高速公路可变速度标志神经网络控制

针对高速公路可变速度控制是一个非线性时变系统,难于用数学模型准确建模这一特点,提出了神经网络控制方法.阐述了神经网络学习算法,设计了高速公路可变速度标志神经网络控制器,并对控制器进行了仿真研究.仿真结果表明,该方法切实可行,具有实用价值.     

襄十高速公路K373+040～K373+180段滑坡整治工程大截面抗滑桩施工技术

本介绍了一项严重影响襄十高速公路安全营运的重点控制性工程中的大截面抗滑桩施工工艺流程及技术方案，并着重对基坑涌水对抗滑桩施工安全、质量方面的危害及处理方案进行了阐述。     

Modeling of optimal control for urban freeway corridor under incident conditions

Traffic control and management are effective measures to solve the problem of traffic congestion. The optimal control model for freeway corridor is developed under incident conditions, which is in the form of minimization of the sum of the square of the difference between traffic demand and capacity at each intersection and on the freeway bottleneck section. The model optimizes control parameters of phase splits at arterial intersections, off-ramp diversion rates at upstream off-ramps and on-ramp diversion rates at downstream on ramps. Finally, the objective function is discussed and it is showed that the optimal control model is simple and practical.     

消息中间件的排队论模型研究

1 引言分布式和跨平台用户需求的增加,使得中间件(middle-ware)技术应运而生。中间件是位于上层应用和下层服务之间,提供更简单可靠和增值服务的通用服务。中间件技术是IT界研究的热点之一。中间件一般可以分为事务处理中间件、分布式面向对象中间件和消息中间件。当应用程序需要在不同的网络硬件平台、不同的操作系统乃至不同的网络协议上通信时,适合选用消息中间件(MOM),因为此时应用程序     

Exploring a Bayesian hierarchical approach for developing safety performance functions for a mountainous freeway

While rural freeways generally have lower crash rates, interactions between driver behavior, traffic and geometric characteristics, and adverse weather conditions may increase the crash risk along some freeway sections. This paper examines the safety effects of roadway geometrics on crash occurrence along a freeway section that features mountainous terrain and adverse weather. Starting from preliminary exploration using Poisson models, Bayesian hierarchical models with spatial and random effects were developed to efficiently model the crash frequencies on road segments on the 20-mile freeway section of study. Crash data for 6 years (2000–2005), roadway geometry, traffic characteristics and weather information in addition to the effect of steep slopes and adverse weather of snow and dry seasons, were used in the investigation. Estimation of the model coefficients indicates that roadway geometry is significantly associated with crash risk; segments with steep downgrades were found to drastically increase the crash risk. Moreover, this crash risk could be significantly increased during snow season compared to dry season as a confounding effect between grades and pavement condition. Moreover, sites with higher degree of curvature, wider medians and an increase of the number of lanes appear to be associated with lower crash rate. Finally, a Bayesian ranking technique was implemented to rank the hazard levels of the roadway segments; the results confirmed that segments with steep downgrades are more crash prone along the study section.     

论高速公路城市出入口区域土地利用

当前,高速公路城市出入口区域土地开发的多主体、多类型、多手段现象,直接导致城乡结构混杂、边缘结构无序、土地产权属性模糊等矛盾。合理定位高速公路城市出入口区域土地开发,可加强城市和乡村、城市与城市之间的联系,并优化城市自身的协调功能。     

Quantifying effects of ramp metering on freeway safety

This study presents a real-time crash prediction model and uses this model to investigate the effect of the local traffic-responsive ramp metering strategy on freeway safety. Safety benefits of ramp metering are quantified in terms of the reduced crash potential estimated by the real-time crash prediction model. Driver responses to ramp metering and the consequent traffic flow changes were observed using a microscopic traffic simulation model and crash potential was estimated for a 14.8 km section of I-880 in Hayward, California and a hypothetical isolated on-ramp network. The results showed that ramp metering reduced crash potential by 5-37% compared to the no-control case. It was found that safety benefits of local ramp metering strategy were only restricted to the freeway sections in the vicinity of the ramp, and were highly dependent on the existing traffic conditions and the spatial extent over which the evaluation was conducted. The results provide some insight into how a local ramp metering strategy can be modified to improve safety (by reducing total crash potential) on longer stretch of freeways over a wide range of traffic conditions.     

