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Identification of Black Zones on National Highways: A Case Study of an Indian City

机译:识别国家高速公路上的黑色区域:以印度城市为例

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This paper examines the relation between black zone and number of feeder roads joining the four lane, undivided, 8.5 km long National Highway. Primary and secondary data were collected about geometrical features, number of intersection created due to feeder road per kilometer, accident number/frequency and locations. To indentify the black zone on the study stretch, three methods are used such as an accident rate method, accident frequency method and accident severity. After ascertaining a black zone, a technique of negative binomial distribution with gamma distribution and prediction error minimization method was applied for identifying minimum distances in between two feeder road and/or intersection to avoid creation of black zones. The results of this study suggest that feeder network intersection characterized by left turning do in fact play a role in creating black zones other than the traffic flow and geometry. The prediction model suggests that the number of accidents reduces gradually, if feeder road traffic merges with national highway in between minimum spacing should be more than 750 m to 1 km. The performance of the model was successfully tested on the basis of three statistical measures, mean- absolute error (MAE), root mean square error (RMSE) and normalized error (NER). This work suggests that black zone can be prevented through controlling merging of left turning feeder road traffic to national highway traffic by providing minimum in-between safe spacing.
机译:本文研究了黑色区域与连接四车道,无分隔的8.5公里长的国道的支线道路数量之间的关系。收集了有关几何特征,每公里因支线公路而产生的交叉点数量,事故次数/频率和位置的主要和次要数据。为了识别研究范围内的黑色区域,使用了三种方法,例如事故率方法,事故频率方法和事故严重性。确定黑色区域后,应用具有伽马分布的负二项分布技术和最小化预测误差的方法来识别两条支路和/或交叉路口之间的最小距离,以避免产生黑色区域。这项研究的结果表明,以左转弯为特征的支线网络交叉口实际上在创建交通流量和几何形状以外的黑色区域方面发挥了作用。该预测模型表明,如果支线公路交通与国道合并,且最小间距在750 m至1 km之间,则事故数量会逐渐减少。该模型的性能已基于三种统计量度进行了成功测试,均值-绝对误差(MAE),均方根误差(RMSE)和归一化误差(NER)。这项工作表明,可以通过提供最小的安全间隔来控制左转支线道路交通与国家高速公路交通的合并,从而防止出现黑区。

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