Browsing by Subject "Red light running"
Now showing 1 - 2 of 2
Results Per Page
Sort Options
Item Automated Enforcement of Red-Light Running & Speeding Laws in Minnesota: Bridging Technology and Public Policy(Center for Transportation Studies, University of Minnesota, 2009-10) Adams, John S.; Vandrasek, Barbara J.This report reviews the use of technology for automated enforcement of traffic laws around the world and across the United States, especially red-light running and speeding, with a focus on Minnesota. Automated enforcement to tag red-light runners and speeders is common internationally and domestically. The report reviews evidence and suggests how Minnesota can use automated enforcement to improve safety, cut deaths and injuries, and reduce the appalling annual cost of property damage due to motor vehicle crashes. Citizens of libertarian bent resent laws requiring that they protect themselves while allowing society to absorb extraordinary costs when they or others are injured or killed in traffic crashes. Others express fundamental resentment of “intrusive government” at all levels and the traffic rules governments impose. Thus, linking automated enforcement technology with effective and politically acceptable public policy presents genuine public safety and public-health challenges. Chapters summarize the high cost of crashes; problems and behaviors linked to red-light running and speeding; case studies of automated enforcement of traffic laws; the short-lived Minneapolis “Stop-on-Red” program; the yellow-light phase controversy; Minnesota litigation ending the Minneapolis program; diverse political cultures and debates across the U.S. concerning automated enforcement; and best practices for implementing automated enforcement legislation and programs. Five appendices summarize legal issues surrounding automated enforcement of traffic laws.Item Estimation of Crossing Conflict at Signalized Intersection Using High-Resolution Traffic Data(Minnesota Department of Transportation, 2017-03) Liu, Henry X.; Davis, Gary A.; Shen, Shengyin; Di, Xuan; Chatterjee, IndrajitThis project explores the possibility of using high-resolution traffic signal data to evaluate intersection safety. Traditional methods using historical crash data collected from infrequently and randomly occurring vehicle collisions can require several years to identify potentially risky situations. By contrast, the proposed method estimates potential traffic conflicts using high-resolution traffic signal data collected from the SMART-Signal system. The potential conflicts estimated in this research include both red-light running events, when stop-bar detectors are available, and crossing (i.e. right-angle) conflicts. Preliminary testing based on limited data showed that estimated conflict frequencies were better than AADT for predicting frequencies of angle crashes. With additional validation this could provide a low-cost and easy-to-use tool for traffic engineers to evaluate traffic safety performance at signalized intersections.