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 4,100 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 1,500 vph based on both kinds of tests, and GEV fits the data well for the flow levels above 1,500 vph based on the area test only.     

Exploring pedestrian surrogate safety measures by road geometry at midblock crosswalks: A perspective under mixed traffic conditions

Pedestrian safety is generally assessed using frequency of crashes, based on historical data, for a given transportation facility. However, the lack of good and reliable crash data has hampered its apposite analyses and in evaluating the effectiveness of pedestrian safety programs. To overcome this gap, traffic conflict technique (TCT) which relies mainly on the observations of critical traffic situations for safety analysis were developed. However, the applicability of TCTs and related measures under varying non-lane based heterogeneous traffic conditions prevailing in countries such as India is not widely explored. This paper attempts to evaluate pedestrian safety at urban midblock crosswalk using different surrogate safety measures, including vehicle crossing speed, post encroachment time (PET), yielding compliance of driver as well as pedestrian, and conflict rate. The number of conflicts were observed to increase as the average vehicle crossing speed increases, indicating that pedestrians are extremely vulnerable while crossing the road. The PET value for the smaller vehicles, such as two-wheelers and three-wheelers, is recorded to be lower than the heavy vehicles, such as trucks and buses. With the addition of one lane, there is a significant decrease in the PET value. The average PET values for the vehicle on eight-lane divided road is less than the six-lane divided, four-lane divided, and two-lane undivided roads. Further, the yielding compliance of the driver as well as of the pedestrian depends on the crossing speed of the approaching vehicle and the type of road geometry. Further, the rate and severity of conflicts increased with a decrease in the pedestrian crossing speed. The yielding behaviour of the drivers as well as the pedestrian's yielding compliance varies by location, highlighting the effect of individual and demographic characteristics on pedestrian crossing behaviour.     

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非复合分布模型可用于分析交通流量达1 800 vph的车辆时间间隔,但并不适用于更高交通流量的情况.为解决此类问题,提出了一些基于复合分布的模型.但这类模型的参数标定过程复杂,在一定程度上限制了其应用.针对流量介于1 900 vph到4 100 vph的车辆时间间隔,本文分别采用5种复合分布模型进行分析,即指数-极值分布(EEV)、对数正态-极值分布(LEV)、威布尔-极值分布(WEV)、威布尔-对数正态分布(WLN)和指数-对数正态分布(ELN).然后采用两种方法进行拟合优度检验——基于累计函数分布检验(CDF)和双样本(Cramer-von Mises)&K样本(Anderson-Darling)检验.结果表明,在分析车辆时间间隔方面,威布尔-极值分布(WEV)是最佳的复合分布模型,在Cramer-von Mises检验和K样本Anderson-Darling检验中均具有良好的一致性.     

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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.