The consistency of horizontal alignment at tunnel entrance and exit zone

2016 ◽  
pp. 1679-1689
Author(s):  
Zhen Yang ◽  
Haifeng Han ◽  
Ziyi Xiong ◽  
Donglin Lei
2016 ◽  
pp. 1679-1689
Author(s):  
Zhen Yang ◽  
Haifeng Han ◽  
Ziyi Xiong ◽  
Donglin Lei

2003 ◽  
Vol 30 (6) ◽  
pp. 1042-1054 ◽  
Author(s):  
Yasser Hassan

Many models have been developed to evaluate the operating speeds on two-lane rural highways. However, provided information usually lacks details essential to assess their applicability at locations other than where they were developed. This paper presents a procedure to interpret raw data collected on three horizontal curve sites of different two-lane rural highway classes in Ontario. The speed observations were categorized into three vehicle classes (passenger car, light truck, and multi-axle heavy truck) and four light condition categories (day, night, and two transition periods). The minimum headway and percentile value to define the operating speed were examined, and a revision of the current practice deemed not warranted. The findings also indicated that operating speeds do not depend on the time or vehicle class. Finally, the horizontal alignment affects the operating speed, but the speeds of the two travel directions on a horizontal curve may differ even with little contribution of the vertical alignment.Key words: highway geometric design, operating speed, traffic composition, traffic counters, ambient light, acceleration, deceleration.


Author(s):  
Zijian Guo ◽  
Tanghong Liu ◽  
Wenhui Li ◽  
Yutao Xia

The present work focuses on the aerodynamic problems resulting from a high-speed train (HST) passing through a tunnel. Numerical simulations were employed to obtain the numerical results, and they were verified by a moving-model test. Two responses, [Formula: see text] (coefficient of the peak-to-peak pressure of a single fluctuation) and[Formula: see text] (pressure value of micro-pressure wave), were studied with regard to the three building parameters of the portal-hat buffer structure of the tunnel entrance and exit. The MOPSO (multi-objective particle swarm optimization) method was employed to solve the optimization problem in order to find the minimum [Formula: see text] and[Formula: see text]. Results showed that the effects of the three design parameters on [Formula: see text] were not monotonous, and the influences of[Formula: see text] (the oblique angle of the portal) and [Formula: see text] (the height of the hat structure) were more significant than that of[Formula: see text] (the angle between the vertical line of the portal and the hat). Monotonically decreasing responses were found in [Formula: see text] for [Formula: see text] and[Formula: see text]. The Pareto front of [Formula: see text] and[Formula: see text]was obtained. The ideal single-objective optimums for each response located at the ends of the Pareto front had values of 1.0560 for [Formula: see text] and 101.8 Pa for[Formula: see text].


2021 ◽  
pp. 106145
Author(s):  
Zhenlong Li ◽  
Guanyang Xing ◽  
Xiaohua Zhao ◽  
Haijian Li

1987 ◽  
Vol 19 (2) ◽  
pp. 35-44 ◽  
Author(s):  
C. Bourdy ◽  
A. Chiron ◽  
C. Cottin ◽  
A. Monot

Author(s):  
Bekir Bartin ◽  
Sami Demiroluk ◽  
Kaan Ozbay ◽  
Mojibulrahman Jami

This paper introduces CurvS, a web-based tool for researchers and analysts that automatically extracts, visualizes, and analyzes roadway horizontal alignment information using readily available geographic information system roadway centerline data. The functionalities of CurvS are presented along with a brief background on its methodology. The validation of its estimation results are presented using actual horizontal alignment data from two different roadway types: Route 83, a two-lane two-way rural roadway in New Jersey and I-80, a freeway segment in Nevada. Different metrics are used for validation. These are identification rates of curved and tangent sections, overlap ratio of curved and tangent sections between estimated and actual horizontal alignment data, and percent fit of curve radii. The validation results show that CurvS is able to identify all the curves on these two roadways, and the estimated section lengths are significantly close to the actual alignment data, especially for the I-80 freeway segment, where 90% of curved length and 94% of tangent section length are correctly matched. Even when curves have small central angles, such as the ones in Route 83, CurvS’s estimations covers 71% of curved length and 96% of tangent section length.


2014 ◽  
Vol 21 (8) ◽  
pp. 3411-3418 ◽  
Author(s):  
Liu Yang ◽  
Jian-long Zheng ◽  
Rui Zhang
Keyword(s):  

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