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Traffic wave

About: Traffic wave is a research topic. Over the lifetime, 2106 publications have been published within this topic receiving 62117 citations. The topic is also known as: phantom traffic jam & ghost jams.


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Journal ArticleDOI
Boris S. Kerner1
TL;DR: In this article, a model of a heavy bottleneck is presented and the evolution of the traffic phases in congested traffic, synchronized flow and wide moving jams, is studied under a continuous non-limited increase in bottleneck strength, where the average flow rate within a congested pattern allowed by the heavy bottleneck decreases continuously up to zero.
Abstract: Spatiotemporal features and physics of vehicular traffic congestion occurring due to heavy highway bottlenecks caused for example by bad weather conditions or accidents are found based on simulations in the framework of three-phase traffic theory. A model of a heavy bottleneck is presented. Under a continuous non-limited increase in bottleneck strength, i.e., when the average flow rate within a congested pattern allowed by the heavy bottleneck decreases continuously up to zero, the evolution of the traffic phases in congested traffic, synchronized flow and wide moving jams, is studied. It is found that at a small enough flow rate within the congested pattern, the pattern exhibits a non-regular structure: a pinch region of synchronized flow within the pattern disappears and appears randomly over time; wide moving jams upstream of the pinch region exhibit a complex non-regular dynamics in which the jams appear and disappear randomly. At greater bottleneck strengths, wide moving jams merge onto a mega-wide moving jam (mega-jam) within which low-speed patterns with a complex non-regular spatiotemporal dynamics occur. We show that when the bottleneck strength is great enough, only the mega-jam survives and synchronized flow remains only within its downstream front separating free flow and congested traffic. Theoretical results presented can explain why no sequence of wide moving jams can often be distinguished in non-homogeneous traffic congestion measured at very heavy bottlenecks caused by bad weather conditions or accidents.

65 citations

Journal ArticleDOI
TL;DR: A two-dimensional cellular automaton is introduced to model the flow and jamming of vehicular traffic in cities and the dependence of the average velocity of cars on the global traffic density is investigated.
Abstract: A two-dimensional cellular automaton is introduced to model the flow and jamming of vehicular traffic in cities. Each site of the automaton represents a crossing where a finite number of cars can wait approaching the crossing from each of the four directions. The flow of cars obeys realistic traffic rules. We investigate the dependence of the average velocity of cars on the global traffic density. At a critical threshold for the density the average velocity reduces drastically caused by jamming. For the low-density regime we provide analytical results which agree with the numerical results.

64 citations

Posted Content
TL;DR: In this article, a treasure trove of detailed information can be recovered from 0-1 detector data, despite the anonymity and the spatial discreteness of the measurements, if one analyzes the data with the right tools Field data from a 5-lane freeway in Oakland, California (see Figure 1) is used to demonstrate the ideas
Abstract: Ask most commuters and they will agree that congestion has reached an intolerable level To reduce this congestion, engineers need detailed traffic information Highly detailed information is also prized by traffic scientists, as a prerequisite to improve current traffic theories Ideally, engineers and scientists would like to obtain from field data the position of each vehicle on a particular facility at every moment in time The technology to record space-time vehicle trajectories on a massive scale is in its infancy; therefore, analysts must work with much less data Many freeways are equipped with primitive sensors that can record only anonymous vehicle passages at specific locations with a time series of 0's and 1's Typically, these detectors are installed on all lanes at sites, called stations, which are spaced about 1 km apart This article will show that, despite the anonymity and the spatial discreteness of the measurements, a treasure trove of detailed information can be recovered from 0-1 detector data, if one analyzes the data with the right tools Field data from a 5-lane freeway in Oakland, California (see Figure 1) is used to demonstrate the ideas

63 citations

Journal ArticleDOI
TL;DR: In this paper, the authors proposed an approach that increases the capacity of heavily congested intersections by opening up exit lanes for left-turn traffic dynamically with the help of an additional traffic light installed at the median opening (the presignal).
Abstract: Many congested intersections have a heavy traffic volume on movements for which capacity is insufficient because of geometric limitations. An unconventional approach that increases the capacity of heavily congested intersections is presented: this approach opens up exit lanes for left-turn traffic dynamically with the help of an additional traffic light installed at the median opening (the presignal); this situation is referred to as exit lanes for left-turn (EFL) control. An optimization problem for EFL control was formulated as a mixed-integer nonlinear program, in which the geometric layout, main signal timing, and presignal timing were integrated. The mixed-integer nonlinear program was solved by transformation into a series of mixed-integer linear programs. The latter problem can be solved with the standard branch-and-bound technique. The results of extensive numerical analysis and VISSIM simulation showed that the EFL approach could increase intersection capacity and reduce traffic delay substantial...

63 citations

Journal Article
TL;DR: Models to estimate the safety of a signalized intersection on the basis of information about its traffic flow and accident history are provided and, on this basis, judge whether an intersection is unusually hazardous.
Abstract: Models to estimate the safety of a signalized intersection on the basis of information about its traffic flow and accident history are provided They are based on data from 145 intersections in Metropolitan Toronto Several insights were obtained during the development of the models First, logically sound models require that the frequency of collisions be related to the traffic flows to which the colliding vehicles belong and not to the sum of the entering flows Second, it is therefore necessary to categorize collisions by the movement of the vehicles before the collision and not by the initial impact type, as is customary Third, the relationship between collision frequency and the related traffic flows is at times unexpected in form For each of the 15 accident patterns, an equation is given to estimate the expected number of accidents and the variance using the relevant traffic flows When data about past accidents are available, estimates based on traffic flow are revised with a simple equation Several practical questions can now be answered Given the traffic flow for a signalized intersection, one can predict how many and what kinds of accidents should be expected to occur on it; one can also show the probability density function (pdf) of the estimate Knowledge of the pdf allows the determination of what an unusually high number of accidents would be on such an intersection If the traffic flow of the intersection changes from year to year, one can estimate what changes in safety should be attributed to changes in flow Also, one can correctly compare the safety of several intersections that have different flow patterns Most important, one can estimate safety when both flows and accident history are given and, on this basis, judge whether an intersection is unusually hazardous This method of estimation is recommended for accident warrants in the Manual on Uniform Traffic Control Devices

62 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202314
202237
202120
202017
201919
201822