Cellular automaton model simulating spatiotemporal patterns, phase transitions and concave growth pattern of oscillations in traffic flow

The Intelligent Driver Model with stochasticity – New insights into traffic flow oscillations

“…The PCF model is simulated according to [19], i.e., realisations of the analytical distributions of the displacements are added to the locations in each time step which is set equal to its time-gap parameter τ = T = 1 s (see below), so no discretisation errors incur. Figure 7 displays simulations of a platoon car-following experiment in China [26] (red symbols in the right column) where, in each row, only one of the three instability mechanisms is activated, namely string instability (top row), white acceleration noise (middle), and action points (bottom). Because of the stochastic nature, the detailed dynamics changes from run to run, so we show the growth of the speed standard deviation along the platoon for 10 realisations (right column).…”

An Extended Car-Following Model Considering the Drivers’ Characteristics under a V2V Communication Environment

“…In 1998, Helbing et al [21] pointed out that the OV model had an unrealistic acceleration (deceleration) and proposed a generalized force (GF) model based on Bando et al In 2001, Jiang et al [22] proposed a comprehensive full velocity difference (FVD) model based on the GF model, as shown in Equation (9). In addition, for the purpose of a comparative analysis, the FVD model was selected as the control model.…”

“…For the sake of safe driving, this study assumed that the maximum DSRC guarantees up to 400 m; that is, R = 200 m. Therefore, = 0.07 can be computed by Equation (2), and the value of can be adjusted according to the upper limit of the maximum length of the V2V covered region. Moreover, it is challenging to compute the analytical solution of Equations (9) and (10), so we used the Runge-Kutta method to discretize them, that is,…”

“…In conclusion, the performance of traffic flow was improved by taking drivers' characteristics into account under the V2V communication situation for car-following theory.Sustainability 2020, 12, 1552 2 of 18 by Nagel and Schreckenberg [7]. Since then, different scholars have used the cellular automata model to explore traffic phase transitions, spatiotemporal patterns of traffic flow, and mixed traffic flow (i.e., mixed bicycle traffic flow) [8][9][10]. Additionally, because the car-following model can describe complex traffic phenomena from the micro perspective, it has attracted considerable attention from scholars.…”

Reversible Transitions in a Cellular Automata‐Based Traffic Model with Driver Memory

“…Previous models simply aimed to capture vehicle physics or drivers' passive responses to brake lights, such as the slowstart or braking propagation e ects [16][17][18][19][20]. By contrast, these more recent models are based on the idea that vehicles autonomously coordinate their velocities by accelerating or decelerating, enabling them to simulate the spatiotemporal patterns and phase transitions seen in tra c ows [14,15,[21][22][23]. Although there have been several competing approaches, coordinating vehicle velocities in response to the tra c spacing appears to be an important factor in representing phase transitions and reversible/complex tra c patterns [23][24][25].…”