He fitted the THW data collected on interstate 279 during the morning peak hour with a normal distribution, a Pearson distribution, a lognormal distribution and a shifted lognormal distribution. Mei and Bullen (1993) investigated the applicability of the THW distribution model in a high-density traffic flow environment. The test results show that the double-shifted negative exponential distribution model can fit most of the data. Griffiths and Hunt (1991) proposed a double-shifted negative exponential distribution model to describe the THW distribution by analyzing data from 86 groups of single-lane THW samples collected in the UK with a total of 82,388 cases. Cowan evaluated the model by collecting 1,324 THW samples on a two-lane highway in both directions in Sydney, and the results showed that the M3 model could fit the naturalistic driving data well. Based on Berthon distribution, Cowan (1975) obtained the M3 distribution by improving the distribution model. By analyzing data from Interstate 71 in Ohio, USA, Tolle verified that the lognormal distribution fits the THW better than the other two distributions. Tolle (1976) explored the fit performance of the composite exponential distribution, the Pearson Type III distribution and the lognormal distribution to THW. Based on Buckley’s study, Wasielewski (1979) used a semi-Poisson model to explore the relationship between the variation of THW and traffic flow of following vehicles on highways. Therefore, many researchers use distribution model to describe THW.īuckley (1968) proposed the semi-Poisson model and verified its good fitting performance using peak-hour THW data from a 6-lane highway in Sydney. That’s why it is difficult to obtain THW characteristics of drivers. However, the observed THW values are not constant even if the environment is completely identical due to factors such as differences of driver’s ability in perceptions, data processing, taking actions and heterogeneity of vehicle performance ( Li and Chen, 2017). What’s more, it can also provide indicator references for longitudinal-related ADAS and AD standards, e.g. fault tolerant time interval and fault reaction time interval and controllability (C) to avoid a specified harm or damage ( Mao et al., 2021 Zhu et al., 2020 Chen et al., 2021 Shangguan et al., 2021). In concept phased of Functional Safety (FuSa) development, THW distribution can be used to determine key time indicators, e.g. During Safety of The Intended Functionality (SOTIF) development, THW distribution can provide references for requirement metric extraction, scenario-based testing and quantitative evaluation. In the traffic safety domain, time headway (THW) is defined as the elapsed time between successive vehicles in a single lane of traffic ( Biswas et al., 2021).
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Published in Journal of Intelligent and Connected Vehicles. Copyright © 2022, Ruilin Yu, Yuxin Zhang, Luyao Wang and Xinyi Du.
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