Work zone items influencing driver speeds at roadworks: worker, driver and expert perspectives

Roadworks are essential to a safe and efficient road network, yet somewhat paradoxically the necessary work is often associated with increased risk to motorists and workers, as well as with traffic flow disruptions. A major source of increased crash risk at roadwork sites (work zones) is poor speed limit compliance. Speeding in work zones is examined in existing literature to the extent that major issues are known and some effective countermeasures are identified. However, as speeding remains a major problem in work zones, influences on driver behaviour arguably need to be better understood to achieve greater compliance and thus realise further gains in road safety. Current research on safety at Queensland roadwork sites has examined the views of workers, measured work zone speed profiles, and conducted an online survey of drivers (N=410). This paper focuses on survey participants’ ratings of 12 specific work zone items (including traffic control measures) in terms of their influence on speed choice. Repeated measures ANOVA revealed statistically significant differences (p<0.001) in the ratings of these items, with the most influential including visible presence of workers, visible police presence, and speed feedback displays. Those rated least influential included ’roadwork speed limits are enforced’ and ‘reduce speed’ signs and increased fines for speeding in work zones. The paper considers the alignment of these findings with those from other sources, including worker interviews and the literature, to provide a consolidated assessment of the influence of work zone items on driver speeds.

Investigation of pedestrian crashes on two-way two-lane rural roads in Ethiopia

Understanding pedestrian crash causes and contributing factors in developing countries is critically important as they account for about 55% of all traffic crashes. Not surprisingly, considerable attention in the literature has been paid to road traffic crash prediction models and methodologies in developing countries of late. Despite this interest, there are significant challenges confronting safety managers in developing countries. For example, in spite of the prominence of pedestrian crashes occurring on two-way two-lane rural roads, it has proven difficult to develop pedestrian crash prediction models due to a lack of both traffic and pedestrian exposure data. This general lack of available data has further hampered identification of pedestrian crash causes and subsequent estimation of pedestrian safety performance functions. The challenges are similar across developing nations, where little is known about the relationship between pedestrian crashes, traffic flow, and road environment variables on rural two-way roads, and where unique predictor variables may be needed to capture the unique crash risk circumstances. This paper describes pedestrian crash safety performance functions for two-way two-lane rural roads in Ethiopia as a function of traffic flow, pedestrian flows, and road geometry characteristics. In particular, random parameter negative binomial model was used to investigate pedestrian crashes. The models and their interpretations make important contributions to road crash analysis and prevention in developing countries. They also assist in the identification of the contributing factors to pedestrian crashes, with the intent to identify potential design and operational improvements.

Intelligent collision avoidance, control and guidance

This paper reviews a variety of advanced signal processing algorithms that have been developed at the University of Southampton as part of the Prometheus (Programme for European traffic flow with highest efficiency and unprecedented safety) programme to achieve an intelligent driver warning system (IDWS). The IDWS includes the detection of road edges, lanes, obstacles and their tracking and identification, estimates of time to collision, and behavioural modelling of drivers for a variety of scenarios. The underlying algorithms are briefly discussed in support of the IDWS.

A neurofuzzy approach to future intelligent driver support systems

This paper reviews a variety of advanced signal processing algorithms that have been developed at the University of Southampton as part of the Prometheus (PROgraMme for European Traffic flow with Highest Efficiency and Unprecedented Safety) research programme to achieve an intelligent driver warning system (IDWS). The IDWS includes: visual detection of both generic obstacles and other vehicles, together with their tracking and identification, estimates of time to collision and behavioural modelling of drivers for a variety of scenarios. These application areas are used to show the applicability of neurofuzzy techniques to the wide range of problems required to support an IDWS, and for future fully autonomous vehicles.

