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车辆间隙定义为到达路段某特定点相邻车辆的间隔.本文研究了非双车道异质混合交通情形下的车辆间隙模型，此类交通流在诸如印度等一些发展中国家普遍存在.其特征是具有大量“零间隙”的情况，这主要由给定车道宽度下车辆同时到达的情形所致.其另一特征是较大车头时距带来的尾区数据量偏大.然而，一些学者运用轻尾分布对车辆间隙进行建模并得到了满意的结果.原因主要是：(1)尾区数据融入单个面元；(2)分布拟合时卡方检验等方法具有一定的局限性.此外，一些学者还建议对同一范围的交通流数据采用其他一些分布进行拟合，产生了分布选择的分歧.同时，可表征任何分布拟合状况的面元在间隙模型中的作用逐渐减小.因此，本文就以上问题对现有间隙模型的研究进行进一步和标准化的分析.本文还分析了两种具有较好尾模型特性的广义Pareto分布(GP)、广义极值分布(GEV)、其他几种传统分布对550 vph至4 100 vph流量范围内车辆间隙建模，且使用基于区域和基于距离检验的方法进行分布拟合检验.结果表明，无论使用哪种检验方法，广义Pareto分布对于大于1 500 vph流量的间隙数据在整体和尾区均能得到较好的拟合效果.广义极值分布在使用基于区域的检验方法时对于大于1 500 vph流量的间隙数据产生较好的拟合效果.

Time Gap Modeling under Mixed Traffic Condition:A Statistical Analysis

This paper attempts to model vehicular time gap, which is defined as the time interval between any two successive arrivals of vehicles at a reference point of measurement on a road segment.Such an approach is justified under the non lane based heterogeneous traffic conditions prevailing in developing countries such as India, characterized by many “zero” time gaps due to simultaneous arrivals within a given road width.In addition, time gap data are characterized by a significant amount of data in the tail region due to long headways.Nevertheless, many researchers of time gap modeling have used light tailed distributions that modeled time gaps satisfactorily due to two reasons:(a) The tail data was merged into a single bin; and (b) goodness of fit tests such as the Chi square test, which has many limitations, were used.Further, some researchers have suggested different distributions for the same range of traffic flows, leading to ambiguity in distribution selection.In addition, bin size, which dictates the degree of fit of any distribution, has been ascribed very less importance in time gap modeling.Hence, this paper tries to consolidate and standardize the existing research in time gap modeling research by addressing all these issues.Two new distributions, namely Generalized Pareto (GP) and Generalized Extreme Value (GEV) with better tail modeling properties, have been proposed along with other conventional distribution to model vehicular time gaps over a wide range of flow from 550 vph to 4100 vph.Two types of goodness of fit tests, namely Area based and Distance based tests, have been used.It has been found from the study that GP distribution fits the time gap data well (overall and tails) up to a flow range of 1500 vph based on both kinds of tests, and GEV fits the data well for the flow levels above 1500 vph based on the area test only.