A data-driven operational model for traffic at the Dallas Fort Worth International Airport

Airports are on the front line of significant innovations, allowing the movement of more people and goods faster, cheaper, and with greater convenience. As air travel continues to grow, airports will face challenges in responding to increasing passenger vehicle traffic, which leads to lower operational efficiency, poor air quality, and security concerns. This paper evaluates methods for traffic demand forecasting combined with traffic microsimulation, which will allow airport operations staff to accurately predict traffic and congestion. Using two years of detailed data describing individual vehicle arrivals and departures, aircraft movements, and weather at Dallas-Fort Worth (DFW) International Airport, we evaluate multiple prediction methods including the Auto Regressive Integrated Moving Average (ARIMA) family of models, traditional machine learning models, and DeepAR, a modern recurrent neural network (RNN). We find that these algorithms are able to capture the diurnal trends in the surface traffic, and all do very well when predicting the next 30 minutes of demand. Longer forecast horizons are moderately effective, demonstrating the challenge of this problem and highlighting promising techniques as well as potential areas for improvement.Traffic demand is not the only factor that contributes to terminal congestion, because temporary changes to the road network, such as a lane closure, can make benign traffic demand highly congested. Combining a demand forecast with a traffic microsimulation framework provides a complete picture of traffic and its consequences. The result is an operational intelligence platform for exploring policy changes, as well as infrastructure expansion and disruption scenarios. To demonstrate the value of this approach, we present results from a case study at DFW Airport assessing the impact of a policy change for vehicle routing in high demand scenarios. This framework can assist airports like DFW as they tackle daily operational challenges, as well as explore the integration of emerging technology and expansion of their services into long term plans.     

“大敦煌”“大交通”背景下的现代艺术设计教学研究

兰州交通大学研究丝路文化、敦煌文化、研究交通的条件得天独厚，在这样的条件和背景下思考，研究建立一个的“大敦煌”、“大交通”现代艺术设计教学体系，有着重要的现实意义。     

Pattern of road traffic injuries: one year hospital-based study in Western Nepal

Road traffic injuries and deaths caused by motor vehicles is a growing public health problem all over the world. Inter-country or regional differences in the pattern of injury by road users have significant implications in determining prevention policies. The present study was conducted to evaluate the pattern of injuries in 217 hospitalized trauma patients admitted during 1 year, 2003. The majority of the injuries (54%) involved motorcycles. The highest incidence amongst the male population was in the age group of 16 – 30 years. Head and face injuries and injuries to the lower limbs comprised 58.1% and 50.7% of all injuries respectively. The bones of the lower limbs were most commonly fractured. To conclude, the traffic casualties of motorcyclists and pedestrians are considered a major problem and the preventive measures to reduce these transport-related injuries are discussed in this study.     

基于绿色交通系统的德国城市环保交通管理策略

以绿色交通为主导的城市环保变通管理是德国城市得以可持续发展的一个重要策略.环保变通管理包括:优先发展公共交通,区域化交通安宁,促进自行车发展,汽车停车管理等一系列措施.研究揭示出环保变通管理对于德国城市摆脱汽车变通依赖的重要作用.     

毫米波交通管制系统研究动向

<正> 推行外贸代理制是外贸体制改革的一项重大举措。外贸代理制所涉及的代理行为与我国《民法通则》所规定的民事代理既有相同点也有不同之处。《民法通则》第四章第二节对代理制度作了规定,按照《民法通则》第63条之规定,公民、法人都可以通过代理人实施     

基于PCA-SVM的交通事件检测研究

随着自动控制等技术的不断发展,智能事件自动检测系统为高速公路事件检测提供了有效的途径。但传统的检测方法因为交通数据样本有限、输入交通特征过于冗余、参数设置无法达到最优等问题的影响而无法获得更优的性能。文中采用I-880实测数据集,运用主成分分析方法对交通输入特性进行特征选择,构建支持向量机事件检测模型,最后用粒子群算法选择支持向量机模型参数,获得了满意的检测效果,为设计高性能的事件检测算法提供了方法,具有一定的现实意义。     

A hybrid clustering and classification approach for predicting crash injury severity on rural roads

As a threat for transportation system, traffic crashes have a wide range of social consequences for governments. Traffic crashes are increasing in developing countries and Iran as a developing country is not immune from this risk. There are several researches in the literature to predict traffic crash severity based on artificial neural networks (ANNs), support vector machines and decision trees. This paper attempts to investigate the crash injury severity of rural roads by using a hybrid clustering and classification approach to compare the performance of classification algorithms before and after applying the clustering. In this paper, a novel rule-based genetic algorithm (GA) is proposed to predict crash injury severity, which is evaluated by performance criteria in comparison with classification algorithms like ANN. The results obtained from analysis of 13,673 crashes (5600 property damage, 778 fatal crashes, 4690 slight injuries and 2605 severe injuries) on rural roads in Tehran Province of Iran during 2011–2013 revealed that the proposed GA method outperforms other classification algorithms based on classification metrics like precision (86%), recall (88%) and accuracy (87%). Moreover, the proposed GA method has the highest level of interpretation, is easy to understand and provides feedback to analysts.     

交通文化建设是交通行业思想政治教育的新理念

交通文化建设是行业精神文明建设的重要组成部分,是行业精神文明建设的拓展和升华。进入新世纪,适应全面建设小康社会的新形势,加快交通现代化建设的步伐,必须大力加强交通文化建设。     

浅析职工上下班途中的工伤认定

随着2011年1月1日新《工伤保险条例》的正式实施,新条例进一步明确和扩大了职工上下班途中工伤认定范围,却也引发了一系列新的思考,诸如上下班途中如何界定、非本人主要责任范围的确定、非交通事故能否认定为工伤等问题,都值得我们认真去探讨和研究。本文将结合具体案例探讨职工上下班途中工伤认定条文的理解和适用问题。     

基于博弈论的城市道路交通拥挤收费分析

针对城市道路交通拥挤不断加剧,交通供需不平衡矛盾日益尖锐的现实问题,在分析城市道路交通拥挤收费基本原理的基础上,应用博弈论的分析方法,针对交通出行车与交通管理者之间的拥挤收费进行了博弈分析,以期对我国实施该政策具有借鉴意义。