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Editorial Briefing
Active traffic management: adaptive traffic signal control

Jan 2014

Active traffic management: adaptive traffic signal control

Active traffic management (ATM) uses advanced technologies (computing, communication, and electronics) and traffic management centers to improve roadway traffic flow. Adaptive traffic signal control is an ATM solution for reducing traffic congestion through intersection signal (traffic light) optimization using real-time data. The essential components of the control system are roadside traffic sensors, a central computer (control center), traffic-signal controllers at the intersections, and a fiber-optic or wireless communication system. In recent years, Los Angeles and New York City have made significant investments in upgrading their traffic signals to adaptive control. See also: Active traffic management; Data communications; Highway engineering; Optimal control theory; Optimization; Traffic-control systems; Ubiquitous transportation network sensors

Editorial Briefing
Potential impacts of self-driving cars

Jan 2017

Potential impacts of self-driving cars

In the short term, the future of autonomous-vehicle technology is hard to unravel, but two reports (May 2017) offer some insight into what we might expect. A multidisciplinary research team from eight universities reported in the e-print repository arXiv how automated vehicles affect traffic flow. The research team found that on a test track, which mimicked a single-lane stretch of road, when one out of 21 or 22 vehicles (about 5 percent) was automatically controlled, researchers could eliminate stop-and-go waves, known as "phantom traffic jams." The team accomplished this by adjusting the automated vehicle’s (AV) speed and distance from the vehicle in front of it, and by estimating the average speed of the vehicles in front of the AV. In addition to controlling traffic flow, having an AV in the mix reduced braking and fuel consumption. Based on the simplicity of their control strategy, the researchers concluded that even though self-driving cars are years away from acceptance, implementing similar controls in available technology, such as adaptive cruise control, intelligent infrastructure, and connected vehicles, could reduce congestion in traffic flow. See also: Connected vehicles; Intelligent transportation systems; Intelligent vehicles and infrastructure; Intervehicle communications; Self-driving cars; Transportation engineering

Editorial Briefing
Simulations of pedestrian behavior applied to traffic management

Jan 2015

Simulations of pedestrian behavior applied to traffic management

The flow of pedestrians through public spaces and thoroughfares is frequently a concern of architects, engineers, and planners, as well as building owners and operation managers, who may want to identify potential traffic bottlenecks, reduce congestion, and improve safety. Commercially available pedestrian software models have become highly useful tools for those purposes: They can simulate and graphically present the movement of individuals, small groups of people, and large crowds for analysis. Simulation models do not provide specific answers to pedestrian traffic problems. Instead, they allow users to test various scenarios to see what approaches might work best. For example, a transportation engineer designing a train platform might run simulations for stairway placement to optimize pedestrian clearance times. See also: Architectural engineering; Computer-aided engineering; Computer graphics; Model theory; Railway engineering; Simulation; Software; Transportation engineering

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