases) with the increasing of green lights duration (T-g) at low (high) values of vehicles injection rate (alpha). Moreover, by taking CO2 as the order parameter, the phase diagram shows that the system can be in four different phases (I, II, III, and IV) depending on a and T-g. The transition from phase II (I) to phase III (II) is second order, while the transition from phase II to phase IV is first order. To reduce the traffic emission and enhance the speed, two strategies were proposed. Simulation results show a maximum reduction of 13.6% in vehicles' emissions and an increase of 9.5% in the mean speed when adopting self-organizing intersection (second strategy) at low and intermediate a. However, the first strategy enhances the mean speed up to 28.8% and reduces the traffic emissions by 3.6% at high a. Therefore, the combination of both strategies is recommended to promote the traffic efficiency in all traffic states. Finally, the model results illustrate that the system shows low traffic emission adopting symmetric lane-changing rules than asymmetric rules.