evaluation-an-exclusive-spur-dike-u-turn-design-with-radar-collected

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation

Traditional U-turn designs can improve operational features obviously, while U-turn diversions and merge segments still cause traffic congestion, ...

evaluating-effect-roadside-parking-on-dual-direction-urban

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street

Roadside parking is a common traffic phenomenon in China. Narrow urban streets, high parking demands, and a shortage of parking lots force the public to ...

determination-friction-coefficients-icy-pavements-under-different

Determination of the Friction Coefficients of Icy Pavements Under Different Amounts of Snowfall

Ice on road surfaces can lead to a significant decrease in the friction coefficient, thus endangering driving safety. However, there are still no studies ...

Chang'an University

Xi’an University of Posts and Telecommunications

The traditional U-turn design has significantly improved traffic operations for relieving traffic congestion. However, the U-turn diversion and merge segments still cause traffic conflicts and delays. In this paper, an exclusive spur dike U-turn lane (ESUL) is proposed with the aim of addressing the disadvantages of the traditional U-turn design. ESUL provides a separate U-turn lane to diverge, decelerate, U-turn, accelerate and merge without interacting with through traffic. The effectiveness of ESUL is demonstrated through a field data investigation, simulation and analysis with VISSIM software. The proposed design is evaluated in terms of three parameters: travel time, delay and number of stops. Compared to the traditional U-turn design, ESUL can reduce travel time by 29.15%, delay by 66.70% and the number of stops by 100% at most. The results showed that ESUL has better performance than the traditional U-turn design and could be implemented to reduce traffic congestion and the potential hazards caused by U-turn maneuvers.

Evaluating the sustainable traffic flow operational features of an exclusive spur dike U-turn lane design.

Poor visual conditions in tunnels can easily cause traffic accidents, and it is difficult for emergency services to reach these areas. As an economical and effective visual guiding device, delineators have attracted wide attention. Based on the actual alignment of the Qinling Mountain No.1, No.2 and No.3 tunnels of the G5 Expressway in Xi'an City (Shaanxi Province, China), this paper designs a simulation experiment. Through a simulator study and a questionnaire survey, this paper discusses how delineators affect drivers' visual characteristics (including fixation area and pupil size) in different settings and with different road alignments. Twenty-five subjects participated in this research. The results show that setting delineators in tunnels can continuously guide drivers' vision and attract their attention to focus on the pavement. Compared with setting only pavement delineators, setting wall delineators and pavement delineators together can provide better guiding effects and ensure driving safety in both straight and curved sections. In addition, when driving in tunnels equipped with delineators, especially tunnels with both wall delineators and pavement delineators, the participants exhibited a smaller pupil diameter and lower pupil diameter change rate. In terms of the relationship between pupil size and road alignment, the results indicated that regardless of what type of delineator was used, the drivers exhibited the smallest pupil size and lowest pupil change rate when driving on the straight section compared with the curved sections.

ArticleTotal : 3	Year
Publication title Cited by 4	2020
Publication title	2023
Publication title	2023

Article	Year
Evaluating the sustainable traffic flow operational features of an exclusive spur dike U-turn lane design. PloS one\| PubMed ID: 30969985	2019
Evaluating the impact of setting delineators in tunnels based on drivers' visual characteristics. PloS one\| PubMed ID: 31851692	2019

Yang Shao

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