SMART

A major European project aimed at delivering green traffic management systems in European cities and towns will be officially launched today (January 18) at the National Space Centre in Leicester, UK. The three-year project, known as ‘THE ISSUE’ (Transport Health Environment – Intelligent Solutions Sustaining Urban Economies), will support scientists, engineers and development agencies from several different European regions to work together, using the latest space and information technologies, to develop more effective methods of easing road congestion and improving the urban environment. It is hoped that the final research outcomes will be used to influence future policy and the implementation of traffic management systems that benefit public health and safety.

Read the full story at http://traffictechnologytoday.com/news.php?NewsID=35996.

Author: Kristina Bjoran

Many high-end cars today come equipped with brake assist systems, which help a driver use the brakes correctly depending on particular conditions in an emergency. But what if the car could apply the brakes before the driver even moved?

This is what German researchers have successfully simulated, as reported in the Journal of Neural Engineering. With electrodes attached to the scalps and right legs of drivers in a driving simulator, they used both electroencephalography (EEG) and electromyography (EMG) respectively to detect the intent to brake. These electrical signals were seen 130 milliseconds before drivers actually hit the brakes—enough time to reduce the braking distance by nearly four meters.

Seated facing three monitors in a driving simulator, each subject was told to drive about 18 meters behind a computer-driven virtual car traveling at about 100 kilometers per hour (about 60 mph). The simulation also included oncoming traffic and winding roads. When the car ahead suddenly flashed brake lights, the human drivers also braked. With the resulting EEG and EMG data, the researchers were able to identify signals that occurred consistently during emergency brake response situations.

“None of these [signals] are specific to braking,” says Stefan Haufe, a researcher in the Machine Learning Group at the Technical University of Berlin and lead author of the study. “However, we show that the co-occurrence of these brain potentials is specific to sudden emergency situations, such as pre-crash situations.” So while false positives from the signal are possible, the combination of EEG and EMG data makes a false positive much less likely.

Read the full article at http://www.technologyreview.com/biomedicine/38159/?p1=A1

Author: Keith Barry

Smartphone apps may be the key to getting people out of their cars and onto mass transit.

An interesting study of commuters in Boston and San Francisco found people are more willing to ride the bus or train when they have tools to manage their commutes effectively. The study asked 18 people to surrender their cars for one week. The participants found that any autonomy lost by handing over their keys could be regained through apps providing real-time information about transit schedules, delays and shops and services along the routes.

Though the sample size is small, the researchers dug deep into participants’ reactions. The results could have a dramatic effect on public transportation planning, and certainly will catch the attention of planners and programmers alike. By encouraging the development of apps that make commuting easier, transit agencies can drastically, and at little cost, improve the ridership experience and make riding mass transit more attractive.

Read the full article at http://www.wired.com/autopia/2011/04/how-smartphones-can-improve-public-transit/

By Tyler C. Folsom at QUEST Integrated, Inc.

Abstract: The trends of Personal Rapid Transit (PRT) and Automated Highways are converging. The result may be a new transportation mode built around robotic vehicles. This paper outlines how technology can transform transportation, making it more convenient, safer, more sustainable and less subject to congestion. Such a system could utilize existing infrastructure, but split highway lanes in half with vehicles less than a meter wide. This paper presents lessons from several relevant vehicles that the author’s research teams have worked on. It describes open source work in progress and invites participation from other researchers.

Read the full paper at http://www.enviroteach.com/RoboticTransportationMode.pdf