Nonlinear-map model for split effect on vehicular traffic through periodic signals

We study the effects of both split and cycle time on dynamical behavior of vehicles moving through a sequence of traffic lights on a highway, where the traffic lights turn on and off periodically. The dynamical model of vehicular traffic controlled by signals is expressed in terms of a nonlinear map. The vehicle exhibits complex behavior with varying split and cycle time. The tour time between signals shows a self-similar behavior. When split s_p is lower than 0.5, vehicular traffic shows a similar behavior as that of s_p=0.5, while vehicular traffic of s_p  >0.5 is definitely different from that of _sp?0.5$s_p?0.5$. The algebraic expression among the tour time, cycle time, and split is derived.     

Clustering and maximal flow in vehicular traffic through a sequence of traffic lights

We study the maximal current (maximum traffic capacity) of vehicular traffic through a sequence of traffic lights on a highway, where all signals turn on and off synchronously. The dynamical model of vehicular traffic controlled by signals is expressed in terms of a nonlinear map, where the excluded-volume effect is taken into account. The dynamical behaviors of vehicles are clarified by analyzing traffic patterns. The clustering of vehicles varies with the cycle time of signals. The maximum current is closely connected to vehicular clustering. Clustering of vehicles is controlled by varying both split and cycle time of signals. The dependence of the maximal current on both split and cycle time is derived.     

Tour time in a two-route traffic system controlled by signals

We study the dynamic behavior of vehicular traffic in a two-route system with a series of signals (traffic lights) at low density where the number of signals on route A is different from that on route B. We investigate the dependence of the tour time on the route for some strategies of signal control. The nonlinear dynamic model of a two-route traffic system controlled by signals is presented by nonlinear maps. The vehicular traffic exhibits a very complex behavior, depending on the cycle time, the phase difference, and the irregularity. The dependence of the tour time on the route choice is clarified for the signal strategies.     

Vehicular motion in 2D city traffic network with signals controlled by phase shift

We study the dynamic behavior of vehicular traffic through the series of traffic lights controlled by phase shift in two-dimensional (2D) city traffic network. The nonlinear-map model is presented for the vehicular traffic. The city traffic network is made of one-way perpendicular streets arranged in a square lattice with traffic signals where vertical streets are oriented upwards and horizontal streets are oriented rightwards. There are two traffic lights for the movement to north or that to east at each crossing. The traffic lights are controlled by the cycle time, split, and phase shift. The vehicle moves through the series of signals on a path selected by the driver. The city traffic with a heterogeneous density distribution is also studied. The dependence of the arrival time on cycle time, split, phase shift, selected path, and density is clarified for 2D city traffic. It is shown that the vehicular traffic is efficiently controlled by the phase shift.     

Traffic states and fundamental diagram in cellular automaton model of vehicular traffic controlled by signals

We present a cellular automaton (CA) model for vehicular traffic controlled by traffic lights. The CA model is not described by a set of rules, but is given by a simple difference equation. The vehicular motion varies highly with both signals’ characteristics and vehicular density. The dependence of tour time on both cycle time and vehicular density is clarified. In the dilute limit of vehicles, the vehicular motion is compared with that by the nonlinear-map model. The fundamental diagrams are derived numerically. It is shown that the fundamental diagram depends highly on the signals’ characteristics. The traffic states are shown for various values of cycle time in the fundamental diagram. We also study the effect of a slow vehicle on the traffic flow.     

Regularization and control of irregular vehicular motion through a series of signals at disordered intervals

We study the control and regularization of irregular motion of a vehicle moving through the series of traffic signals positioned at disordered intervals. All signals are controlled by both cycle time and phase shift. The nonlinear dynamic model of the vehicular motion controlled by signals is described in terms of the stochastic nonlinear map. The vehicle exhibits a very complex behavior with varying both cycle time and strength of disordered intervals. The delay or advance of tour time is compensated by synchronizing the phase shift with disordered intervals. The irregular motion induced by the disordered configuration of signals is regularized for various values of cycle time.     

Effect of irregularity on vehicular traffic through a sequence of traffic lights

We present the stochastic nonlinear-map model of vehicular traffic controlled by irregular signals. The signal’s interval, the split of signal, and the offset time changes irregularly from signal to signal on a roadway. We study the effect of irregularity on dynamical behavior of vehicular traffic through a sequence of traffic lights. The vehicle exhibits the very complex behavior with varying cycle time. When the strength of irregularity is small, the arrival time does not change with irregularity for some values of cycle time, while it changes for other values of cycle time. The region in which the arrival time changes is expanding with increasing irregularity’s strength. The region map (phase diagram) is shown in the cycle time-irregularity’s strength space.     