Multi-agent based traffic simulation and integrated control of freeway corridors: Part 1 simulation and control model

Freeway corridors consist of urban freeways and parallel arterials for alternative use. Ramp metering in freeways and signal control in arterials are contemporary traffic control methods that have been developed and applied in order to improve the traffic conditions of freeway corridors. However, most existing studies have focused on either optimal ramp metering in freeways or progressive signal strategies between arterial intersections. For efficient control of freeway corridors, ramp metering and signal control must be considered simultaneously, as otherwise the control strategies for freeway operation may disturb arterial traffic. On the other hand, traffic congestion and arterial bottlenecks that arise with increasing traffic volume at peak hours and ineffective signal operation may cause problems with accessibility to freeway ramps and degrade the urban freeway’s ability to act as a through-traffic process. This research dynamically estimates the traffic stream between an urban freeway and its ramps according to changes in the freeway structure, traffic passing demand, and control methods due to restricted valid information. The results are then compared with those from other methods. Finally, the integrated control in the urban freeway traffic axis is optimized based on the expected traffic stream, by using design of experiment (DOE), neural network (NN), and a simulated annealing algorithm. This paper was recommended for publication in revised form by Associate Editor Kyongsu Yi Myung-Won Suh is a professor of Mechanical Engineering. During 1986–1988, he worked in Ford motor company as researcher. During 1989–1995, he worked in technical center of KIA motors. He took a BS Degree in Mechanical Engineering from Seoul National University and an MS Degree in Mechanical Engineering from KAIST, South Korea. He obtained his Doctorate at University of Michigan, USA, in 1989. His research areas include the structure and system optimization, advanced safety vehicle and reliability analysis & optimization. Chul-Ho Bae is a PhD candidate at Sungkyunkwan University in Suwon, South Korea. He accomplished fellowship work as researcher at Mississippi State University, USA, in 2003 and 2005. He worked in Institute of Advanced Machinery and Technology (IMAT) as a Research Assistant in 2004. He was a part time Lecturer in computer aided Mechanical Engineering of Ansan College of Technology, Suwon Science College, and Osan College during 2004–2005. He took a BS Degree in Mechanical Design and an MS Degree in Mechanical Engineering from the Sungkyunkwan University. His research interests include computer aided engineering, reliability engineering, and optimization.     

Predicting crash likelihood and severity on freeways with real-time loop detector data

Real-time crash risk prediction using traffic data collected from loop detector stations is useful in dynamic safety management systems aimed at improving traffic safety through application of proactive safety countermeasures. The major drawback of most of the existing studies is that they focus on the crash risk without consideration of crash severity. This paper presents an effort to develop a model that predicts the crash likelihood at different levels of severity with a particular focus on severe crashes. The crash data and traffic data used in this study were collected on the I-880 freeway in California, United States. This study considers three levels of crash severity: fatal/incapacitating injury crashes (KA), non-incapacitating/possible injury crashes (BC), and property-damage-only crashes (PDO). The sequential logit model was used to link the likelihood of crash occurrences at different severity levels to various traffic flow characteristics derived from detector data. The elasticity analysis was conducted to evaluate the effect of the traffic flow variables on the likelihood of crash and its severity.The results show that the traffic flow characteristics contributing to crash likelihood were quite different at different levels of severity. The PDO crashes were more likely to occur under congested traffic flow conditions with highly variable speed and frequent lane changes, while the KA and BC crashes were more likely to occur under less congested traffic flow conditions. High speed, coupled with a large speed difference between adjacent lanes under uncongested traffic conditions, was found to increase the likelihood of severe crashes (KA). This study applied the 20-fold cross-validation method to estimate the prediction performance of the developed models. The validation results show that the model's crash prediction performance at each severity level was satisfactory. The findings of this study can be used to predict the probabilities of crash at different severity levels, which is valuable knowledge in the pursuit of reducing the risk of severe crashes through the use of dynamic safety management systems on freeways.     

Presence of passengers: does it increase or reduce driver's crash potential?

This study examines the impact of passengers on the driver's crash potential on freeways. To estimate the impact, a set of bivariate probit models were developed using the 5-year (1999-2003) crash records on a 36.3-mile stretch of Interstate-4 freeway (I-4) in Orlando, Florida. Bivariate probit models identify the correlation between potentially inter-related choices of three passenger characteristics and three crash characteristics. The analysis using bivariate probit models showed that there exist strong correlations between passenger and crash characteristics. It was found that drivers generally display safer driving behavior when they are accompanied by passengers, and more passengers reduce driver's crash potential. It was also found that younger driver's crash potential increases with the presence of a younger passenger only. In addition, the analysis of crash type using traffic flow parameters at the time of crashes showed that young drivers with only younger passengers are more likely to be involved in single-vehicle crashes in high-speed and low-volume conditions. The findings in this study provide insight into how the presence of passengers has an impact on driver behavior and traffic safety in various conditions.