A driving force model for non-strict priority crossing behaviors of right-turn drivers

Lin, Dianchao; Ma, Wanjing; Li, Li; Wang, Yinhai

doi:10.1016/j.trb.2015.10.007

Cited by 17 publications

(14 citation statements)

References 31 publications

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“…Notably, the spring forces are similar to what several authors refer to as social force (Helbing & Tilch, 1998; Lin, Ma, Li, & Wang, 2016). The stiffness of the multispring k 1 and the spring oscillator k 2 represents the different motivations of the driver, which an individual experiences in each vehicle.…”

Section: Methodology Of Vehicle Interactions Modelingsupporting

confidence: 73%

Behavioral decision‐making model of the intelligent vehicle based on driving risk assessment

Zheng

Huang

Wang

et al. 2019

Computer aided Civil Eng

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Intelligent‐driving technologies play crucial roles in reducing road‐traffic accidents and ensuring more convenience while driving. One of the significant challenges in developing an intelligent vehicle is how to operate it safely without causing fear in other human drivers. This paper presents a new behavioral decision‐making model to achieve both safety and high efficiency and also to reduce the adverse effect of autonomous vehicles on the other road users while driving. Moreover, we attempt to adapt the model for human drivers so that users can understand, adapt, and utilize intelligent‐driving technologies. Furthermore, this paper proposes a combined spring model for assessing driving risk. Thus, we analyze some driving characteristics of drivers and choose “safety” and “high efficiency” as the two main factors that are pursued by drivers while driving. Based on the principle of least action, a multiobjective optimization cost function is established for the decision‐making model. Finally, we design six unprotected left‐turn scenarios at a T‐intersection and three unprotected left‐turn scenarios at a standard two‐lane intersection for applying simulation algorithm and provide a decision‐making map for developing intelligent‐driving technologies. Based on the principle of least action, this paper demonstrates that optimization theory can give insight into drivers’ behavior and can also contribute to the development of intelligent‐driving algorithms. The experimental results reveal that the behavioral decision‐making model can always avoid collision accidents on the premise of ensuring certain efficiency, and it can achieve 97.01%, 94.52%, 96.67%, 91.18%, 101.27%, 83.33%, 102.94%, 103.03%, and 105.77% of time to intersection's maximum pass rate in the considered nine scenarios.

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Section: Methodology Of Vehicle Interactions Modelingsupporting

confidence: 73%

Behavioral decision‐making model of the intelligent vehicle based on driving risk assessment

Zheng

Huang

Wang

et al. 2019

Computer aided Civil Eng

View full text Add to dashboard Cite

show abstract

“…The result showed that drivers generally make their decision about 1 s before arriving at the conflict point, and the vehicle speed difference between the two directions was the most important factor that affected a driver’s decision-making process. As there is no strict priority in conflicts between right-turn vehicles and through non-motorized vehicles in most countries, by analogizing the maneuver of moving vehicles, Lin et al developed a micro-level driving force model, which includes the safety driving force and efficiency driving force for right-turn drivers which constitute the dominant party during the non-strict priority crossing process ( 18 ). Through sensitivity analysis, they identified the influence of vehicle type, non-motorized traffic flow rate, and non-motorized traffic speed on the average right-turn speed, and offered support for the rationality of the non-strict priority.…”

Section: Literature Reviewmentioning

confidence: 99%

Capacity Modeling of Permitted Left-Turn Signalized Intersections with Probabilistic Priority

Wang

Yang

Tian

et al. 2020

Transportation Research Record

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Probabilistic yielding behavior is commonly observed at permitted left-turn signalized intersections in China. Nevertheless, its impact on traffic capacity has not been explored in existing research. The uniqueness of this traffic operation is that neither left-turn traffic nor through traffic holds an absolute priority. Based on queuing theory, this research developed an analytical capacity model that took into account the probabilistic priority phenomenon at permitted left-turn signals. To validate the developed capacity model, stochastic simulations with different combinations of left-turn traffic yielding rates and through-traffic flow rates were performed. It was found that modeling results from the analytical model precisely matched the stochastic simulation results. In comparison with traditional capacity estimation models that assumed through traffic holds an absolute priority, this research revealed that when through-traffic flow rate is around 1000 vehicles per hour (vph), the capacity of left-turn traffic increased from 233 vph to 536 vph when left-turn traffic yielding rates decrease from 1 to 0.2; whereas when left-turn traffic yielding rates decreased from 0.2 to 0, left-turn capacity increased sharply to 1200 vehicles per hour (vph). In this regard, it is critical to take into account the probabilistic left-turn yield behavior when developing permitted left-turn signal warrants.

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“…According to traffic laws, vehicular traffic turning left and facing a permitted phase must yield the right-of-way to oncoming traffic; however, this situation is not the case in some countries, including China, Norway, and Finland. Drivers in these countries may not follow the full compliance with the official priority rules and instead fail to yield [1][2][3]. Leftturning vehicles always attempt to choose the shortest path where the potential conflict point is nearer to themselves than through-vehicles.…”

Section: Introductionmentioning

confidence: 99%

A Method for Determining the Capacity of an Exclusive Left Lane with a Permitted Phase under Nonstrict Priority

Bai

Song

et al. 2019

Advances in Civil Engineering

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A new method has been developed for estimating the capacity of an exclusive left lane with a permitted phase under nonstrict priority. Different from maneuvers under strict priority, these left-turning vehicles were released in the form of a left-turn group. A field survey was first conducted to explore the maximum number of vehicles in a left-turn group, and the releasing process of the permitted left turns. e observations revealed that (1) the maximum number is related to the intersection geometry and (2) the releasing process includes two stages: the first left-turn group crossing at the beginning of a permitted phase and the following leftturn groups crossing using gaps provided by opposing right turns. Next, a method based on probability theory and these observation results were applied to estimate the capacity of an exclusive left lane. e procedure contains two stages and eight steps. Finally, the estimation of the left-turn capacity using the proposed model was validated by comparing the capacity from the strict priority and actual maximum volumes.

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A driving force model for non-strict priority crossing behaviors of right-turn drivers

Cited by 17 publications

References 31 publications

Behavioral decision‐making model of the intelligent vehicle based on driving risk assessment

Behavioral decision‐making model of the intelligent vehicle based on driving risk assessment

Capacity Modeling of Permitted Left-Turn Signalized Intersections with Probabilistic Priority

A Method for Determining the Capacity of an Exclusive Left Lane with a Permitted Phase under Nonstrict Priority

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