Vehicular motion on a selected path in a 2d traffic network controlled by signals

We study the dynamic behavior of vehicular traffic through a series of traffic lights on selected paths in a two-dimensional (2d) traffic network. The city traffic network is made of one-way perpendicular streets arranged in a square lattice with traffic signals where vertical streets are oriented upwards and horizontal streets are oriented rightwards. A vehicle moves through the series of signals on a path selected by the driver. The selected path is one of the straight, zigzag, and random paths in a 2d traffic network. The vehicular motion on a selected path is presented by the nonlinear-map model. Vehicular traffic exhibits very complex behavior with varying selected paths, cycle times, and vehicular density. The dependence of the arrival time on cycle time, selected path, and density is clarified for 2d city traffic.     

Control of vehicular traffic through a sequence of traffic lights positioned with disordered interval

We study the dynamical behavior of vehicular traffic through a sequence of traffic lights which are positioned with inhomogeneous interval on a roadway and turn on and off periodically with the synchronized strategy. The dynamics of vehicular traffic controlled by traffic lights is described in terms of the stochastic nonlinear map. When the interval between traffic lights fluctuates highly, vehicles cannot move together with the same tour time. While vehicles can move together with the other at less inhomogeneous interval between traffic lights for specific values of cycle time. If heterogeneity of traffic-light's interval is higher, it becomes more difficult to control vehicles moving together. The phase diagram (region map) is presented for controlling the vehicular traffic.     

Vehicular traffic through a self-similar sequence of traffic lights

We study the dynamical behavior of vehicular traffic through a sequence of traffic lights positioned self-similarly on a highway, where all traffic lights turn on and off simultaneously with cycle time T_s. The signals are positioned self-similarly by Cantor set. The nonlinear-map model of vehicular traffic controlled by self-similar signals is presented. The vehicle exhibits the complex behavior with varying cycle time. The tour time is much lower such that signals are positioned periodically with the same interval. The arrival time T(x) at position x scales as (T(x)-x)∝x^d_f, where d_f is the fractal dimension of Cantor set. The landscape in the plot of T(x)−x against cycle time T_s shows a self-affine fractal with roughness exponent α=1−d_f.     

Vehicular traffic flow through a series of signals with cycle time generated by a logistic map

We study the dynamical behavior of vehicular traffic through a series of traffic signals. The vehicular traffic is controlled with the use of the cycle time generated by a logistic map. Each signal changes periodically with a cycle time, and the cycle time varies from signal to signal. The nonlinear dynamic model of the vehicular motion is presented by a nonlinear map including the logistic map. The vehicular traffic exhibits very complex behavior on varying both the cycle time and the logistic-map parameter a$a$. For a>3$a>3$, the arrival time shows a linear dependence on the cycle time. Also, the dependence of vehicular motion on parameter a$a$ is clarified.     

Effect of restart at signals on traffic flow through a series of signals

We study the effect of restart at signals on the vehicular traffic controlled by a series of signals. The Nagel–Schreckenberg model (NS model) and Fukui–Ishibashi model (FI model) are applied to the vehicular motion. In the FI model, the step-by-step acceleration is not taken into account but the acceleration effect is included in the NS model. It is shown that the difference between both models results in the restart effect at signals. The extended version of the NS model with signals is formulated by the difference equation. The restart at signals has an effective effect on the traffic flow. The fundamental diagram changes highly by the restart effect. The dependences of mean speed on the cycle time are shown.     

Vehicular motion in counter traffic flow through a series of signals controlled by a phase shift

We study the dynamical behavior of counter traffic flow through a sequence of signals (traffic lights) controlled by a phase shift. There are two lanes for the counter traffic flow: the first lane is for east-bound vehicles and the second lane is for west-bound vehicles. The green-wave strategy is studied in the counter traffic flow where the phase shift of signals in the second lane has opposite sign to that in the first lane. A nonlinear dynamic model of the vehicular motion is presented by nonlinear maps at a low density. There is a distinct difference between the traffic flow in the first lane and that in the second lane. The counter traffic flow exhibits very complex behavior on varying the cycle time, the phase difference, and the split. Also, the fundamental diagram is derived by the use of the cellular automaton (CA) model. The dependence of east-bound and west-bound vehicles on cycle time, phase difference, and density is clarified.     

