A dilemma study of the traffic flow system emerged due to the lane-change by follower's tailgating effect

Abstract

In this study, an artificial traffic system, which is generated on a computer by utilizing the computational technique, has been developed by establishing brilliant lane-changing criteria for the Cellular Automata (CA) traffic model to figure out adequate strategies for cooperative driving that can be implemented in actual traffic systems for optimum use of existing road facilities. We investigate the flow efficiency and social dilemma, which embody the tension between the demanded road facility and the existing road facility, that emerged due to the defector drivers in a traffic flow system, who are highly aggressive in driving and impose threatening/pushing effects on their preceding while they are tailgating. The evolutionary game theory, which is one of the most efficient tools in the decision-making process, has been utilized to identify the Social Efficiency Deficit (SED), which means the dilemma strength of those games. We introduced a new lane-changing protocol for the preceding vehicle, considering the threatening effects given by the aggressive follower. This investigation explored several case studies defining various strategies for cofactors and defectors. We conducted a series of multi-agent simulations on this traffic flow system and experienced the Prisoner's Dilemma (PD) and the Quasi-Prisoner's Dilemma game with diverse dilemma strengths for four different strategies for cooperators and defectors.

Keywords: Evolutionary game; Follower’s tailgating effect; Intelligent traffic system; Social dilemma in traffic system.

Fig. 1

These schematic diagrams describe the strategies plan for the cooperator and defectors from Case I to Case IV.

Fig. 2

These diagrams, panels (a) and (b), describe the game structure of the traffic flow field and quantify the dilemma strength of this game, panel (c), for Case I.

Fig. 3

These line curves stand for the vehicle’s demography of each lane, panel (a) for lane 1, panel (b) for lane 2, and panel (a) for lane 3 for Case I.

Fig. 4

The panels (a) and (b) explain the payoff structures of the traffic flow field for metastable and congested regions respectively, and panel (a) justifies the vigorousness of social dilemma for Case II.

Fig. 5

The curves represent the total number of vehicles and the existing ratio of cooperators and defectors for each lane, (a) for lane 1, (b) for lane 2, and (c) for lane 3, in Case II.

Fig. 6

The line curves in panels (a) and (b) represent the payoff matrix of synchronizing and congested states, respectively, for Case III. And the dilemma level of this case has been justified by the panel (c).

Fig. 7

The plots in panels (a), (b), and (c) suggest the existing number of total vehicles and portion of cooperators and defectors in lane 1, lane 2, and lane 3, respectively, for Case III.

Fig. 8

The line graphs in (a) and (b) describe the game matrix of metastable and congested areas, respectively, for Case IV. The panel (c) shows the dilemma intensity of this game formed in Case IV.

Fig. 9

The line plots in diagrams (a), (b), and (c) figure out the total number of vehicles and fractions of cooperators and defectors in each lane, lane 1, lane 2, and lane 3, for Case IV.