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Register a new userComplex Fundamental Diagram of Traffic Flow in the Deep Lefortovo Tunnel (Moscow)
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The fundamental diagram for tunnel traffic is constructed based on the empirical data collected during the last two years in the deep long branch of the Lefortovo tunnel located on the 3$^text{rd}$ circular highway of Moscow. This tunnel of length 3 km is equipped with a dense system of stationary radiodetetors distributed uniformly along it chequerwise at spacing of 60 m. The data were averaged over 30 s. Each detector measures three characteristics of the vehicle ensemble; the flow rate, the car velocity, and the occupancy for three lanes individually. The conducted analysis reveals an original complex structure of the fundamental diagram.
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In recent years, several approaches for modelling pedestrian dynamics have been proposed and applied e.g. for design of egress routes. However, so far not much attention has been paid to their quantitative validation. This unsatisfactory situation belongs amongst others on the uncertain and contradictory experimental data base. The fundamental diagram, i.e. the density-dependence of the flow or velocity, is probably the most important relation as it connects the basic parameter to describe the dynamic of crowds. But specifications in different handbooks as well as experimental measurements differ considerably. The same is true for the bottleneck flow. After a comprehensive review of the experimental data base we give an survey of a research project, including experiments with up to 250 persons performed under well controlled laboratory conditions. The trajectories of each person are measured in high precision to analyze the fundamental diagram and the flow through bottlenecks. The trajectories allow to study how the way of measurement influences the resulting relations. Surprisingly we found large deviation amongst the methods. These may be responsible for the deviation in the literature mentioned above. The results are of particular importance for the comparison of experimental data gained in different contexts and for the validation of models.
The 3D fundamental diagrams and phase portraits for tunnel traffic is constructed based on the empirical data collected during the last years in the deep long branch of the Lefortovo tunnel located on the 3rd circular highway in Moscow. This tunnel of length 3 km is equipped with a dense system of stationary ra-diodetetors distributed uniformly along it chequerwise at spacing of 60 m. The data were averaged over 30 s. Each detector measures three characteristics of the vehicle ensemble; the flow rate, the car velocity, and the occupancy for three lanes individually. The conducted analysis reveals complexity of phase states of tunnel traffic. In particular, we show the presence of cooperative traffic dynamics in this tunnel and the variety of phase states different in properties. Besides, the regions of regular and stochastic dynamics are found and the presence of dynamical traps is demonstrated.
During the attempt to line up into a dense traffic people have necessarily to share a limited space under turbulent conditions. From the statistical point view it generally leads to a probability distribution of the distances between the traffic objects (cars or pedestrians). But the problem is not restricted on humans. It comes up again when we try to describe the statistics of distances between perching birds or moving sheep herd. Our aim is to demonstrate that the spacing distribution is generic and independent on the nature of the object considered. We show that this fact is based on the unconscious perception of space that people share with the animals. We give a simple mathematical model of this phenomenon and prove its validity on the real data that include the clearance distribution between: parked cars, perching birds, pedestrians, cars moving in a dense traffic and the distances inside a sheep herd.
In this paper, urban traffic is modeled using dual graph representation of urban transportation network where roads are mapped to nodes and intersections are mapped to links. The proposed model considers both the navigation of vehicles on the network and the motion of vehicles along roads. The roads capacity and the vehicle-turning ability at intersections are naturally incorporated in the model. The overall capacity of the system can be quantified by a phase transition from free flow to congestion. Simulation results show that the systems capacity depends greatly on the topology of transportation networks. In general, a well-planned grid can hold more vehicles and its overall capacity is much larger than that of a growing scale-free network.
Mitigating traffic congestion on urban roads, with paramount importance in urban development and reduction of energy consumption and air pollution, depends on our ability to foresee road usage and traffic conditions pertaining to the collective behavior of drivers, raising a significant question: to what degree is road traffic predictable in urban areas? Here we rely on the precise records of daily vehicle mobility based on GPS positioning device installed in taxis to uncover the potential daily predictability of urban traffic patterns. Using the mapping from the degree of congestion on roads into a time series of symbols and measuring its entropy, we find a relatively high daily predictability of traffic conditions despite the absence of any a priori knowledge of drivers origins and destinations and quite different travel patterns between weekdays and weekends. Moreover, we find a counterintuitive dependence of the predictability on travel speed: the road segment associated with intermediate average travel speed is most difficult to be predicted. We also explore the possibility of recovering the traffic condition of an inaccessible segment from its adjacent segments with respect to limited observability. The highly predictable traffic patterns in spite of the heterogeneity of drivers behaviors and the variability of their origins and destinations enables development of accurate predictive models for eventually devising practical strategies to mitigate urban road congestion.
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