Size-resolved particle distribution and gaseous concentrations by real-world road tunnel measurement

Measurements of aerosol particle number size distributions (15-700 nm), CO and NOx were performed in a bus tunnel, Australia. Daily mean particle size distributions of mixed diesel/CNG (Compressed Natural Gas) buses traffic flow were determined in 4 consecutive measurement days. EFs (Emission Factors) of Particle size distribution of diesel buses and CNG buses were obtained by MLR (Multiple Linear Regression) methods, particle distributions of diesel buses and CNG buses were observed as single accumulation mode and nuclei-mode separately. Particle size distributions of mixed traffic flow were decomposed by two log-normal fitting curves for each 30 minutes interval mean scans, all the mix fleet PSD emission can be well fitted by the summation of two log-normal distribution curves, and these were composed of nuclei mode curve and accumulation curve, which were affirmed as the CNG buses and diesel buses PN emission curves respectively. Finally, particle size distributions of diesel buses and CNG buses were quantified by statistical whisker-box charts. For log-normal particle size distribution of diesel buses, accumulation mode diameters were 74.5～87.5nm, geometric standard deviations were 1.89～1.98. As to log-normal particle size distribution of CNG buses, nuclei-mode diameters were 21～24 nm, geometric standard deviations were 1.27～1.31.

A penalty-based genetic algorithm for the migration cost-aware virtual machine placement problem in cloud data centers

In the past few years, the virtual machine (VM) placement problem has been studied intensively and many algorithms for the VM placement problem have been proposed. However, those proposed VM placement algorithms have not been widely used in today's cloud data centers as they do not consider the migration cost from current VM placement to the new optimal VM placement. As a result, the gain from optimizing VM placement may be less than the loss of the migration cost from current VM placement to the new VM placement. To address this issue, this paper presents a penalty-based genetic algorithm (GA) for the VM placement problem that considers the migration cost in addition to the energy-consumption of the new VM placement and the total inter-VM traffic flow in the new VM placement. The GA has been implemented and evaluated by experiments, and the experimental results show that the GA outperforms two well known algorithms for the VM placement problem.

Modelling, calibrating, and validating car following and lane changing behaviour

Car following (CF) and lane changing (LC) are two primary driving tasks observed in traffic flow, and are thus vital components of traffic flow theories, traffic operation and control. Over the past decades a large number of CF models have been developed in an attempt to describe CF behaviour under a wide range of traffic conditions. Although CF has been widely studied for many years, LC did not receive much attention until recently. Over the last decade, researchers have slowly but surely realized the critical role that LC plays in traffic operations and traffic safety; this realization has motivated significant attempts to model LC decision-making and its impact on traffic. Despite notable progresses in modelling CF and LC, our knowledge on these two important issues remains incomplete because of issues related to data, model calibration and validation, human factors, just to name a few. Thus, this special issue will focus on latest developments in modelling, calibrating, and validating two primary vehicular interactions observed in traffic flow: CF and LC.

Power extraction studies in a gasdynamic laser

A generalized two‐dimensional flow‐radiation coupled model to extract power from a gasdynamic laser is proposed. The model is used for the study of power extraction from a 9.4‐μm CO2 downstream‐mixing gasdynamic laser, where a cold CO2+H2 stream is mixed with a vibrationally excited N2 stream at the nozzle exits. This model is developed by coupling radiation with the two‐dimensional, unsteady, laminar and viscous flow modeling needed for such systems. The analysis showed that the steady‐state value of 9.4‐μm intensity as high as 5×107 W/m2 can be obtained from the system studied. The role of H2 relaxant in the power extraction process has also been investigated.

Fiber Bragg Grating Sensor Instrumentation for Parking Space Occupancy Management

This work aims at providing an effective parking management system by reducing the drivers' searching time for vacant car-parking space, in turn improving the traffic flow in the car park areas. This is achieved by the use of Fiber Bragg Grating Sensor (FBG) sensor instrumentation in vehicle parking management system. Present work involves embedding an array of FBG sensors underground in the parking space, then determining the strain changes on the FBG sensor due to load applied by the vehicle parked in the parking space, occupancy of the parking space is determined. To validate the FBG sensor parking management system, three most common cases have been considered. This closed loop FBG parking management system can give real-time feed-back to space-guidance display board helping the driver in maneuvering the vehicle to the appropriate parking space. The proposed technique offers optimized usage of parking space for the various segments of cars and also facilitates in a conjoined automated billing system, as compared to conventional method of parking systems.

Towards a Comprehensive Approach to Quantifying and Mapping Ecosystem Services Flows

25 p.