Green-wave control of an unbalanced two-route traffic system with signals

We introduce the preference parameter into the two-route dynamic model proposed by Wahle et al. The parameter represents the driver’s preference for the route choice. When the driver prefers a route, the traffic flow on route A does not balance with that on route B. We study the signal control for the unbalanced two-route traffic flow at the tour-time feedback strategy where the vehicles move ahead through a series of signals. The traffic signals are controlled by both cycle time and phase shift (offset time). We find that the mean tour time can be balanced by selecting the offset time successfully. We derive the relationship between the mean tour time and offset time (phase shift). Also, the dependences of the mean density and mean current on the offset time are derived.     

Effect of speed fluctuations on a green-light path in a 2d traffic network controlled by signals

When a vehicle moves through a series of green lights, avoiding red signals in a two-dimensional (2d) city traffic network, the vehicle describes a characteristic trajectory (green-light path) and the travel time has a minimal value. The green-light path depends on the cycle time, split, signal-control strategy, and fluctuations of vehicular speed. We clarify the effect of speed fluctuations on a green-light path in a 2d traffic network controlled by signals. Even if an extremely small quantity of speed fluctuation is added, the green-light path changes greatly. It is shown that the root-mean square (RMS) of the deviation from the mean path depends highly on the cycle time. Also, the dependence of the green-light path on the speed-fluctuation strength is shown under a constant value of cycle time.     

Randomness control of vehicular motion through a sequence of traffic signals at irregular intervals

We study the regularization of irregular motion of a vehicle moving through the sequence of traffic signals with a disordered configuration. Each traffic signal is controlled by both cycle time and phase shift. The cycle time is the same for all signals, while the phase shift varies from signal to signal by synchronizing with intervals between a signal and the next signal. The nonlinear dynamic model of the vehicular motion is presented by the stochastic nonlinear map. The vehicle exhibits the very complex behavior with varying both cycle time and strength of irregular intervals. The irregular motion induced by the disordered configuration is regularized by adjusting the phase shift within the regularization regions.     

Bunching and transition of vehicles controlled by a sequence of traffic lights

We study the dynamical behavior of many vehicles with different desired velocities, moving through a sequence of traffic lights on a single-lane highway, where the traffic lights turn on and off periodically with the synchronized strategy. The dynamics of vehicular traffic controlled by traffic lights is described in terms of the nonlinear maps. For specific values of cycle time, the group (cluster) of vehicles exhibits the bunching without extending over the highway. It is found that two types of traffic states appear: the one is the bunching traffic and the other is the extended traffic. In the bunching traffic, all vehicles move together with the same tour time, while vehicles spread over the highway in the extended traffic. The dynamical transition between two traffic states occurs at specific values of cycle time. The phase diagram (region map) is presented.     

Vehicular motion through a sequence of traffic lights controlled by logistic map

We study the dynamical behavior of a single vehicle through the sequence of traffic lights controlled by the logistic map. The phase shift of traffic lights is determined by the logistic map and varies from signal to signal. The nonlinear dynamic model of the vehicular motion is presented by the nonlinear map including the logistic map. The vehicle exhibits the very complex behavior with varying both cycle time and logistic-map parameter a. For a>3, the dependence of arrival time on the cycle time becomes smoother and smoother with increasing a. The dependence of vehicular motion on parameter a is clarified.     

Green-light paths in city traffic controlled by signals

When a vehicle moves through a series of green lights with avoiding red signals in the traffic network, the travel time has a minimal value and the vehicle draws a characteristic trajectory. We study the trajectories (green-light paths) of a vehicle for various values of both cycle time and split at the synchronized and random-phase strategies. The trajectory depends highly on both signal's characteristics and control strategy. We clarify the dependence of green-light paths on both cycle time and split. At the random phase strategy, the vehicle draws a trajectory of the random walk. It is shown where the vehicle arrives if a driver selects the green-light path.     

Influences of overtaking on two-lane traffic with signals

Based on the cellular automata method (CA method), two-lane traffic flow with the consideration of overtaking is investigated. Discrete equations are proposed to describe the traffic dynamics by using the rules of CA model. Influences of signal cycle time (t_s) and vehicular density (ρ) on the mean velocity 〈v〉 and mean overtaking times 〈c〉 of the traffic flow are discussed. The effects of slow vehicles and road barricades on the traffic flow are also studied. Simulation results shows that the vehicular density and the signal cycle time have significant influences on the traffic flow. The mean velocity of the traffic flow could keep a comparatively large value when ρ≤0.45. For a certain value of ρ, 〈v〉 displays a serrated fluctuation with t_s. Therefore, there may exist a certain combination of ρ and t_s which optimizes the traffic flow efficiency. As compared with the results in Nagatani (2009) [7], the model proposed here and the simulation results which took into account the effects of signal cycle time, slow vehicles, and road barricades on the traffic flow with overtaking allowed, can reflect the situation of traffic flow in a more realistic way.