The primary objective of this work is to explore how drivers react to flashing green at signalized intersections. Through video taping and data procession based on photogrammetry, the operating speeds of vehicles befo...The primary objective of this work is to explore how drivers react to flashing green at signalized intersections. Through video taping and data procession based on photogrammetry, the operating speeds of vehicles before and after the moment when flashing green started was compared using paired-samples T-test. The critical distances between go and stop decisions was defined through cumulative percentage curve. The boundary of dilemma zone was determined by comparing stop distance and travel distance.Amber-running violation was analyzed on the basis of the travel time to the stop line. And finally, a logistic model for stop and go decisions was constructed. The results shows that the stopping ratios of the first vehicles of west-bound and east-bound approaches are 41.3% and 39.8%, respectively; the amber-light running violation ratios of two approaches are 31.6% and 25.4%, respectively;the operating speed growth ratios of first vehicles selecting to cross intersection after the moment when flashing green started are26.7% and 17.7%, respectively; and the critical distances are 48 m and 46 m, respectively, which are close to 44 m, the boundary of dilemma zone. The developed decision models demonstrate that the probability of go decision is higher when the distance from the stop line is shorter or operating speed is higher. This indicates that flashing green is an effective way to enhance intersection safety,but it should work together with a strict enforcement. In addition, traffic signs near critical distance and reasonable speed limitation are also beneficial to the safety of intersections.展开更多
基金Supported by Shandong Provincial Natural Science Foundation(No.ZR2009AQ016)the Fundamental Research Funds for the Central Universities(No.09CX05004A)
基金Project(51208451)supported by the National Natural Science Foundation of ChinaProject(10KJB580004)supported by the Natural Science Foundation for Colleges and Universities of Jiangsu Province,ChinaProject supported by the New Century Talents Project of Yangzhou University,China
文摘The primary objective of this work is to explore how drivers react to flashing green at signalized intersections. Through video taping and data procession based on photogrammetry, the operating speeds of vehicles before and after the moment when flashing green started was compared using paired-samples T-test. The critical distances between go and stop decisions was defined through cumulative percentage curve. The boundary of dilemma zone was determined by comparing stop distance and travel distance.Amber-running violation was analyzed on the basis of the travel time to the stop line. And finally, a logistic model for stop and go decisions was constructed. The results shows that the stopping ratios of the first vehicles of west-bound and east-bound approaches are 41.3% and 39.8%, respectively; the amber-light running violation ratios of two approaches are 31.6% and 25.4%, respectively;the operating speed growth ratios of first vehicles selecting to cross intersection after the moment when flashing green started are26.7% and 17.7%, respectively; and the critical distances are 48 m and 46 m, respectively, which are close to 44 m, the boundary of dilemma zone. The developed decision models demonstrate that the probability of go decision is higher when the distance from the stop line is shorter or operating speed is higher. This indicates that flashing green is an effective way to enhance intersection safety,but it should work together with a strict enforcement. In addition, traffic signs near critical distance and reasonable speed limitation are also beneficial to the safety of intersections.
文摘针对松材线虫病的在森林传播会导致毁灭性病虫害问题,以松褐天牛在松材线虫传播过程中的媒介作用为基础,构建了基于饱和发生率、人工防治延迟时滞等非线性变化特征的生态侵染模型.模型将媒介昆虫分为易感天牛和染病天牛,通过分析饱和发生率、人工防治时滞对生态系统稳定性的影响,模拟求解并预测松材线虫病对森林生态侵染的发生趋势.案例研究的结果表明:当人工防治不存在时间延迟(τ=0)时,天牛种群将在一段时间后达到稳态,病害不会发生大规模爆发;当人工防治力度低于24%左右时,易感天牛和染病天牛的种群数量随防治呈上升趋势,无法达到防治效果;当人工防治力度超过24%左右时,防治力度越强,染病天牛的种群数量越少且减小速率越快,可以达到防治效果;人工防治延迟的临界时滞为16 d.
