Topic
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.
Papers published on a yearly basis
Papers
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13 Dec 2007
TL;DR: In this paper, the optimal signal parameter to cancel traffic congestion at an intersection is calculated by grasping the traffic congestion circumstances of a road leading to the intersection based on probe information collected from a probe vehicle.
Abstract: PROBLEM TO BE SOLVED: To calculate the optimal signal parameter to cancel traffic congestion at an intersection, by grasping the traffic congestion circumstances of a road leading to the intersection based on probe information collected from a probe vehicle. SOLUTION: In a traffic control center 5, probe information, including the information of the location and speed of a vehicle at every traveling time is collected, and the traffic congestion tailing location of a road leading to the intersection is grasped based on the collected probe information, and the optimal green signal time of each traffic light 6 is controlled so that traffic congestion of the intersection is solved from the traffic congestion tailing location of each road that connects to the intersection. COPYRIGHT: (C)2009,JPO&INPIT
10 citations
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10 citations
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TL;DR: The distributions of traffic mix and driver type are shown to have significant effect on intersection performance and patterns of flow, based on a minimum space rule.
Abstract: This paper presents a model for heterogeneous traffic at simple X and T-intersections of a single lane and two lane urban roads. Traffic at the intersections is controlled by a set of lights, operated to one of several fixed-time schemes. The heterogeneous traffic consists of both short (e.g. cars) and long (buses/trucks or equivalent) vehicles and is modelled, using a one-dimensional two-component cellular automaton. For intersections, we consider the implications for both homogeneous and heterogeneous traffic flows, based on a minimum space rule. For longer vehicles, this implies occupation of multiple road cells. The distributions of traffic mix and driver type are shown to have significant effect on intersection performance and patterns of flow. Example findings are presented and simple validation of the model basis is given, using available, but limited, local Dublin field data.
10 citations
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01 Oct 2014TL;DR: Methods for predicting traffic decongestion using congestion patterns, a new method of representation using branched spatiotemporal chains, and a method for measuring similarities between patterns are proposed.
Abstract: Analysis of patterns in big traffic data can predict when road congestion events will dissipate. Intelligent transportation systems (ITS) organizations or drivers have interest in these pattern-based prediction about traffic congestion. In this paper, the authors propose methods for predicting traffic decongestion using congestion patterns. First, the authors propose a new method of representation using branched spatiotemporal chains. The method describes spatiotemporal changes in congestion for multiway branched roads. Second, the authors suggest a method for measuring similarities between patterns. This method can find the historical pattern that is most similar to the current congestion pattern. It then estimates the end time for the current congestion as that of the historical pattern. The authors performed experiments to compare the similarity of the estimated end times with real decongestion times for actual congestion events.
10 citations
01 Mar 1999
TL;DR: In this paper, a traffic model that can be used in predicting traffic congestion is presented, where traffic data from a congested rural road are used to show that traffic delays and vehicle accumulations between any two generic observers located inside a road section can be predicted from the traffic counts measured at the extremes of the section.
Abstract: This report presents a traffic model that can be used in predicting traffic congestion. A study is described in which traffic data from a congested rural road are used to show that traffic delays and vehicle accumulations between any two generic observers located inside a road section can be predicted from the traffic counts that are measured at the extremes of the section. Two specifics are noteworthy about this traffic model: 1) it does not require recalibration on the day of the experiment; and, 2) it works well despite what appears to be location-specific driver behavior.
10 citations