Computing turn delay in city road network with GPS collected trajectories

<span class="hit">In</span> this paper, we aim to mine <span class="hit">turn</span> <span class="hit">delay</span> at different times and <span class="hit">turn</span> types <span class="hit">in</span> <span class="hit">city</span> <span class="hit">road</span> <span class="hit">network</span> based on personal <span class="hit">GPS</span> collected trajectories. We provide a method to effectively solve the problem for <span class="hit">computing</span> <span class="hit">turn</span> <span class="hit">delay</span>. By using this method, we can rapidly process massive trajectory data, to explore and predict <span class="hit">turn</span> <span class="hit">delay</span> <span class="hit">in</span> <span class="hit">city</span> <span class="hit">road</span> <span class="hit">network</span>. Through map-matching and pre-processing work for trajectory data, we firstly extract <span class="hit">turn</span> <span class="hit">delay</span> records from the time that people pass across a <span class="hit">road</span> intersection. Limited by the range of trajectory collection, these <span class="hit">turn</span> <span class="hit">delay</span> records cannot cover all <span class="hit">road</span> intersection and all different times. Therefore, we secondly propose a prediction model based on Neural <span class="hit">Networks</span> to handle these records. <span class="hit">In</span> this prediction model we have considered both geography neighborhood effect and topological relationship of <span class="hit">road</span> intersections. Finally, we tested the efficiency of this method through cross-validation by using 8986 trajectories derived from 165 pedestrians <span class="hit">in</span> a time period of three years. It demonstrates that the proposed method can obtain a higher accuracy of <span class="hit">turn</span> <span class="hit">delay</span> prediction than traditional methods which usually ignore topological characteristics of <span class="hit">road</span> intersections. Copyright 2011 ACM

不同上方来水模式下工程堆积体坡面的植被调控

为揭示植被格局对工程堆积体坡面水沙调控的影响，采用野外模拟径流冲刷试验，分析了 4 种上方来水模式（均 匀型、峰值前型、峰值中型和峰值后型）下坡面 5 种覆草格局（裸坡、坡顶聚集、坡中聚集、坡底聚集和带状格局）的 侵蚀特征。结果表明：水流功率与土壤剥蚀率之间相关性最高且呈极显著幂函数关系（R2 =0.47～0.72，P&lt;0.01），是描述 堆积体侵蚀动力机制的最优参数。植被格局的减流效益在 12.23%～49.62%之间，减沙效益在 12.92%～80.54%之间，减 沙效益高于减流效益；带状和坡顶聚集格局的平均减流减沙效益分别为 43.87%、58.09%和 30.55%、54.41%，显著优于 其他植被格局，在治理堆积体水土流失时应优先考虑这两种植被格局。植被格局下侵蚀泥沙中砂粒含量较对照小区减小 了 18.79%～35.80%，黏粒含量增加了 3.56%～10.69%，表明植被对砂粒的拦截效果显著；侵蚀泥沙颗粒体积分形维数主 要由黏粒体积分数决定，两者呈极显著线性相关关系（R2 =0.90，P&lt;0.01）。植被格局的砂粒富集率较对照小区相对减小， 黏粒富集率相对增加，体积分形维数增大；侵蚀泥沙中黏粒和砂粒迁移方式以团粒为主，粉粒则以单粒为主。该研究可 为工程堆积体水土流失植被防控措施的配置提供参考。</div