Bayesian network for red-light-running prediction at signalized intersections

Abstract

Red-light-running (RLR) is an important reason for the large number of intersection-related fatalities, injuries, and other losses. The accurate RLR prediction can effectively reduce crashes caused by RLR behavior. The RLR prediction is usually composed of two parts: the vehicle’s stop-or-go behavior and the arrival time when the vehicle reaches the stop line. Previous stop-or-go prediction models are usually based on embedded traffic sensors using machine learning algorithms. While based on the continuous trajectories collected by radar sensors, RLR prediction can be conducted more effectively. In this paper, a probabilistic stop-or-go prediction model based on the Bayesian network (BN) is proposed for RLR prediction. We extend the deterministic output into the probabilistic output, which provides decision-makers with greater autonomy. The causality of BN improves the interpretability of the prediction model. The BN model is calibrated and tested by the continuous trajectories data measured by radar sensors installed at a signalized intersection. We not only consider the movement measurements of individual vehicles (e.g., speed and acceleration), but also take into account the car-following behavior. As a comparison, different machine learning models and the model based on the inductive loop detection (ILD) are adopted. The results show that the proposed BN model has a high prediction accuracy and performs better in the feature interpretation. This paper provides a new way for probabilistic RLR prediction based on continuous trajectories, which will significantly improve traffic safety of signalized intersections.     

Analyzing urban bus service reliability at the stop, route, and network levels

Improving the reliability of bus service has the potential to increase the attractiveness of public transit to current and prospective riders. An understanding of service reliability is necessary to develop strategies that help transit agencies provide better services. However, few studies have been conducted analyzing bus reliability in the metropolis of China. This paper presents an in-depth analysis of service reliability based on bus operational characteristics in Beijing. Three performance parameters, punctuality index based on routes (PIR), deviation index based on stops (DIS), and evenness index based on stops (EIS), are proposed for the evaluation of bus service reliability. Reliability involves routes, stops, punctuality, deviation, and evenness. The relationship among the three parameters is discussed using a numerical example. Subsequently, through a sampling survey of bus lines in Beijing, service reliability at the stop, route, and network levels are estimated. The effects of route length, headway, the distance from the stop to the origin terminal, and the use of exclusive bus lanes are also analyzed. The results indicate low service reliability for buses in Beijing and a high correlation between service reliability and route length, headway, distance from the stop to the origin terminal, and the provision of exclusive bus lanes.     

上海长江隧道连接通道水平冻结法施工

主要介绍了上海长江隧道连接通道冻结法施工技术,包括冻土帷幕设计、冻结孔布置、连接通道开挖支护、控制冻胀和融沉的措施等,同时对连接通道的主要技术创新措施进行了分析。     

Vulnerability analysis for large-scale and congested road networks with demand uncertainty

To assess the vulnerability of congested road networks, the commonly used full network scan approach is to evaluate all possible scenarios of link closure using a form of traffic assignment. This approach can be computationally burdensome and may not be viable for identifying the most critical links in large-scale networks. In this study, an “impact area” vulnerability analysis approach is proposed to evaluate the consequences of a link closure within its impact area instead of the whole network. The proposed approach can significantly reduce the search space for determining the most critical links in large-scale networks. In addition, a new vulnerability index is introduced to examine properly the consequences of a link closure. The effects of demand uncertainty and heterogeneous travellers’ risk-taking behaviour are explicitly considered. Numerical results for two different road networks show that in practice the proposed approach is more efficient than traditional full scan approach for identifying the same set of critical links. Numerical results also demonstrate that both stochastic demand and travellers’ risk-taking behaviour have significant impacts on network vulnerability analysis, especially under high network congestion and large demand variations. Ignoring their impacts can underestimate the consequences of link closures and misidentify the most critical links.     

偏压连拱隧道施工方法研究

文章采川三维有限元数值模拟研究了偏压连拱隧道不同施工顺序下拱顶下沉、中墙稳定性及初期支护受力特征.研究结果表明,先开挖浅埋侧时,拱顶沉降较小,中墙在施工中的稳定安全系数较大、弯矩较小,初期支护受力较大;对于浅埋偏压连拱隧道,围岩变形及中墙在施工中的稳定性控制更为重要.所以,从有利于围岩变形、中墙稳定性控制以及中墙受力的角度出发,宜采用先开挖浅埋侧的施工方法.     

地铁暗挖隧道二次衬砌施工质量控制浅析

文章根据深圳轨道交通二期工程安托山站—侨香站暗挖区间的二次衬砌施工经验,分析了地铁暗挖隧道二次衬砌施工质量的影响因素以及二次衬砌施工过程中容易发生的质量问题,并提出了具体的控制措施。通过经验总结,使地铁施工的管理、质量控制、施工通病防治上升一个台阶。     

富水砂层中暗挖隧道施工沉降控制技术

富水砂层中暗挖隧道施工是地铁施工中的一大难点,常常因沉降过大而引发安全事故。在深圳地铁安-侨区间隧道工程中,通过洞内帷幕注浆、水平旋喷桩、地表井点降水等辅助措施做到了超前控制,在解决隧道开挖安全问题的同时有效地减少了沉降量;施工过程中严格执行了"管超前、短进尺、强支护、早封闭、勤量测"的方针,控制了时空效应,解决了富水砂层中暗挖隧道沉降过大的问题。文章重点分析了沉降原因及控制沉降的关键技术和有效措施。     

光面爆破技术在城市地铁隧道施工中的应用

文章从光面爆破机理、光面爆破技术中各主要参数的确定,以及光面爆破在实际工程中的应用几个方面出发,以深圳地铁安-侨区间隧道硬岩段施工为例,研究分析了如何利用光面爆破技术来提高城市地铁浅埋暗挖法的光爆效果,特别是近距离穿越重要建(构)筑物时如何有效进行光面爆破。     

等截面、等量排风管道静压分布和风口开度计算

本文详细介绍了等截面多风口等量排风长管道内压力分布基本式理论推导和风口开度调节计算方法,并根据实际地下工程测定数据验证其理论计算可靠性,可以供地下工程设计计算和工程调试中作借鉴和参考。     

兰渝线新龙凤隧道涌水分析

文章结合兰渝线新龙凤隧道发生的岩溶涌水突泥地质灾害问题,在对隧道地质环境及岩溶发育特征分析研究的基础上,从隧道所处地段的岩溶发育强度、岩溶水动力垂直及横向分带,以及大气降雨条件等方面分析了隧道涌水的原因,认为新龙风隧道发生的涌水是揭露中小型岩溶管道所致,涌水的主要控制因素为大气降雨.