Motorway crash duration and its determinants: do durations vary across motorways?

Traffic congestion has been a growing issue in many metropolitan areas during recent years, which necessitates the identification of its key contributors and development of sustainable strategies to help decrease its adverse impacts on traffic networks. Road incidents generally and crashes specifically have been acknowledged as the cause of a large proportion of travel delays in urban areas and account for 25% to 60% of traffic congestion on motorways. Identifying the critical determinants of travel delays has been of significant importance to the incident management systems, which constantly collect and store the incident duration data. This study investigates the individual and simultaneous differential effects of the relevant determinants on motorway crash duration probabilities. In particular, it applies parametric Accelerated Failure Time (AFT) hazard‐based models to develop in‐depth insights into how the crash‐specific characteristic and the associated temporal and infrastructural determinants impact the duration. AFT models with both fixed and random parameters have been calibrated on one year of traffic crash records from two major Australian motorways in South East Queensland, and the differential effects of determinants on crash survival functions have been studied on these two motorways individually. A comprehensive spectrum of commonly used parametric fixed parameter AFT models, including generalized gamma and generalized F families, has been compared with random parameter AFT structures in terms of goodness of fit to the duration data, and as a result, the random parameter Weibull AFT model has been selected as the most appropriate model. Significant determinants of motorway crash duration included traffic diversion requirement, crash injury type, number and type of vehicles involved in a crash, day of week and time of day, towing support requirement and damage to the infrastructure. A major finding of this research is that the motorways under study are significantly different in terms of crash durations; such that motorway 1 exhibits durations that are on average 19% shorter compared with the durations on motorway 2. The differential effects of explanatory variables on crash durations are also different on the two motorways. The detailed presented analysis confirms that looking at the motorway network as a whole, neglecting the individual differences between roads, can lead to erroneous interpretations of duration and inefficient strategies for mitigating travel delays along a particular motorway.     

驾驶特性对交通安全的影响分析

本文基于对人类行为模式的研究,并以此作为参考,将驾驶员的信息处理过程划分为感知、判断、操纵三个阶段。分析感知特性、判断特性、操作特性对交通安全的影响。     

Short/medium-term prediction for the aviation emissions in the en route airspace considering the fluctuation in air traffic demand

This paper proposes a novel short/medium-term prediction method for aviation emissions distribution in en route airspace. An en route traffic demand model characterizing both the dynamics and the fluctuation of the actual traffic demand is developed, based on which the variation and the uncertainty of the short/medium-term traffic growth are predicted. Building on the demand forecast the Boeing Fuel Flow Method 2 is applied to estimate the fuel consumption and the resulting aviation emissions in the en route airspace. Based on the traffic demand prediction and the en route emissions estimation, an aviation emissions prediction model is built, which can be used to forecast the generation of en route emissions with uncertainty limits. The developed method is applied to a real data set from Hefei Area Control Center for the en route emission prediction in the next 5 years, with time granularities of both months and years. To validate the uncertainty limits associated with the emission prediction, this paper also presents the prediction results based on future traffic demand derived from the regression model widely adopted by FAA and Eurocontrol. The analysis of the case study shows that the proposed method can characterize well the dynamics and the fluctuation of the en route emissions, thereby providing satisfactory prediction results with appropriate uncertainty limits. The prediction results show a gradual growth at an average annual rate of 7.74%, and the monthly prediction results reveal distinct fluctuation patterns in the growth.     

Practicability study on the suitability of artificial,neural networks for the approximation of unknown steering torques

For steer-by-wire systems, the steering feedback must be generated artificially due to the system characteristics. Classical control concepts require operating-point driven optimisations as well as increased calibration efforts in order to adequately simulate the steering torque in all driving states. Artificial neural networks (ANNs) are an innovative control concept; they are capable of learning arbitrary non-linear correlations without complex knowledge of physical dependencies. The present study investigates the suitability of neural networks for approximating unknown steering torques. To ensure robust processing of arbitrary data, network training with a sufficient volume of training data is required, that represents the relation between the input and target values in a wide range. The data were recorded in the course of various test drives. In this research, a variety of network topologies were trained, analysed and evaluated. Though the fundamental suitability of ANNs for the present control task was demonstrated.     

A self-regulation traffic-condition-based route guidance strategy with realistic considerations: Overlapping routes,stochastic traffic,and signalized intersections

Variable message signs that provide various types of route guidance information have been widely deployed in large cities. To release proper information only using easily collected data, a simple traffic-condition-based (TCB) route guidance strategy was recently proposed. The strategy works based on the estimation of free-flow and congested traffic conditions and is capable of approximating user optimal equilibrium stably. Due to little consideration of the complexity of urban road networks, the TCB strategy is still away from field applications in urban areas. To further push the strategy toward field tests, this article improves the TCB strategy in the following aspects: supplementing the strategy with a self-regulation ability by considering existing traffic conditions; decomposing link capacity to solve the problem of overlapping routes by comparing link capacity on alternative routes; coping with stochastic traffic; and the impact of signalized intersections by utilizing aggregated data. A scenario for an urban road network in Beijing, China, is simulated to test the improved strategy, and the simulation results clearly indicate the effectiveness of the proposed improvements. The improvements extend the TCB strategy on moderately complicated urban road networks, and still have the advantages of simple diversion rules, easily obtained input data, and stable and effective diversion processes.     

船舶交通动力系统安全突变研究

为揭示系统本质,建立船舶交通风险预控机制,基于突变理论对船舶交通系统的安全突变进行研究。首先定性分析了船舶交通系统的特性,并针对动态性与突变性特征,提出船舶交通系统的突变动力学研究方法,进而建立船舶交通动力系统安全突变模型,最后对系统的安全突变进行了定量的分析研究。     

冯店隧道交通事故特性研究

文章分析了成南高速公路冯店隧道交通事故的时空分布特性。冯店隧道（79～80km）的交通事故主要集中于79．62～80km路段、成都至南充（成南高速公路）方向,多数发生在夜晚,事故形态主要是撞击固定物和侧翻。分析了隧道、道路线形等因素对驾驶员的影响,认为隧道79～80km路段的线形组合和超速行驶不仅诱发事故、也是事故具有明显集中性、方向性和时间性的主要原因。     

北京市小汽车发展的外部效应分析

从公共经济学角度,对北京市小汽车的发展进行了外部效应分析,指出了随着保用量的增长,对社会产生负外部效应,提出了矫正小汽车发展外部效应的措施建议,希望能够为有关政府部门制定决策提供理论借鉴。     

昼夜温度及交通量变化对沥青路面响应及性能的影响

现场沥青路面结构承受的交通量及温度都有随昼夜变化的特性,如果交通量较为集中的时段往往也是地表温度较高的时段,则会对路面结构造成较大的破坏。文章利用试验得到的沥青混合料的动态模量主曲线和时间温度转换因子,结合实测的路面结构温度场对这一问题进行了定量分析。分别分析了沥青混合料的动态模量、路面结构的关键力学响应以及荷载对路面结构的破坏率的昼夜变化特性,并比较了冬天和夏天变化特性的不同;结合不同时间的荷载作用破坏率及实测的交通量小时分布系数进行分析,表明按交通量实际分布情况得到的路面结构使用寿命可以比按传统假设的均匀分布情况小10％以上。在路面结构分析和设计中有必要考虑温度及交通量的昼夜变化特性。     

从南方雨雪冰冻灾害谈北方高速公路除雪抢通措施

通过对2008年春节前南方雨雪冰冻灾害的反思,介绍了针对北方地区高速公路多年来积累的除雪抢通经验与措施。