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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
TL;DR: In this paper, a simple model for studying bottleneck effects of lane changing traffic and aggregate traffic dynamics of a roadway with lane-changing areas is proposed. But the model is limited to highway merging, diverging, and weaving areas.
Abstract: Frequent lane-changes in highway merging, diverging, and weaving areas could disrupt traffic flow and, even worse, lead to accidents. In this paper, we propose a simple model for studying bottleneck effects of lane-changing traffic and aggregate traffic dynamics of a roadway with lane-changing areas. Based on the observation that, when changing its lane, a vehicle affects traffic on both its current and target lanes, we propose to capture such lateral interactions by introducing a new lane-changing intensity variable. With a modified fundamental diagram, we are able to study the impacts of lane-changing traffic on overall traffic flow. In addition, the corresponding traffic dynamics can be described with a simple kinematic wave model. For a location-dependent lane-changing intensity variable, we discuss kinematic wave solutions of the Riemann problem of the new model and introduce a supply-demand method for its numerical solutions. With both theoretical and empirical analysis, we demonstrate that lane-changes could have significant bottleneck effects on overall traffic flow. In the future, we will be interested in studying lane-changing intensities for different road geometries, locations, on-ramp/off-ramp flows, as well as traffic conditions. The new modeling framework could be helpful for developing ramp-metering and other lane management strategies to mitigate the bottleneck effects of lane-changes.

170 citations

Journal ArticleDOI
TL;DR: A framework for a dynamic and automatic traffic light control expert system combined with a simulation model, which is composed of six submodels coded in Arena to help analyze the traffic problem, can control how long traffic signals should be for traffic improvement.
Abstract: Traffic congestion is a severe problem in many modern cities around the world. To solve the problem, we have proposed a framework for a dynamic and automatic traffic light control expert system combined with a simulation model, which is composed of six submodels coded in Arena to help analyze the traffic problem. The model adopts interarrival time and interdeparture time to simulate the arrival and leaving number of cars on roads. In the experiment, each submodel represents a road that has three intersections. The simulation results physically prove the efficiency of the traffic system in an urban area, because the average waiting time of cars at every intersection is sharply dropped when the red light duration is 65s and the green light time duration is 125s. Meanwhile, further analysis also shows if we keep the interarrival time of roads A, B, and C, and change that of roads D, E, and F from 1.7 to 3.4s and the interdeparture times at the three intersections on roads A, B, and C are equal to 0.6s, the total performance of the simulation model is the best. Finally, according to the data collected from RFID readers and the best, second and third best traffic light durations generated from the simulation model, the automatic and dynamic traffic light control expert system can control how long traffic signals should be for traffic improvement.

169 citations

Journal ArticleDOI
TL;DR: A car following model for electric, connected and automated vehicles based on reinforcement learning with the aim to dampen traffic oscillations caused by human drivers and improve electric energy consumption and improve travel efficiency.

168 citations

01 Jan 2009
TL;DR: The method uses 5-min flow and density values for a section of freeway and rapidly and reliably estimates key parameters such as free flow speed, capacity, critical density, congestion wave speed and jam density, which are key inputs to many macroscopic traffic simulation models.
Abstract: This paper will present a method for automated, empirical calibration of freeway traffic flow characteristics. The method uses 5-min flow and density values for a section of freeway and rapidly and reliably estimates key parameters such as free flow speed, capacity, critical density, congestion wave speed and jam density, which are key inputs to many macroscopic traffic simulation models. The method consists of data filtering, capacity identification, and approximate quantile regression steps. The method was used to calibrate a cell transmission model of Interstate-880 in San Francisco Bay Area, California, a 40-mile long urban freeway with lots of recurrent and non-recurrent congestion and with dozens of loop detector stations. The calibrated model reproduced the observed traffic congestion behavior within 9% error for performance measures VMT (vehicle miles traveled), VHT (vehicle hours traveled) and total flow. Also, the empirical results suggest that capacity, defined as the maximum observed 5-minute flow rate over several days, differs from breakdown flow, defined as the flow that is observed just before the freeway section becomes congested.

167 citations

01 Jul 2002
TL;DR: In this article, a method is developed to determine how crash characteristics are related to traffic flow conditions at the time of occurrence, and crashes are described in terms of the type and location of the collision, the number of vehicles involved, movements of these vehicles prior to collision and severity.
Abstract: A method is developed to determine how crash characteristics are related to traffic flow conditions at the time of occurrence. Crashes are described in terms of the type and location of the collision, the number of vehicles involved, movements of these vehicles prior to collision, and severity. Traffic flow is characterized by central tendencies and variations of traffic flow and speed for three different lanes at the time and place of the crash. The method involves nonlinear canonical correlation applied together with cluster analyses to identify traffic flow regimes with distinctly different crash taxonomies. A case study using data for more than 1,000 crashes in Southern California identified twenty-one traffic flow regimes for three different ambient conditions: dry roads during daylight (eight regimes), dry roads at night (six regimes), and wet conditions (seven regimes). Each of these regimes has a unique profile in terms of the type of crashes that are most likely to occur. A matching of traffic flow parameters and crash characteristics reveals ways in which congestion affects highway safety.

164 citations


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