Car-following theory of steady-state traffic flow using time-to-collision

Jin, Sheng; Huang, Zhiyi; Tao, Pengfei; Wang, Dianhai

doi:10.1631/jzus.a1000518

Cited by 25 publications

(11 citation statements)

References 30 publications

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“…To overcome this weakness, TTC (time to collision) was introduced into the basic GMR model. Then it was widely used in micro driving state prediction models [2]. However, CF behaviour is not a mechanical process.…”

Section: Introductionmentioning

confidence: 99%

The Impact of Traffic Environmental Vision Pressure on Driver Behaviour

Chen

Wang

2018

Journal of Advanced Transportation

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Car-following (CF) and lane-changing (LC) behaviours are basic components in driving process. Previous models described them as physical processes with vehicle dynamics and physical criteria. However, drivers’ decisions are greatly influenced by their subjective vision information of various traffic environment elements. To solve this problem, we propose a new concept of traffic environmental vision pressure to explain these two behaviours. The pressure source consists of two parts: nearby vehicles and infrastructures. Pressure models were built to quantify the impact of traffic and roadside infrastructures on these two behaviours. 103 field tests (53 LC and 50 CF) carried out by 40 drivers were conducted to test and calibrate the models. Drivers’ psychological data and vehicle data were collected and postprocessed. Results showed positive relationship between drivers’ psychological stress and vision pressure, which verified the assumption that traffic environmental vision information would have certain effect on driver behaviour. Quantitative thresholds of pressure value were also given and explained with test data. It is concluded that the traffic environmental vision pressure in CF and LC behaviours is quite different, and higher pressure has more impact on behaviour change. We believe that these results will be helpful to study the micro driver behaviour.

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Section: Introductionmentioning

confidence: 99%

The Impact of Traffic Environmental Vision Pressure on Driver Behaviour

Chen

Wang

2018

Journal of Advanced Transportation

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“…Road infrastructure and traffic volume have dramatically increased over the past decades in many countries, resulting in a considerable increase in traffic crashes (Saenghaengtham and Kanongchaiyos, 2006;Hu et al, 2008;Jin et al, 2011;Luoma and Sivak, 2012;Li et al, 2012;2014;Ma et al, 2012;Jin et al, 2013). An analysis on crash frequency can identify factors that affect the crashes to help reduce the number of crashes.…”

Section: Introductionmentioning

confidence: 99%

Application of generalized estimating equations for crash frequency modeling with temporal correlation

Liu

et al. 2014

J. Zhejiang Univ. Sci. A

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Traditional crash frequency modeling uses crash frequency data averaged across multiple years. When data size is small, crash data in each year are used in the modeling to extend the size of the samples. The extension of sample size could create a temporal correlation among crash frequencies of the different years, which could affect the modeling accuracy. The primary objective of this study is to evaluate the application of the generalized estimating equation (GEE) procedures to account for the temporal correlation in the longitudinal crash frequency data. Four-year crash data at exit ramps on a freeway in China were collected for modeling. Based on the same data, traditional generalized linear models (GLMs) were estimated for model comparison. Results showed that traditional GLM underestimated the standard errors of coefficients for explanatory variables. The GEE procedure with an exchangeable correlation structure successively captured the temporal correlation among the crash frequencies of the different years. The GLM with GEE outperformed the traditional GLM in providing a good fit for the crash frequency data. Results of this study can help researchers better understand how various factors affect the crash frequencies at freeway divergent areas and propose effective countermeasures.

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“…The continuous traffic trajectory especially is quite important for the entire traffic behavior analysis, as discussed by Cavanagh and Alvarez [1], Liu et al [2,3], Bie et al [4,5], and Gavrila [6]. Based on the traffic trajectory, scholars have carried out a lot of works including pedestrians' road-crossing behavior [7,8], car following theory [9,10], and prediction model of bus arrival time [11,12]. Based on those researches, the traffic is becoming more secure and convenient.…”

Section: Introductionmentioning

confidence: 99%

Occlusion Processing and Tracking Technology of Multiple Targets in Traffic Video Detection

Zhao

Jiang

et al. 2014

Advances in Mechanical Engineering

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This paper reports an algorithm of traffic multiple targets occlusion processing and tracking based on both the prediction of Kalman filtering operators and the processing of the PCA-SIFT (Principle Component Analysis Scale Invariant Feature Transform) feature with the active operators. The Kalman filtering operators are used for both the prediction of occlusion event and the fast location of occlusion area. The template for each target in the occlusion area is built and the PCA-SIFT feature with the active contour operators is used to estimate and trace the multiple targets separately in the area. Through updating, the occlusion processing and tracking of multiple targets continue until the occlusion ends. Obtained results have shown that the algorithm in the paper can solve multiple targets occlusion and tracking effectively and accurately, comparing with the real trajectories.

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Car-following theory of steady-state traffic flow using time-to-collision

Cited by 25 publications

References 30 publications

The Impact of Traffic Environmental Vision Pressure on Driver Behaviour

The Impact of Traffic Environmental Vision Pressure on Driver Behaviour

Application of generalized estimating equations for crash frequency modeling with temporal correlation

Occlusion Processing and Tracking Technology of Multiple Targets in Traffic Video Detection

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