Shallow water equations: Magic and limitations

D Liang from Cambridge University explains the shallow water equations and their applications to the dam-break and other steep-fronted flow modeling. They assume that the horizontal scale of the flow is much greater than the vertical scale, which means the flow is restricted within a thin layer, thus the vertical momentum is insignificant and the pressure distribution is hydrostatic. The left hand sides of the two momentum equations represent the acceleration of the fluid particle in the horizontal plane. If the fluid acceleration is ignored, then the two momentum equations are simplified into the so-called diffusion wave equations. In contrast to the SWEs approach, it is much less convenient to model floods with the Navier-Stokes equations. In conventional computational fluid dynamics (CFD), cumbersome treatments are needed to accurately capture the shape of the free surface. The SWEs are derived using the assumptions of small vertical velocity component, smooth water surface, gradual variation and hydrostatic pressure distribution.

基于工作流的知识流建模与控制

知识在多个参与者之间的产生、传播与应用称为知识流.在知识密集型组织中,对业务过程的控制和对知识资产的管理具有紧密的依赖关系.工作流管理是实现业务过程控制的重要技术.当前的工作流过程元模型不支持对知识管理机制的表示.为此,提出了一个扩展的工作流过程元模型,以支持业务过程控制与知识管理的集成.在此慕?Ｓ肟刂平?辛松钊氲难芯?提出了一种知识流建模方法,通过 5 类知识流单元对知识传递与重用、人员协作与交流进行表示.针对知识流中的动态因素,研究了基于资源约束、知识需求变化和时间约束的知识流控制方法,以实现自适应的知识流控制,并给出了有关算法.为工作流技术与知识管理技术的有效结合提供了一个有益的途径.

基于网络受限移动对象数据库的交通流统计分析模型

网络动态交通流的统计分析技术是目前移动计算及智能运输系统领域的一个重要研究方向．然而，现有的交通流统计分析方法(如基于固定传感器的方法、高空交通流监视方法、浮动车法等)存在着信息量少、数据处理复杂、精确度及效率低下、通信代价高昂等缺陷．为了有效地提高交通流统计分析的效率与精度，提出了一种基于网络受限移动对象数据库的交通流统计分析方法(network constrained moving objects database based traffic flow statistical analysis．NMOD—TFSA)．通过对移动对象所提交的位置更新信息进行联机统计，NMOD-TFSA能够实时地获取交通网络各部分的动态交通参数．由于在数据采集时考虑了道路网络的拓扑结构，NMOD．TFSA有效地降低了通信及计算的代价；此外，NMoD—TFSA所采集的数据能够反映移动对象完整的时空轨迹，因此为数据分析提供了更为丰富的信息，提高了数据处理的精度．实验结果表明，与目前通行的浮动车法相比，NMOD—TFSA有效地降低了 通信及计算代价，提高了交通流统计分析的精度与灵活性．

移动对象数据库模型、查询语言及实时交通流分析

提出一种移动对象数据库模型——Dynamic Transportation Network Based Moving Objects Database(简称DTNMOD),并给出了DTNMOD中基于移动对象时空轨迹的网络实时动态交通流分析方法.在DTNMOD中,交通网络被表示成动态的时空网络,可以描述交通状态、拓扑结构以及交通参数随时间的变化过程;网络受限的移动对象则用网络移动点表示.DTNMOD模型包含了完整的数据类型和查询操作的定义,因此可以在任何可扩充数据库(如PostgreSQL或SECONDO)中实现,从而得到完整的数据库模型和查询语言.为了对相关模型的性能进行比较与分析,基于PostgreSQL实现了一个原型系统并进行了一系列的实验.实验结果表明,DTNMOD提供了良好的区域查询及连接查询性能.

基于模糊模式识别的车型分类研究

根据目前中国路桥车辆收费标准,提出了一种基于模糊模式识别的车型分类系统。车辆经过环形线圈传感器时,形成感应曲线,提取感应曲线的特征并进行特征分离,利用模糊模式识别方法对车型进行匹配分类。研究结果已在路桥收费系统以及交通流量统计中得到应用。