Vehicle Type–Specific Headway Analysis Using Freeway Traffic Data

Abstract

The distribution of time headway has long been a research topic because headway between vehicles is an important flow characteristic affecting safety, level of service, driver behavior, and capacity of a transportation system. However, almost all reported research on headway is focused on mixed vehicular flow without considering the difference in headway with respect to different vehicle types. In this paper, with the use of 1-day freeway traffic data from Interstate 35 in Austin, Texas, four time headway types based on different combinations of leading vehicles and following vehicles–-car–truck, truck–car, truck–truck, and car–car–-are analyzed at different traffic flow levels. Statistical comparisons between each vehicle type–specific headway and mixed vehicle–type headway are made to verify their significant differences. The paper demonstrates the necessity of vehicle type–specific headway study. The results of the paper not only identify qualitative differences between the vehicle type–specific headways and distributions, but also can be used as a reference for practical applications, especially for improving microscopic traffic simulation models.

Headway distribution models of two-lane roads under mixed traffic conditions: a case study from India

“…The test result showed that the double displaced negative exponential distribution model provided the best fit to the urban freeway headway data and shifted lognormal distribution fits the general purpose lane headways very well. Besides, there are several other mixed models that have been developed and tested over the years in predicting headway distributions; they are respectively combined normal distribution and shifted negative exponential distribution, combined negative exponential distribution and shifted negative exponential distribution [1], generalized queuing model (GQM) [16] and semi Poisson distribution etc. Some mixed distributions were developed based on the assumption that headway consists of two components; following and free.…”

“…The reason behind this decision was due to certain advantages that the K-S test offers over the chisquare test; K-S test can use data with a continuous distribution and there is no minimum frequency per test interval [1]. The K-S test statistic is calculated by determining the difference between the cumulative percentage of the measured frequency and the cumulative percentage of the expected frequency.…”

“…Study on this parameter is important particularly in context to capacity analysis, safety studies, car following and lane changing behavior modelling and level of service evaluation [1]. Capacity of a roadway and saturation flow rate of an intersection is the reciprocal of minimum time headway; in a way that rear end collision does not occur even in the event of sudden stop of leading vehicles.…”

A cellular automata model for car–truck heterogeneous traffic flow considering the car–truck following combination effect

“…However, the above studies [3][4][5]8,9] did not recognize that the driving behavior also depends on the lag vehicle type (car or truck). Ye's study [10] first explores the impact of the lag vehicle type on traffic flow. He concluded that the four types of car-truck following combination should be taken into account in the study of the car-truck heterogeneous traffic flow: car-following-car (CC), car-following-truck (CT), truck-following-car (TC) and truck-following-truck (TT).…”

Analysis of vehicle headway distribution on multi-lane freeway considering car–truck interaction

“…They classified the vehicles into four types: car-car (C-C), car-truck (C-T), truck-car (T-C), and truck-truck (T-T) and built the proposed distribution models for them, but they did not study the relationship with the environmental factors. 21 Weng et al also studied the headway distribution by vehicle types for expressway work zone. The headway type is classified as same as Ye and Zhang.…”